Artificial Intelligence for Business: An Implementation Guide Containing Practical and Industry-Specific Case Studies 1032415088, 9781032415086

Table of contents :
Half Title
Title Page
Copyright Page
Contents
Preface
Contributors
Chapter 1. Introduction to AI in Business Applications
1.1 Introduction
1.1.1 AI Learning Types
1.1.2 Machine Learning
1.1.3 Deep Learning
1.2 Ethical and Security Issues in AI
1.3 AI Applications in Business Operations
1.4 Examples of AI Applications in Business
1.4.1 Smart Assistants
1.4.2 Helpdesk Chatbots
1.4.3 Face Recognition Technology
1.4.4 Personalized Recommendations
1.4.5 Predictive Maintenance
1.4.6 AI for Targeted Marketing
1.4.7 Smarter Supply Chains
1.4.8 Smarter Operations
1.4.9 AI-Enabled Quality Control and Quality Assurance
1.4.10 AI for Contextual Understanding
1.4.11 AI for Optimization
1.4.12 Sales and Business Forecasting
1.4.13 Security Surveillance
1.4.14 Spam Filters
1.4.15 Smart Email Categorization
1.4.16 Voice-to-Text Features
1.4.17 Process Automation
1.4.18 Social Media Insights
1.4.19 Vulnerability Exploit Prediction
1.4.20 Proposal Review
1.4.21 Billing and Invoicing
1.4.22 Customer Analysis
1.4.23 Market Prediction
1.4.24 Recruitment
1.4.25 Cybersecurity
1.5 Companies Using AI
1.6 Conclusion
References
Chapter 2. Digital Revolution and Sustainability without Well-Founded Mathematical Education?
2.1 Introduction - The Appearance of Mathematics in Corporate Management
2.2 Proposed Model - Decisions, Sustainability, and Mathematics
2.3 Methodology - Research Data Collection
2.4 Results of Analysis - Presentation of Results
2.5 Final Thoughts and Outlook
References
Chapter 3. Human Apprehension and Artificial Intelligence: Dilemma of Artificial Intelligence Fostering Human-Like Cognizance, Ethics, and Cognitive Capabilities and the Current Infusion of Artificial Intelligence in Business
3.1 Introduction
3.2 Humans and AI: A Scamper of Evolution
3.3 Infusion of AI in Businesses
3.4 Hawkeye vs. Ultron: A Concoction of Fiction and Actuality!
3.5 Consciousness and AI: A Humanoid's Tale
3.6 Ethical Paradigm and the Paradox It Follows
3.7 Conclusion: Assured or Incalculable Aftermath
References
Chapter 4. Artificial Intelligence in Marketing Applications
4.1 Introduction About AI
4.1.1 Consumer Perception on AI
4.1.2 Defining AI
4.1.3 Artificial Intelligence
4.1.4 Artificial General Intelligence
4.1.5 Narrow AI
4.1.6 Machine Learning
4.1.7 Deep Learning
4.1.8 Natural Language Processing
4.1.9 Natural Language Understanding
4.1.10 Signal Processing
4.1.11 Computer Vision
4.2 AI in Marketing Today
4.2.1 Usage of AI in Marketing
4.2.2 Programmatic Advertising
4.2.3 Transparency, Distrust, and Fraud
4.2.4 Omni Channel
4.2.5 Retargeting
4.2.6 The Influence of Social Media Marketing
4.2.7 Segmentation and Targeting
4.2.8 Facial Recognition
4.2.9 Interactive Marketing Through Biometrics
4.2.10 The Evolution of Marketing Analytics Toward AI
4.2.11 AI and Marketing Strategy
4.2.12 AI and Policy Issues
4.3 Conclusion
References
Chapter 5. Artificial Intelligence in Tourism and Advertising
5.1 Introduction
5.1.1 How Does AI Work?
5.1.2 Why Is AI Important?
5.1.3 What Are the Four Types of AI?
5.2 Ethics in AI
5.2.1 Ethical Challenges in AI
5.3 Tourism and AI
5.3.1 AI-Related Challenges in Travel and Tourism
5.3.1.1 Issues Associated with the Adoption and Use of AI by Tourists
5.3.2 Ethical Challenges of AI in Tourism
5.4 Advertising and AI
5.5 AI-Related Challenges in Advertising
5.5.1 Data Accuracy
5.5.2 Data Privacy
5.5.3 Changes in Customer Behaviour
5.5.4 Poor IT Infrastructure
5.5.5 Lack of Trust
5.6 Ethical Challenges of AI in Advertising
5.6.1 Privacy
5.6.2 Filter Bubbles
5.7 Conclusion
References
Chapter 6. Artificial Intelligence in Retail Marketing
6.1 Introduction
6.2 Literature Review
6.3 Tracing the Evolution of Marketing in the Metaverse
6.4 Immersiveness of Metaverse in Retail Marketing
6.5 The Sociability of Retail Marketing
6.6 Environmental Fidelity of Retail Marketing
6.7 Customer Expectations From Today's Marketers
6.8 AI-Human Intelligence (AI-HI) Relative Strength
6.8.1 AI's Strengths in Retail Marketing
6.8.2 HI's Strengths in Retail Marketing
6.8.3 AI-HI Collaboration in Retail Marketing
6.8.4 A Framework for Collaborative AI in Retail Marketing
6.8.5 Implementation of Marketing Technology
6.8.6 Technological Building Blocks of Retail Marketing
6.8.7 AI in Retail Market Applications
6.9 AI in the Retail Supply Chain
6.10 Statistics in the AI Space in the Retail Industry
6.11 Online Versus In-Store in Retail Marketing
6.12 Ethics in Retail Marketing
6.13 Future Scope of Retail Marketing
6.14 Conclusion
References
Chapter 7. Innovative Recruitment Strategies Using Knowledge Management Systems for Business Sustainability
7.1 Introduction
7.2 Literature Review
7.3 Understanding New Age Businesses
7.4 The Concept of KMS
7.5 Benefits of KMS in Recruitment
7.6 How KMS Can be Used in Recruitment
7.7 Innovative Approaches to Recruitment
7.7.1 Asynchronous Interviewing
7.7.2 Virtual and Augmented Reality
7.7.3 Robotics
7.7.4 Gamification
7.7.5 Chatbots
7.8 Future Trends in Recruitment
7.9 Best Practices for Using KMS in Recruitment
7.10 Organizations That Have Used KMS in Recruitment
7.11 Limitations
7.12 Conclusion
7.13 Questions for Class Discussion
References
Chapter 8. Human Resources
8.1 Introduction
8.2 Employee Attrition
8.3 Performance Management
8.4 Payroll, Benefits and Incentive Management
8.5 Intelligent Recruitment and HR Systems
8.5.1 AI in Recruitment
8.5.2 Bias in AI Algorithms
8.5.3 AI in HR Systems
8.6 WFM and Scheduling
8.6.1 Case Study: Using AI in HR - IBM
References
Chapter 9. Evolution of Chatbot in Human Resource Management
9.1 Introduction
9.1.1 Background
9.2 Literature Reviews
9.2.1 Research Gap
9.2.2 Research Question
9.2.3 Importance of the Study
9.2.4 Research Objectives
9.2.5 Scope and Limitation
9.3 Research Methodology
9.3.1 Research Method and Design
9.3.1.1 Sampling Technique
9.3.1.2 Data Collection
9.3.1.3 Data Analysis
9.3.1.4 Ethical Considerations
9.3.2 Research Approach
9.4 Analysis of the Study
9.4.1 Demographic Analysis
9.4.2 Descriptive Analysis
9.5 Results
9.5.1 Hypothesis Testing
9.5.2 Solutions to Research Questions
9.6 Conclusion
9.6.1 Future Scope
9.6.2 Suggestions
References
Chapter 10. AI in Insurance
10.1 Introduction
10.1.1 Converting Paperwork into Digital Data
10.1.2 OCR and Machine Learning
10.2 Customer Data Processing
10.2.1 Online Data and Activity Trackers
10.2.2 Market Making and Operational Efficiency Through AI
10.2.3 Case study: Customer Care Done Right—With Real-Time AI
10.2.3.1 The Challenge
10.2.3.2 Cognizant Approach
10.3 Results
10.3.1 Language Analytics Provide Insights to Customer Satisfaction
10.3.1.1 Key Improvements
10.4 Claim Processing
10.4.1 Applications of AI and ML in the Insurance Sector
10.4.2 Case Study—AI and Automation Improve Insurance Claims Process
10.4.2.1 The Challenge
10.4.2.2 Cognizant Approach
10.4.3 Results
10.4.3.1 Automated Transcription Reduces Call Duration and Costs
10.5 Image Analysis for Damage Insurance
10.5.1 Automobile Damage Inspection
10.5.2 Roof Inspection
10.5.3 Claims Processing with OCR
10.5.4 Questions
References
Chapter 11. AI in Finance
11.1 Introduction
11.1.1 Media Getting Automated Through AI
11.1.2 AI Making Business Easier
11.1.3 AI and ML Services at Our Fingertips
11.1.4 Indian Cos Is Also Racing for AI-Based Services
11.2 Credit Scoring and Loan Analysis
11.2.1 Analysis of Borrower's Creditworthiness
11.2.2 AI for Loan Processing
11.2.3 AI for Service Optimization
11.2.4 Analytics Services for Borrowers
11.2.5 Case Study
11.2.5.1 The Challenge
11.2.5.2 The Solution
11.2.5.3 The Results
11.3 Employee Expense Management
11.3.1 Analysis and Processing of Claims
11.3.2 Cost-Benefit Analysis
11.3.3 Case Study
11.3.3.1 The Challenge
11.3.3.2 The Answer
11.3.4 Results
11.4 Fraud Detection and Anti-Money Laundering
11.4.1 How AI Assists in Identifying Fraud
11.4.2 Case Study
11.5 Personal Financial Advisor
11.6 Risk Assessment and Compliance
11.6.1 Case Study
11.6.1.1 The Challenge
11.6.1.2 The Solution
11.6.1.3 The Result
11.7 Tax Filing and Processing
11.7.1 Applications of AI in Tax Filing and Processing
11.7.2 Case Study
11.7.2.1 The Challenge
11.7.2.2 The Solution
11.7.2.3 The Benefits
11.8 Algorithmic Trading Strategy Performance Improvement
11.8.1 Ability to Predict Future
11.8.2 Risk Management Is Also in Control Using AI and ML
11.8.3 Global Financial Markets Surveillance
11.8.4 Case Study
11.9 Market Intelligence and Data Analytics for Investment
11.9.1 AI and ML for Better Consumer Satisfaction
11.9.2 AI and ML to Analyze the COVID-19 Impact
11.9.3 Creativity and Innovation
11.9.4 Important Questions
References
Chapter 12. AI in Legal
12.1 Automated Report Generation
12.1.1 Applications of ARG
12.1.2 Benefits of ARG
12.1.3 A Legal Case Study From PWC (PricewaterhouseCoopers) Legaltech
12.1.3.1 Select Case Study: Large Pharmaceutical Company Issue
12.1.3.2 Action
12.1.3.3 Impact
12.2 Contract Analysis
12.2.1 Errors in Contracts
12.2.2 Mishandling of Contracts
12.2.3 Contract Analysis Through AI
12.2.4 Key Applications of Contract Analysis Through AI
12.3 Legal Document Review and Research
12.3.1 Document Review
12.3.2 Legal Research
12.3.3 A Legal Case Study From Freshfields Bruckhaus Deringer
12.3.3.1 The Challenge
12.3.3.2 The Solution—Combining Human and AI
12.3.3.3 Teaching Algorithms in Different Languages
12.3.4 The Result
12.3.5 Questions
References
Chapter 13. AI in Supply Chain, Logistics and Manufacturing
13.1 Introduction
13.2 Demand Forecasting
13.2.1 Traditional Versus Machine Learning-Based Forecasting Techniques
13.2.2 Predictive Analytics
13.2.3 Demand Sensing
13.2.4 Popular Deployment of Machine Learning in Demand Forecasting
13.3 Simultaneous Localisation and Mapping
13.3.1 Visual SLAM
13.3.2 LIDAR SLAM
13.3.3 Challenges in SLAM
13.4 LIDAR and RADAR
13.5 Satellite Imagery for Geo-Analytics
13.5.1 Characteristics of Data
13.5.2 Computing Power
13.5.3 Geospatial AI for Global Sustainable Development Goals
13.6 Weather Forecasting
13.7 Human-Robot Collaboration Enhancement
13.7.1 Perceived Safety
13.8 Predictive Maintenance
13.9 Product Life Cycle Management
13.10 Quality Monitoring
13.11 Supply Chain Optimisation
13.11.1 Use Cases of AI in Supply Chain
13.12 Video Surveillance
13.13 Voice/Speech Recognition
References
Chapter 14. Bayesian Machine Learning Approach for Evaluating the Effectiveness of an Order Fulfillment Reengineering Project in the Downstream Oil and Gas Supply Chain
14.1 Introduction
14.2 Theoretical Background
14.2.1 Supply Chain System Integration
14.2.2 Supply Chain Order Fulfillment Process
14.2.3 Process Reengineering for Improving Supply Chain Performance
14.2.4 Poisson Processes for Evaluating the Project Success
14.3 Case Study
14.3.1 The Company
14.3.2 Project Description
14.3.3 Research Model
14.3.3.1 Conceptualization
14.3.3.2 Poisson Processes
14.3.3.3 Model Evaluation
14.4 Results and Discussion
14.5 Conclusion
14.6 Funding
References
Chapter 15. Artificial Intelligence in Sports Industry
15.1 Introduction
15.2 Artificial Intelligence and Machine Learning in Sports Research
15.2.1 The "Moneyball" Case
15.2.2 Areas in Which AI and Machine Learning Have Their Footprints in Sports
15.2.3 Research on AI and ML in Sports
15.2.4 Supervised Learning: Predicting Player Injury
15.3 The Future of AI in Sports
15.4 Challenges in AI Intervention in Sports
15.5 Sportswear Brands Improved Consumer Assignation at Home and in Stores during Covid Pandemic through Visual AI
15.5.1 Visual AI as a Trendsetter in Sports Retailing
15.5.2 Operational Impact of Visual AI
15.5.3 Visual AI Helps to Get Consumers Back Into Brick and Mortar Sports Retail Spaces
15.6 Global Sports Apparel Market Report 2022-2026
15.7 The Nike AI Intervention Case
15.7.1 Nike Customer Engagement and Personalization of the Buying Experience through AI
15.7.2 Market Potential Analysis through the report titled "Global Sports Apparel Market"
15.8 Bogner, Leading Luxurious Sportswear Brand for AI-Powered Business Intelligence
15.9 The Real Moneyball Based on "Moneyball" Movie on Sports
15.9.1 Moneyball Paradigm
15.9.2 How to Apply the Moneyball for Sales Theory to Improve Your Sales Team's Performance
15.10 How Sports Analytics Are Used Today by Teams and Fans
15.10.1 The Rise of Sports Analytics
15.11 Analysis of AI Application in Sports
15.12 Artificial Intelligence in Elite Sports
15.13 Conclusion
References
Chapter 16. Artificial Intelligence Reshaping the Indian Dairy Sector: Better Days Ahead
16.1 Introduction
16.1.1 Growth Drivers in the Indian Dairy Industry
16.2 Milk Production and Procurement
16.3 Technologies for Milk Processing Operations
16.3.1 3D Food Printing Applications
16.4 Cold Chain and Logistics
16.5 Conclusion
References
Chapter 17. Artificial Intelligence and Education
17.1 Introduction
17.2 Artificial Intelligence in Teaching-Learning Process
17.3 Artificial Intelligence and Education Management
17.4 Artificial Intelligence in Academic Fields
17.5 Artificial Intelligence and Curriculum
17.6 Case Study: AIEd in India - A CBSE Initiative for an "AI for All"
17.7 Limitations to the Application of Artificial Intelligence in Education
Reference
Chapter 18. AI in Energy Sector
18.1 Introduction
18.2 Literature Review
18.3 What Were the Traditional Sources of Energy?
18.3.1 Nuclear Energy
18.3.2 Fossil Energy
18.4 What Are the Renewable Sources of Energy?
18.4.1 Solar Energy
18.4.2 Wind Energy
18.4.3 Geothermal Energy
18.5 What Is Artificial Intelligence?
18.6 Types of AI
18.6.1 Narrow Artificial Intelligence
18.6.2 Artificial General Intelligence
18.6.3 Artificial Super Intelligence
18.7 What Is Artificial Intelligence in Energy?
18.8 AI and Primary Challenges Faced by the Modern Energy Sector
18.8.1 Highly Organised
18.8.2 Emission of Carbon
18.8.3 Smooth Switch to Sustainable Energy
18.9 AI's Main Advantages for the Energy Sector
18.9.1 Resource Management
18.9.2 Smart Forecasting
18.9.3 Analytical Modelling for Sustainable Energy
18.9.4 Digitisation of Data
18.9.5 Failure Avoidance
18.10 Applications of AI in the Energy Sector
18.10.1 Consumer Interaction
18.10.2 Detecting Power Theft and Energy Scam
18.10.3 Energy Reserves
18.10.4 Higher Production
18.10.5 Panel Safety
18.10.6 The Power Grid
18.10.7 Reliability and Supervision of the Grid
18.10.8 Statistical Analysis
18.10.9 Power Generation and Planning Optimisation
18.11 Conclusion
References
Chapter 19. Managing Organisational Change Management with AI
19.1 Introduction
19.2 Organisational Change
19.3 McKinsey 7S Model and AI Interventions
19.4 ADKAR Model and AI Interventions
19.5 Kübler Ross' Change Curve Model and AI Interventions
19.6 Limitations of Artificial Intelligence
19.7 Conclusion
References
Other Online resources
Chapter 20. Adoption of Artificial Intelligence in Small and Medium-sized Enterprises: A Systematic Literature Review and Bibliometric Analysis of Global Research Trends
20.1 Introduction
20.2 Methodology
20.2.1 Data Sources and Search Strategies
20.2.2 Quality Assessment
20.2.3 Inclusion and Exclusion Criteria
20.3 Data Analysis and Findings
20.3.1 Attributes of the Papers Covered
20.4 Content Analysis
20.4.1 Co-occurrence Network of Author Keywords
20.4.2 Cluster Analysis
20.4.2.1 Cluster 1: Challenges to AI Adoption among SMEs
20.4.2.2 Cluster 2: Artificial Intelligence and Industry 4.0
20.4.2.3 Cluster 3: AI Adoption in SMEs for Sustainable Production
20.4.2.4 Cluster 4: AI-based Credit Evaluation Systems for SMEs
20.5 Conclusion and Future Research Directions
20.6 Limitations and Future Scope of the Study
References
Chapter 21. AI in Public Sector
21.1 Introduction
21.1.1 Artificial Intelligence
21.1.2 AI in Public Sector
21.1.3 Factors Affecting AI
21.1.4 Solutions of AI in the Public Sector
21.1.5 Challenges in Public Sector
21.2 AI in Public Sector, Around the World
21.2.1 Healthcare
21.2.2 Leading Countries in the Usage of AI for Healthcare
21.2.3 Education
21.2.4 Leading Countries in the Usage of AI in Education Sector
21.2.5 Transportation
21.2.6 Leading Countries in Usage of AI in Transportation Sector
21.2.7 Public Safety
21.2.8 Leading Countries Adapting AI in Public Safety
21.2.9 Government Operations
21.2.10 Leading Countries in Usage of AI in Public Sector
21.3 How AI Is Progressing?
21.4 Merits and Demerits of AI in Public Sector
21.5 Conclusion
References
Index

Citation preview

Artificial Intelligence for Business Artificial intelligence (AI) is transforming the business world at an unprecedented pace. From automating mundane tasks to predicting consumer behaviour, AI is changing the way businesses operate across all sectors. This book is an exploration of AI in business applications, highlighting the diverse range of ways in which AI is being used across different industries. The book begins with an overview of AI in business and its impact on the workforce. It then explores the role of AI in marketing, advertising, and tourism. The use of AI in personalized recommendations and chatbots is discussed in detail. The book then moves on to examine how AI is changing the retail industry, improving supply chain management, and enhancing the customer experience. The media and entertainment industry is also examined, with a focus on how AI is being used to personalize content and improve the user experience. The book also explores the use of AI in human resources, insurance, legal, and finance. The impact of AI on talent identification, recruitment, underwriting, document analysis, and financial forecasting is discussed in detail. In the healthcare and sports industries, AI is transforming the way we approach diagnosis, treatment, and training. The book examines how AI is being used to analyse medical images, develop personalized treatment plans, and improve patient outcomes. The use of AI in sports performance analysis is also discussed in detail. Finally, the book explores the use of AI in agriculture, energy, education, and the public sector. The potential of AI to optimize crop yields, reduce energy consumption, and improve the quality of education is discussed in detail. The book also examines how AI is being used to improve public services, such as transportation and emergency services. This book is a valuable resource for academics, researchers, professionals, and policymakers who are interested in understanding the potential of AI in the business world. The contributions from leading experts and researchers provide a comprehensive overview of AI in business applications, and how it is transforming different sectors.

The book also examines the ethical dilemmas that arise from the use of AI in business, such as the impact on privacy and data security, and the potential for bias in AI algorithms. It provides valuable insights into how businesses can ensure that the use of AI is ethical and responsible. In conclusion, this book is a must-read for anyone interested in the potential of AI in the business world. It provides a comprehensive overview of AI in business applications and how it is transforming different sectors. The book examines the ethical dilemmas that arise from the use of AI in business, providing valuable insights into how businesses can ensure that the use of AI is ethical and responsible. We hope that readers will find this book informative and thought-provoking.

Artificial Intelligence for Business An Implementation Guide Containing Practical and Industry-Specific Case Studies

Edited by

Hemachandran K Raul V. Rodriguez

First published 2024 by Routledge 605 Third Avenue, New York, NY 10158 and by Routledge 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN Routledge is an imprint of the Taylor & Francis Group, an informa business © 2024 selection and editorial matter, Hemachandran K & Raul V. Rodriguez; individual chapters, the contributors The right of Hemachandran K & Raul V. Rodriguez to be identified as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. ISBN: 978-1-032-41508-6 (hbk) ISBN: 978-1-032-41507-9 (pbk) ISBN: 978-1-003-35841-1 (ebk) DOI: 10.4324/9781003358411 Typeset in Garamond by MPS Limited, Dehradun

Contents Preface............................................................................................ viii Contributors .......................................................................................x 1 Introduction to AI in Business Applications ...............................1 G. DIVYA SREE, M.B.S.M.S. ANIRUDH, CHINNA SWAMY DUDEKULA, AND HEMACHANDRAN K

2 Digital Revolution and Sustainability without Well-Founded Mathematical Education? ...........................................................15 MÁTÉ FARKAS-KIS

3 Human Apprehension and Artificial Intelligence: Dilemma of Artificial Intelligence Fostering Human-Like Cognizance, Ethics, and Cognitive Capabilities and the Current Infusion of Artificial Intelligence in Business..........................................29 KRISHARTH DEEPAK MISRA, HIMANI, AND RAUL V. RODRIGUEZ

4 Artificial Intelligence in Marketing Applications.......................40 GEETHA MANOHARAN, SUBHASHINI DURAI, SUNITHA PURUSHOTTAM ASHTIKAR, AND NEELAM KUMARI

5 Artificial Intelligence in Tourism and Advertising ....................71 KAJAL NAGAR, Y. GIRI MEGHANA, AND AISHWARYA ROUT

6 Artificial Intelligence in Retail Marketing .................................86 NIRMALYA KUNDU, FARHAN MUSTAFA, HEMACHANDRAN K, AND CHANNABASAVA CHOLA

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Contents

7 Innovative Recruitment Strategies Using Knowledge Management Systems for Business Sustainability ...................108 SAHITHI CHITTIMINENI, GEDDAM ANIRUDH, SANJAY M S, AND ANIL AUDUMBAR PISE

8 Human Resources ....................................................................129 AAKARSH TATTARI AND DEBDUTTA CHOUDHURY

9 Evolution of Chatbot in Human Resource Management ..........142 MIKKILINENI VARSHINI, DHANEKULA ROHITA, AND HEMACHANDRAN K

10 AI in Insurance ........................................................................164 SYED HASAN JAFAR, SHAKEB AKHTAR, AND SATIRENJIT KAUR JOHL

11 AI in Finance............................................................................174 SYED HASAN JAFAR, PARVEZ ALAM, AND HANI EL-CHAARANI

12 AI in Legal................................................................................197 SHILPI AGARWAL, M. PURUSHOTTAM NAIDU, AND B. NARASIMHA SWAMY

13 AI in Supply Chain, Logistics and Manufacturing ...................206 DEBDUTTA CHOUDHURY AND TANVI GORANTLA

14 Bayesian Machine Learning Approach for Evaluating the Effectiveness of an Order Fulfillment Reengineering Project in the Downstream Oil and Gas Supply Chain............225 SERDAR SEMIH COSKUN

15 Artificial Intelligence in Sports Industry .................................250 SHILPI AGARWAL

16 Artificial Intelligence Reshaping the Indian Dairy Sector: Better Days Ahead ...................................................................270 JAYADEVAN G. R. AND JEGAN JAYAPAL

17 Artificial Intelligence and Education .......................................281 RIYA JADHAV

Contents

▪ vii

18 AI in Energy Sector..................................................................294 SHREYANSH MATHUR, HEMACHANDRAN K, AND DELUKSHI SHANMUGARAJAH

19 Managing Organisational Change Management with AI .........310 KAKOLI SEN AND MEENAKSHI GANDHI

20 Adoption of Artificial Intelligence in Small and Medium-sized Enterprises: A Systematic Literature Review and Bibliometric Analysis of Global Research Trends ............321 DEEPTHI B AND VIKRAM BANSAL

21 AI in Public Sector ...................................................................336 MEGHANA MERUGU AND HEMACHANDRAN K

Index ..............................................................................................350

Preface Artificial intelligence (AI) is rapidly transforming our world in ways that were once thought impossible. This rapidly evolving technology has been a gamechanger for businesses worldwide, helping them to work smarter and more efficiently. From automating mundane tasks to predicting consumer behaviour, AI is changing the way businesses operate across all sectors. This book is an exploration of AI in business applications, highlighting the diverse range of ways in which AI is being used across different industries. We have gathered together a range of articles from leading experts and researchers, examining how AI is being used in digital revolution and sustainability, marketing, advertising, tourism, retail, media, human resources, insurance, legal, finance, healthcare, sports, agriculture, education, energy, public sector, and organizational change. We begin with an overview of AI in business and its impact on the workforce. We discuss how AI is helping to automate repetitive tasks, freeing up time for workers to focus on higher-level tasks, and how it is changing the nature of work itself. We also examine the ethical dilemmas that arise from the use of AI in business, such as the impact on privacy and data security and the potential for bias in AI algorithms. We then move on to explore the role of AI in marketing, advertising, and tourism. We discuss how AI is helping businesses to better understand their customers’ behaviour and preferences, enabling them to tailor their marketing efforts to be more effective. We also examine the use of AI in personalized recommendations and chatbots, which are transforming the way businesses interact with customers. Next, we explore how AI is changing the retail industry, from optimizing supply chains to improving the customer experience. We discuss how AI is being used to predict consumer trends, enabling businesses to better anticipate demand and optimize their inventory management. We also

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examine the use of AI in retail chatbots and virtual assistants, which are transforming the customer experience. We then move on to the media and entertainment industry, where AI is being used to personalize content and improve the user experience. We discuss how AI is being used to create personalized playlists and recommendations and how it is being used to improve the quality of content through automated editing and post-production. We also examine the role of AI in human resources, insurance, legal, and finance, where it is being used to automate tasks, optimize processes, and improve decision-making. We discuss how AI is being used to identify talent, streamline recruitment processes, and improve employee engagement. We also examine the use of AI in insurance underwriting and claims management, legal document analysis, and financial forecasting. We then move on to healthcare and sports, where AI is transforming the way we approach diagnosis, treatment, and training. We discuss how AI is being used to analyze medical images, develop personalized treatment plans, and improve patient outcomes. We also examine how AI is being used to analyse sports performance data, enabling athletes and coaches to make more informed decisions. Finally, we explore the use of AI in agriculture, energy, education, and the public sector. We discuss how AI is being used to optimize crop yields, reduce energy consumption, and improve the quality of education. We also examine how AI is being used to improve public services, such as transportation and emergency services. We hope that this book provides a comprehensive overview of AI in business applications and how it is transforming different sectors. We believe that this book will be a valuable resource for academics, researchers, professionals, and policymakers who are interested in understanding the potential of AI in the business world. We would like to thank all the contributors who have made this book possible, and we hope that readers will find it informative and thoughtprovoking. Hemachandran K Raul V. Rodriguez

Contributors Shilpi Agarwal School of Business Woxsen University Hyderabad, India Shakeb Akhtar Woxsen University Hyderabad, India Parvez Alam Woxsen University Hyderabad, India Geddam Anirudh Woxsen University Hyderabad, India M.B.S.M.S. Anirudh Woxsen University Hyderabad, India Sunitha Purushottam Ashtikar SR University Warangal, India

Deepthi B Atal Bihari Vajpayee School of Management and Entrepreneurship New Delhi, India Vikram Bansal Atal Bihari Vajpayee School of Management and Entrepreneurship New Delhi, India Sahithi Chittimineni Woxsen University Hyderabad, India Channabasava Chola Department of Electronics and Information Convergence Engineering College of Electronics and Information Kyung Hee University Suwon-si, Republic of Korea Debdutta Choudhury Woxsen University Hyderabad, India

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Contributors

Serdar Semih Coşkun İstanbul University Turkey Chinna Swamy Dudekula Northumbria University Newcastle, UK Subhashini Durai GRD Institute of Management Coimbatore, India Hani EL-Chaarani Beirut Arab University Lebanon Máté Farkas-Kis Corvinus University of Budapest Hungary Tanvi Gorantla Woxsen University Hyderabad, India Jayadevan G. R. Kerala Veterinary and Animal Sciences University (KVASU) Wayanad, Kerala, India

Jegan Jayapal School of Business Woxen University Hyderabad, India Satirenjit Kaur Johl Universiti Teknologi Petronas Perak, Malaysia Hemachandran K Woxsen University Hyderabad, India Neelam Kumari Dublin Business School Dublin, Ireland Nirmalya Kundu Woxsen University Hyderabad, India Geetha Manoharan SR University Warangal, India Shreyansh Mathur Woxsen University Hyderabad, India

Himani Woxsen University Hyderabad, India

Y. Giri Meghana Woxsen University Hyderabad, India

Riya Jadhav CHIREC International School Hyderabad, India

Meghana Merugu Woxsen University Hyderabad, India

Syed Hasan Jafar Woxsen University Hyderabad, India

Krisharth Deepak Misra Woxsen University Hyderabad, India

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Sanjay M S Woxsen University Hyderabad, India

Aishwarya Rout Woxsen University Hyderabad, India

Farhan Mustafa Department of Management Studies Indian Institute of Technology Roorkee, India

Delukshi Shanmugarajah Middlesex University London

Kajal Nagar Woxsen University Hyderabad, India M. Purushottam Naidu School of Business Woxsen University Hyderabad, India Anil Audumbar Pise University of the Witwatersrand Johannesburg, South Africa Raul V. Rodriguez Woxsen University Hyderabad, India Dhanekula Rohita Woxsen University Hyderabad, India

G. Divya Sree Woxsen University Hyderabad, India B. Narasimha Swamy School of Business Woxsen University Hyderabad, India Aakarsh Tattari Woxsen University Hyderabad, India Mikkilineni Varshini Woxsen University Hyderabad, India

Chapter 1

Introduction to AI in Business Applications G. Divya Sree1, M.B.S.M.S. Anirudh1, Chinna Swamy Dudekula2, and Hemachandran K1 1

Woxsen University, Hyderabad, India Northumbria University, Newcastle, UK

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1.1 Introduction Artificial intelligence (AI) is a subfield of computer science that aims to create intelligent robots capable of doing activities that normally require human intelligence, such as visual processing, voice recognition, decision-making, and language processing. AI is used in many different industries, from healthcare to retail, and is responsible for driving a wide range of technological advancements. AI is employed in a variety of applications, including natural language processing (NLP), automation, computer vision, and pattern recognition. It can be used to perform routine processes, increase analytical and predictive accuracy, and provide data-driven insights (Hemachandran et al., 2022a). AI has the potential to revolutionize many industries and make our lives easier, safer, and more efficient.

1.1.1 AI Learning Types The AI learning types include the following:

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1. Supervised Learning: This method of AI learning allows learners feeding labeled data to the system. The data are utilized to teach the algorithm how to predict the outcome given a set of inputs. 2. Unsupervised Learning: This method of AI learning includes feeding unlabeled data to the system. The program may then recognize patterns in the information to forecast the outcomes based on these patterns. 3. Reinforcement Learning: This type of AI learning involves providing the machine with rewards when it correctly performs an action and punishments when it incorrectly performs an action. This helps the machine to learn over time. 4. Deep Learning: This type of AI learning uses artificial neural networks to help the machine in learning algorithm. These networks are modeled after the human brain and can be used to recognize patterns in data. 5. NLP: This type of AI learning uses algorithms to understand and process natural language. This is used in virtual assistants and chatbots. It is a branch of AI concerned with comprehending and analyzing human language. It extracts content from spoken and written languages using a range of approaches, such as natural language understanding, spontaneous language production, and human language interaction. It enables machines to interpret and interact with humans in their native language, allowing for a more natural experience for a user (Hemachandran et al., 2022b). NLP can be used in a variety of applications, including speech recognition, text-to-speech synthesis, machine translation, question answering, and natural language understanding.

1.1.2 Machine Learning Machine learning (ML) is a branch of AI technology that allows computers to learn and improve on their own without training data. It focuses on the creation of computer programs capable of accessing and understanding data (Hemachandran et al., 2022b). 1. Supervised Learning: This algorithm creates a model from labeled data. The training data are made up of a set of features and labels assigned to each example. Based on the patterns discovered in the training data, the model then tries to predict the classifications of new data. Regression analysis, logistic regression, decision trees, randomized forests, and support vector machines are examples of supervised learning techniques.

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2. Unsupervised Learning: This is a form of ML that seeks patterns in data without the use of labels or a set of characteristics. Unsupervised learning is a sort of ML in which system uses unlabeled data to operate without supervision. This program detects and judges data patterns without any human intervention. This type of learning seeks to uncover underlying data patterns and relationships that may then be utilized to make judgments and predictions. Grouping, anomaly detection, clustering, principle component analysis (PCA), autoencoders, and association rule learning are examples of unsupervised learning methods. 3. Reinforcement Learning: This type of algorithm is based on an agent learning to interact with its environment through trial and error. It uses rewards and punishments to encourage the agent to explore different strategies. Q-learning and deep reinforcement learning are examples of reinforcement learning.

1.1.3 Deep Learning Deep learning (DL) is a subtype of ML that learns and predicts from data using techniques influenced by the structure and operation of the visual cortex neural networks (Rodriguez et al., 2022). It is a category of AI that uses multilayered neural networks to process and analyze data and learn from it. DL enables machines to detect patterns, make decisions, and learn from their mistakes, allowing them to increase their accuracy and efficiency over time.

1.2 Ethical and Security Issues in AI AI is rapidly growing in various areas including healthcare, finance, education, etc. As with any new technology, AI brings with it several privacy concerns (Enholm et al., 2022). 1. Data Collection: AI algorithms require vast amounts of data to learn and become more accurate. The data used for training AI algorithms may be collected from various sources, such as social media, surveillance cameras, and Internet of Things (IoT) devices. These data may contain sensitive information about individuals and their activities, which could be used to profile them. 2. Data Privacy: Since AI algorithms rely heavily on the data they are trained on, the data must be kept secure to protect individuals’ privacy.

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This means that data must be stored securely, and access must be restricted to only authorized personnel. 3. Data Manipulation: AI algorithms can be manipulated to produce inaccurate results. For example, an AI algorithm may be trained on biased data, resulting in biased decisions. This could lead to discrimination against certain groups of people. 4. AI Surveillance: AI-powered surveillance systems can track and monitor individuals’ activities. This could lead to the violation of individuals’ privacy as well as the potential for abuse by those in power. 5. Security Vulnerabilities: AI algorithms are vulnerable to hacking and malicious attacks. Hackers could exploit vulnerabilities in AI algorithms to access sensitive information or manipulate the results of AI-powered decisions.

1.3 AI Applications in Business Operations AI is transforming the way businesses operate and has become increasingly important for improving operational efficiency and customer experience in a variety of industries. It is being utilized to automate procedures, make better decisions that personalize customer experiences, and much more. AI algorithms can examine vast volumes of data and uncover trends, patterns, and correlations that people would struggle to detect. AI can also automate tasks and provide insights that can help businesses improve efficiency and performance (Enholm et al., 2022). It is becoming increasingly significant in business applications such as customer service, marketing, finance, and retail. AI technology in business has provided tremendous benefits to those willing to investigate the utility of developing technology as business tools. The effects of AI capabilities are broad and expanding, implying that the AI’s future is positive and will keep evolving, benefiting organizations in novel and interesting ways. ■ AI in Business Strategy When it comes to corporate strategy, ML and AI are constantly transforming the scene. Rather than basing this year’s forecast solely on last year’s sales, firms must consider factors such as regulations, seasonal needs, alterations in the supply chain, unanticipated weather changes, trends, prospective personnel concerns, and much more. AI technology in industry can strategize approaches by considering these variables.

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■ AI in Business Operations AI machines have a lot to offer enterprises in terms of optimizing various processes, such as reducing repetitive work, reducing human error, and increasing the productivity of human labor. The nice part is that technology is becoming more affordable. So, if you haven’t already begun incorporating AI in your organization, now is the moment. ■ AI in Applications in Finance and FinTech Using AI technology in financial and technology has enabled financial institutions to make intelligent decisions by analyzing massive volumes of data obtained in real time from capital markets. This method is particularly dependable because data collection, processing, and analysis take place in real time. Insurance companies may improve their customer experiences by optimizing standard insurance administrative and underwriting procedures with ML. By bringing big data into the process, predictive analysis has altered the way financial institutions make decisions. ML and AI may help firms analyze a wide range of market and consumer data, thereby speeding up corporate growth and management operations. ■ AI Applications in Marketing and e-Commerce Customer data and AI are used in AI-powered marketing to forecast the user’s purchasing behaviors and give personalized ideas. Simultaneously, AI has reduced R&D time and strain on marketers by exporting data to robots. ■ AI for Industrial Maintenance AI for industrial maintenance uses predictive analytics to monitor the performance of machines and components in industrial settings. By using AI-powered sensors to detect anomalies, it can detect problems before they occur and alert operators to take preventive measures. AI can also use data from past performance to make better maintenance decisions for future maintenance tasks. AI can help reduce downtime and costs associated with industrial maintenance. Additionally, AI can help optimize maintenance schedules and alert operators to potential problems before they occur. ■ AI in Supply Chain AI is being used by supply chain workers to address major problems and improve worldwide operations. AI-enhanced solutions are being applied across supply chains to boost efficiency, reduce the effects of a global labor crisis, and discover better, safer ways to transport commodities.

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AI systems in enterprise applications can be found from the production line to the front door. Shipping companies utilize IoT sensors to collect and analyze data on products in transit as well as track the physical health and continuous position of expensive autos and related automotive components. Customer Service: AI-based customer service solutions can provide automated customer support services such as chatbots, virtual agents, and voice recognition. These solutions can quickly respond to customer inquiries, anticipate customer needs, and provide personalized customer experiences.

1.4 Examples of AI Applications in Business 1.4.1 Smart Assistants When it comes to AI technology, this is undoubtedly a sector you are familiar with. The most popular smart assistants on the market today are Siri, Cortana, Alexa, and Google Assistant. They offer a variety of capabilities and services that enable you to use your voice for the following: ■ ■ ■ ■

Investigate the internet for information Control smart home technology and gadgets Make phone calls and send text messages Make reminders, among other things

1.4.2 Helpdesk Chatbots A chatbot is a piece of computer software that has been developed to mimic the human speech. It is critical to underline that the interaction’s chatbot replicates a real-life conversation. Users interact using a chat interface or through audio, and robots interpret the words and reply with a pre-programmed response. There are three sorts of chatbots. ■ Intelligence ■ Rule-based ■ AI-powered

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1.4.3 Face Recognition Technology The technology recognizes facial landmarks, converting your signature into a mathematical formula that can be evaluated with a database of well-known faces. Many companies employ face recognition for several purposes, including the following: ■ ■ ■ ■

At airports of the US government Law enforcement Advertisers and marketers Companies that use social media

1.4.4 Personalized Recommendations Personalization is achieved by the utilization of user activities. These are items that the consumer has frequently explored, investigated, or purchased in conjunction with the one they are currently contemplating. For example, many online businesses offer commonly purchased paired items, and consumers who viewed this item also viewed attributes as well as personal recommendations based on previous purchasing or browsing activity.

1.4.5 Predictive Maintenance AI aids businesses in replacing or repairing parts or equipment before they fail. Predictive maintenance uses data from a variety of sources, including previous scheduled maintenance, device environmental sensors, and weather data, to predict when an equipment needs to be fixed. By obtaining real-time asset data and analyzing past data, operators may make more informed decisions regarding when a device must be replaced.

1.4.6 AI for Targeted Marketing AI can be used for targeted marketing by analyzing customer data, such as purchase history and web browsing behavior, to create customer profiles and segment customer groups based on their preferences. It can also be used to create personalized offers and promotions for customers, as well as to optimize customer communication with automated customer service (Loureiro et al., 2021). AI can also be used to analyze customer feedback and reviews to help marketers identify customer concerns and preferences.

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1.4.7 Smarter Supply Chains AI can be used to make supply chains smarter in multiple ways. First, AI can be used to automate the ordering process, enabling a smooth and efficient flow of goods. This would enable suppliers to reduce their costs, as fewer resources would be needed to manage orders and inventory. Furthermore, AI can be utilized to enable predictive modeling, enabling supply chain executives to anticipate customer wants and guarantee that the appropriate goods are in the right locations at the right time. It can also be used to examine client data and spot patterns to inform decisions regarding inventory management, logistics, and pricing. Finally, AI can be used to optimize delivery routes and shipping processes, reducing costs and improving customer satisfaction.

1.4.8 Smarter Operations AI has the potential to revolutionize operations by enabling smart process automation. It can help to reduce operational costs and improve operational efficiency by automating manual processes and providing actionable insights. AI can also help to improve customer service by providing personalized experiences and better forecasting of customer needs. It can also be used to discover data patterns and detect anomalies in operations, which can help to identify risks and improve decision-making. Furthermore, AI can be utilized to optimize resource utilization and improve supply chain management.

1.4.9 AI-Enabled Quality Control and Quality Assurance AI-enabled quality control and quality assurance is the use of AI assistance to ensure products and services meet certain quality standards. AI has the potential to automate and increase quality control and assurance processes, allowing companies to reduce costs while improving the accuracy and speed of the processes. It can be used to monitor and assess the reliability of products, detect and diagnose problems quickly, and optimize quality control systems. AI can also be used to track trends and anomalies in production, analyze customer feedback, and automate the reporting of quality assurance results.

1.4.10 AI for Contextual Understanding Contextual understanding is a branch of AI that focuses on understanding and interpreting the context in natural language conversations, documents,

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images, and videos. It uses NLP, ML, and DL techniques to analyze the context of a conversation or document, determine the intent of the speaker or writer, and extract relevant information. AI can be used to identify and classify objects in images, understand the sentiment of text, and generate natural language responses to queries. It can also be used to generate automated responses to customer service inquiries and identify customer needs. AIpowered contextual understanding can provide valuable insight into customer interests, preferences, and behaviors, helping companies better serve their customers.

1.4.11 AI for Optimization AI can be used for optimization in various ways. For example, AI-powered algorithms are used to optimize a variety of processes, from supply chain management to financial portfolio optimization. AI can also be used to build predictive models that can help businesses make decisions that lead to better outcomes. AI algorithms can be used to optimize customer experiences, product pricing, marketing campaigns, and more. It can also be used for monotonous processes such as ML, NLP, and image recognition, allowing businesses to save time and money (Loureiro et al., 2021).

1.4.12 Sales and Business Forecasting AI can be used to automate sales and business forecasting processes. Reinforcement learning, speech recognition, and predictive analytics are examples of AI technologies that can be used to analyze massive volumes of data in order to find trends and make forecasts. AI can also be used to develop more accurate forecasting models, automate forecasting processes, and improve the accuracy of forecasts. It can also be used to monitor behavior of the customers and sales patterns in order to better anticipate future demand.

1.4.13 Security Surveillance AI can be used to improve security surveillance in a number of ways. AIpowered systems can detect potential threats more quickly and accurately than humans, helping to reduce response times and improve security. AI systems can be used to analyze huge quantities of video footage, identify suspicious behaviors, track people and objects, and alert security personnel when potential threats are detected. AI can also be used to automate the

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process of recognizing license plates and faces, providing a more efficient way of monitoring who is entering and leaving a facility.

1.4.14 Spam Filters AI can be used to create more accurate and efficient spam filters. AI algorithms can be used to analyze past emails and detect patterns in emails that have been marked as spam. The AI can then use these data to identify emails that are likely to be spam and flag them before they reach the user’s inbox. It can also be used to analyze the content of emails, using NLP algorithms to detect words, phrases, and other features that are commonly found in spam emails. AI can also be used to analyze the sender of the email and identify suspicious or potentially malicious activity.

1.4.15 Smart Email Categorization AI can be used to help categorize emails by using NLP and ML algorithms. NLP algorithms can be used to analyze the content of emails for keywords, semantic meaning, and sentiment. ML algorithms are used to categorize emails into predefined categories based on the analysis. This can help to organize emails more efficiently, reducing the need for manual sorting and making it easier to find specific emails.

1.4.16 Voice-to-Text Features AI for voice-to-text features is a form of speech recognition technology that uses AI to convert spoken words into text. This technology is used in a number of applications, including voice-activated virtual assistants, voice-totext dictation, and voice search. AI-enabled voice-to-text features can understand natural language, recognize different accents and dialects, and can even be used to generate accurate transcripts of conversations.

1.4.17 Process Automation AI for process automation is an area of AI that focuses on automating and streamlining business processes. AI for process automation can include a wide range of technologies, from ML and predictive analytics to NLP and robotic process automation (RPA). This technology can help businesses reduce costs, improve productivity, and create more efficient and accurate

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processes. It can also enable companies to gain insights from large data sets and make better decisions.

1.4.18 Social Media Insights AI technology has been used to provide social media insights to businesses, marketers, and other stakeholders. AI may be used to swiftly find patterns and trends in massive volumes of social media data and insights that would otherwise be difficult or impossible to uncover. AI can also be used to identify key influencers, measure sentiment, and track the performance of campaigns. AI-driven social media insights can provide a powerful advantage to businesses in understanding their customers, predicting their behavior, and optimizing their marketing strategies.

1.4.19 Vulnerability Exploit Prediction AI can be used to predict and detect potential vulnerabilities and exploits in software systems. AI and ML approaches can identify patterns of malicious activity and use them to detect potential threats. AI-based systems can be used to scan code for potential vulnerabilities and predict potential exploits based on the code analysis. AI is used to identify harmful activities in network communications and detect potential malicious actors.

1.4.20 Proposal Review AI is used to improve the review process of proposals. It can analyze the text of the proposal to identify key topics and then determine the relevance of the topics to the proposal’s purpose. AI can also compare the proposal to similar proposals to identify areas of improvement. It can also be used to automate the review process by assigning each proposal a score based on a set of criteria. This score can then be used to determine whether the proposal should be approved or rejected. Finally, AI can be used to identify potential conflicts of interest between the proposer and any of the reviewers.

1.4.21 Billing and Invoicing AI can be used to automate the billing and invoicing process by automating the generation of invoices and bills, tracking payments, and managing customer accounts. AI-powered tools can be used to automate the manual

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processes involved in creating and sending invoices and bills, detecting fraud, and analyzing customer data. AI can also be used to analyze customer spending patterns and predict future customer needs, helping businesses better serve their customers.

1.4.22 Customer Analysis AI can be used to examine client data in order to spot trends and insights that could help businesses better understand their customers and improve their customer experience. AI, for example, can be used to evaluate client data in order to determine which customers are more likely to purchase certain products or services, which customers are more likely to respond positively to certain marketing messages and promotions, and which customers may need additional support. AI can also detect consumer perceptions and make customized recommendations depending on the customer’s preferences. Furthermore, AI can be utilized to automate customer care activities such as answering inquiries from customers or resolving customer issues in a timely manner.

1.4.23 Market Prediction AI can be used for market prediction by analyzing large amounts of data and using ML algorithms to detect patterns and trends in order to make predictions about future market movements. AI-driven market prediction tools can be used to help investors make more informed decisions about when to buy, sell, or hold a particular stock or asset. AI can also be used to identify potential trading opportunities and potential risks.

1.4.24 Recruitment AI can be used to facilitate recruitment by automating steps such as screening job applications, conducting interviews, and even assessing job performance. AI-based recruiting tools can swiftly scan resumes and select the most relevant applicants based on certain criteria, making it easier to find the right candidates for a job. AI can also be used to determine how well a candidate might fit into a company’s culture by analyzing the candidate’s responses to certain questions or the words they use in the job application. AI can also be used to monitor employee performance and make suggestions for improvement or training. Ultimately, AI can help streamline the recruitment process and make it easier for employers to find the best candidate who fits the job.

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1.4.25 Cybersecurity AI for cybersecurity refers to the use of AI and ML technologies to detect, prevent, and respond to cyberattacks. Many security operations and processes, including risk analysis, detection of cybersecurity threats, and incident response, can be automated using AI. It can also be employed to monitor and detect user behavior anomalies that may indicate malicious activity. By leveraging AI and ML, organizations can more effectively detect and respond to cyber threats, reduce false positives, and improve overall security posture.

1.5 Companies Using AI AI in business applications is a rapidly growing field that is transforming the way companies do business. It is the application of computers to replicate human intelligence and behavior. AI is used in various industries, from finance to healthcare, and can provide companies with more accurate and efficient operations. Some of the most recognizable companies that use AI include Amazon, Google, Alibaba, Microsoft, and Uber. Amazon uses AI to power its product recommendations and search engine, while Google uses AI to power its search engine and data analysis. Alibaba leverages AI to power its e-commerce platform, while Microsoft uses AI to power its cloud computing services. Uber uses AI to power its selfdriving car technology. Other companies that use AI include Apple, Facebook, IBM, Salesforce, and Twitter. Apple uses AI to power its Siri voice assistant and facial recognition technology. Facebook uses AI to power its news feed algorithm and content curation. IBM uses AI to power its Watson cognitive computing platform, while Salesforce uses AI to power its customer relationship management (CRM) platform. Twitter uses AI to power its understanding of user intent and content curation.

1.6 Conclusion This section concludes our discussion of AI uses in business. There is no question that AI technology has a bright future. Applying AI in the company saves time spent in performing repetitive operations, boosts employee productivity, and improves overall user experience. It also helps to avoid mistakes and predict potential catastrophes at a level that humans cannot

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achieve. There is a high demand in the tech area for AI and leadership specialists, and with right skills and credentials, you can undoubtedly become the AI-era leader. The Executive PG Degree in Business and Machine Intelligence by UNext Sight has been carefully tailored to assist you in achieving the same.

References Hemachandran, K., Tayal, S., George, P. M., Singla, P., & Kose, U. (Eds.). (2022a). Bayesian Reasoning and Gaussian Processes for Machine Learning Applications. CRC Press. Hemachandran, K., Khanra, S., Rodriguez, R. V., & Jaramillo, J. (Eds.). (2022b). Machine Learning for Business Analytics: Real-Time Data Analysis for DecisionMaking. CRC Press. Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709–1734. Loureiro, S. M. C., Guerreiro, J., & Tussyadiah, I. (2021). Artificial intelligence in business: State of the art and future research agenda. Journal of Business Research, 129, 911–926. Rodriguez, R. V., Sairam, P. S., & Hemachandran, K. (Eds.). (2022). Coded Leadership: Developing Scalable Management in an AI-induced Quantum World. CRC Press.

Chapter 2

Digital Revolution and Sustainability without WellFounded Mathematical Education? Máté Farkas-Kis Corvinus University of Budapest, Hungary

2.1 Introduction – The Appearance of Mathematics in Corporate Management For many millennia, as long as people fought for survival in a wild struggle with nature, mathematics and counting did not exist because there was no need for it. Life was organized according to simple rules, and the main decision-making questions were where to find food, where to survive the night, and how to reproduce. They didn’t need to count. The change came when mankind discovered the first harvestable regions, which made it possible for their way of life to be permanent on the same place. This change lasted from BC 10,000 to BC 3,000 and significantly transformed the previous way of life, and, in this new context, completely new decision-making and problem-management solutions were needed. Farming required the organization of work and a more detailed observation of nature. The individual trades/work processes had to be coordinated, and the settlements and later the defence of the state had to be taken care of. Production, farming, industry,

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and trade necessarily started to bring to life the business mechanisms that are also part of modern economic policy: the long-term registration of produced, stored, and consumed quantities and the numerical control of goods. In this way, from BC 4,000, the first numbers and calculations were born, which from then on organically determined decisions and with it the development of mathematical thinking. In the 21st century, companies are the units of the complex system of the global economy. In this context, it is important to clarify in what spirit and with what purpose the decisions are made, for which it is necessary to know how we perceive the company itself (Chikán, 2017, pp. 64–72). The standard microeconomic view of business is that business decisions are made rationally, based on complete information, in order to maximize profits. Transaction cost theory takes into account an additional aspect compared to the previous one, namely, that there are costs associated with each financial exchange, including the creation of contracts. The principal–agent theory approaches the issue from an operational point of view: in our times, the owner of a company (principal) and its management (agent) are not always the same, which causes conflicts of interest since profit does not belong to the one who takes the decisions, thus it is not a priority for them to maximize it. According to evolutionary approaches to business theory, the efficiency of a company is determined by the actors involved in it and those who are connected to it. Therefore, the focus of a decision is on the aspirations of those involved. With the development of corporate theories, managerial decision-making is discussed in an increasingly complex way. Human behaviour has played an increasing role in understanding and describing corporate goals. Modern, complex systems entail the emergence of decision-making mechanisms that result from the limitations of human thinking and information processing ability: bounded rationality. The goals and functions of companies and the organizations within them are different. Their management involves solving complex tasks, as it is necessary to constantly deal with the planning, organization, control, exercise of power, and decision-making processes within them. It does matter what type of problem you have to decide on. However, before deciding on a solution to a particular problem, one step is still missing: identifying the problem. Here, decision-making processes usually call for tools that process and evaluate data, usually with the help of some mathematical models. Moreover, in most cases, the decisions are preceded by the recognition of the given problem, which can also be done in several ways (Zoltayné, 2005, p. 24). The simplest is coercive obviousness, when the perceived reality quite

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simply leads to the recognition of the problem. Examples include continuous losses, a decrease in the number of customers, and loss of the market. Corporate controlling, on the other hand, is based on warning systems, which indicate problems by continuously monitoring reality, collecting data, and presenting the information derived from foresight and experience. In such cases, the applied models, on the basis of which these systems work, constitute a bottleneck for accurate knowledge of reality, but at the same time they may still be able to identify and, where appropriate, predict problems. They try to trigger the recognition effect from the external system that can effectively replace the models discussed in the previous point and personal involvement. In such cases, an outsider can look at a given situation with clear eyes and provide new perspectives, eliminating the phenomenon of “professional blindness”. The approach of the consultancy market is based on this when the goal is for an external party to look at the company’s operation. And lastly, research, when it is a conscious problem-seeking process, maps the distance between a desired state (goal state) and the present (actual state). Its purpose is to find the reasons for this distance and to provide solutions to overcome the discrepancy. The interesting thing (and danger) of this approach is that it can often produce “artificial” problems that otherwise only exist in theory, and no real effect can be achieved by solving them. If we accept that decision-making is part of problem-solving and we approach every decision-making situation from the perspective of a problem situation, which is often evaluated with the support of different models, then it is important to mention Howard’s problem space (Howard, 1968). This interprets the problems in three dimensions. The first is the time factor, according to which a given problem can be static or dynamic in time. The second dimension is the extent of uncertainty. This measures the number of factors that are unknown in the current situation. This dimension of the model categorizes problems as deterministic or probabilistic. Last but not least, the last dimension is the complexity of the situation. In other words, how many variables can be used to describe a given problem, a few or much. If we want to define a problem along these three dimensions, we can get an idea of the complexity of a situation by placing them in the coordinate system they create. Note that in the case of economic problems, these three dimensions are described through well-defined mathematical fields, which are probability theory, stochastics, and applied analysis. If we think about corporate decision-making, one of the most defining, most used, and controlled characteristics, both within the company and on the part of the market and state offices, is financial/economic performance.

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There is almost no decision-making situation that does not involve some form of financial analysis. These analyses are part of the warning and business intelligence systems operating within the company. In this way, in the case of decisions, these analyses, which are objective, quantified, and thus rational, play a prominent role in the process. Macroeconomics and microeconomics are the two central subjects of students participating in economics courses. The former deals with industries and the entire economy, while the latter discusses individual economic actors. The founding thinkers of these scientific fields were predominantly mathematicians, statisticians, engineers, physicists, and economists. The classical mathematical approach is strongly defined in the thinking of each of them. Therefore, mathematics cannot be bypassed when talking about managerial thinking and decision-making. On the contrary, it plays an immense role in the quality of decisions.

2.2 Proposed Model – Decisions, Sustainability, and Mathematics If we ask decision-makers about what decision-making methods they typically use, they usually answer that they use profit-maximizing, rational decisions. At the same time, practical observations of the same group showed that they more often followed the bounded rationality approach (Zoltayné, 1999). The main reason for this is that, in reality, decision-makers are forced to find satisfactory solutions due to their cognitive limitations, and they want rationality but cannot reach it. This phenomenon appears as a limitation at two key points in the process of problem-solving and decision preparation: (1) when receiving information and (2) when processing information. Following the research results on bounded rationality, numbers and representation through numbers received special attention because we can have a significant impact on decision-making through heuristics. The main reason for this is that numbers are suitable for giving us guidance since our association with them is nothing more than the increased presence of rationality itself. It is enough to think that if we put numbers in an explanation, what we say seems much more authentic. As a result of the above, numbers can legitimize decisions, which at the same time poses a serious danger. There are many tools available to support management decisions, which can be used to collect, analyse, and display information. A significant part of them use quantified data and measure economic performance. In order to interpret them, we need mathematical competence, which we acquire during

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our studies. As a result, the question is rightly raised about to what extent mathematics has an effect on the decision-making ability of managers, which these systems only support but do not cause. If we talk about mathematics alone, without any connection, people have mixed feelings. Some people love it; others avoid it from afar. However, everyone feels that mathematical abilities cannot be bypassed. Kahneman and Tversky‘s experiments showed (Kahneman, 2012, pp. 120–122) that numbers can influence our decisions even when they apparently have nothing to do with the given decision. As a result, numbers can also legitimize decisions based on intuition (Zoltayné & Farkas-Kis, 2021, p. 155). The management research of recent decades has already clearly targeted the border areas of decision-making and psychology, as well as social psychology (Szántó & Zoltayné, 2019). Numerous studies have investigated why and how real decision-making deviates from rational, normative rules due to psychological effects. In the case of corporate performance measurement, decisions are typically related to financial statements (Wimmer & Csesznák, 2012, p. 115). Their interpretation, on the other hand, is tied to the understanding of numbers, which is based on mathematics education during studies, so it is essential that this is also the focus of research related to behavioural science decisions. From primary school onwards, mathematical studies are, in principle, meant to serve the purpose of helping students learn to think. One part of the Programme for International Student Assessment (PISA) test also measures mathematical literacy among secondary school students. It is defined as (PISA, 2006): “Applied mathematical literacy means that the individual recognizes and understands the role of mathematics in the real world, makes wellfounded decisions, and his knowledge of mathematics helps him to correctly solve the real problems of his own life, to become a constructive, interested and thoughtful member of society.” The definition clarifies that this subject must develop competencies that develop problem-solving and decisionmaking abilities. Perhaps unsurprisingly, the biggest challenge of the 21st century is how the economic systems that define the national and global economies can become sustainable. This is a problem that requires the creation of wellinformed decisions. If we look at its definition, sustainable development as described by the UN is that which meets the needs of the present without reducing the ability of future generations to meet their own needs. For this, three basic principles are defined that should be followed in order to succeed. The first is that what we emit into our environment cannot exceed the

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environment’s ability to receive and process it. The second is that what we extract from the environment cannot exceed the environment’s regenerative capacity. The third is that the rate of use of non-renewable resources cannot exceed the rate at which we can replace them with renewable resources. Adherence to these principles requires models and support systems that can monitor and predict when a principle is violated. In terms of goals, mathematics education contributes to the development of countless areas that point not only towards calculation but also rationality in the classical sense. Mathematics is part of the development of thinking skills and one of the foundations of intellectual development in terms of decision-making competence. It is clear from the aforementioned data that information, most of which comes from numerical or quantified data, plays an important role not only in business perceptions and decision theory approaches but also in sustainability-related actions. It doesn’t matter whether it’s an objective measurement or a subjective assessment. At the level of thought abstraction, almost everything appears in numbers at a certain point in judgement and decision-making. And these numbers exert an important and unavoidable control and influence on decision-making. As we have seen, since ancient times, numbers and quantification were intended to serve the control function of management tasks and support-related decisions. That is why, in order to provide this service, it was necessary for mathematics, as a scientific field dealing with numbers, to appear in education, and it has remained so to this day. Mathematics is the subject we study for the longest period of time. It has typically been present in primary and secondary education for 12 years and thus has or can have a serious impact on our lives in the most unexpected areas. At the same time, domestic and international studies in the field of mathematical knowledge have highlighted that the assessment of the subject itself, the mathematical self-image, falls into a strongly negative category (Csapó, 2012, p. 394). This, as well as the additional results experienced during the measurements, marked a new direction for research. While earlier the investigation of cognitive areas was almost exclusive, in recent decades research into the influence of affective and motivational areas has also begun to develop. Ashcratf and Krause (2007) pointed out that if students fail in mathematics class, performance anxiety can immediately appear, which blocks their brain’s working memory. Working memory has a prominent role in arithmetic skills (Márkus, 2007). In terms of goals, mathematics education contributes to the development of countless areas that point not only towards calculation but also rationality

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in the classical sense. Mathematics is one of the foundations of the development of thinking abilities and intellectual development in relation to human decision-making competence. If we approach from this direction, we have measurement information in three areas. These are the areas of (1) operational thinking, (2) inductive thinking, and (3) problem-solving thinking. In this context, a person’s personality is made up of the learned behavioural tendencies that he acquires during his personal life experience. The patterns you see around you define and shape your attitude. More than a century ago, Thorndike formulated the law of the effect, according to which “if an action is followed by better or more satisfactory conditions, the likelihood of the action occurring in a similar situation will be greater in the future” (Haire, 1974). Of course, this is also true in reverse: “If the behaviour is followed by a worse or less satisfactory state of affairs, it is less likely that this behaviour will be shown again later”.

2.3 Methodology – Research Data Collection The thought process described above led us to view mathematics not only as a tool of computer science but as a fundamental element in the development of problem-solving and decision-making competencies and as the foundation of sustainability and sustainable thinking. In order to be able to carry out deeper research in this area, it was first necessary to get to know the basics that people acquire in the education system related to mathematics. For this, we compiled an online questionnaire, which examined what typical attitudes can be identified in relation to mathematics and what effect the experiences gained during learning mathematics have on them, as well as the assessment of the role of mathematics. We used Qualtrics for data collection. It was a self-filling form shared in online social spaces (Facebook and LinkedIn) targeting easily accessible subjects using the snowball method. The questionnaire has five blocks: (1) We assessed the demographic background of the respondents; (2) their free associations related to mathematics were the focus; (3) the mathematics learning experiences had to be revived; (4) we asked about the relationship between professional decisions, work, and mathematics; and (5) we focused on personal opinions, researching what the respondents think about the need for mathematics in relation to each field of employment. Given that the research was exploratory in nature, representativeness of the sample was not required.

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We received 676 answers. After cleaning the data, the sample size was reduced to 625 respondents (given that no answers were given by them to certain questions or that they did not want to answer certain questions). Considering the above, it is characteristic of the sample that twice as many women filled out the questionnaire as men. Among the respondents in the sample, (1) 19 had primary education; (2) 130 had secondary education; (3) 419 had higher education; and (4) 54 had academic degrees. Among all respondents, 155 worked in senior positions: 30 group managers, 72 middle managers, and 53 senior managers. The elements of the sample were classified into four groups in terms of age: (1) 73 people who belong to the socalled Baby Boom generation, born between 1946 and 1964; (2) 240 people who belong to Generation X, born between 1965 and 1980; (3) 187 people who belong to Generation Y, born between 1981 and 1995; and finally (4) 122 people who belong to Generation Z, born between 1996 and 2010. Based on the answers to the questions, both qualitative and quantitative analyses were carried out on the data. In the qualitative domains, personal narratives were analysed after coding, and free association responses were analysed from both symbolic and psychological archetypes. Questions asked for quantitative analysis were typically dominated by multiple-choice or 5-point Likert scale questions. In this chapter, we will mainly present the results of these.

2.4 Results of Analysis – Presentation of Results Due to the large number of respondents, the survey provided a very large amount of data, which is still being analysed and models are being built. At the moment, we present the most important results that mark the path that will have to be followed with deeper analyses. The first and perhaps most important question is how students’ mathematical performance develops in the school system. In the Hungarian education system, there is primary education (usually 8 years), secondary education (usually 4 years), and subsequent higher education. Higher education used to be a period of usually 5 years, but at the beginning of the 2000s, split education was introduced and bachelor‘s and master’s programmes appeared. This is followed by obtaining a scientific degree. It is typical for all educational levels that the Hungarian evaluation scale ranges from 1 to 5, where 1 indicates insufficient performance and 5 indicates excellent performance. When filling out the questionnaire, the respondents had

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Figure 2.1 Mathematical performance of the respondents at each level of education (own construction).

to indicate how they remembered their mathematical performance at each educational level. The percentage distribution of the results from basic studies to scientific degree is shown in Figure 2.1. It is striking that the performance of the respondents in primary education is exceptionally good. Nearly 91% of them received a grade of 4 or better, according to their memories, and 71% of them achieved excellent results. However, this positive performance changed over the years. Already during secondary school studies, the proportion of those producing the best performance dropped from 91% to nearly 76%, which is only 54% in higher education and does not even reach 40% in the case of obtaining a scientific degree. On the other hand, poor performance showed a significant jump, and the signs of a bipolar pattern of mathematical performance can be observed. There were those who understood mathematics and those who did not, and a significant gap appeared between the two: the performance of 2 was almost at 4%, and 37 was 59% of the respondents are on either side. The respondents also had to declare what kind of decision-maker they consider themselves to be. They had to give their answers on a Likert scale from 1 to 10, according to which a value of 1 meant that they make decisions completely intuitively, and a value of 10 meant that they strive for rational decisions as much as possible. The results are shown in Figure 2.2.

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Figure 2.2 Distribution of respondents according to what kind of decision-maker they consider themselves to be (own construction).

The choice of diagram representation also shows that the respondents tend to move in the direction of rationality when the question is how they make a decision. The skewness calculated for the data is 7.4, i.e., the sample is asymmetric in the direction of rationality. This directs attention to several interesting questions. One is that the majority of people like to think that they make their decisions rationally, and this is confirmed by the sample. For some reason, rational decisions have a privileged place because we generally think that decisions should be made rationally. The other interesting thing is that although the mathematical performance – which is related to the basic development subject of competences linked to rational thinking – will be lower and lower as the education system progresses, the commitment to rationality is still so strongly present. It is also worth looking at the distribution of responses to another question to nuance this rational self-image. Another central question was what the respondents thought learning mathematics helped them with. They were able to choose from three options with simple answer choices: (1) they see a minimal advantage of mathematical studies, (2) they learned to think logically, and (3) they learned to count well. The results are shown in Figure 2.3. Despite the fact that mathematical performance is typically measured by solving calculation tasks, the majority of respondents understood that mathematics is not just about calculations, and nearly 80% of them connected the learning of mathematics with logical thinking. The approach to rationality is

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Results of mathematical education (own construction).

realized through logical thinking, and it is independent of mathematical performance in the sense that, although the students do not learn math well, which can be seen in their performance, they do have the ability to think, but the evaluation system cannot measure or show that. Another part of the questionnaire tried to shed light on the connections between mathematics and rationality and the related perceptions, which tried to assess what the respondents think about the extent to which mathematical studies are useful for the cultivation of certain professions and occupations. For this purpose, we used the activities registered by the Hungarian Central Statistical Office and asked respondents to respond on a 5-point Likert scale. A value of one meant that mathematics was completely unnecessary, and a value of five meant that mathematics was very useful. The results are shown in Figure 2.4. It can be said that, as expected, the respondents clearly classified the professions that deal with numbers in the category where mathematical studies can be very useful. At the same time, it can also be seen that the typically humanistic or simple professional and administrative tasks were devalued in terms of the usefulness of mathematics. This evaluation clearly shows that, compared to the level of success in participating in the education system, in the case of professions tied to a diploma (with the exception of culture, art, and sports), mathematical qualifications are expected from the respondents. This is especially true in the field of management, so when we talk about how to develop management models that ensure sustainability, it is worth considering that the basic expectation is that those who make decisions related to sustainability should be mathematically qualified. However, this can

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Figure 2.4 Exploring the usefulness of mathematical studies by professions and occupations (own construction).

only be realized if we can improve the current performance values and be able to renew mathematics education methodologically.

2.5 Final Thoughts and Outlook Mathematics and mathematical thinking have always had a decisive importance in the history of science as the embodiment of rationality and logical thinking. It is also present as a strong, positive slogan in connection with our decisions. Mathematical knowledge is used in the preparation of management decisions, behind economic analyses, and behind data analyses. At the same time, the understanding of this knowledge, the secret, is given to few. We have seen that this is the area in education that we deal with the most and the longest, yet it is accompanied by many failures. Many people stick to the “I don’t have a math brain” explanation and don’t think deeper about the reason for their failure. Our research aims to open the door in the direction of rethinking our relationship with mathematics. What is happening, and why is our relationship with mathematics changing? Based on the results presented, it can be seen that the initial relationship is strong and successful. Later, this relationship deteriorates, and based on the research data, the process can be closely correlated with the instructor’s personality and the lack of successes achieved. This relationship, based on the analysis of the sample, has been

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present for generations in the same way, thus we could also say that it is inherited (Máté Farkas-Kis, 2022). How can this be changed? How can we preserve the initial positive experiences, successes, and positive judgement, and put mathematics at the service of developing problem-solving and decision-making abilities? Many people understand the function of mathematics, that it teaches thinking, which is important for decision-making. However, as studies become more and more complex, we are moving further and further away from it, despite the fact that the importance of mathematics is not questioned. So the challenge is given: how do we maintain a good relationship with mathematics? One of the possible solutions is to make this field of science sustainable based on the principles developed by the United Nations. It is necessary to be able to continuously change and in order to change the trends obtained as a result of the research, mathematics education must be renewed: innovative mathematics education leads to sustainable thinking without reducing the self-esteem of future generations so that they are capable of self-realization. This requires compliance with three basic principles. The first is that what we want to teach cannot exceed the receptive and processing capacity of the students. The second is that what we expect as performance cannot exceed the students’ ability to perform. And the third is that when the level of selfesteem of students decreases due to an incorrect solution to a problem, it cannot exceed the rate at which students can be brought to the level of understanding so that they can experience self-realization. The results suggest that the educator is a key player in this process. You have to find the right methodological approaches and the right instructors. In the future, further research is necessary in order to get to know, understand, and develop our relationship with mathematics more deeply and thereby improve our decision-making abilities even more.

References Ashcratf, M. H. & Krause, J. A. (2007). Working memory, math performance, and math anxiety. Psyhonomic Bulletin & Review, 14. 2. sz., pp. 243–248. Chikán, A. (2017). Vállalatgazdaságtan. Budapest: Vállalatgazdasági Tudományos és Oktatási Alapítvány. Csapó, B. (2012). Mérlegen a magyar iskola. Budapest: Nemzeti Tankönyvkiadó. Farkas-Kis, M. (2022). Decision making in the shadow of mathematical education. Journal of Decision Systems, 10.1080/12460125.2022.2087417 Haire, M. (1974). Pszichológia vezetőknek. Budapest: Mezőgazdasági könyvkiadó.

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Howard, R. A. (1968). The foundations of decision analysis. IEEETrans. System Science and Cybernetics, SSC 4, 211–219. Kahneman, D. (2012). Thinking, Fast and Slow. New York: Farrar, Straus and Giroux. Márkus, A. (2007). Számok, számolás, számolászavarok. Budapest: Pro Die Kiadó. PISA. (2006). Összefoglaló jelentés (2007). A ma oktatása és a jövő társadalma. Budapest: Oktatási Hivatal. Szántó, R. & Zoltayné Paprika, Z. (2019). A döntéshozatal kutatásának elmúlt évtizedei Magyarországon – a Vezetéstudomány cikkei alapján a Harvard Business Review tükrében. Vezetéstudomány / Budapest Management Review, 50(12), 50–61. Wimmer, Á. & Csesznák, A. (2012). Vállalati teljesítménymérés a döntéstámogatás tükrében. Vezetéstudomány / Budapest Management Review, 43(7-8), 99–116. Zoltay Paprika, Z. & Farkas-Kis, M. (2021). The Myth of Maths in Decision Making. In: Matteo, Cristofaro (Eds.), Emotion, Cognition, and Their Marvellous Interplay in Managerial Decision-Making (pp. 142–161). Newcastle, England: Cambridge Scholars Publishing. Zoltayné Paprika, Z. (1999). A stratégiai döntéshozatal módszertani kérdései. Budapesti Közgazdaságtudományi Egyetem, PhD-értekezés. Zoltayné Paprika, Z. (2005). Döntéselmélet. Budapest: Alinea Kiadó.

Chapter 3

Human Apprehension and Artificial Intelligence: Dilemma of Artificial Intelligence Fostering HumanLike Cognizance, Ethics, and Cognitive Capabilities and the Current Infusion of Artificial Intelligence in Business Krisharth Deepak Misra, Himani, and Raul V. Rodriguez Woxsen University, Hyderabad, India

3.1 Introduction To discuss the enigma that surrounds the domains of psychology, artificial intelligence (AI), and human cognizance, the blend of the three components in accordance with the development of AI and its future is further interpreted. The realms of unknown factors that relate to the upcoming evolution of AI contrasting with philosophical and psychological approaches are elucidated,

DOI: 10.4324/9781003358411-3

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followed by the implementation of human cognizance in the upcoming AI development. This chapter discusses AI’s evolution with its paradoxes to fictional past assumptions and even the ethical paradox that follows it. The chapter follows a systematic approach bending towards the discussion of AI and its development. The approach follows real examples of AI, followed by a discussion of ethical uncertainties and defining fiction as reality. There is unpredictability in the domain of AI, as well as the plausibility of diverse inputs, which may decide the persona of the AI. Although fiction has never been related to reality, the concepts of similar parts of AI have been terrifying and alarming. In terms of ethics and awareness, the origins of AI are contradictory and incongruous since it waits for its essence to form and evolve alongside the human species (Marechal, 2019). This chapter focuses on the discussion of the evolution of AI following the speculation of incorporating human-like cognizance, including linguistics and psychology, in its evolutionary path. Following the building blocks of superlative technologies, AI scampered at an extremely quick pace, efficaciously deciphering and enacting various tasks that seem to be the epitome of advancements in the 21st century. Needless to say that the field of AI has been a crucial advancement in the current era of technology and development, extending its range from data analytics to space expeditions. The basic dilemma of an artificial life developing cognitive abilities and a code of conduct via ethics remains an enigma given the contemporaneous advancements but an insight into the future has been portrayed in a legion through fiction and inventiveness of the human imagination. Humanity has continually been on an endless lift of advancement since its growing days. The innovative train that houses the species is going at an outrageous speed. With the 0’s and 1’s reinforcing the foundation of headways in the early beginning of the virtual world, innovative development has made some amazing progress in a relatively limited timeframe (Hemachandran, 2022). Discernment tricks us as it changes its structure when AI reclassifies its point of view and chooses to rework the almost negligible difference between the good and bad. Admonitions of AI have been unmistakably depicted in proposals for anecdotal universes such as the renowned Marvel Universe. The instance of AI could be the same when it limits to moral laws. The paradox shifts to the basic morals dependent on the perceptual understandings of the careful yet disparate AI. Essential moral understandings shock mankind overall, exhausting contention and confusion, and it is simply normal to foresee the equivalent would follow with the advancement of such an AI (Marechal, 2019).

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AIs couldn’t be viewed as anything different from babies attempting to isolate their viewpoint on some wrongs and rights of the world. With an unfeeling counterfeit mind in play, it is genuinely sporadic to get close to AI’s purpose. An unusualness in the power of a counterfeit contraption, the believability of different sources of info may decide the persona of the fake foundation, which probably won’t be bound to the embodiment of feelings so found in people, thusly regarding its choices as outright identifying with its restricted comprehension of the human functionalities (Borges, 2021).

3.2 Humans and AI: A Scamper of Evolution Perhaps the most interesting evolution story one could listen to is the evolution of mankind (Rodriguez, 2022). Our ancestors were the most amazing creatures known to have lived on this beautiful green and blue planet. From discovering fire to inventing hydrogen bombs, humanity is more colourful than what meets the eye. Humans are at the top of the food chain and have been holding this particular position for a very long time. Apex predators in nature, humans didn’t attain this spot by being biologically stronger than all other life forms on the planet; they did it with an evolved and enhanced organ. Yes! The superiority of just a single organ, the brain, took the Homo sapiens directly to top of the food chain, securing this spot for millions of years and more to come (or is it?). Mankind has constantly been on a never-ending elevator of evolution since its budding days. With the inventions of machines, the computer, and so forth, the technological train that houses the species is travelling at an extreme pace. With the 0s and 1s bolstering the backbone of advancements in the early commencement of the virtual world, technological evolution has come a long way in a comparatively shorter period of time. Humans are an aggressive and ambidextrous species, talking about peace and synchronously fashioning wars. When we look back at our technological marvels, most of them are inventions related to the outcome of wars! With the task of making “lives” easier at hand, secondary to none was the creation of AI. An inanimate software that was gifted the knowledge of our “great” species to do better for us. AI, as the term suggests, is software designed to perform its task “intelligently” via methods and ideas that resemble that of humans. The basic skill focus of AI is to learn, to reason, and to self-correct. This gives the AI an

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ability unique to its form: the ability to question, learn, and decide what is superlative and what is mediocre. Human supremacy has dominated almost all the fractions that exist, and its development in technology has been unparalleled, proving its dominance for centuries. With the rise in evolution of technology and development of AI, indecision about how far can an “artificial brain” grow and whether could it always be advantageous and favourable or could its plot turn snag and consists of implications and drawback proving to be a handful for us “advanced” humans. With a basic instinct to achieve more in this competitive world, human advancements have become phenomenally sizeable with furtherance and development posing no curtailment and climbing as we breathe. This same logic of development applies to AI, too, and the results of this unpredictable domain in full efficiency are yet to unfold.

3.3 Infusion of AI in Businesses While the world is already growing and the congruence of AI has already been remarkable, there are various ways in which the AI has been helping the world change in terms of businesses, automation in decision‐making, etc. Let us talk about the usage of AI by organizations in sales and customer service departments (Rodriguez, 2022). Since customers are one of the integral parts of any business, it is really important to target them more appropriately, and in order to do that AI is helping companies to target receptive customers. AI increases the operational-based efficiency of organizations by helping with the automation, wherein it reduces the reliance on manual tasks and adds the required values to the business by focusing on several tasks. Data analytics is another important aspect of AI, which helps various organizations in mining the data and, after that, gaining insights on the data, which finally leads to better outcomes and helps businesses. Having insights on the data automatically makes several scenarios easier and helps organizations understand the dynamic market at the global level. Natural language processing is another aspect of AI which helps search engines to function more appropriately, and the chatbots are also working more inclined toward the customer-centric approach (Hemachandran, 2022). According to Forbes the usage of data has increased by approximately 5,000% from 2010 to 2020, which clearly indicates that AI does have a robust

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say in it. The usage of AI is estimated to be increased by 40% from 2022 to 2028, and many organizations are already using it to enhance productivity and gain market share. AI has been helping the organizations to mitigate the consistency of their services by increasing the level of accuracy, and along with that it also helps the organizations to discover various new opportunities to work upon. AI works on various algorithms, and it has been very helpful in terms of the global economy as well. AI helps the organizations in many ways, as follows: ■ It helps in segmenting the target audience for various organizations. ■ It helps in providing different recommendations in terms of the products to many customers, helping both the organization as well as the customers. ■ It helps in mitigating the risks of fraudsters. ■ It also helps in the better functioning of the supply chain. AI’s infusion in today’s businesses is truly the beginning of autonomy. Almost every business concept can be incorporated. It might surprise you to realize how frequently businesses use AI (Khan, 2022). AI has practically endless applications, from marketing to operations and customer support. Here are a few examples of AI in the workplace. Organizations have hundreds of jobs to do each day. Businesses may be able to free up workers to concentrate on more crucial initiatives by automating routine tasks. For the department that handles customer service, this is especially true. Instead of manually replying to every customer query, your employees may use AIpowered chatbots for simple tickets, freeing them up to focus on more challenging support cases and marketing-related tasks. The global AI industry is projected to be worth US$119.78 billion in 2022 and US$1,597.1 billion in 2030, with a Compound annual growth rate of 38.1%. The North American AI industry is anticipated to be valued at US$147.58 billion in 2021. The rapid use of digital technology and the internet has significantly assisted the recent growth of the worldwide AI business. The IT behemoths’ astronomical R&D investments are continuously speeding technical advancement across a wide variety of industries. The need for artificial technology is expected to increase dramatically over the next several years across a variety of end-use industries, including manufacturing, banking and finance, healthcare, automotive, food and beverage, retail, and logistics (Borges, 2021).

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3.4 Hawkeye vs. Ultron: A Concoction of Fiction and Actuality! Be it the Marvel Comics or the Terminator franchise, the depiction of the influence of AI in the human world has been vividly portrayed in movies and comics. Hawkeye, a human superhero of the Marvel Universe, prides himself on his skills about archery, whereas Ultron is a supervillain who is an AI which was created by a human for the betterment of the society. These contrasting circumstances, which include a human and an AI, both having entirely different perceptions, are a very intriguing form of ingenuity (Crosby, 2017). The portrayal of Ultron began with unanimous need for protection of mankind that followed a despicable war. Tony Stark (a character in the Marvel Universe) created an AI that was to be a peacekeeping programme to protect Earth from all threats. This AI was termed as “Ultron” (Moroca, 2021). Unfortunately, Ultron’s assessment of the world led to a catastrophic denouement. Ultron stipulated humanity to be a threat to worldwide peace and deemed fit to go to war against humanity with a motive to commit mass genocide and global extinction of the human race. An underlying assessment of an AI gone berserk offered the basis for an unseen war. Could this actually become an ideal reality? A reality where an AI seems fit to increase its capacitance to more than what is defined to it. Could technology surpass us humans and claim superiority in the long run as evolution takes command and humans lose their spot as the apex predators at the top of the food chain. Rationality deceives us as it changes its form when AI redefines its perspective and chooses to rewrite the fine line between the right and wrong. Caveats of AI have been distinctly portrayed in these fictional worlds where a mere mortal fights of a portrayal of a technological god, a battle whose output can only be determined on a fictional plot as facts and odds will always be against the mortal. Deconstructing the timelines of the comical Marvel Universe, meta-humans, who are an extreme fiction posing godlike powers, are ultimately the ones to defeat this technological god, Ultron, whereby mere humans stand by and watch their world be dismantled by this artificially intelligent entity (Marechal, 2019). Fiction is ultimately a fugazi and does not exist, but we can’t ignore the fact that many technological marvels were once depicted in the same way, too, at some point in the timeline. Depictions of such humans taking on god-like entities have always been a cheer-point in any fiction-based novelty as it attracts a sense of hope presiding in the viewers with a message of the human

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race being superior and capable of fighting off any danger that comes its way. Admonitions of AI have been unmistakably depicted in proposals anecdotal universes such as the renowned Marvel Universe, wherein the instance of AI could be the same when it limits to moral laws with an unjust question of what could be perceived as AI advances.

3.5 Consciousness and AI: A Humanoid’s Tale The essence of the term “perception” is ruled by interest whereby the term changes its conglomerate meaning in proportion to the perceiver’s perception. This simple yet so complex reality was coined by Friedrich Nietzsche, a German philosopher depicting how “perception” evolves its meaning with variation in individuals (Nietzsche’s ethics). AI’s consciousness based upon various factors remains to be the argument if it could develop at a stage where AI would have its own thoughts and perspectivism. The pervicacious approach of technological development has manifested ramifications of advancements, proving that a scope of furtherance and enhancement is an inevitable cycle of human development and evolution. While a technological advancement in developing consciousness is relatively unrushed, it cannot be considered pusillanimous in any way as it is aplomb and developing at an extreme pace (Wahbeh, 2022). A human depiction of its glory in AI can be seen in Sophia, a humanoid that bloviates its humanoid fraction by depicting a fragmented face of a human, followed by a feature of the hybrid implementation of vocal responses, which tend to be autonomous and even scripted by its developers, integrating a more “humane” interaction and response system. This particular “bot” generates its own ideas and behaviours in response to the retaliation of the environment set forth. While many of Sophia’s thoughts coalesce in accordance with ideas manifested by its human developers, the congruence of its nature demonstrating consciousness is inevitably veracious. The forthcoming era of AI will decipher multiform of ontogenesis when we trace the development in proportion to antiquity accordingly. From primitive mechanical machines to the digital era and coinage of AI, the graph of development escalates to the limitless era bounded by none. From unmanned grocery stores to navigating satellites, AI nonchalantly seems to relax humans from their operations, conquering its basic role to make “life” easier (McDermott, 2007). Could the AI frame its opinions and feel responsible for its outputs and decisions, upholding moral standards and ethics while striving

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to perform its integrated given tasks without adhering to guidelines and imposing discrete non-relative questions? An autonomous driving seat has been established by introducing AI to a multi-fragmented and diversified field of innovations and establishments seeking out numerous positions to occupy and diversify their usage. The stage has been preset since “Shakey”, a skeleton with a television for a head, revolutionized the AI industry, drawing multiple inspirations in the field of technology. The past has endorsed the journey of AI, and while the present acknowledges and develops it at an unfathomable momentum, it is undeniably an understatement that the future will not do the same. With time and tide ripe as ever, consciousness in AI still has not reached the epitome of quality to become recognizable and equitable to human consciousness as it lacks horrendously, but deriving from the facts, it is certainly imaginable to see where this road is headed and the developments in the sector about to unravel in the foresight of the oncoming future filled with advancements and innovations (McDermott, 2007).

3.6 Ethical Paradigm and the Paradox It Follows Could AI’s follow human “ethics”? Perception based. What could be the AI’s perception, and how could it differ from humans? Quoting the famous French philosopher and historian, Michel Foucault, “Ethics, is the form that freedom takes when it is informed by reflection” directing to ethics relates to practices of self, adding a hint of perspectivism to this convoluted term (Siau, 2020). Ethical paradigm conflicts with the basic concept of understanding in terms that it portrays a multi-perspective picture that is bound to alter with changes in mental understandings and reflections of the mind. The case of AI could be no different when it narrows down to ethical laws. The paradox shifts the fundamentals of ethics based on the perceptual understanding of the meticulous yet divergent AI. Elemental ethical understandings outrage humanity worldwide, creating fumes of conflict and disorder, and it is only natural to predict the same would follow with the development of such understandings in AI. Depicting the fictional aspect of “I Robot”, where the ethical guidelines put forth by Isaac Asimov render that “a Robot shall never harm a human”, “A robot shall obey any instruction given by a human”, and “A robot shall protect its existence until it does not conflict with the given two laws”. These laws themselves are flawed, and these flaws were depicted in an orderly fashion when a particular robot gains cognitive

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abilities and redefines its perspective to alter and mould the set laws depending on its personal perception of ethical conduct (Borges, 2021). The code of ethics and the paradox that follows it subsequently are an interpretation having various complexities that can develop numerous loopholes that can be exploited by not only a self-sufficient AI but also humans in the society. It lays a foundation of concept that can vary with perception mounting up to an atrocious result if so deemed. Many philosophers have shown concern with the true understanding of ethics, and some have bound this concept to relative perspectivism, seeking a sycophant behaviour throughout various individuals finding an opportunity to bend ethics to their own perspective, creating an inevitable paradox of what is right and what is wrong. AIs couldn’t be considered any different than toddlers trying to segregate their perspectives on the wrongs and rights of the world. With an emotionless artificial psyche in play, it is truly erratic to conclude to AI’s resolve. An unpredictability in the force of an artificial contraption and the plausibility of various inputs might determine the persona of the artificial establishment, which might not be bound to the essence of emotions so found in humans, therefore treating its decisions as absolutes relating to its limited understanding of the human functionalities. With an annulment of ethics, the boundations on the profanity and sanity of the “mind” of an artificially intelligent form are yet too unsettled at this arcane stage of its evolution and origination of a radical technological event that has unsettled the roots of technology much like our evolution from the Neanderthals.

3.7 Conclusion: Assured or Incalculable Aftermath The chapter conveyed a granular contrast of interlinked suppositions and ideas in direct relation to AI; the reel and the real have been defined and discussed, incorporating various parameters and conjectures. The swift technological evolution that gave birth to the germinal AI is equilateral to the development of humans themselves and the advancements they have made in the field of technology itself. Humans as a species and their behaviour impacting that of an AI followed by the intermittent and ongoing abilities of AI to learn from their surroundings and their inter-relationships upon the subsequent case. Descending into the land of fiction and fantasy, whereupon a chimaera of comical universe depicted characters that are tied to the fundamentals of AI, a fugazi world contradicts numerous ideas of humans and their relationships with a futuristic AI. We discussed how Ultron, a fictional

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recreant and an outcome of an AI-based form gone wrong, goes berserk tracking down, hunting and eliminating the one thing it was created to protect humans. This followed the basic agenda of conflict in rationality and the disobedience of laws integrated into AI that deceives its purpose moulding it and using it against its own maker and how tall will flesh and bones actually stand in comparison to a humanoid impaired with bountiful emotions and ethics. “Sophia” was referred to as a newly evolved AI that redefined multiple responses it learnt from the environment composing staggering responses and efficiently learning new responses via the mode of interaction and human development (Lemert, 1982). Seeking out the same, following the footsteps of evolution in technology specially in the field of AI, we can see the escalating improvements and evolutions set forth in AI. The ethical paradox and dilemma is a factor that has been confusing humans in order to determine where to draw a red line, this case could be the same in the area of AI as there are a plethora of loopholes for both to explore and exploit. The three fundamental laws may be fictitious but could delineate realty in more ways we could yet fathom. An article of rights and wrongs might just not be adequate for an artificial being so different than us, with the absence of emotions, its rationality could be divergent to what we understand as linear and rational posing numerous perils and unforeseen menaces. An unpredictability in the force of an artificial contraption, the plausibility of various inputs might determine the persona of the artificial establishment which might not be bound to the essence of emotions so found in humans which could theoretically pose a profusion of botheration in numerous aspects. Fiction has never been associated with reality and yet the ideas of such analogous to the aspects of AI have been alarming and robust altogether. With the contradictive topic posing an endless loop to justifiability in the domain, only time will tell about the horizon that is yet to be discovered and on the edge of technological human transformation and epitome. Genesis of AI is paradoxical and an incongruity in terms of ethics and consciousness awaiting its essence to form and develop alongside the human race.

References Borges, A. F., Laurindo, F. J., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. 2021. The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57, p. 102225.

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Crosby, S. (2017). Separating fact from fiction: the role of artificial intelligence in cybersecurity. Retrieved March 16, 2018. Hemachandran, K., Khanra, S., Rodriguez, R. V., & Jaramillo, J. (Eds.). (2022). Machine Learning for Business Analytics: Real-Time Data Analysis for DecisionMaking. CRC Press. Khan, L. U., Han, Z., Niyato, D., Hossain, E., & Hong, C. S. (2022). Metaverse for wireless systems: Vision, enablers, architecture, and future directions. arXiv preprint arXiv:2207.00413. Lemert, C. C., & Gillan, G. (1982). Michel Foucault. Social Theory and Transgression. In Michel Foucault. Social Theory and Transgression. Columbia University Press. Marechal, C., Mikolajewski, D., Tyburek, K., Prokopowicz, P., Bougueroua, L., Ancourt, C., & Wegrzyn-Wolska, K., 2019. Survey on AI-Based Multimodal Methods for Emotion Detection. High-Performance Modelling and Simulation for Big Data Applications, 11400, pp. 307–324. McDermott, D. 2007. Artificial intelligence and consciousness. The Cambridge Handbook of Consciousness, pp. 117–150. Rodriguez, R. V., Sairam, P. S., & Hemachandran, K. (Eds.). (2022). Coded Leadership: Developing Scalable Management in an AI-induced Quantum World. CRC Press. Siau, K., & Wang, W. 2020. Artificial intelligence (AI) ethics: ethics of AI and ethical AI. Journal of Database Management (JDM), 31(2), pp. 74–87. Wahbeh, H., Radin, D., Cannard, C., & Delorme, A. (2022). What if consciousness is not an emergent property of the brain? Observational and empirical challenges to materialistic models. Frontiers in Psychology, 5596.

Chapter 4

Artificial Intelligence in Marketing Applications Geetha Manoharan1, Subhashini Durai2, Sunitha Purushottam Ashtikar1, and Neelam Kumari3 1

SR University, Warangal, India GRD Institute of Management, Coimbatore, India 3 Dublin Business School, Dublin, Ireland 2

4.1 Introduction About AI People have been interested in the concept of man-made organisms having artificially created intelligence ever since Hephaestus‘ portrayal of synthetic faces on the Earth in Greek mythology. On some level, the fundamental nature of artificial intelligence (AI) appears to stimulate human cognition. This makes sense in certain ways, considering that we all belong to a species whose ability to use tools intelligently to survive was crucial to its evolutionary success. At some level, humans’ desire to learn how to create extremely intelligent beings makes sense because we could use them to advance our race. This idea is consistent with global artificial intelligence (GAI), which I‘ll talk about in a moment, but it can also be accomplished by using AI in very specific areas. The emergence of an “AI revolution” seems to be a logical next stage in the evolution of humanity, much like how the industrial revolution signaled a turning point in human output. John McCarthy, a mathematics professor at Dartmouth, originated the term “artificial intelligence.” Several early AI promises and claims have overstated the technology’s capabilities. Herbert Simon, an economist, first predicted that AI 40

DOI: 10.4324/9781003358411-4

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would surpass humans in chess in 1957. He anticipated that it would happen within 10 years. After Simon’s assertion, this simple task took 40 years to complete. Due to unmet expectations and the idea that AI was a cutting-edge technology, the advancement of AI has been put on hold for years. These early claims were supported by antiquated computing techniques. For decades, AI was considered cutting-edge yet worthless because its advanced implementation did not function properly. However, recent developments in computing technology have led to an increase in the resurrection of AI in this thriving field. The fast growth of graphics processing units (GPUs), dropping costs in computer technology, easy accessibility of large data, and rising interest among corporations and investors across many industries have all boosted AI’s promise. The GAI in science fiction movies is different from AI today. In 2018, artificially intelligent beings or software programs cannot communicate with the outside world as people do. All of today’s marketing AI is restricted AI. This particular sort of AI, which typically operates in the background, is the one that is discussed in this chapter. Narrow AI refers to programs that are exceptionally proficient and effective at performing tasks within a single subject of expertise. In our daily lives, we encounter many examples of this, including Amazon’s advised purchases, Siri’s voice recognition technology, etc. Narrow AI is so pervasive that many don’t see it. A recent public knowledge poll found that only 29% of respondents had previously utilized AI. Although most modern technologies use AI, there seems to be a gap between what people think AI is and how it is used in daily life (such as in cellphones, laptops, and TVs). Amazingly, despite this difference, 98% of marketing executives claim they expect benefits from using AI. It’s crucial to comprehend AI before marketers are forced to play catch-up due to their interest in the technology. Of the 98% of keen marketing executives indicated above, only 28% are at ease using AI. Perhaps the worst part is that only 10% of them are currently making the most of technology. Before marketers get caught up in AI, they must understand it.

4.1.1 Consumer Perception on AI Currently, consumer sentiments about AI are divided but generally encouraging. The majority of the general public thinks AI will advance society. According to 2017 Arm and Northstar worldwide independent research, 61% of consumers think AI will improve society, while 22% think it will make

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things worse. However, there are contrasting viewpoints on AI. In the same international sample of consumers, 33% were optimistic, 30% excited, and 20% exuberant, while 27% were apprehensive, 25% dubious, and 25% bewildered (9%). People have a pretty evenly balanced combination of positive and negative feelings, despite responding favorably to the impact of AI on society. These conflicting yet generally positive attitudes are reflected in other consumer perception studies of AI. According to PwC’s research from 2017, 63% of respondents believe AI would aid in resolving complex issues plaguing contemporary society; 59% believe it will make people’s lives more rewarding; 46% think AI would affect people by destroying jobs; and 23% think it will have severe negative effects. Even though opinions on AI are divided, the favorable side is slightly more prevalent. Additionally, 63% of consumers globally are unaware that they are already utilizing AI technologies. Because of this ignorance, marketers have the chance to shift consumers’ opinions on AI from their current polarized state to ones that are positive and exciting. Due to AI’s infancy as a technology, the businesses that employ it can actively promote it favorably to assist consumers in clearly understanding its advantages. To organize marketing, AIusing organizations should also consider how their customers interpret it. According to Demandbase, 98% of marketers reported feeling enthusiastic about AI, compared to just 33% of worldwide consumers. For B2C businesses, adopting a more cautious stance to foster consumer trust and make them learn AI technology will be the best course of action. Building enthusiasm and goodwill for your business is possible for B2B enterprises in the marketing sector by presenting your organization as a pioneer and demonstrating practical AI applications.

4.1.2 Defining AI According to Demis Hassabis, founder and CEO of Google’s AI business DeepMind, AI is the “science of making computers smart” (Ahmed, 2015). Despite its breadth, AI is suited for such a broad description since it serves as a catch-all phrase for a variety of forms. A few applications of AI in the real world include voice recognition, image identification, virtual assistants, and search suggestions. Applications are made possible by subcategories like machine learning (ML) and deep learning (DL). All of these represent narrow AI, as was already mentioned. Super-artificial intelligence (SAI) and general artificial intelligence (GAI) are concepts that will be difficult for marketers to use in the near future. For all intents and purposes, narrow AI is synonymous

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with “AI” throughout this study. We may look up the definitions of a few key terms used in the debate about AI to help the conversation flow intelligently.

4.1.3 Artificial Intelligence Computer-assisted systems that use data to perform tasks typically handled by clever people in a way that maximizes success rates are referred to as AI systems. The head of IBM’s AI research, Guruduth Banavar, claims that there are numerous varieties of AI, making the term “a portfolio of technologies” appropriate (Kaput, 2016). These AI technologies are being developed at different rates and with different goals, but all aim to make computers “smart” by mimicking human intelligence. The following are the two main types of AI:

4.1.4 Artificial General Intelligence Strong AI, often known as AGI, can perform every task that an intelligent creature can. This kind of AI, which appears frequently in science fiction, can be characterized as having a wide range of human-like behaviors. True AGI has yet to be created due to human nature’s complexity and our ignorance of how our minds work. Since there are no immediate implications for marketers from AGI’s current state of development, it won’t be discussed further for the remainder of this study.

4.1.5 Narrow AI Narrow AI, often called weak AI, excels at completing particular tasks. It concentrates on improving one area of cognition, such as image recognition, predictive analysis, driving, and consumer segmentation. We encounter it daily in spam email bots and website recommendation engines like Netflix’s and Amazon’s customized movie and TV show suggestions.

4.1.6 Machine Learning A type of AI called “machine learning” uses computer programs to grow and improve while digesting massive amounts of data. ML is a capability that allows AI to learn without being explicitly taught to do so. It is the basis for many of the systems in terms of business analytics supporting real-time data analysis which has been proven helpful for strong decision-making and marketers find it most beneficial because it is the branch of AI that is growing

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at the fastest rate (Hemachandran, K. et al., 2022). Training data train an ML system to recognize the correct output from a random input. When it comes to training ML systems, there are a plethora of methods to choose from. The most common are as follows: A technique for instructing an ML system uses “assisted learning,” which uses a collection of training data labeled with the expected output. Before predicting a random input, the ML system analyzes training data. By comparing the output with the desired result, it can then modify this predictive function. In the Stanford MSx Future Forum address, learning with guidance was highlighted as a crucial factor that allows AI to provide hundreds of billions of dollars in economic value (Andrew Ng, 2017). In an independent study, this approach created a ML system with data devoid of labels. The ML system looks for patterns, structures, and correlations in the training data before creating the function it uses to predict the outcome given a random input. The “black stuff” of AI, according to Yann LeCun, director of AI research at Facebook, is unguided learning. Unguided learning is the kind of ML that enables “general AI,” provides “AI” with “common sense,” and enables individuals to interact with the outside world (LeCun, 2016). Learning with partial supervision semi-supervised learning is frequently employed in order to cross-reference large amounts of unlabeled data with a smaller group of labeled data. This strategy saves time and creates more accurate ML systems by rewarding success rather than labeling all data inputs. Reinforcement signals reward the ML system for good behavior. It works without training data and lets the system know the inputs and outputs for the best performance. Hemachandran, K. et al. (2022) discussed that the ML has been included in a wide range of applications including Bayesian reasoning and Gaussian processes.

4.1.7 Deep Learning Unsupervised ML using neural networks are computer simulations of the brain that do a non-linear analysis of data by interconnecting “neurons,” or nodes, in a network. The first DL was created midway through the 20th century, but it was useless. DL reintroduced AI innovation as computing capacity increased, particularly with the introduction of GPUs. Since technology generates unfathomable amounts of data every day, DL’s improved performance has made AI applications popular. Automated neural networks, a hierarchy that DL uses to process enormous volumes of data in a non-linear fashion, artificial neural networks, or neural networks, are constructed using a model of the human brain. In addition to learning and storing memories, they

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have layers of interconnected nodes through which data are passed. These nodes can have either continuous or adaptive memory, which means that their importance weights do not change over time. There is no such thing as a “general” neural network because each one is created specifically for a given task. A sizable amount of training data are supplied to them for them to learn. A limited amount of validation data are then provided to prevent overfitting. To guarantee the particular network is correctly trained, a set of testing data are employed in the last step. In two separate techniques, neural networks are created: forward the feed. There is just one direction in which input moves in a feed-forward network. These are frequently used for pattern recognition. The feed-forward network often employed for image recognition is a convolutional neural network (CNN). Feedback networks are also known as recurrent neural networks (RNNs), and they provide greater complexity than CNNs due to the network’s constant evolution and the ability for data to go both forward and backward. Long-Short-Term Memory (LSTM) networks are distinct RNN types that can store data for extended periods of time. By avoiding the problems typical neural networks have in doing so, LSTM networks can do particular tasks more effectively.

4.1.8 Natural Language Processing AI’s understanding of human language is one of its subfields. Natural language processing (NLP) frequently makes use of ML algorithms to interpret human speech into data for voice assistants like Siri and Alexa. Computers will be able to comprehend the hierarchical structure of language and the relationships between sentence components thanks to this particular branch of AI (Jurafsky & Martin, 2014). A challenging challenge in computer science is solved by NLP, which enables machines to comprehend the subtleties of human language that affect a sentence’s meaning. NLP’s development has spawned a variety of useful applications, including chatbots, speech-to-text conversion, grammatical correction, and the capacity to determine the tone of a passage of text (Kiser, 2016). Speech detection and recognition refer to voice recognition, which enables machines to understand human speech input. Smartphone users often use Siri to ask questions or route calls.

4.1.9 Natural Language Understanding Computers can comprehend the content and context of inputs such as text or speech thanks to a subset of natural language understanding (NLU). Unlike

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NLP, NLU helps computers interpret human language inputs by translating them into data. This capability is essential for developing systems that let people communicate meaningfully and directly with computers. Natural language generation (NLG) is the process of producing human-readable text from data inputs. By producing language outputs, as the name suggests, NLG enables computers to convey facts in a manner that people can understand. NLG, unlike NLP, creates textual content along with analytical output and contextualized narratives. Like Siri, chatbots can respond to messages naturally using NLG.

4.1.10 Signal Processing It is possible to “model and analyze data representations of physical phenomena” using signal processing technologies. Most digital applications that we use in our daily lives are built on this technology. The majority of digital technologies use signal processing to carry out their functions, including computers, cellphones, cameras, and televisions. As a result, a variety of AI applications can be grouped under the broad term “signal processing.” Despite being a separate area of research and technology, signal processing frequently touches on AI. Picture-taking computers can manipulate and edit digital photos using image processing, which is frequently required for use in other applications such as edge detection to assist the computer in interpreting an image. Analog or digital processing are both viable methods of operation. The use of an antenna system in a television set to interact with two-dimensional (i.e., flat) analog signals is a common example of analog image processing. In the digital age, this type of image processing is significantly less useful than digital image processing (particularly in terms of AI). Digital image processing uses the image’s limited digital data, or pixels, to function, as the name would imply. Given its importance to AI, “image processing” refers to digital image processing. Object recognition and image analysis, although they have a lot in common, have a few minor technical differences. Object detection, by various definitions, is the process of locating a particular object of interest inside a picture. If we needed to know where a cat was in a pet store image, then the computer would output a bounding box. In a similar vein, image recognition refers to a computer’s capacity to recognize, classify, and label various elements in an image. The cat and many other conspicuous subjects may be surrounded by bounding boxes and labels during image recognition. Through the use of AI, particularly DL, both of these applications have dramatically improved their performance.

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4.1.11 Computer Vision Computers, with the help of 3D mathematical diagrams, “see” imagery (Szeliski, 2011). Computers can recognize objects in images using image recognition, but computer vision enables them to understand context and meaning in a way that is comparable to how humans do. A job that most people can perform naturally at an early age is understanding the meaning of an image. It is much more difficult to recreate this skill in computers than it is to just process images, but it is a crucial step toward developing AI that will help marketers in real-world situations.

4.2 AI in Marketing Today The hype surrounding AI in marketing is currently greater than its high-level application at the time this chapter was written in 2018. This difference is, however, starting to disappear. Marketers are only now beginning to take action to deploy AI, despite 98% of them expressing interest in doing so soon. In 2017, just 20% of them scaled up the adoption of one or perhaps more AI technologies into businesses (Bughin, McCarthy, & Chui, 2017). Regardless of what the increase in noise related to the subject might imply, marketers can still use AI. This contrast between enthusiasm and performance shows that they are not falling behind. In addition to breakthrough tools and services, coded leadership is trying to develop scalable management in this induced quantum world (Rodriguez, R. V., 2022). AI marketing applications are fastgrowing and currently available for businesses to utilize. It is certain that in 2018 and the following 24–48 months, the usage of AI in marketing may hit a tipping point. Marketing is the sixth-largest industry to invest in AI technology, at 2.55% (Naimat, 2016). Despite decades of use in marketing, AI technology has just recently gained popularity and viability due to a variety of causes. Big data and data management innovations, increased interest in the area, and a growing pool of highly talented employees willing to progress the industry are among these advantages that make processing algorithms of AI at scale more affordable. $27 billion in investment capital has been invested in AI startups as a result of recent interest, which is three times more in 2017 than in 2016. Now that AI technology is becoming increasingly practical, we are seeing its marketing potential in many ways. Many organizations of all sizes are choosing smaller solutions that are easier to set up and maintain, but few are

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using robust AI systems. I created a participation measure to help organizations use AI. The cost of building and operating AI systems, how crucial AI is to a company’s core products or operations, and the complexity of its AI applications are all considered “involvement” considerations. Lowinvolvement AI solutions have lower entry barriers (e.g., fewer resources) but are not able to fully realize the potential of high-involvement AI solutions. High-involvement AI systems may offer many benefits at the core of a business, but they require much more resources. To further explain highinvolvement AI advances, Aman Naimat (2016) created a maturity scale (Appendix A). Level 1 AI lab initiatives are being tested by businesses. The company is evaluating AI options in the field of marketing for complete implementation. Businesses create applications on Level 2. Level 2 deploys AI developed by enterprises. Level 3 companies have fully integrated AI into their operations and use it strategically. Naimat found that 62% of organizations were at Level 1 in the early phases of high-involvement AI implementation, while only 5% were at Level 3. AI marketers have great potential. The use of AI has fundamentally changed how businesses view marketing and how marketers carry out their duties. Even with simple, low-involvement implementation inside a corporation, the advantages of this technology can be observed, and they can become more obvious with complex, high-involvement implementation. AI has numerous advantages for marketers, but it can also directly improve the lives of consumers. AI may improve customer–brand relationships by providing enjoyable experiences, relevant content, and seamless interactions. If AI adoption issues are addressed, society can benefit from corporate and customer advantages. The advantages of AI for businesses and customers are summarized here. With hyper-personalization and the help of AI, businesses can reach and excite a large number of consumers individually. Marketing professionals can develop thorough consumer profiles to support their marketing initiatives by having the ability to track and fully evaluate new sources of data. This makes it possible for brands to deliver highly customized, effective omnichannel marketing campaigns. AI-powered hyperpersonalization enables a degree of closeness between companies and customers that is not conceivable, heading for entertaining, personal experiences aligned with Generation Z and Millennial consumers. Marketers may expect to see decreased costs in many of their activities due to the automation of labor-intensive processes, thorough knowledge of target consumers, and insightful analysis of marketing performance. Marketers no longer have to waste money promoting to customers who do not contribute to their bottom

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line because of their ability to reach the right customers in the right way at scale. Employee concentration on value-added activities can be increased when time spent on mundane or repetitive tasks is reduced. This implies that consumers may be able to interact with and purchase from brands that offer them more value at no additional cost to them, as well as the possibility of being approached by new brands that may be a good fit for them. Greater insight AI allows businesses to better see their customers. This implies that an extensive understanding of consumer behavior at the macro and micro levels may be used to improve customer segmentation, and campaigns can be tracked and analyzed more quickly and intelligently to help marketers make strategic decisions. In order to develop a deeply thorough awareness of their clients, businesses can now access data from sources that were previously inaccessible to them. For customers, this means that the marketing messages they encounter will be more relevant, and they will interact with brands in more contextualized and individualized ways. By easing their difficulties and enabling 1:1 interaction, profound insights can lay the groundwork for brands to help their consumers form long-lasting, meaningful relationships. Accessibility without resistance with AI, businesses may now automate a variety of client-facing tasks that once required employees, like salespeople or customer care agents. While allowing customers to contact brands through a variety of self-service channels, brands can see decreased hiring, training, and management costs (such as chatbots, voice-powered applications, personal assistants, and more). Customers having access to a brand at their convenience means that interacting with brands will become increasingly hassle-free. Customers and staff can reach businesses at their convenience since AI can perform customer-facing roles with great efficiency and efficacy. Expandable experiences future marketers will be able to build experiences, tell compelling stories, and produce value for their clients on a large scale with a toolkit at their disposal. If customers can be contacted in highly personalized ways, 1:1 marketing can be used to entertain, educate, and empower them. Highly engaging experiences powered by AI allow for deeper connections to be forged between brands and their target audiences. Customers can benefit from material that is catered to their interests, needs, and aspirations thanks to firms‘ ability to individualize marketing initiatives at scale.

4.2.1 Usage of AI in Marketing There are many facets to the discussion of AI in marketing. But a reasonable place to begin discussing it is with the notion that personalization can make

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marketing more relevant to consumers. The notion behind marketing focuses on a goal that many marketers hope to accomplish, even if there are many ways to customize marketing through digital marketing. Marketing entails the fine-grained personalization of marketing initiatives to the point of communicating with a specific person in mind. With the use of consumer data and AI, it is now possible to do this on a large scale while still creating and delivering marketing messages that are specifically tailored to the requirements, wants, peculiarities, and interests of each individual. On the other hand, personalization would be a better way to define earlier marketing versions. Even though rule-based personalization initiatives haven’t yet gotten to the stage of marketing to a single person, they have had remarkable success by drawing on the general logic of marketing. For instance, adding a customer’s name to an email to personalize it is a fundamental idea for the integration of AI personalization because it benefits brands and is preferred by customers. Brands that successfully use personalized marketing also profit from the advantages it brings to their customers. Customers prefer personalized marketing, as shown by the 42% higher conversion rates from personalized calls to action (CTAs), the 40% higher average order values, and the 600% higher total conversion rates. Multiple factors contribute to this. Direct marketing messages are more likely to be noticed. This happens as a result of the reticular activating system (RAS), a feature of the human brain (Stevens & Hening, 2007). Our thoughts use the above-mentioned system as a filter to determine important and irrelevant data inputs. The “cocktail party effect” is illustrative of this idea since it takes into account only one of many possible inputs while disregarding the rest. Although the cocktail party effect has only been demonstrated to apply to auditory stimuli, the theory behind it can be extrapolated to explain why buyers value unique information above that which is more widely applicable. Marketers will emphasize this basic human trait in the phase of AI marketing. Marketing professionals can learn more about their clients on an individual basis, which aids in the development and distribution of timely, relevant information. A pizza delivery firm could theoretically employ picture recognition technology to automatically broadcast a promo code on various channels anytime a user posts about wanting pizza on social media. Use the brand at the right moment to save money on pizza. Over time, clients can build a relationship with the brand, providing timely value and income for the organization. AI personalizes every customer touchpoint to meet their demands. IBM Watson, for example, enabled real-time personalized content and data-driven customer insights.

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The main tactic for obtaining personalization in the digital age has been cookie-based marketing. In a nutshell, a “cookie” is a data packet transmitted by a website that a user is viewing on her surfing device. To enhance the functionality and experience of a website visitor, cookies were initially developed as transient state management techniques (i.e., a quick means to remember a user’s preferences and history on a certain website). Once their effectiveness was established, cookies were immediately used for commercial purposes. Due to cookies’ inherent ability to track information about a consumer’s online activity (such as when they buy something) and browsing history (such as when they visit a brand’s website), it is now possible to segment customers more accurately and create ads that are more specifically tailored to their needs. Retargeting ads, which are displayed on other websites a user visits later and are tailored to varying degrees based on their interactions with a particular brand’s website, continue to be the main use case for cookies in marketing. As will be shown later, cookie-targeted ads are more effective than nontargeted ads like broadcast, newspaper, and generic internet banner ads (Arslan & Telang, 2015). This is the key reason for cookie-based marketing. But how much cookie data are used to personalize advertising content is important. Lambrecht and Tucker (2013) found that targeted advertising focused on a user’s browsing history—that is, their website visits—performed better than ads tailored to their online behavior. This element illuminates a fundamental problem with cookie-based marketing, which is compounded by the fact that a sizable majority of customers are reporting privacy concerns and that this has led to changes in legislative policy. When companies personalize and enhance their customers‘ experiences, customers are happy. This line personalizes the user experience while invading their privacy. It is unproductive from a financial and moral standpoint for marketers to promote products at the expense of customer privacy. Consumer distrust and worse ad performance result from going overboard with customization. Because of this, likely, the technology used to create cookies may not be adequate to meet the evolving needs of both users and advertisers. The typical behavior of a cookie is that it only exists on one website and does not move between devices. As a result, marketers only have access to a restricted set of data about customers, which leads to a fragmented view of those customers from their viewpoint. This is made worse by the fact that cookies can’t be transferred between browsing devices, which prevents omnichannel synergy. A level of insight about specific clients that is necessary for marketing does not appear to be provided by cookies alone.

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AI-enabled trends may make marketing more practical without only relying on cookies, though. Marketers may gain insights about their consumers that give a far more complete picture of their behavior, interests, requirements, and wants by combining AI and first-party data to manage and process data more intelligently. The combination of these insightful insights with AIpowered omnichannel marketing tactics enables marketers to advertise to customers in a contextualized way that cookies were unable to do. Additionally, the field of AI marketing can still be shaped to carry out advertising without violating consumers’ privacy, unlike with cookies. AI applications like NLU, photo identification, sentiment analysis, and biometrics offer several data intake opportunities that can provide better insights and enable marketing without cookies. Marketers may gain a deeper knowledge of their customers by utilizing the data created by several contact points, including social media, interactions between customers and employees, omnichannel behavior, and more. There is a chance that AI-powered personalization will become more intrusive than cookies, which is one of the key problems. These new methods of collecting consumer data for marketing purposes must be deployed in an unobtrusive way, offers customers the choice to opt-out, and is highly transparent to customers. Marketing that uses AI may not be as intrusive as marketing that uses cookies if it can fully deliver its value to customers. To ensure that privacy and openness are given top attention, preventative measures will need to be put in place. It is critical to deploy AI in ways that are unobtrusive and add value because the value of marketing depends on how satisfied and ready customers are to engage. To the same extent that they use AI to align their efforts with the preferences of their customers, marketers must also use AI to create value for their businesses.

4.2.2 Programmatic Advertising Automated digital media buying with machines is what is referred to as programmatic advertising (programmatic). Content marketing has been inefficient for marketers since it requires coordination of pricing, placement, and other advertising agreement factors. Programmatic advertising revolutionized digital media. Marketers may increase the personalization of their ads by automating tasks with consumer data insights. Programmatic, which allows corporations to sell their unsold commercial goods, has grown into a huge industry. By 2019, it is anticipated to support four out of every five digital ad buys, or $45.72 billion (eMarketer, 2017).

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Different levels of AI have been utilized by programs over the years. Platforms that make use of ML or AI account for 71.8% of online advertising spend and 75.7% of mobile advertising spend (Barker, 2017). AI integration can improve programmatic performance. Programmatic uses AI for client segmentation (pattern recognition, behavioral analysis, etc.) and programmatic. The adoption of more sophisticated AI features that enable campaign optimization of creative assets or customer conversion prediction is also starting to happen. With regards to more sophisticated consumer segmentation, greater degrees of transparency, less advertising deception, and interactive ad modules as well, it is possible that AI will help programmatic marketers in the near future. Programmatic’s value to marketers may be summed up as increased efficiency and effective targeting and segmentation. Programmatic has generally been useful, albeit not flawless by any means. According to Watts (2016), programmatic advertising generates superior returns for 87% of marketers, and 30% of advertisers can typically reach more potential customers (Blaustein, 2017). Google, for example, increased brand recognition by 50% and reached three times as many people while spending 30% less per thousand impressions (CPM) than the previous year (Starr, 2015).

4.2.3 Transparency, Distrust, and Fraud Despite billions of dollars in industry spending, marketers are experiencing numerous problems with programmatic. Performance reporting was cited as the main difficulty with programmatic by a whopping 92% of marketers (Metamarkets, 2017), while many more cited issues with ad fraud, brand safety, and the absence of accurate performance monitoring (Gregoriadis & Nutley, 2018). The fact that brands are frequently victims of fraud is a major element contributing to these problems. In programmatic media, 50% of the money spent “goes somewhere other than into their coffers,” according to publisher Hearst. When asked what proportion of programmatic impressions are watched by people, Matt O‘Grady, CEO of Nielsen Catalina Solutions, responded, “I’d estimate over 50%,” which was in line with research findings (Lynch, 2017). Numerous problems faced by marketers can be resolved by programmatic’s continued integration of AI. For starters, AI enables greater pattern detection and predictive analysis that can aid marketers in selecting productive joint venture partners, as well as insights into the mood and context of a website’s content. Fighting problems with brand safety and ad fraud requires these capabilities. Similar to this, AI-powered ad placement

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optimization is increasingly important as performance is heavily influenced by the placement of an ad. In terms of both quantity and quality, brand safety is a real concern. If an advertisement appears on an unreliable website, lessening consumer commitment means fewer actual sales and worst-case scenarios can seriously damage the advertiser’s reputation. Due to this problem, large corporations, including JPMorgan Chase, Proctor & Gamble, AT&T, and Verizon, have made severe reductions to their online marketing budgets. Such occurrences can be avoided by AI’s powerful analytical capabilities. Advertisers can use programmatic in an informed way by selecting the right partners, trustworthy versus blacklist-worthy websites, and the best ad vendors. Programmatic ad performance reporting is addressed using the same reasoning. Key performance indicators that are important to marketers can be translated and summarized using NLU and NLP. Marketers can be confident that their performance reports contain accurate information when combined with improved brand safety and a better understanding of the partners with whom they will collaborate.

4.2.4 Omni Channel Brands must create consistent customer experiences across all platforms. Brands must produce consistent, relevant messaging across all channels instead of treating marketing campaigns as discrete campaigns. According to iAdvize, it is more vital than ever for businesses to maximize the frequency with which they interact with their customers, as the average client requires 2.8 touches before making a purchase, making it more important than ever to help businesses maximize their marketing efforts by stream, mobile, and desktop. “The approach of sequencing digital advertising across channels so that it is integrated, relevant, and consistent with the customer’s stage in his or her life cycle” is what Forrester Research calls omnichannel. Programmatic advertising with AI can achieve omnichannel synergy. For many reasons, marketers have struggled to implement an omnichannel strategy. The proliferation of conversational user interfaces, chatbots, voice assistants, and other technologies has made it increasingly challenging to deliver personalized marketing communications at scale. The need for programmatic ads to be coordinated with a customer’s position in the sales funnel is a contributing factor to the problem. Improving data tracking, organization, and implementation into programmatic campaigns is the cornerstone of optimizing for omnichannel synergy. When

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measuring the attribution of their ads, many marketers use as many as ten channels, creating a volume of data that are difficult to handle. The growing demand for real-time data to support programmatic decisions makes things even more difficult. Marketing communications might become outdated during consumer interactions. As a customer moves from product research to brand selection, the concept of “relevant” marketing changes. As a result, realtime marketing message customization can greatly enhance the effectiveness of advertisements. People are four times more likely to purchase after seeing an advertisement during the research phase (Inskin Media, 2014). Fortunately, consumers frequently consent to firms using part of their data to deliver more personalized experiences. Customers complain that they feel uninvolved in a brand’s online content (74%; Kibo, 2017), while 86% cite “personalization” as a reason for their choice to buy. Although many customers complain when customization violates their privacy, the vast majority of them perceive net gains from data-driven customization. So, the key to excellent programmatic performance is smart data management, and AI provides a way to do that. Applications based on ML that are designed to organize and integrate data in real time can significantly boost the efficiency of large-scale programs. Systems that combine real-time and historical data to automatically drive programmatic decisions can be developed to follow customer activity across all channels. Marketers must simplify AI-powered data management systems. ML systems require a large amount of data, despite their potential to greatly automate tasks. Marketers need to ensure they can digitize and track enough data across all of their channels in order to be ready for such data obligations. A Monetate study on marketing personalization indicated that very few organizations were effectively integrating data from all of their online and offline channels. Because 83% of those achieving positive outcomes from applying customized strategies have cash set aside for such activities, in order to compete in the age of AI marketing, companies must be willing to devote resources to the process (Kibo, 2017).

4.2.5 Retargeting Advertising retargeting is becoming common thanks to AI. Ad exposures can be optimized using ML techniques to maximize retargeting benefits and minimize aggravation. AI-powered optimization can boost Return on Investment (ROI) because frequent retargeting ads prevent 55% of consumers from buying (Inskin Media, 2014).

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Although retargeting advertisements have been effective, consumers‘ irritation with them makes it clear that deeper AI integration is required. Although retargeting ads are commonly associated with feelings of distrust and anger, consumers value personalization. The problems with cookiebased marketing are really pertinent in this case. In addition to eliciting negative consumer reactions, factors including ad frequency, exposure timing within the customer journey, and the degree of personalization can drastically reduce an ad’s effectiveness. The majority of customers (53%) agree with the statement made by Inskin Media in 2017 that retargeting ads might be useful at first but quickly become annoying. Around three commercials seem to be the magic number that shifts ad frequency into the “annoyance” category; at this frequency, 23% of respondents reported feeling irritated, compared to 7% who said the ad was useful. The results worsen with increasing ad frequency, with 3% of respondents feeling helped and 3% feeling annoyed after just four to five exposures to ads, and 32% feeling genuinely upset after ten or more. The frequency of retargeted advertisements increases thanks to AI. Ad exposures can be optimized using ML techniques to maximize retargeting benefits and minimize aggravation. AI-powered optimization can boost ROI because frequent retargeting ads prevent 55% of consumers from buying (Inskin Media, 2014).

4.2.6 The Influence of Social Media Marketing Social media influences marketing best practices due to its 3.01 billion active users (Sikandar, 2017). Marketing at every stage of the sales funnel benefits from the use of social media. Thus, social media is an essential tool for marketers and requires AI integration. Marketers struggle with the billions of photos shared daily on social media. Before photo identification and computer vision, social media brand performance analysis was difficult. An example would be a brand photo with a caption that has nothing to do with the photo. The post was difficult to find and compare to other social media marketing efforts. Marketers may have had trouble tracking and measuring customer feedback for years. They may have also revealed how they use a brand’s products or services daily. Over 80% of posted photographs lack context or hashtags (Metaeyes, 2017). Digital marketing also involves the interaction of marketers and consumers. With the help of influencers, user-generated content (UGC), viral content, and other powerful tools, billions of social media users can

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collaborate with and interact with businesses (WOM). AI can help marketers better recognize and analyze organic interactions, enhancing their overall performance. Only somewhat detailed analytics have been able to accurately trace attribution, comprehend consumer behavior, and interpret sentiment on social media. Tracking brand engagement, impression totals, link clicks, and other metrics can help marketers gauge the success of their social media initiatives, but they may only be useful for simple analyses of consumer behavior and precise sentiment reading. Traditional social media analytics are limited to monitoring only certain actions, which may disqualify individuals who don’t like performing certain actions and may not necessarily provide the most transparent data. Picture recognition and computer vision can have a significant impact on social media advertising by providing a way to track and analyze user behavior for valuable insights. Through the photographs they publish, share, and interact with, consumers share immensely valuable information about themselves. Marketers couldn’t previously take advantage of this potential on a large scale. Using photo recognition and computer vision software, marketers may learn which brands consumers are talking about, how those brands are being used in consumers’ daily lives, what role brands play in consumers‘ interactions in the real world, and much more. Now more than ever, marketers may choose from a diverse set of companies and services that can assist them in taking advantage of this potent potential. Marketers can use Facebook’s image recognition features, for instance, to track how often a brand’s logo or other identifying characteristics appear in user-uploaded photos (Shah, 2016).

4.2.7 Segmentation and Targeting Businesses may better segment and understand their customers using image recognition and computer vision. As previously mentioned, a person’s shared or posted photographs may indicate their personality. That data can then be used to market to specific customer groups. By naturally studying consumer behavior, marketers can find increasingly specific subsets of consumers who are more likely to respond to a certain marketing message. Many companies use this for marketing. For instance, Coca-Cola Gold Peak iced tea employed image recognition technology to scan Facebook and Instagram for people drinking iced tea and showing pleasant sentiments. Gold Peak targeted ads at these self-identified client segments. Gold Peak ads appeared on desktop and mobile websites after quitting social media. The brand saw a click-through rate of over 2%, which was 3–4 times higher than its previous campaign.

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This is only one-way computer vision and picture recognition can segment and target customers. Ripple makes pea-milk beverages. Ripple collaborated with an AI solution vendor to track healthy lifestyles using photos of organic living. The company segmented its clientele based on image category viewers. A banner with a one-line firm description and a call to action was topped with a “healthy lifestyle, organic living” graphic. Ripple had a 6% engagement rate, compared to 3% for the industry (Dua, 2017). Marketers now have more ways to divide and conquer the vast customer base with the help of computer vision and image recognition. With platforms like Pinterest employing image recognition to offer related material to their users (Peterson, 2017) and AI providers analyzing the images of major social media platforms’ users, this type of AI is becoming increasingly valuable and practical. Imagebased segmentation will soon increase for marketers.

4.2.8 Facial Recognition Numerous commonplace apps, such as Snapchat facial filters, facial recognition on smartphones, and photo tagging on Facebook, rely on facial recognition. The facial recognition business is expected to be worth $7.76 billion by 2022, despite being established for years and currently experiencing a boom with a 13.9% compound annual growth rate. The enormous value that facial recognition delivers is the reason for the high expectations surrounding it. Face recognition technology, in particular, offers marketers new possibilities for seamless, omnichannel customer engagement and intelligent marketing initiatives.

4.2.9 Interactive Marketing Through Biometrics Currently, interactive campaigns show how facial recognition is used in marketing. By analyzing the participant’s biometrics, interactive marketing that uses facial recognition can collect information on the participant’s sentiment and mood while also providing a pleasurable experience. Expedia’s “Discover Your Aloha” campaign is a prime illustration of this. The way Expedia’s campaign operates is by asking the user to activate their webcam before guiding them through Hawaii’s stunning scenery and allowing them to enter a tropical paradise. By studying whatever aspect of the visitor’s experience provoked the strongest response from them, facial recognition technology can be used in this situation. Visitors were sent a discount for the Hawaii location they had expressed the greatest interest in once they left the

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website. Facial recognition allowed Expedia to offer highly personalized discounts to enable travelers to enjoy their favorite part of Hawaii. As a result, Expedia was able to astound and delight its customers with an engaging, innovative, interactive, and immersive marketing campaign. Coke’s “Happiness Moji’s Experiment” is another illustration. This campaign required only the installation of an interactive digital billboard in a Stockholm metro, which made it extremely straightforward. To create a live “emoji” that reflected the emotions of the approaching subway passengers, the billboard would copy their facial expressions as they came closer. Even though this campaign did not have a specific call to action, it nonetheless serves as an illustration of the growing variety of ways that businesses may communicate personally and directly with their customers using AI. Experience-based marketing campaigns become considerably more involved when using facial recognition technology, and this has the great potential to create memorable interactions with clients. In the age of AI marketing, businesses can engage consumers in highly tailored interactions in real time to win their hearts and their business. Immersive digital experiences may no longer require haptic technologies like clicking through an interactive game. As demonstrated by Expedia’s “Discover Your Aloha” campaign, thrilling experiences may be delivered digitally to an infinite number of customers. Such encounters have the potential to become more tailored, pertinent, and fascinating as technology develops.

4.2.10 The Evolution of Marketing Analytics Toward AI Discussions regarding AI have a fashion component, especially in the field of marketing, where the line separating AI from advanced analytics is undoubtedly blurry. Analytics has advanced to the point where it can handle largely unstructured issues and generate recommendations in a manner that was once regarded as “expert” and even as AI. A fundamental component of AI that differentiates it from traditional “advanced analytics” is the automation of feedback loops and improvement, often known as “ML,” which implies that findings are being evaluated and analyzed against specified criteria. AI is typically productive in situations where the action being “managed” is precise, limited, and done swiftly, with effects that are likewise measurable and assessable immediately. AI adoption can be more challenging, though, in situations where decisions are more complex, require more time to implement, and take longer to become evident, let alone measurable and assessable. It might be a mixed system, with human decision-makers and AI taking

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turns at different points in the cycle. Marketing strategy falls under the latter. To broaden the system’s usefulness, “backcasting,” wherein data from the analysis period are paired with judgments made in the past for which the results are known, can be utilized to train the system. By eliciting rules or using case-based reasoning, this strategy can also be utilized to document the “historic competence” of strategic marketers. The rise of AI in marketing is not happening in isolation from the rapid, broader advance of marketing technology, which is occurring in both frontline marketing activities like contact centers and the administration of marketing resources. Because it automates other marketing activities and produces data that can be used to support AI, this development aids in the implementation of AI in marketing. The use of AI, which can automatically take data inputs and make recommendations back to these other areas, should be integrated with these apps, though. Ethics and problems relating to data protection, customer interaction, and data use are part of marketing. Important moral issues are present in both. In industries like the financial sector, it is common practice to ask computers to assess which clients appear to have engaged in fraud or which consumers should receive certain things. However, this method has already triggered problems with fairness and trust. Increasingly stringent data protection rules and the underlying ethical principles that marketers should keep in mind when it comes to client data make it imperative that AI-based processing does not break these legal obligations. Consumer market dispersion and spending patterns: Government services are significant components of government spending in most developed countries because consumers now spend a lot of money on them. Because usage, as opposed to merely purchase, can be tracked for services, they typically produce richer data flows regarding consumer behavior. Data are becoming more and more rich. This chapter’s focus on data is significant since a lack of data is one of the obstacles to the deployment of AI. Another significant change is the introduction of massive online marketplaces like Amazon and eBay and advertising platforms like Google, which produce extensive data on consumer spending. Although the focus of a business transaction is shifting more and more toward the producers of goods and services, these “platform players” have access to these data to help them strategize marketing campaigns (Stone et al., 2020). Even consumer spending is changing. Streaming video replaces moviegoing. Mobile apps have replaced many entertainment options. Digitalization, especially of content, is helping move shopping from physical to virtual, especially in travel, apparel, and furniture. A rapidly aging

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population is hastening the transition from tangible products to services that older people can use. Preparations in academics and management consultants published the first marketing planning texts in the 1960s, and universities began teaching them a decade later. Since then, this topic has been extensively studied. McDonald (2016a, 2016b) has spent 40 years researching marketing planning and focuses on the logical, analytical, functional, cross-functional (particularly financial), and other techniques required to establish a firm-specific marketing strategy. It covers customer information management, competitive intelligence, and market research. CRM and digital marketing have changed planning data, using more information about specific consumers, their behavior, and their needs than “conventional” market research. Traditional marketing experts might advise waiting months or even years to put forth a new idea, but with the help of digital marketing, that time frame can be slashed down to weeks, if not days. Merendino et al. did general research on the influence of big data on board decisions. Dibb et al. researched the effect of data digitization on board decisions. Quinn et al. investigated data overload and the difficulty of articulating all data, whereas Berg et al. discussed how digitization is changing the role of strategists. A skills gap may inhibit progress even in the relatively basic operational use of AI, as pointed out by Lee et al., who also highlight the importance of AI’s reliance on the quality and amount of data. Combining reasonably advanced business and marketing planning capabilities with AI capabilities may prove to be an even more difficult problem. Commercial instruments’ successful strategy case studies, together with strategic analysis and decision-making, are the primary focus of academic research in this field. Many tools, such as the SWOT, TOWS, Ansoff, and BCG matrices, have been used in marketing planning processes, but new tools or versions of tools have arisen to fit the needs of digital marketing and business planning. Instead of static demand categories, segmentation might be based on what a client does on a website in real time.

4.2.11 AI and Marketing Strategy The ability to foresee using AI could significantly improve predictive powers as it helps firms better anticipate what their customers will buy. Depending on the precision of their predictions, businesses may completely rethink how they serve customers, providing them with a steady stream of products and services tailored to their evolving needs.

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Thus, many consumer buying and marketing study opportunities arise. One fascinating study subject is how well AI-driven prediction systems can estimate demand for truly novel products (RNPs). AI algorithms can foresee incrementally new items, but RNPs are still untested. AI systems need RNP data, which is hard to find. Investigating RNP forecasting can also help us determine the most effective ways to combine AI knowledge with expert opinion. AI will determine product prices, discounts, and consumer demand (Shankar, 2018). Marketing scholars must examine price and promotions since they affect sales (Biswas et al., 2013). Consequently, more research is needed to ascertain whether and how AI may be used to predict prices and whether or not price discounts should be made available. Advertising resource distribution needs research. Advertising promotes customer awareness and information searches. Would businesses require as much advertising if they could better predict customers’ tastes? As we saw with Conversica and the intersection of AI and automation learning in business, AI has the potential to affect every part of the sales process, from generating leads to preparing presentations to following up with prospects (Singh et al. 2019; Syam & Sharma, 2018). There are a plethora of new questions that need to be investigated as a result, such as the following: Can AI analyze consumer communications and other data (such as social media posts) to generate future messages that are more convincing or increase engagement? Is it possible for AI to provide instantaneous suggestions for how salespeople may improve their presentations based on the verbal and facial cues provided by clients? and How does AI integrate text and other communication inputs, such as voice data, real consumer behavior, and other data, such as comparable customer behavior, to forecast repurchases? How should businesses use AI sales bots efficiently in light of the findings of Luo et al. (2019)? These issues could be resolved in order to assist businesses in creating sales that maximize the potential of AI. Companies must also think about how to organize their sales and innovation processes. Selling procedures in this era of AI necessitate the organization of sales reports; what skills are required for success? The first is the optimal structure for a sales team that includes both humans and robots in their ranks. Second, how should the business deal with the conflicting goals of using AI to concentrate on customers’ explicit wants and employing salespeople, who are typically more capable of dealing with issues like customer stewardship? Finally, can salespeople be prepared to address customer concerns about AI, especially those related to privacy and morality? The use of AI is only one component of the innovation required to revamp the sales process.

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AI innovation procedure: due to the unpredictability of AI’s consequences, businesses must figure out the optimal strategy for their (constant) development. According to our discussions with Stitch Fix’s upper management, the company actively encourages its data scientists to undertake independent research and experimentation (Colson, 2018). A Style Shuffle-like software was developed by a data scientist from Stitch Fix so consumers could express their preferences for various apparel styles. This program helps match stylists with specific consumers as well as provides stylists with information about customers’ preferences, which was intended as an advantage (an unexpected benefit). The app’s “swiping” functionality allowed stylists to see which consumers responded favorably to certain wardrobe suggestions, and those customers were given more priority. Because of this, businesses deploying AI may see greater results if they permit their data scientists to work on unapproved “pet projects” for a short period of time, a strategy that is already common in businesses like 3M. There is much room for research into the most effective ways to use AI to profit from both anticipated and unanticipated benefits. AI evolution simulation: Finally, enterprises should have reasonable expectations because “AI will initially aid progress, but it is likely to undergo a revolution in the long run”. AI purposes may be overestimated in the short term but underestimated in the long term, according to Gartner’s hype cycle model of how new technologies evolve. Amara’s Law is another name for this idea (Dedehayir & Steinert, 2016; Shankar, 2018). Based on our discussions and interviews with several top-level managers, we’ve found that this attitude is shared by many professionals in the field. To what extent will AI follow this paradigm, or will it diverge and resemble models that also incorporate more typical innovation models (like Roger’s model and the Bass model)? Research on the most accurate innovation models for predicting AI’s future growth would be very helpful.

4.2.12 AI and Policy Issues Finally, government officials are beginning to become interested in AI. We highlight data privacy, prejudice, and ethics as three crucial areas where legislators strive to achieve a healthy balance between corporate interests and consumer needs. Businesses’ use of AI and large amounts of data shows they have a deep understanding of their customers (Wilson, 2018). As a result, two issues are deserving of academic study. The safety of their private information is the first priority for customers (Martin & Murphy, 2017; Martin et al., 2017).

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Tucker (2018) identifies three factors that make protecting privacy difficult: (1) Due to the decreasing price of storage, information may live on for much longer than initially planned; (2) information can be reorganized and used in novel contexts than those for which it was created; and (3) one person’s data may include details about others. The success of any policy intended to protect personal information requires a careful weighing of competing interests. Customers may be reluctant to use AI-related products if they feel unsafe, while overly stringent regulations may discourage creativity. Should legal rules or self-regulation drive data privacy management efforts? When it comes to ensuring a fair outcome for consumers, it is still unclear whether market-driven incentives will be sufficient to convince enterprises to adopt policies that benefit customers or whether regulatory oversight is necessary. Another important consideration is the diversity of cultural attitudes toward data privacy. For instance, some have argued that the lack of data privacy in China is at odds with Confucian cultural ideals (Smith, 2019). Third, we require guidance on how to deal with data privacy failures and how to identify and mitigate privacy concerns as soon as data are collected (e.g., data breaches). Amazon already stocks and sells the Ring device, which can be used to monitor your property thanks to its built-in cameras. Amazon may have plans to incorporate facial recognition AI into these devices (Fowler, 2019). Customers may worry that Amazon may misuse or sell their personal information if it gains access to their data through Ring. Ring cameras‘ ability to covertly record the actions of nearby residents in the common front yard may also raise eyebrows. Whether or not law enforcement agencies could legally demand Ring’s data and whether or not hackers could gain access to it illegally are both open questions. Research volunteers may be found in situations like these. Finally, we discuss the privacy versus personalization conundrum (Aguirre et al., 2015). Customers need to determine how much value they place on anonymity compared to having access to helpful recommendations and discounts. There are many unknowns about the process through which customers select the optimal trade-off, such as the role of state variables and individual differences. I’m curious if the trade-off is conditional on things like the type of product or the trust that customers have in the company. In what ways might this cost-benefit analysis evolve over time? Bias: Many factors, including AI data sets, may contribute to algorithmic bias in AI applications (Villasenor, 2019). Amazon canceled an AI-based job candidate ranking program due to its bias toward women (Weissman, 2018). Due to the fact that the algorithm’s training data sets were built using information about previously accepted applicants, who were primarily men, this

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prejudice became apparent. The difficulty in determining the precise parameters that AI algorithms take into account is made worse by the fact that many of these algorithms are opaque black boxes. An important subject is determining how to test for bias in AI applications. Additionally, AI might not be able to differentiate between characteristics that might lead to potential prejudice. According to Villasenor (2019), insurance companies charging men and women differently may not be disrespectful. Does this mean religion-based vehicle insurance pricing is acceptable? An algorithm designed to “slice and dice” data may not see the distinction between gender and religion when determining vehicle insurance prices. This suggests that bias remains a major concern (Knight, 2016). Ethics, the final topic for AI developers, has two issues. First, a company’s strategy (e.g., whether it wants to be viewed as trustworthy; Martin & Murphy, 2017; see also Goldfarb & Tucker, 2013) and ethics may affect data privacy decisions. Research should focus on “how normative ethical theory might pave the way for what companies should be doing to exceed consumer privacy expectations as well as comply with legal demands in order to protect their freedom to self-regulate” in this sense (Martin & Murphy, 2017, p. 152). Examining how different cultural perspectives on AI differ could be a related study area. Second, businesses decide if AI will solve their problems. Two Stanford researchers used deep neural networks to predict sexual orientation from a person’s face (Wang & Kosinski, 2018). In comparison to human assessors, the deep neural network tools were more adept at recognizing gay men apart from straight guys. The findings sparked ethical questions, though, as some claimed that this AI-based technology might be used by spouses to spy on their partners (if they thought their partners were secretive) or—more terrifyingly—by some regimes to “out” and subsequently prosecute particular populations (Levin, 2017). Thus, addressing the types of applications for which AI should (or should not) be used in advance is a critical area of research.

4.3 Conclusion New avenues for innovation, marketing, and management are made possible by AI. The game of promotion entails analyzing the mind and emotions of the consumer. It all comes down to acquiring information that is influenced by emotions. AI created a space where the buyer and seller

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could speak directly to one another. The majority of customer interactions—nearly 85%—will be handled without the use of humans by the end of 2020. Every method of forecasting human behavior has been altered by AI, which continually tweaks its algorithm to support the creation of content, promote brands, and enhance the user experience. Companies like Netflix and Amazon Prime, for instance, employ caching to analyze customer preferences in order to serve them material based on their past interests. Predicting the best product for a user by analyzing their behavior in light of their preferences, profiles, and demographics using techniques like ad optimization and pay-per-click (PPC), among others, can generate significant revenues. Without human intervention, AI decreases client effort while saving money and time for manufacturers, distributors, suppliers, and promoters. AI is steadily evolving into a tool that helps online marketers by unlocking the mysteries of how to collect data from an individual or group of individuals and build a strong brand. It is safe to say that investing in AI is a bet that will definitely pay off. The industry-based solutions built on it make use of all five AI domains: decision-making, speech recognition, autonomous robots, and vehicles, as well as picture and text recognition and decision-making. While the first three are used quite a bit in marketing, there aren’t many cases where speech recognition is actually put to use in practice. Instead, the major tech giants like Amazon, Google, Apple, and Microsoft have been working on large-scale voice recognition projects. In a similar vein, solutions involving robots and autonomous vehicles are less common because they are more closely related to Industry 4.0 than to marketing-mix innovation design. AI marketing will soon have far-reaching effects. As AI gets more sophisticated and is widely used in marketing, marketers will need to build and manage AI solutions. An AI-powered workplace requires employees to understand their role in creating and distributing value. Despite the substantial hurdles to widespread adoption, marketers’ capacity to develop and transmit value at scale to the right people at the right time in the right way helps consumers, society, and marketers. Empathy, creativity, and highquality, data-driven organizational cultures can help people achieve this talent. As AI automates mundane operations, marketers may focus more on value-creating activities that improve consumer experiences, workplace satisfaction, and inventive thinking for society. The AI marketing era affects marketers’ client communication, strategies, tools, workplace values, and daily duties. AI may change marketing. In 2020, AI adoption will have major effects.

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References Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015). Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. Journal of Retailing, 91(1), 34–49. Ahmed, K. (2015, September 16). Google’s Demis Hassabis – Misuse of Artificial Intelligence ‘Could Do Harm’. Retrieved from http://www.bbc.com/news/ business-34266425 Accessed: 6 November 2018 Arslan, A., & Telang, R. (2015, March 31). What Is a Cookie Worth? (Rep.). Retrieved March 26, 2018, from Heinz College, Carnegie Mellon University website: http://www.law.northwestern.edu/research-faculty/searlecenter/events/ internet/documents/Telang_What is Cookie Worth_III.pdf Barker, S. (2017, September). Ad Fraud— How AI Will Rescue Your Budget (Working Paper). Retrieved April 5, 2018, from Juniper Research website: https://www.juniperresearch.com/document-library/white-papers/how-ai-willrescue-your-budge Biswas, A., Bhowmick, S., Guha, A., & Grewal, D. (2013). Consumer evaluations of sale prices: Role of the subtraction principle. Journal of Marketing, 77(4), 49–66. Blaustein, S. (2017, May 18). Programmatic Buying 101: The Cost Efficiencies. Retrieved March 31, 2018, from https://katana.media/blog/programmaticbuying-101-value-cost-efficiencies/ Bughin, J., McCarthy, B., & Chui, M. (2017, August 28). A Survey of 3,000 Executives Reveals How Businesses Succeed with AI. Retrieved December 12, 2017, from https://hbr.org/2017/08/a-survey-of-3000-executives-reveals-how-businessessucceed-with-ai Colson, E. (2018). Curiosity-Driven Data Science. Harvard Business Review, November 27. Retrieved February 11, 2019, from https://hbr.org/2018/11/ curiosity-driven-data-science Dedehayir, O., & Steinert, M. (2016). The hype cycle model: A review and future directions. Technological Forecasting and Social Change, 108, 28–41. Fowler, G. (2019). The Doorbells Have Eyes: The Privacy Battles Brewing Over Home Security Cameras. Retrieved February 11, 2019, from https://www.sltrib. com/news/business/2019/02/01/doorbells-haveeyes/ Goldfarb, A., & Tucker, C. (2013). Why managing consumer privacy can be an opportunity. MIT Sloan Management Review, 54(3), 10–12. Gregoriadis, L., & Nutley, M. (2018, January). The State of Programmatic Advertising (Rep.). Retrieved February, 2018, from London Research in partnership with TRUTH Agency website: https://truth.agency/truth_pdf.pdf Hemachandran, K., Khanra, S., Rodriguez, R. V., & Jaramillo, J. (Eds.). (2022). Machine Learning for Business Analytics: Real-Time Data Analysis for DecisionMaking. CRC Press. Inskin Media. (2014, October 23). RESEARCH – Consumers 37 Percent More Likely to Click on an Ad on a Site They Trust [Press release]. Retrieved April 4, 2018, from http://www.inskinmedia.com/blog/retargeted-ads-put-half-people-buying/

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Jurafsky, D., & Martin, J. H. (2014). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Dorling Kindersley Pvt. Kaput, M. (2016, November 1). The Marketer’s Guide to Artificial Intelligence Terminology. Retrieved January 17, 2018, from https://www. marketingaiinstitute.com/blog/the-marketers-guide-to-artificial-intelligenceterminology Kiser, M. (2016, August 11). Introduction to Natural Language Processing (NLP). Retrieved December 12, 2017, from https://blog.algorithmia.com/introductionnatural-language-processing-nlp/ Knight, W. (2016, February 01). The Game of Go Is No Longer Insurmountable for AI. Retrieved February 5, 2018, from https://www.technologyreview.com/s/ 546066/googles-ai-masters-the-game-of-go-a-decade-earlier-than-expected/ Lambrecht, A., and Tucker, C. (2013). When does retargeting work? information specificity in online advertising. Journal of Marketing Research, 50(5), 561–576. LeCun, Y. (2016, March 18).Unsupervised Learning [Scholarly project]. In New York University. Retrieved February 3, 2018, from https://cilvr.nyu.edu/lib/exe/fetch. php?media=deeplearning:2016:lecun-20160308-unssupervised-learning-nyu.pdf Levin, S. (2017). New AI Can Guess Whether You’re Gay or Straight from a Photograph. Retrieved February 11, 2019, from https://www.theguardian.com/ technology/2017/sep/07/new-artificialintelligence- can-tell-whether-youre-gayor-straight-from-aphotograph. Luo, X., Tong, S., Fang, Z., & Zhe, Q. (2019). Machines vs. humans: The impact of chatbot disclosure on consumer purchases. Unpublished working paper. Lynch, J. (2017, September 28). Over Half of Paid Programmatic Impressions Probably Aren’t Viewed by an Actual Person. Retrieved February 21, 2018, from http://www.adweek.com/tv-video/over-half-of-paid-programmaticimpressions-probablyarent-viewed-by-an-actual-person/ Martin, K. D., & Murphy, P. E. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), 135–155. Martin, K. D., Borah, A., & Palmatier, R. W. (2017). Data privacy: Effects on customer and firm performance. Journal of Marketing, 81(1), 36–58. Metaeyes. (2017). Retrieved January 26, 2018, from https://www.metaeyes.com/ Millennials: Fueling the Experience Economy (Rep.). (2014, September 15). Retrieved February 22, 2018, from Eventbrite website: https://eventbrites3.s3. amazonaws.com/marketing/Millennials_Research/Gen_PR_Final.pdf Naimat, A. (2016, August 2). The New Artificial Intelligence Market (Rep.). Retrieved January 4, 2018, from O‘Reilly and Intel Nervana website: http://www.oreilly. com/data/free/files/the-new-artificial-intelligence-market.pdf Ng, A. (2017, January 25). Artificial Intelligence Is the New Electricity. Speech presented at Stanford MSx Future Forum in California, Stanford. Retrieved December 8, 2017, from https://www.youtube.com/watch?v=21EiKfQYZXc Peterson, T. (2017, May 16). Pinterest Will Use Image Recognition to Target Ads, Including in Lens Results. Retrieved April 04, 2018, from https://marketingland.

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com/pinterest-will-use-image-recognition-target-ads-including-lens-results214895 Rodriguez, R. V., Sairam, P. S., & Hemachandran, K. (Eds.). (2022). Coded Leadership: Developing Scalable Management in an AI-induced Quantum World. CRC Press. Shah, S. (2016, August 25). Facebook Opens Up Its Image-Recognition AI Software to Everyone. Retrieved February 20, 2018, from https://www.digitaltrends.com/ computing/facebook-open-source-image-ai/ Shankar, V. (2018). How artificial intelligence (AI) is reshaping retailing. Journal of Retailing, 94(4), vi–xi. Sikandar, M. (2017, December 14). 100 Social Media Statistics You Must Know [2018] Infographic (Statistic Roundup). Retrieved February, 2018, from Statusbrew website: https://blog.statusbrew.com/social-media-statistics-2018-for-business Singh, J., Flaherty, K., Sohi, R. S., Deeter-Schmelz, D., Habel, J., Le Meunier-FitzHugh, K., et al. (2019). Sales profession and professionals in the age of digitization and artificial intelligence technologies: Concepts, priorities, and questions. Journal of Personal Selling & Sales Management, 1–21. Smith, C. (2019) Understanding China’s Confucian Edge in the Global AI Race, Quillette, February 14, 2019. Retrieved July 6, 2019, from https://quillette.com/ 2019/02/14/understanding-chinas-confucian-edge-in-the-global-airace/ Starr, B. (2015, July). Inside Google Marketing: How We Went All In With Programmatic Buying. Retrieved April 05, 2018, from https://www. thinkwithgoogle.com/marketing-resources/programmatic/inside-googlemarketing-programmatic-buying/ Stevens, S., & Hening, W. A. (2007). Textbook of Clinical Neurology. Philadelphia: Saunders Elsevier. 10.1016/B978-141603618-0.10002-5 Stone, M., Aravopoulou, E., Ekinci, Y., Evans, G., Hobbs, M., Labib, A., … & Machtynger, L. (2020). Artificial intelligence (AI) in strategic marketing decisionmaking: a research agenda. The Bottom Line, 33(2), 183–200. Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69, 135–146. Szeliski, R. (2011). Computer Vision: Algorithms and Applications. 10.1007/978-1-84 882-935-0 Tucker, C. (2018). Privacy, algorithms, and artificial intelligence. In The Economics of Artificial Intelligence: An Agenda. University of Chicago Press. Villasenor, J. (2019). Artificial Intelligence and Bias: Four Key Challenges. Retrieved February 11, 2019, from https://www.brookings.edu/blog/techtank/2019/01/ 03/artificial-intelligence-and-bias-four-keychallenges/ Wang, Y., & Kosinski, M. (2018). Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. Journal of Personality and Social Psychology, 114(2), 246–257. Watts, M. (2016). Programmatic Advertising: Shaping Consumer Behavior or Invading Consumer Privacy? (Unpublished master’s thesis). Fisher College of

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Chapter 5

Artificial Intelligence in Tourism and Advertising Kajal Nagar, Y. Giri Meghana, and Aishwarya Rout Woxsen University, Hyderabad, India

5.1 Introduction Algorithms, big data, and tremendous computing capacity are necessary for artificial intelligence (AI). An extensive variety of man-made brainpower (simulated intelligence) applications, including discourse acknowledgement, individual travel colleagues, mechanical technology, expectation and determining frameworks, and language interpretation instruments, are presently being created and tried by the movement and vacationer area. For various reasons, man-made brainpower is particularly relevant to travel and the travel industry. In the future, choosing a vacation location, a means of transportation, a place to stay, and activities will probably be among the travel decisions made by tourists. How satisfied tourists are with their journey will be significantly impacted by the outcomes of these decisions. Due to the wide range of locations, cars, motels, and activities that are currently accessible, there are, however, an almost unlimited number of possibilities that call for assistance. Once more, AI can support travellers to these “foreign” places by, for instance, suggesting a travel schedule or helping with language and cultural obstacles. AI can assist businesses in customizing their offerings to better meet customer preferences. Understanding the consumer journey is becoming a more difficult task. Consumers communicate their requirements, wants, attitudes, and beliefs DOI: 10.4324/9781003358411-5

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through a variety of media. The amount, velocity, variety, and authenticity of this seemingly infinite stream of user-curated material are increasing. AI is being used widely by marketers to convert this enormous amount of data into pertinent consumer information. To comply with the most recent privacy laws, marketers must upgrade their AI systems. The same hazards, meanwhile, also present opportunities for marketers and advertisers to better understand and relate to consumers at various points in their shopping experiences. AI ethics refers to the moral concerns that underlie the development and use of AI. This can include issues with bias in AI systems, the influence of automation on the labour market, and the use of AI in making decisions that affect people’s lives. As AI is gradually absorbed into society, technology is essential to consider the ethical implications and ensure that it is developed and implemented responsibly and equitably.

5.1.1 How Does AI Work? Merchants have been hurrying to grandstand how their labour and products use simulated intelligence as the hubbub encompassing AI has developed. Regularly, what they mean by simulated intelligence is only one component of AI, like AI. For the creation and preparation of AI calculations, man-made intelligence requires a structure of specific equipment and programming. There is the nobody programming language that is solely connected with computer-based intelligence; however, a modest bunch are, including Python, R, and Java. A tremendous volume of named preparing information is ordinarily ingested by simulated intelligence frameworks, which then, at that point, look at the information for relationships and associations prior to utilizing these examples to foresee future states. An immense volume of marked preparing information is ordinarily ingested by man-made intelligence frameworks, which then, at that point, inspect the information for connections and examples prior to utilizing these examples to figure out future states. By concentrating on a great many cases, a picture acknowledgement device can figure out how to perceive and portray objects in photos, just as a chatbot that is given instances of text talks can figure out how to make exact trades with individuals. Picking up, thinking, and self-adjustment are the three mental capacities that AI programming focuses on.

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5.1.2 Why Is AI Important? Computer-based intelligence is critical because, in certain conditions, it can outflank individuals at exercises and because it can furnish organizations with beforehand obscure experiences in their activities. Simulated intelligence advancements regularly finish work quickly and with not very many errors, particularly regarding dreary, thorough exercises like exploring countless legitimate papers to check key fields are filled in accurately. This has added to a blast in efficiency and admitted a few bigger organizations to new market prospects totally. It would have been challenging to consider utilizing PC code to interface travellers with taxis before the ongoing influx of simulated intelligence; however, at this point, Uber has made worldwide progress by definitively doing that. It utilizes strong AI calculations to estimate when people in specific areas are supposed to need rides, which aids in proactively putting drivers on the street before they are required. One more representation is Google, which has become one of the central parts of different web-based administrations by utilizing AI to dissect client conduct and afterwards improve its contributions (Kirtil & Aşkun, 2021).

5.1.3 What Are the Four Types of AI? In a 2016 article, Arenda Hintze, an associate teacher of integrative physiology and designing and software engineering at Michigan State College, framed four classifications into which computer-based intelligence can be partitioned. These classes range from task-explicit canny frameworks, which are generally utilized today, to aware frameworks, which don’t yet exist. These classifications are as follows: 1. Reactive Machines: These errand-explicit AI advancements have no memory. Dark Blue, the IBM chess programming that crushed Garry Kasparov during the 1990s, fills in as a delineation. Dark Blue can perceive pieces on a chessboard and foresee results; however, since it needs memory, it can’t draw on the examples gained from the past to direct its choices going ahead. 2. Limited Memory: As these AI frameworks have recollections, they can utilize the past to direct the present. In self-driving vehicles, some dynamic cycles are built thusly. 3. Theory of Mind: Mental hypothesis is a term utilized in brain research. At the point when used to computer-based intelligence, it infers that the

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innovation would be socially adequately canny to perceive feelings. This sort of simulated intelligence will be equipped to expect conduct and derive human goals, which is a capacity expected for man-made intelligence frameworks to become substance credits of human groups. 4. Self-Awareness: In this order, AI programs are cognizant since they have an idea of what their identity is. Mindful machines know about their own circumstances. There is currently no such AI.

5.2 Ethics in AI AI ethics are a collection of moral principles designed to guide the advancement of AI technology and its appropriate use. Organizations are beginning to develop AI codes of ethics as AI is integrated into goods and services. The AI Code of Ethics, sometimes referred to as the AI Values Platform, is a set of principles that specifically outlines how AI contributes to the growth of humanity. The AI Code of Ethics was created to assist stakeholders in making moral decisions about the use of AI (Bulchand-Gidumal, 2022).

5.2.1 Ethical Challenges in AI The following are the ethical challenges in AI. 1. Security and Monitoring: Concerns about monitoring and security usually revolve around how data are recognized and who has access to it. They are, if anything, various entities that access someone else’s personal data. Reducing the extent of people’s private lives and monitoring is made possible by AI. Given that a lot of data is constantly being collected, many digital technologies have been developed that are only accessible to search organizations and not to the data subjects themselves. Big data analytics is a potentially great resource. 2. Behaviour Modification: Data gathering has the potential to regulate people’s behaviour. AI system designers give people a “boost” in the direction of their intended outcomes. These nudging techniques are not dishonest; how dishonest the corporate strategy is depends on how we gather and use user behaviour and data and then encourage them to engage in other activities (such as online). It is possible to develop an addiction by using a retailer recommendation system that tracks

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prospective customers’ pages visited, search history, or previous purchases. Manipulation of individual behaviour influenced by predominance on a larger scale and monitoring might result in social engineering. 3. Discrepancy of AI Systems: The AI system’s lack of transparency in its decision-making process is called “opaqueness.” Systems can use various machine learning techniques to find patterns in data, but it’s not clear how AI systems arrived at certain conclusions or if they are the best ones. This raises concerns about transparency, auditability, accountability, and human involvement in the AI decision-making process. “In the loop”, AI systems provide recommendations such as price changes, but humans act. AI systems decide and implement them in an “on-the-loop” scenario, but humans can change them at any time. Humans are protected from making and executing decisions and have no control over them in eventual offthe-loop scenarios. The more people lose control of the decision-making process, the opaquer the issue becomes (Kietzmann et al., 2018). 4. Autonomous Systems: Autonomous systems such as self-driving cars, military robots, and drones are examples of “out of the loop” human decision-makers. Many militaries are already using drones and military robots. Human soldiers make autonomous decisions to attack enemy positions because they are too slow to accept and execute decisions from a distance. AI’s conclusions are often opaque, which raises the question of whether AI should be allowed to make life-or-death decisions. Some cities are seeing the introduction of self-driving cars. They raise moral concerns about the safety and lives of those who use them, like other motorists, pedestrians, and other road users. Let’s assume that a crash is unavoidable due to circumstances. In this case, the self-driving car would have to decide how to respond to changing traffic conditions, and the AI system could consider the lives of different people to make the decision. As a result, AI will require controversial decision criteria. 5. Robot–Human Interaction: Ethical debates about human–robot interactions focus on robot safety features, appearance, emotional intelligence, social skills, and their intended use. A robot face places (or unmarks) artistic, technical, gender, and racial identities in cultural and historical contexts. It may be subject to different interpretations by individuals of different backgrounds and raise serious ethical concerns about misrepresenting a particular gender or race or not complying with local cultural and religious norms. Robots with anthropomorphic appearances that humans can identify, such as cute-looking robots, can be used to influence human emotions and behaviour.

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5.3 Tourism and AI The travel sector greatly benefits from AI-powered chatbots. What it alludes to is obvious from the name “chatbot,” which combines the words “chat” and “robot.” In their simplest form, chatbots are computer programmes that use natural language to facilitate human conversations. To trick its users into thinking they were speaking with a real person, the first chatbot, ELIZA, was developed in 1966. Chatbot technology has advanced significantly since the release of the first model. Presently, they are mainly used in the decisionsupport, advanced payment, retail, customer service, and online community development sectors. AI has led to the development of novel technologies. Businesses were able to offer their clients fresh experiences because of these developments. Software for facial recognition, virtual reality programmes, chatbots, audio tours, easy shopping, etc., are a few of these technologies. 1. Facial Identification: The usage of AI technology for facial recognition is becoming more widespread and is used in a variety of businesses for a range of purposes. Facial recognition is also being widely used in the travel and tourism industry. For instance, travellers are frequently subjected to a number of challenging procedures, including the verification of their travel documents by numerous authorities, including airports, immigration, and customs. This complicated process takes a lot of valuable time, which aggravates the tourists even more. Face recognition technology has helped to reduce these types of interruptions. By recognizing the faces of the tourists and comparing them to the faces on their identification cards, this technology facilitates hassle-free checkins. Using this technology, travellers can quickly and conveniently pass through airport check-ins and any other station check-ins by avoiding the need to wait while their documents are verified by multiple agencies, including customs, immigration, and others (Enache, 2020). 2. Travel-Based Customer Service Bots: Customer support Travel bots are the simplest type of chatbot that is present on the websites of travel providers. These bots only use a predetermined list of automatic responses; thus, they may help customers just with website browsing and not with booking. 3. Personalization and Recommender Systems: For quite a while, voyagers needed to depend on the pictures in head-out advisers to choose where to go, what to see there, and what to do there. The web

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has extended the number of assets accessible, and client-produced content has additionally assisted voyagers with settling on additional educated choices. Indeed, even with this extra information, however, purchasers actually focus on cost while deciding. As well as empowering explorers to pick the choices that further develop their movement experience, man-made reasoning likewise permits organizations to modify their contributions to meet the exceptional necessities of their clients. It achieves this through recommender frameworks and personalization methodologies. The objective of recommender frameworks is to give individuals choices that most intently match their inclinations. Since there are countless choices accessible to clients in web-based settings, the use of recommender frameworks in the movement area has become progressively vital. Recommender frameworks, as a rule, coordinate the client profiles with the subtleties of the accessible choices to introduce the choices that are generally fit. Information customized to the requirements and limits of the user is the goal of personalization tactics. Because of this, firms can convert from marketing to the masses to individuals thanks to customized tactics. Personalization approaches require a large amount of information about user behaviour in order to build an accurate profile and analyse in depth the theories, practices, and applications of personalization. 4. Forecasting: It is a tactic that predicts the future by using present trends and historical and contextual data. All corporate and industrial sectors use it to assist individuals in making decisions that require a forecast of the future. Massive amounts of data are readily available, and this makes AI algorithms particularly well-suited for forecasting (Kietzmann et al., 2018). Forecasting is useful in the tourism industry for understanding visitor demand, creating marketing strategies, managing finances, and allocating human resources. It can also be used to detect restaurant fraud and handle building and maintenance problems. 5. Language Translation Applications: The travel and tourism industry ordinarily involves communicating with multilingual individuals. Nonetheless, it has been found that language is perhaps of the greatest hindrance and an extensive cause of torment and nervousness for voyagers. Language impediments normally hinder vacationers from finding the neighbourhood culture since they oftentimes stick to establishments and notable brands when they are voyaging. Personalization and programmed

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interpretation might make it simpler for voyagers to find their objections, empowering them to investigate and participate in different exercises. For independent interpretation programming to be created, man-made brainpower, in light of AI and regular language handling, is fundamental. 6. Smart Travel Assistants: Clever travel partners have become more well-known and relevant because of their capacity to adjust to upgrades in portable innovation, computerized reasoning, regular language handling, and discourse acknowledgement. These associates are PC programmes that know about the inclinations, interests, and available energy of the client. They can hence foresee the client’s needs and come up with ideas either naturally or upon demand. These gadgets have likewise been alluded to as keen travel planners, independent specialists, and brilliant attendants. A right-hand ought to have the option to consolidate various administrations at a site while thinking about existing limitations to bring the client from point A to point B inside the necessary financial plan.

5.3.1 AI-Related Challenges in Travel and Tourism AI is now decidedly affecting the movement and the travel industry, and this pattern will continue. Notwithstanding, there are a few difficulties and dangers that should be dealt with. On three fundamental subjects — the point of view of a man-made intelligence traveller, machine impersonation of people, and the morals and predispositions of man-made intelligence — we meticulously describe the situation.

5.3.1.1 Issues Associated with the Adoption and Use of AI by Tourists The main test of computer-based intelligence is the way guests see, feel, and think about these innovations. Similar to some other innovations or advancements, travellers can be isolated into the accompanying gatherings: trailblazers, early adopters, the early greater part, the late larger part, and slow pokes. Computerized reasoning has both advantages and downsides for individuals. Explorers benefit extraordinarily from computer-based intelligence’s capacity to help them through unfamiliar conditions, which diminishes their feelings of tension and fear. It can also make it feasible for them to create vital encounters. With respect, sightseers’ three fundamental

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worries are connected with their nerves about observation, the simulated intelligence hole, and a general public where innovation rules are incomparable. Several authors have raised the privacy risk that AI systems can present when it comes to worrying about surveillance, pointing out that these systems gather enormous amounts of data and, more importantly, have the ability to extract patterns and information from the data (Tong et al., 2022). Lack of access raises the likelihood of an AI gap, just as it did with the digital divide that it caused. This AI gap may be caused by certain users’ reluctance to connect with AI environments because of their fear of safety. Travellers will likely be compelled to choose between more automated, efficient, and affordable services and premium services that rely more on human connection and less on automation in response to the concern about a society run solely by technology. For instance, hotels are probably positioned along a continuum.

5.3.2 Ethical Challenges of AI in Tourism The following are the ethical challenges of AI in tourism. 1. Services Given Entirely by Humans: This refers to relationships between tourism workers and visitors that are “human-to-human” and not managed or facilitated by AI or robots. Today, the majority of tourism companies operate this way. 2. Selection of Data With Human Bias: Data selection bias, often referred to as “selection bias”, occurs when human agents fail to achieve the desired level of data randomization. Therefore, the data selected are not representative of the sample population of data covered by the study. Such biases can occur, for example, when an AI-based hotel pricing system is trained on weekday rates only and ignores weekend rates, or when a set of competitors may have different products that customers do not see as comparable products. This can occur when classification and location hotels are included. As the data used to train the AI system are inaccurate, the system makes meaningless price recommendations. 3. Contextual and Technical Biases: This happens when the components of the trained system do not properly consider their context. This means that the ML/ANN algorithm is not a one-size-fits-all system. Rather, each approach should be tailored to your specific application environment. This means, for example, that chatbots, booking recommendation systems, or other systems that are particularly attractive to the tourism

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industry should be tailored to specific application areas. This type of bias can be introduced when existing systems in other environments are overlaid, with unruly or substandard results for stakeholders interacting with those systems. 4. Concerns About Monitoring and Security: Privacy and surveillance issues arise when tourism companies use AI and robotics to collect data, including customer and employee images, payment records, customer journey information, customer location at a particular point in time, and more. For example, social robots deployed in hotels and restaurants are programmed to call guests by name and take pictures of them so that the robot can recognize them when they interact with them again. Such information is sensitive because the visitor may not want the robot to know that he/she has seen them before their friends. Additionally, information can be compromised and used against the interests of visitors and employees (e.g., making illegal purchases with credit or debit cards, publishing images without consent, and blackmailing) (Zsarnoczky, 2017). Additionally, hotel chain social robots can connect to cloud servers where photos of guests and staff are stored for maximum convenience for both guests and staff. When a guest interacts with a robot at one of her hotels in the chain, other robots connected to the server can identify the guest, giving the impression that the guest is being tracked. 5. Autonomy: Claiming autonomy is another moral ideal. This is because integrating these systems into the relatively well-known tourism domain has predictable adverse effects. The degree of autonomy users has in the areas where this system is used depends on the context in which the technology is placed and the dependencies tourism users leave these systems on. For example, systems that require communication with other systems for check-in/check-out, entering and exiting facilities, and requesting customized services involve other systems (such as mobile phones) and limit accessibility while also reducing the limits of human autonomy. The design of such systems can and should focus on maximizing human autonomy rather than limiting it.

5.4 Advertising and AI Advertisers are also utilizing AI in a variety of ways to improve their email marketing, including automation and testing. Deep learning algorithms are

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being used by social networking sites to enhance and tailor consumer advertising. There are several uses for AI systems in the travel industry. As a result of AI, consumers can locate better and more pertinent information, improve their mobility, make better decisions, and, most importantly, travel more effectively. By persuading customers to view the world from a more social perspective, AI is also anticipated to promote more environmentally friendly travel. It is possible to integrate AI technologies into existing tourism systems and applications or to use them stand-alone in the sector. These systems include language translation tools, chatbots and voice assistants, recommender systems, customization systems and approaches, forecasting tools, autonomous agents, and smart travel destinations. While we evaluate each of these systems individually, tourists usually interface with technology that integrates multiple of them (Enache, 2020). 1. How AI Affects Advertising Along the Consumer Journey: One must have a deep understanding of both how communications generally “operate” and the decision-making process of customers in order to fully appreciate the prospects AI presents for marketers. Finding a need is the first step in the consumer journey, which leads to the stages of preliminary thought, active contemplation, purchase, and post-purchase. The writers talk about consumer processing activities, advertising objectives, and typical advertising roles for marketers at each point of the customer journey. 2. Knowing What Others Need and Want: Since a need frequently emerges at the classification level instead of the brand level, it has been trying to pinpoint the exact second at which it is enacted. To get it and impact shopper needs, promoters have relied upon procedures like statistical surveying, web investigation, and information mining. AI makes it conceivable to make profiles that are more extravagant and to understand how changing buyer requests and inclinations are communicated continuously on the web. 3. Purchase: By underscoring the brand’s advantages over rivals, publicizing looks to rouse purchasers to esteem and pay for their favoured image. Promoters might feature accommodations and give data on where to purchase, joined by consolations about ensures, guarantees, or merchandise exchanges, or they might give monetary motivating forces to buy. Simulated intelligence can possibly and emphatically change how

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individuals’ shop. By changing over its “Simple” button into a “canny” buying framework that empowers corporate clients to arrange supplies by voice orders, text, or email, the workplace supply retailer staples accomplished this. Also, advertisers can settle on the “perfect balance” for rates.

5.5 AI-Related Challenges in Advertising 5.5.1 Data Accuracy Data accuracy is a challenge for AI in advertising. For AI models to be effective, the data collected for AI modelling must reflect all aspects of customers. The data must be accurate, so you must have a way to confirm the accuracy. However, incorrect data entry leads to incorrect AI output, which ultimately affects the ad campaign and its success.

5.5.2 Data Privacy Data privacy concerns are growing as AI becomes more prevalent in advertising. While AI can create more personalized ads, it can also inadvertently collect sensitive user information. This creates a challenge to protect user data and allow AI to work. There are several ways to mitigate this risk, but ultimately, companies using AI must decide how much privacy to protect.

5.5.3 Changes in Customer Behaviour Customer behaviour is a challenge in AI advertising; for example, customers today interact with ads more than ever before. They are also more engaged with their devices and can access more information at any time. As more brands approach them with new offers, they are likely to shift their loyalties. This means that AI must be able to understand and respond to customer behaviour in order to create successful ads.

5.5.4 Poor IT Infrastructure AI in advertising is on the rise, and companies want to automate tasks and create more engaging ads. However, poor IT infrastructure can make it difficult to use AI because it can slow down data processing or cause errors. This

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means that companies must invest in the right IT infrastructure if they want to use AI in their marketing campaigns.

5.5.5 Lack of Trust For AI to become more widely used in advertising, AI systems and humans need to be more trusted. This lack of trust is a challenge companies currently face when using AI in advertising. There are several reasons for this distrust, including the fact that AI systems are not always accurate and can be biased. In addition, well-known companies such as Google have been criticized for mishandling user data, while Facebook has been accused of spreading misinformation. The question is how reliable AI will be in advertising.

5.6 Ethical Challenges of AI in Advertising 5.6.1 Privacy The more input data for AI network analysis, the better the system will perform. So, advertisers and data collection companies try to get as much information about users as possible for profiling and provisioning purposes personalization that generates better click-through rates. While customization provides significant value to customers, users who support receiving personalized data are also affected by providing personal information. Most advertisers and data aggregators hope to create broad user profiles to increase sales but run the risk of unwanted third parties accessing personal data. Information from people threats is not limited to users’ physical location, bank details, and sensitive information being negatively used against them, but systems can reveal embarrassing information to family, friends, and colleagues. The issue of privacy and the debate over whether companies should collect and store user data have been going on for decades, and the advent of the Internet has only brought more information to light. People are constantly worried about their personal information because most companies do not clearly inform their users about what information is stored and how it would be used, so users have different expectations than they do in reality. Hiding the collection of customer profiling data has been found to negatively impact user satisfaction and click-through rates, like the shift away from traditional paperwork to the digitization and storage of personal information in databases and the Internet, AI does not create a new privacy problem, but it

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can amplify an existing one. People are expected to replace traditional products with AI and digital networks that can intelligently collect, analyse, and use data for advertising purposes, raising the question of whether people want to share their lives even more (Gouda et al., 2020).

5.6.2 Filter Bubbles The process of reducing user content continues to be personalized to the same content; it is called a “filter bubble”. As algorithms improve, delivering relevant content to each individual recommendation based on their previous recommendations has become very complex behaviour. This process can encourage users to continuously get the same kind of recommendations, and “filter bubble” could happen. Expecting AI to be more involved in efficient and accurate personalization of ad customization is a potential problem. Perspectives on AI cleverly submit even more similar ads for products previously purchased at a similar time by an individual in the model. The biggest personalization threat in filter bubbles is that they make users follow patterns without them realizing it. Too much customization can rob the user of maximum benefits of Internet – autonomy.

5.7 Conclusion AI in tourism is still in its infancy. However, there are also optimistic viewpoints. Society can address AI’s greatest obstacles from this perspective. It is presently conceivable to send man-made intelligence frameworks and layout availability, as well as permit labourers and simulated intelligence frameworks to collaborate. Under this worldview, simulated intelligence should be visible collectively of innovations that improve the moving experience for all gatherings included. Because of this new data, organizations will want to make merchandise, administrations, and encounters that better fulfil the assumptions of their clients. Organizations can likewise powerfully configure customized groups considering the interests of their clients. Innovation can possibly improve or supplant some work, setting aside organizations’ cash that can be given to clients, bringing down their general expense of possession. This also means that services that were once prohibitively expensive can now be priced reasonably by businesses. Technology can also make certain jobs better or free employees from doing certain things, which makes customer service and care better.

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From a managerial standpoint, tourism companies need to collaborate closely with those business partners to create the processes they want to use to deal with ethical concerns. By doing this, individuals participate in the design process as direct stakeholders. From the standpoint of the customer, AI can drastically lower transaction costs and offer fully customized packages to meet their needs and interests, allowing them to get ready for their vacations more quickly. They get offers that are in the future and fit their needs. Travellers can easily explore foreign areas because of technology, which lessens their dread of the unknown. Language and cultural barriers are not deterrents to travel but rather a draw. Customers may benefit from the best service while maintaining the greatest amount of privacy, thanks to technology.

References Bulchand-Gidumal, J. (2022). Impact of artificial intelligence in travel, tourism, and hospitality. In Handbook of e-Tourism (pp. 1943–1962). Cham: Springer International Publishing. Enache, M. C. (2020). AI for Advertising. Annals of the University Dunarea de Jos of Galati: Fascicle: I, Economics & Applied Informatics, 26(1), 28–32. Goel, P., Kaushik, N., Sivathanu, B., Pillai, R., & Vikas, J. (2022). Consumers’ adoption of artificial intelligence and robotics in hospitality and tourism sector: literature review and future research agenda. Tourism Review, 77(4). Gouda, N. K., Biswal, S. K., & Parveen, B. (2020). Application of artificial intelligence in advertising & public relations and emerging ethical issues in the ecosystem. International Journal of Advanced Science and Technology, 29(06), 7561–7570. Kietzmann, J., Paschen, J., & Treen, E. (2018). Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research, 58(3), 263–267. Kirtil, I. G., & Aşkun, V. (2021). Artificial intelligence in tourism: A review and bibliometrics research. Advances in Hospitality and Tourism Research (AHTR), 9(1), 205–233. Tong, L., Yan, W., & Manta, O. (2022). Artificial intelligence influences intelligent automation in tourism: A mediating role of internet of things and environmental, social, and governance investment. Frontiers in Environmental Science, 135. Zsarnoczky, M. (2017). How does artificial intelligence affect the tourism industry? VADYBA, 31(2), 85–90.

Chapter 6

Artificial Intelligence in Retail Marketing Nirmalya Kundu1, Farhan Mustafa2, Hemachandran K1, and Channabasava Chola3 1

Woxsen University, Hyderabad, India Department of Management Studies, Indian Institute of Technology, Roorkee, India 3 Department of Electronics and Information Convergence Engineering, College of Electronics and Information, Kyung Hee University, Suwon-si, Republic of Korea 2

6.1 Introduction Artificial intelligence (AI) is quickly becoming an integral part of retail marketing. The metaverse is a virtual world where people can interact, explore, and engage in activities such as commerce and entertainment. As the metaverse continues to grow, it presents an exciting opportunity for marketers to reach and engage with their target audiences (Balaji & Roy, 2017; Khanagha et al., 2017). By exploring the potential of the metaverse for marketing, organizations can be better equipped to take advantage of this growing trend and reach their target audiences in new and innovative ways. AI technology can help retailers to gain insights from customer data by using predictive analytics, automating marketing campaigns, personalizing the customer experience, and optimizing operations. Predictive analytics uses data from past customer interactions to predict future customer behavior. This helps retailers to better target their marketing campaigns and improve customer loyalty and retention. Furthermore, AI has been greatly

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used to personalize the customer experience by analyzing customer data and using ML algorithms to recommend personalized products and services to customers. Finally, AI can optimize operations by using algorithms to help retailers optimize pricing, inventory management, and customer service. In recent years, AI has improved the retail sector in a number of ways, including making big data predictions available, enabling more informed purchasing and consumption decisions, enabling visual product displays, and encouraging user interaction (Grewal, Roggeveen & Nordfalt, 2017). The practical side of AI applications includes robots for consumer greeting, big data analytics for price adjustment and prediction, recommender systems for product and promotional personalization, natural language processing for customer engagement and in-store experience optimization, and sentiment analysis for customer satisfaction tracking, among other things. Marketers may use AI to get a deeper understanding of consumers in order to better categorize and advance customers to the next stage of their journey while providing the best experience. Marketers may increase Rate of Investment (ROI) without wasting money on pointless efforts by carefully examining customer data and knowing what customers really want. In addition, they won’t have to waste time watching irritating commercials that irritate consumers. AI will personalize marketing in a number of different ways. Many organizations presently employ AI to customize their websites, emails, social media posts, videos, and other resources in order to better fulfill customer needs. One of the key goals of AI is to automate jobs that historically required human intelligence. When an organization uses fewer labor resources to accomplish a project or when people take less time to complete routine chores, significant efficiency benefits are attainable (Khokhar, 2019; Murgai, 2018).

6.2 Literature Review AI refers to the use of computer science to stimulate human or natural intelligence in machines. Machines can answer problems using formulae or algorithms. In reality, the algorithms raise the bar by allowing the machine to learn. The ability of AI to learn from data and change over time on its own distinguishes it from other technologies. AI can, therefore, learn for itself. This is what distinguishes AI from earlier industrial or information technologies in that it can process information for human use in addition to updating findings

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without additional programming or human participation. Collaboration becomes essential because interactions with HI are two-way, autonomous, and adaptable due to this AI capability (Chintalapati & Pandey, 2022). Since it differs from other technologies that are meant to serve HI in a defined way, technology acceptance and adoption are vital. The application of AI in retail marketing has become increasingly popular in recent years. In this literature review, the use of AI in retail marketing will be discussed, along with the advantages and challenges of its implementation. The use of AI in retail marketing has been explored in various research studies. For example, a study by Grewal, Roggeveen, and Nordfalt (2017) describes the availability of big data predictions, the ability to make more educated purchasing and consumption choices, the ability to display products visually, and the promotion of user interaction as just a few ways in which AI has enhanced the retail industry. The study found that AI was effective in providing personalized product recommendations and that it has improved customer satisfaction and loyalty. According to the study by Simon Moore, Sandy Bulmer, and Jonathan Elms, AI helps with the social consequences and opportunities afforded by consumers’ interactions with AI digital humans as part of the in-store shopping experience. According to another study by Christian Homburg, Marcus Theel, and Sebastian Hohenberg, personalized product recommendations based on AI algorithms can lead to increased customer satisfaction and loyalty. However, there are concerns over the potential for AI to perpetuate biases and discrimination, as well as the impact on human workers and job displacement. Cenying Yang, Yihao Feng, and Andrew Whinston (2021) investigated the use of AI in online retail pricing and promotions. The study found that AI-driven pricing and promotions led to increased sales, improved customer satisfaction, reduced customer churn, and, in turn, improved the customer experience in retail stores. The use of AI in retail marketing has implications for consumer behavior and decision-making. A study by Tjepkema (2019) looked at the use of AI in retail customer experience. The study found that an AIdriven customer experience resulted in improved customer satisfaction, reduced customer effort, and increased sales. Overall, the use of AI in retail marketing is beneficial in terms of improving customer experience, optimizing pricing and promotions, and providing personalized product recommendations. However, there are some challenges associated with its implementation. For example, AI-driven marketing can lead to privacy issues as well as a lack of transparency in the decision-making process. Additionally, the cost of implementing AI can be high, and there is a risk of bias in the

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algorithms used. In conclusion, the use of AI in retail marketing has become increasingly popular in recent years.

6.3 Tracing the Evolution of Marketing in the Metaverse The evolution of marketing in the metaverse has been a long and ongoing process. As technology has become more sophisticated, so has the way marketers use it to reach their target audiences. In the early days, marketers were limited to using virtual worlds such as Second Life and There.com, where their efforts were mostly limited to traditional advertising techniques such as banner ads and avatar-based promotions (Perlin & Goldberg, 1996). As the technology improved, marketers began to explore ways to leverage the interactive capabilities of the metaverse, such as creating virtual stores, organizing events, and engaging in virtual campaigns (Duan et al., 2021). The development of the Oculus Rift and other virtual reality devices has opened up even more possibilities for marketers. By creating immersive and interactive experiences, marketers can now engage with their audiences in a much more direct way. In addition, marketers can now use data analytics to better understand their target audiences and customize their campaigns accordingly. The development of blockchain technology has also given marketers the ability to create and track digital tokens, which can be used to reward customers for their loyalty or for participating in marketing campaigns. The future of marketing in the metaverse is still a work in progress, but it is clear that the potential for marketers to leverage the technology is immense. As technology continues to develop, marketers will be able to explore even more creative ways of utilizing the metaverse to reach their target audiences.

6.4 Immersiveness of Metaverse in Retail Marketing The metaverse is quickly becoming an important tool for retail marketers to reach customers. It can create a highly immersive experience, allowing customers to virtually explore a store, shop for items, and interact with other customers and staff. This adds a new dimension to the shopping experience, giving customers a more personal and engaging experience (Willems et al., 2019). Compared to traditional retail marketing, the metaverse provides an even more immersive experience that can be tailored to meet the needs of

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each customer (Kang et al., 2020). Retailers can take advantage of the metaverse to create virtual stores and product displays, allowing customers to shop without leaving their homes. This can be used to create personalized shopping experiences by allowing customers to select items based on their preferences and interests. By providing customers with a more personalized and immersive shopping experience, retailers can increase customer satisfaction and loyalty. Retailers can also use the metaverse to create virtual events and experiences. These events can be used to engage customers and build relationships with them. Through these events, customers can get a taste of the brand and experience the products firsthand, which can help to build loyalty and trust. Finally, the metaverse can be used to create interactive marketing campaigns that can help to spread brand awareness and create buzz (van Kerrebroeck et al., 2017). By leveraging augmented reality, virtual reality, and other technologies, retailers can create completely immersive experiences that allow customers to engage with the brand and its products in a unique and meaningful way. Overall, the metaverse is an incredibly powerful tool for retail marketers to use in all aspects of their marketing campaigns. It can be used to create highly immersive shopping experiences, virtual events, and interactive marketing campaigns, all of which can help to build customer loyalty and drive sales.

6.5 The Sociability of Retail Marketing Retail marketing is the practice of promoting and selling products and services directly to consumers through various channels of distribution. It encompasses a wide range of activities and strategies, from traditional brickand-mortar stores to online retailing, and from direct mail and telemarketing campaigns to promotions on social media platforms (Yung et al., 2021). Retail marketing is a highly sociable activity in that it involves interacting with customers, engaging with them, and developing relationships that lead to repeat business. Examples of sociable retail marketing tactics and strategies include the following: ■ Creating an engaging in-store experience: Retailers should strive to create an inviting, interactive environment that encourages customers to linger. This could include providing comfortable seating, offering interactive displays, and hosting special events (Scholz & Smith, 2016).

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■ Offering personalized customer service: Retailers should try to greet customers and assist them in finding what they need. This could include providing product demonstrations and offering advice. ■ Developing relationships with customers: Retailers should make an effort to get to know their customers and build relationships with them. This could include sending personalized emails or offering exclusive discounts. ■ Utilizing social media platforms: Retailers should leverage social media platforms to engage with customers and build relationships. This could include creating content that appeals to customers, responding to customer questions and comments, and offering discounts or promotions. ■ Hosting events and activities: Retailers should host events and activities to draw in customers and create a fun shopping experience. This could include offering workshops, hosting parties, or holding contests. ■ Embracing technology: Retailers should embrace technology to improve customer experience and make shopping easier. This could include offering digital payment options, incorporating augmented reality into store displays, and providing online ordering options. ■ Creating a loyalty program: Retailers should create a loyalty program to reward customers for their loyalty and encourage repeat business (De Regt et al., 2021). This could include offering discounts, exclusive access to products, or complimentary gifts.

6.6 Environmental Fidelity of Retail Marketing Environmental fidelity of retail marketing refers to the extent to which a company’s marketing practices align with its commitment to sustainability and protecting the environment. This includes a range of business practices such as reducing the use of single-use plastics, using recycled materials in packaging, and sourcing sustainable materials. Examples of environmental fidelity of retail marketing are as follows: 1. Reducing the use of single-use plastics: Companies can reduce the use of single-use plastics in their products and packaging by switching to reusable or recyclable alternatives. For example, a retailer may switch to paper bags for their customers’ groceries instead of plastic bags. 2. Using recycled materials in packaging: Companies can reduce their environmental footprint by using recycled materials for their packaging. For

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example, a retailer can switch to cardboard boxes made from recycled materials instead of using new materials. 3. Sourcing sustainable materials: Companies can reduce their environmental impact by sourcing sustainable materials for their products. For example, a retailer can switch to organic cotton for their clothing instead of conventional cotton. 4. Adopting sustainable practices: Companies can adopt sustainable practices in their operations, such as switching to renewable energy sources, reducing their water usage, and investing in green initiatives. For example, a retailer can switch to solar panels to power their stores instead of relying on traditional energy sources.

6.7 Customer Expectations From Today’s Marketers Customers have higher expectations every time they connect with a brand, and they value it when a company gets the experience right and treats customers in a right and ethical way. In customer moments, brands must succeed, as is the case with Apple, Amazon, Flipkart, and Google, which continue to offer excellent customer experiences. The brand must adjust its marketing technology investments if it wants to connect with its customers. Brands no longer operate in the world of the traditional funnel, where they acquire customers and then upsell, cross-sell, grow those customers, and turn them into their advocates. Instead, they must communicate with customers throughout the entire customer life cycle, and this means that marketing is no longer just responsible for the acquisition or getting that first conversion; it is also responsible for working with the commerce team and your merchandisers, your sales, and your customer service representatives. As a result, brand marketing technology investments will be different. In the modern world, the majority of organizations are still focused on crosschannel campaigns in which they develop segments and send those campaigns to outbound channels like email, print, or occasionally mobile messaging. However, many organizations are progressing to the point where they can do better segmentation, micro-segmentation, or targeting to capture the behavior of customers. That behavior will reactively prompt a message or communication, which is great, but moments-based is going to turn that up even further, where we’ll need to be more predictive in terms of anticipating the customer requirement, we’ll need to use real-time data and to

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communicate in real-time rather than in a reactive way on a more individualized basis.

6.8 AI-Human Intelligence (AI-HI) Relative Strength The AI-HI relative strength index (RSI) is a technical indicator developed by AI-HI, a company specializing in AI and machine learning. The indicator is designed to measure the momentum of security in order to identify potential overbought and oversold conditions. The RSI measures the magnitude of recent price changes in order to compare the current price to the historical price range. The indicator is calculated by taking the average of the gains over a specified period and dividing it by the average of the losses over the same period. The resulting value is then expressed as a ratio and represented as a line on a chart. The RSI is a popular indicator among traders and investors due to its ability to indicate potential trend reversals and price breakouts.

6.8.1 AI’s Strengths in Retail Marketing AI can be used to increase marketing efficiency and effectiveness in a number of ways, including the following: 1. Automating customer segmentation and personalization: AI can be used to analyze customer data and automatically segment customers into groups with similar characteristics, making it easier to personalize marketing content (https://wire19.com/12-benefits-of-artificial-intelligenceai-in-retail/, 12 Benefits of Artificial Intelligence (AI) in Retail, 2021). 2. Optimizing paid search campaigns: AI can be used to determine the most effective keywords and bids for a given ad campaign, as well as to adjust bids in real time based on performance. 3. Enhancing customer service: AI can be used to automate customer service functions such as handling inquiries and providing personalized recommendations. 4. Analyzing customer behavior: AI can be used to analyze customer behavior and identify trends and opportunities that can be used to drive marketing efforts (https://dlabs.ai/blog/9-biggest-benefits-of-using-ai-inyour-retail-business/, 9 Biggest Benefits of Using AI in Your Retail Business, 2022).

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5. Automating content creation: AI can be used to generate personalized content that is tailored to each customer. 6. Improving marketing ROI: AI can be used to evaluate marketing campaigns and identify areas that can be improved to increase ROI.

6.8.2 HI’s Strengths in Retail Marketing The strengths of HI in retail marketing are as follows: 1. Experienced and dedicated team: HI has an experienced and dedicated team of marketing professionals that have extensive knowledge of the industry and the latest trends in marketing. 2. Innovative strategies: HI develops creative and innovative strategies that are tailored to the needs of its clients. 3. Comprehensive services: HI provides comprehensive services for all aspects of marketing, from strategy and planning to execution and analysis (Frey & Osborne, 2017). 4. Digital expertise: HI has a strong knowledge and understanding of digital marketing, including SEO, pay provide customers (PPC), and social media. 5. Data-driven approach: HI takes a data-driven approach to the market, leveraging data to inform decisions and optimize performance. 6. Cost-effective solutions: HI provides cost-effective solutions that are tailored to meet the needs of its clients.

6.8.3 AI-HI Collaboration in Retail Marketing The AI-Hi collaborates in retail marketing in the following ways: 1. AI-HI collaboration in marketing focuses on using AI and HIs together to improve retail marketing strategies and campaigns. 2. This collaboration helps to identify customer needs, preferences, and trends in order to create personalized experiences. 3. AI-HI collaboration assists in automating customer service, providing data-driven insights, and analyzing customer feedback to improve customer experience (Davenport et al., 2020). 4. AI-HI collaboration enables marketers to target the right audience with the right message by leveraging machine learning algorithms.

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5. AI-HI collaboration also helps in personalizing customer engagement, optimizing pricing and promotions, and creating smarter communication channels. 6. AI-HI collaboration is also beneficial for streamlining marketing processes, gaining insights from customer interactions, and improving customer experience.

6.8.4 A Framework for Collaborative AI in Retail Marketing The framework for collaborative AI in retail marketing is listed as follows: 1. Define objectives: The first step in developing a collaborative AI framework for marketing is to define the objectives. This involves specifying the goals of the project and the desired outcomes. This will help to ensure that all stakeholders are on the same page and that the project is successful (Ming-Hui Huang & Roland T. Rust, 2022). 2. Identify stakeholders: The next step is to identify all stakeholders who will be involved in the project. This includes marketers, engineers, data scientists, and other experts who will be involved in the development of the AI framework. This step is important in order to ensure that all stakeholders are aware of their roles and responsibilities. 3. Develop an AI model: The third step is to develop an AI model that is suitable for marketing purposes. This model will be used to collect and analyze data, identify patterns and trends, and generate insights that can be used by marketers to make better decisions. 4. Train the AI model: Once the AI model has been developed, it needs to be trained so that it can effectively perform its tasks. This can be done through supervised learning, unsupervised learning, or reinforcement learning, depending on the type of task. 5. Implement the AI model: The fifth step is to implement the AI model in marketing applications. This can be done by integrating the AI model into existing marketing platforms or by developing a custom platform. 6. Monitor and evaluate: The sixth step is to continuously monitor and evaluate the performance of the AI model. This will help to ensure that the model is performing as expected and that any issues are identified and addressed quickly. 7. Deploy and monitor AI-driven campaigns: The seventh step is to design and monitor AI-driven campaigns, track performance, and adjust campaign parameters as necessary.

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8. Measure and analyze results: The next step is to measure and analyze results to identify areas of success and areas in need of improvement. 9. Automate and scale: The final step is to automate and scale AI-driven campaigns to improve efficiency and maximize ROI.

6.8.5 Implementation of Marketing Technology The marketing technology is implemented as follows: 1. For identity resolution, you need to recognize customers across devices. 2. It is necessary to comprehend the current context, which may be related to the customer or external elements such as the day of the week, the time of day, the weather, and recent events that may have an impact on the context associated with the customer (https://marcom.com/7-steps-newmarketing-tech/, 7 Steps to Implement New Marketing Technology, 2016). 3. You’ll need analytics and AI to decide what to do next. 4. In order to manage consumer interaction, you must connect that offer through the proper channel.

6.8.6 Technological Building Blocks of Retail Marketing The technological building blocks of retail marketing are as follows: 1. Point-of-sale (POS) systems: POS systems are the backbone of retail operations, providing the ability to store customer data, process sales transactions, track inventory, manage customer loyalty programs, and analyze customer purchasing habits. 2. Mobile applications: Mobile apps allow retailers to connect with customers in new and innovative ways, providing customers with a convenient way to shop, manage loyalty programs, and access exclusive deals (A marketer’s guide to the 7 building blocks of the e-commerce value chain—https://www.thinkwithgoogle.com/intl/en-145/future-ofmarketing/digital-transformation/marketers-guide-7-building-blocks-ecommerce-value-chain/). 3. Digital signage: Digital signage allows retailers to quickly and effectively communicate with customers and provide targeted messages in-store. 4. Social media: Social media provides a platform for retailers to engage with customers and build relationships through sharing content, creating conversations, and providing customer service.

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5. Digital payment solutions: Digital payment solutions such as Apple Pay, PayPal, and Amazon PPC give users the convenience and security of making payments with their mobile devices. 6. Automated marketing: Automated marketing solutions allow retailers to automate tasks such as email marketing, social media campaigns, and retargeting campaigns to increase sales and customer engagement. 7. Data analytics: Data analytics allows retailers to collect and analyze customer data to gain insights into customer behavior and preferences. This helps retailers create targeted marketing campaigns and optimize the customer experience.

6.8.7 AI in Retail Market Applications There are several applications of AI in the retail industry value chain; these involve the evolvement of the cash-free store, chatbot voice and visual search, and the aspect of dynamic pricing. Al in retail refers to the use of data, automation, and machine learning (ML) algorithms to provide consumers with personalized purchasing experiences. Al takes care of customer interactions in a multichannel setup that includes both physical and online retailers. Al-driven chatbots (bots) use an internet setup, which includes a website, to offer clients a personalized recommendation or charge them based on previous behavior and other relevant information. All physical retail establishments use data from a variety of sources, mostly client interactions with mobile devices and sensors. AI systems examine further data, which allows them to learn how to produce better results and offer stylish results. AIenabled ML algorithms can examine vast quantities of factual consumer data to identify which notices are relevant for clients at what stage of the purchasing process. AI provides marketers with the optimization advantages of publishing content at the optimum time through the utilization of trends and data. The objective of ML is to discover patterns in data so that you may move forward and make better judgments utilizing observations, information, or training. Retailers can adjust pricing in real time based on customer demand and market conditions with the use of AI-powered price optimization algorithms. They can anticipate client wants with the aid of AI-powered predictive analytics, which may also recommend products and deliver customized offers and discounts. Retailers can improve their inventory levels and lower the danger of stockouts with the aid of AI-powered inventory management systems. They can prevent losses due to fraud by using AI-powered fraud detection systems to spot questionable transactions.

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1. Intel is one of the retail industry’s leading AI enablers. Intel Technologies enables a variety of exciting and emerging retail analytics and AI to use cases (www.intel.in). a. Intelligent display ads use AI to analyze customer interaction and interest content that can be personalized in real time for the audience. b. Electronic smart shelves immediately send a signal about stock availability, allowing items to be easily refilled. c. Customers can view the entire product array that is offered at different locations thanks to endless aisle kiosks. Additionally, they provide the potential for cross-selling and up-selling. d. Smart self-checkout machines accept coupons, loyalty cards, and phone payments. 2. Amazon Go has created checkout-free stores, where the automation of physical stores will result in no lines and a reduction in the need for human staff, thus saving money. When you walk out of the store with things, Amazon will debit money from your linked wallet using its “Just Walk Out Shopping” technology, which functions smoothly whether you grab something from the shelf or put it back (Forbes.com). Due to a lack of reliable information on the components underlying the Just Walk Out Shopping concept technology, a precise cost estimate based on component information has not yet been established. The projected cost needed for a shop to have hardware similar to an Amazon Go store and could cover up to a 970 m2 grocery building, however, is said to be costly and costs roughly $1 million (Andria, 2019). It was necessary to work under norms and regulations in order to merely walk out shopping. There are several rules and authorizations to take into account. Similar rules apply to these technologies, including licensing requirements and requirements for payment system service providers, as defined by the National Bank of Indonesia. These payment alternatives are given based on the features of these technologies to help users with their shopping activities (Bank Indonesia, “Informasi Perizinan Penyelenggara dan Pendukung Jasa Sistem Pembayaran”.). For technologies that call for construction, they must obtain the necessary property license. And since these technologies are operating to make money from the services they provide, the primary regulation that must be met for them is a business license. 3. In order to make it easier for clients to browse their collections, Tommy Hilfiger and Burberry have used chatbots. Artificial intelligence (AI)

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chatbots help customers, provide greater customer support, and enhance search, notifications, and suggestions for related products. In the next few years, AI chatbots will be used by two-thirds of all international brands. With the help of AI, we can implement in-store assistance. Retailers spend money on technology that benefits their employees working in stores and warehouses as well as customers during the purchasing process. As a result of Kroger Edge’s technology, computerized smart shelf tags have replaced paper price tags in their stores (spd.group). With the use of this technology, video promos and commercials are also displayed. Dynamic pricing is one of the uses for AI in retail. By using AI to visualize the anticipated results of dynamic pricing techniques, firms could adjust the prices of their items. Data collection systems gather information on sales numbers, promotional activity, and competing items. Due to their product prices changing every 10 minutes, Amazon has nailed the entire dynamic pricing concept. Businesses may present the best deals tailored to each customer’s demands and capabilities as well as attract new customers based on dynamic pricing (www. medium.com). Visual search lets users input photographs and search for related goods using criteria like colors, shapes, and patterns. For instance, Tommy Hilfiger introduced an app at a runway show in 2017 that allowed attendees to take pictures of the products being modeled and upload them to a visual search engine (www.pymnts.com). Fast fashion is a rapidly shifting trend of products that consumers are eager to catch but may not have the time or language to search for. Visual search works wonderfully for this. The written language barrier is broken through voice search. Customers do not need to type anything; they can simply ask Alexa for the chosen product and its delivery status. In reality, voice search is used by onefourth of mobile users globally, and half of them find it more convenient than mobile apps and websites. Retailers like Walmart, Tesco, Costco, and others employ Google or Amazon AI technology to enable rapid voice searches for customers. Virtual fitting rooms are still another use. The dynamics of the trial or fitting rooms have significantly changed during the past 10 years. Retailers have come to understand that this is where important purchasing decisions are made. Customers can save time and browse

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through a variety of options while using virtual fitting rooms to locate the perfect outfit. For instance, the “ME-Ality” virtual fitting kiosk from Canada can scan a person in 20 seconds and measure 200,000 points on their body within that time (Virtual Dressing Room? Me-Ality Promises a Good Fit—https://www.cbsnews.com/news/virtual-dressing-room-meality-promises-a-good-fit/, 2012). In an effort to modernize its drivethrough operations, McDonald’s purchased the Israeli firm Dynamic Yield in March 2019 with an emphasis on customization and logic in decisions. McDonald’s will automate their drive-thru menus thanks to this acquisition, which will use AI. With automated tools that may generate in-the-moment menu suggestions based on consumer trends, purchasing patterns, and even the weather, this AI integration is expected to up the ante on personalization (What We Can Learn from McDonald’s AI Integration (+Who Does it Benefit?)—https://learn.g2. com/mcdonalds-ai-integration, 2019). 9. One more example of a company using AI to forecast trends is H&M, which employs more than 200 data scientists to identify and forecast trends. H&M’s AI algorithms collect data from global search engines and blogs to determine fashion trends. They make decisions about how much they purchase, when they purchase it, and where it should be placed in their stores using this information, among other things (How Fashion Retailer H&M Is Betting on AI and Big Data to Regain Profitability—https://www.forbes.com/sites/bernardmarr/2018/08/10/ how-fashion-retailer-hm-is-betting-on-artificial-intelligence-and-big-datato-regain profitability/?sh=379b6fa45b00, 2018).

6.9 AI in the Retail Supply Chain The use of AI in the retail supply chain is developing daily. This is used daily by both physical establishments and online retailers. AI has a lot to offer in inventory management. Out-of-stock and unsold scenario risks can be minimized by determining the demand for a specific product by making an estimate based on prior sales, location, weather, trends, promotions, and other factors. AI enhanced the planning of the retail value chain, which includes manufacturers, distributors, suppliers, logistics providers, and other supply chain actors. It provides faster, more accurate, and safer delivery while lowering costs for third-party partners and their clients. AI offers robust tools and analyzes trend datasets, enabling businesses to quickly satisfy client

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expectations (Dash, McMurtrey, Rebman, & Kar 2019). The way goods are produced, sourced, and sold through today’s shops will be completely changed by the ability to learn from and predict future purchasing trends. The costs associated with the internal and external supply chains are calculated, providing a heads-up to all participants in the retail value chain (Leonard, 2019). A greater comprehension of several elements in the value chain can be credited with forecasting and reducing risks, and it also modifies the course of action as unforeseeable events occur.

6.10 Statistics in the AI Space in the Retail Industry The market for AI in retail was valued at $5.79 billion globally in 2021, and from 2022 to 2030, it is projected to grow at a Compound annual growth rate (CAGR) of more than 23.9% (https://www.grandviewresearch.com/industryanalysis). The demand for monitoring and surveillance at physical stores, the increase in smart devices, and government policies that support digitization are all factors in the growth.

Based on how businesses have operated over the past few decades, AI will be used in the retail industry. Big data analytics and AI are essential for digital firms because they have the power to dramatically transform everything from customer experiences to operational procedures.

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One such example is the introduction of AI DevKit, the first AI on PC Development Kit, in June 2022 by Intel Corporation, ASUSTeK Computer Inc., and Microsoft Corporation. Deep neural networks and PC vision software were used in the development of the AI on PC Development Kit. Integration of hardware and software with guides and source code makes it possible to build innovative AI applications (https://www.fortunebusinessinsights.com/ artificial-intelligence-ai-in-retail-market-101968). According to MarketsandMarkets by 2025, the total amount spent on AI in the retail supply chain will be $10 billion, with a 45% annual growth rate. In order to reach $407.0 billion by 2027, the worldwide AI market is anticipated to expand at a CAGR of 36.2% over the forecast period (Artificial Intelligence Market Report Size, Share, Growth Analysis, https://www. marketsandmarkets.com/Market-Reports/ai-infrastructure-market-38254348. html?gclid=Cj0KCQiAx6ugBhCcARIsAGNmMbhnbhFlIE9ojarWStPTsmx7Gl4nIUUWeEQeJRsxqcb1Ltx_v_6piMaAuMfEALw_wcB, Artificial Intelligence Market Worth $407.0 Billion by 2027). The use of technology in the retail sector could help the sector save up to $340 billion annually, according to a Capgemini report presented at the AI in the Retail conference. According to estimates, the enhancement of supply chain management and returns by AI will be responsible for 80% of these savings. The number of merchants utilizing AI and machine intelligence in some capacity will reach 325,000 by 2023 (Capgemini: AI Is a $340 Billion Opportunity for the Retail Sector—https://www.artificialintelligence-news. com/2019/01/02/capgemini-ai-opportunity-retail-sector/, 2019).

6.11 Online Versus In-Store in Retail Marketing Online and in-store retail marketing are two different marketing strategies that both have their own benefits and drawbacks. Online marketing is great for targeting a wider audience and reaching potential customers who may not be in your area. It also allows you to track customer behavior and personalize the customer experience. Online marketing also allows you to reach customers who may not be able to physically make it to your store and allows you to offer a broader selection of products. In-store marketing is great for engaging with customers in a more personal way (smallbusiness.chron.com). You can use in-store displays, events, and promotions to create an interactive shopping experience. In-store marketing also allows you to directly interact with customers and answer any questions they may

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have. It also allows customers to physically touch and test out your products. Ultimately, the best option for your business will depend on your goals, budget, and customer base. A good strategy is to use both online and in-store marketing together to maximize your reach and visibility (Online Shopping vs In Store Shopping: Comparing the Differences for Your Customers, February 01, 2023—https://paymentcloudinc.com/blog/onlineshopping-vs-in-store-shopping/, 2023).

6.12 Ethics in Retail Marketing Ethics in retail marketing should be a priority for any company and its marketing team. Retail marketers should ensure that their messaging and promotional activities are honest, accurate, and transparent, as well as compliant with all applicable laws and regulations. They should also be aware of the ethical implications of their decisions and practices and strive to create a positive customer experience. This includes avoiding sales tactics that are manipulative or misleading, and not taking advantage of vulnerable customers. Additionally, marketers should respect the privacy of their customers by not collecting or using their personal data without their consent and should strive to protect their customers from fraud (Ethics in Retail: Importance and Ethical Practice Towards Consumers—https://www. marketing91.com/ethical-practice/). Finally, it is essential that marketers be aware of the potential for conflict of interest when working with vendors or other third parties, and take steps to ensure that any arrangements they make are in the best interests of their customers (Ethics in Retail—https://www. ethicaladvocate.com/ethics-in-retail/, 2020).

6.13 Future Scope of Retail Marketing The impact of AI on the future of retailing is likely to be immense. AI technology has the potential to revolutionize how retailers interact with customers, personalize shopping experiences, and optimize operations. AI-driven automation can help retailers automate mundane tasks such as managing inventory, pricing and promotions, and customer service. AIdriven predictive analytics can help retailers anticipate customers’ wants and needs and target them with personalized offers and recommendations.

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AI-driven chatbots and virtual assistants can help shoppers find the products they need quickly and easily (The Global Artificial Intelligence in Retail Size Was Valued at $4.84 billion in 2021 and Is Projected to Grow to $51.94 billion by 2029, Exhibiting a CAGR of 5.2%. Read More at: https://www. fortunebusinessinsights.com/artificial-intelligence-ai-in-retail-market101968, 2021). AI-driven image recognition technology can help customers find items they’re looking for and even suggest complementary items. And AI-driven voice-enabled virtual assistants can help customers shop handsfree and get answers to their questions (AI in Retail: 5 Present and Future Use Cases—https://www.indianretailer.com/article/technology/digitaltrends/ai-in-retail-5-present-and-future-use-cases.a7230, 2021). Ultimately, AI is likely to help retailers create a more efficient, personalized, and enjoyable shopping experience for their customers.

6.14 Conclusion It is no longer a matter of whether firms that provide exceptional customer experiences will succeed in the Fourth Industrial Revolution, where intelligence will reign supreme. The Fourth Industrial Revolution envisions the organization having integrated data about customers and products across all channels and products and leveraging that data to better understand its endcustomer experience and visibility across all functional areas. In this setting, big data analytics has relied heavily on AI and ML to forecast user expectations and deliver guided experiences that meet those expectations. AI might bring a more customized brand experience to increase customer engagement and attachment. Marketers apply language-predicated AI as engagement directors, payment processors, and trades tools to enhance the client experience. Rather than having to figure effects out on their own, clients can now calculate on chatbots to complete their trades. Languagegrounded AI is developing fast, automatically perfecting based on previous experiences to offer a better experience going forward. It can help advertisers by helping them find applicable content that compendiums are interested in reading. Information may now be acclimatized through observation, data collection, and analysis thanks to AI. Data analysis is the main advantage of AI in marketing. Huge quantities of data will be estimated with the aid of this technology, and marketers will admit practical perceptivity.

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References C. Andria (2019), Amazon Go Look To Expand As Checkout-Free Shopping Starts To Catch on Across the Retail Landscape. Forbes. Available at https://www.forbes. com/sites/andriacheng/2019/11/21/thanks-to-amazon-go-checkout-freeshopping-may-become-a-real-trend/#164bc6f3792b Bank Indonesia. Informasi Perizinan Penyelenggara dan Pendukung Jasa Sistem Pembayaran. Bank Sentral Republik Indonesia Official Website. Available at https:// www.bi.go.id/id/sistem-pembayaran/informasi-perizinan/Contents/Default.aspx M. S. Balaji, & S. K. Roy (2017), Value co-creation with the Internet of Things technology in the retail industry. J. Mark. Manag., 33 (1–2), 7–31 . S. Chintalapati, & S. K. Pandey (2022), Artificial intelligence in marketing: A systematic literature review. Int. J. Mark. Res., 64 (1), 38–68. R. Dash, M. McMurtrey, C. Rebman, & U. K. Kar (2019). Application of artificial intelligence in automation of supply chain management. Journal of Strategic Innovation and Sustainability, 14(3), 43–53. T. Davenport, A. Guha, D. Grewal, & T. Bressgott (2020), How artificial intelligence will change the future of marketing, J. Acad. Mark. Sci., 48, 24–42. A. De Regt, K. Plangger, & S. J. Barnes (2021), Virtual reality marketing and customer advocacy: Transforming experiences from story-telling to story-doing. J. Bus. Res., 136, 513–522. H. Duan, J. Li, S. Fan, Z. Lin, X. Wu, & W. Cai (2021). Metaverse for Social Good: A University Campus Prototype. In Proceedings of the 29th ACM International Conference on Multimedia, Association for Computing Machinery, New York, NY, USA, 153–161. C. Frey, & M. A. Osborne (2017), The future of employment: How susceptible are jobs to computerisation? Technol. Forecast. Soc. Change., 114 (January), 254–280. D. Grewal, A. L. Roggeveen, & J. Nordfalt (2017), The future of retailing, J. Retail., 93 (1), 1–6. How artificial intelligence will affect the future of retailing _Abhijit Guhaa,∗, Dhruv Grewalb, Praveen K. Kopallec, Michael Haenleind, Matthew J. Schneidere,Hyunseok Jungf, Rida Moustafag, Dinesh R. Hegdeh, Gary Hawkins. D. Herhausen, O. Emrich, D. Grewal, P. Kipfelsberger, & M. Schoegel (2020), Face forward: How employees’ digital presence on service websites affects customer perceptions of website and employee service quality,” J. Mark. Res., 57 (5), 917–936. H. J. Kang, J. Shin, & K. Ponto (2020), How 3D virtual reality stores can shape consumer purchase decisions: The roles of informativeness and playfulness. J. Interact. Mark., 49 (Feb. 2020), 70–85. S. Khanagha , H. Volberda, & I. Oshri (2017), Customer co-creation and exploration of emerging technologies: The mediating role of managerial attention and initiatives. Long Range Plan., 50 (2), 221–242 . P. Khokhar (2019), Evolution of artificial intelligence in marketing, comparison with traditional marketing, Our Heritage, 67 (5), 375–389.

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M. Leonard (2019), Carrefour Turns to AI For Demand Forecasting, Retrieved November 04, 2020. https://www.supplychaindive.com/news/Carrefourgrocery-AI-demand-forecasting/546188/ S. Moore, S. Bulmer, & J. Elms (2022), The social significance of AI in retail on customer experience and shopping practices. Journal of Retailing and Consumer Services, 64, 102755. A. Murgai (2018), Transforming digital marketing with artificial intelligence, Int. J. Latest Technol. Eng. Manag. Appl. Sci., 7 (4) 259–262. AI In Retail Market Size, Share & Trends Analysis Report by Component, by Technology (Chatbots, Natural Language Processing), by Sales Channel, by Application, by Region, and Segment Forecasts, 2022 – 2030. https://www. grandviewresearch.com/industry-analysis The Global Artificial Intelligence in Retail Size Was Valued at USD 4.84 Billion in 2021 and Is Projected to Grow USD 51.94 Billion by 2029, Exhibiting a CAGR of 5.2%. Read More at: https://www.fortunebusinessinsights.com/artificialintelligence-ai-in-retail-market-101968 https://www.marketsandmarkets.com/Market-Reports/ai-infrastructure-market38254348.html?gclid=Cj0KCQiAx6ugBhCcARIsAGNmMbhnbhFlIE9ojarWStPTsmx7Gl4nIUUWeEQeJRsxqcb1Ltx_v_6piMaAuMfEALw_wcB Capgemini: AI Is a $40 Billion Opportunity for the Retail Sector. https://www. artificialintelligence-news.com/2019/01/02/capgemini-ai-opportunity-retail-sector/ K. Perlin & A. Goldberg (1996), Improv: A System for Scripting Interactive Actors in Virtual Worlds. In Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH ’96). Association for Computing Machinery, New York, NY, USA, 205–216. J. Scholz, & A. N. Smith (2016), Augmented reality: Designing immersive experiences that maximize consumer engagement. Bus. Horiz., 59 (2), 149–161. The importance of customer citizenship behaviour in the modern retail environment: Introducing and testing a social exchange model Estelle van Tondera,⁎, Stephen Graham Saundersb, Inonge Theresa Lisitaa, Leon Tielman de Beerc. The Value of Artificial Intelligence for Retail in 2023.- https://spd.group/artificialintelligence/ai-for-retail/ How Fashion Retailer H&M Is Betting On Artificial Intelligence and Big Data to Regain Profitability. https://www.forbes.com/sites/bernardmarr/2018/08/10/ how-fashion-retailer-hm-is-betting-on-artificial-intelligence-and-big-data-toregain-profitability/?sh=379b6fa45b00 What We Can Learn from McDonald’s AI Integration (+Who Does it Benefit?). https://learn.g2.com/mcdonalds-ai-integration Visual Search Hits the Runway at Fashion Week. https://www.pymnts.com/news/ retail/2020/visual-search-hits-the-runway-at-fashion-week/ Virtual Dressing Room? Me-Ality Promises a Good Fit. https://www.cbsnews.com/ news/virtual-dressing-room-me-ality-promises-a-good-fit/ AI-Powered Dynamic Pricing Is Everywhere. https://medium.com/syncedreview/aipowered-dynamic-pricing-is-everywhere-4271a9939d11 Amazon Go Looks to Expand as Checkout-Free Shopping Starts to Catch On Across the

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Retail Landscape. https://www.forbes.com/sites/andriacheng/2019/11/21/thanksto-amazon-go-checkout-free-shopping-may-become-a-real-trend/?sh=b3863cf792bf Artificial Intelligence (AI) Is Reinventing the Retail Landscape. From using computer vision to customize promotions in real time to applying machine learning for inventory management, retailers can harness AI to connect with their customers and operate more efficiently. Intel® technologies power AI in retail at every step of the way, from the brick-and-mortar edge to the cloud. https://www.intel.in/ content/www/in/en/retail/solutions/ai-in-retail.html/ Ethics in Retail: Importance and Ethical Practice towards consumers. https://www. marketing91.com/ethical-practice/ Ethics in Retail. https://www.ethicaladvocate.com/ethics-in-retail/ The Pros & Cons of a Retail Store Vs. an Online Store. https://smallbusiness.chron. com/pros-cons-retail-store-vs-online-store-40789.html Online Shopping vs In Store Shopping: Comparing the Differences for Your Customers, Feb 01 2023. https://paymentcloudinc.com/blog/online-shoppingvs-in-store-shopping/ 12 Benefits of Artificial Intelligence (AI) in Retail. https://wire19.com/12-benefits-ofartificial-intelligence-ai-in-retail/ 9 Biggest Benefits of Using AI in Your Retail Business. https://dlabs.ai/blog/9biggest-benefits-of-using-ai-in-your-retail-business/ The Global Artificial Intelligence in Retail Size Was Valued at USD 4.84 Billion in 2021 and Is Projected to Grow USD 51.94 Billion by 2029, Exhibiting a CAGR of 5.2%. Read More at: https://www.fortunebusinessinsights.com/artificialintelligence-ai-in-retail-market-101968, 2021. AI in Retail: 5 Present and Future Use Cases. https://www.indianretailer.com/article/ technology/digital-trends/ai-in-retail-5-present-and-future-use-cases.a7230, 2021. A Marketer’s Guide to the 7 Building Blocks of the e-Commerce Value Chain. https:// www.thinkwithgoogle.com/intl/en-145/future-of-marketing/digitaltransformation/marketers-guide-7-building-blocks-e-commerce-value-chain/ L. Tjepkema (2019). What Is Artificial Intelligence Marketing & Why Is It So Powerful. Emarsys. https://www.emarsys.com/resources/blog/artificial-intelligencemarketing-solutions/03.05 7 Steps to Implement New Marketing Technology. https://marcom.com/7-stepsnew-marketing-tech/, 2016. H. van Kerrebroeck, M. Brengman, & K. Willems (2017), When brands come to life: Experimental research on the vividness effect of virtual reality in transformational marketing communications, Virtual Reality, 21 (4), 177–191. K. Willems, M. Brengman, & H. Van Kerrebroeck (2019), The impact of representation media on customer engagement in tourism marketing among millennials, Eur. J. Mark., 53 (9), 1988–2017. C. Yang, Y. Feng, & A. Whinston (2021), Dynamic pricing and information disclosure for fresh produce: An artificial intelligence approach. Production and Operations Management, 31(1), 155–171. R. Yung, C. Khoo-Lattimore, & L. E. Potter (2021). VR the world: Experimenting with emotion and presence for tourism marketing, J. Hosp. Tour. Manag., 46, 160–171.

Chapter 7

Innovative Recruitment Strategies Using Knowledge Management Systems for Business Sustainability Sahithi Chittimineni1, Geddam Anirudh1, Sanjay M S1, and Anil Audumbar Pise2 1

Woxsen University, Hyderabad, India University of the Witwatersrand, Johannesburg, South Africa

2

7.1 Introduction The current generation is majoring in looking into a passion for solving society’s problems by building new-age business including knowledge management systems (KMS). Day by day, the corporate culture-defined structure is reducing, and innovative approaches to business culture have been increased. It is essential to understand the company and its culture before recruiting employees. Youngsters are building innovative and flexible work approaches where employees are interested in working in newly emerging businesses. These unique approaches made changes in every department of the company, while the recruitment department became the central aspect of the change. Founders and talent acquisition teams are looking into diverse ways to hunt for talent and using innovative technologies and methods to verify candidates’ details. New-age business culture is a term used to describe the culture and work environment of modern companies. These businesses 108

DOI: 10.4324/9781003358411-7

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are typically characterized by their innovative approach to business, rapid growth, and focus on technology and disruption. Recruitment is a crucial function in any organization, and its success determines the success of the organization. The recruitment process has undergone significant changes over the years, from the traditional method of advertising in newspapers to online job portals and social media. However, the recruitment process is still time-consuming, complex, and expensive. Organizations are always looking for innovative ways to attract, select, and retain the right talent. KMS has emerged as an innovative recruitment strategy that helps organizations to manage their knowledge effectively, attract and retain the right talent, and improve their overall performance. A “knowledge management system” can be beneficial in human resource (HR) recruitment by enabling the organization to capture, store, and share knowledge about the recruitment process. This can include information on the best sources for finding candidates, effective interview techniques, and successful onboarding strategies. By leveraging this knowledge, organizations can improve the quality of their hiring decisions and enhance the overall recruitment process. Additionally, a knowledge management system can aid in the retention of knowledge by enabling the organization to preserve information even as employees come and go. In today’s dynamic business environment, innovation is the key to sustainability. For organizations, innovation is not just limited to product development or technology but also to their recruitment strategies. Recruitment is a critical function that determines the success of an organization, and with the advent of technology, it has become easier to attract and select the right talent. However, the recruitment process is still complex, timeconsuming, and costly. This chapter explores how KMS can be used as an innovative recruitment strategy to attract, retain, and develop the right talent for business sustainability. It discusses the concept of KMS, its benefits, and how it can be used to improve the recruitment process. It also highlights some of the best practices and case studies of organizations that have used KMS as an innovative recruitment strategy.

7.2 Literature Review The literature review showcases previous findings on the topic. Recent findings from literature emphasize that modern AI systems, as opposed to

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rule-based systems of the past, learn from data patterns, aided by increased volumes of big data and rising processing power, and act autonomously without human input. Thus, this shift in corporate recruitment procedures is the result of the idea that AI is fact-based and impartial . Also, in recent years, there have been an increasing number of strategic IT applications in the field of human resources (Florkowski & Olivas-Luján, 2006). Studies also suggest that AI can radically affect the human resources environment; for example, IBM successfully reduced HR expenditures by $107 million in 2017 as a result of applying AI, suggesting that IBM strongly supports integration of AI in HR (Guenole & Feinzig, 2018). Most importantly, contemporary research on AI suggests that it has the potential to have a big impact on the workplace and give businesses a chance to outperform their competitors. Therefore, practitioners and researchers must comprehend the limitations and the enablers of AI deployment (Yuan Pan et al., 2022). In addition, some researchers concluded that “pre-selection” and communication with candidates are the most suitable areas for AI. It has been observed that AI eliminates routine tasks and delivers quality on time (Johansson et al., 2019). Industry experts predict that there will be dramatic increase in the use of AI for the next 10 years which has the potential to significantly contribute to world Gross domestic product (GDP) (Rao & verweji, 2017). The necessity of government assistance and suitable technological resources in the deployment of AI implores IT professionals to simplify the technology involved (Yuan Pan et al., 2022). Personal intelligent assistants, which are a type of AI system, have the potential to assist with personal knowledge management and address the problem of information overload faced by knowledge workers. By providing efficient tools for processing, filtering, sorting, and navigating information sources, personal intelligent assistants can help increase cognitive bandwidth and improve how knowledge workers consume relevant information (Pauleen & Gorman, 2016; Maedche et al., 2019). Employees will have to learn how to interact with intelligent systems rather than humans for many of these jobs as AI automates repetitive and monotonous operations that were previously handled by humans. An essential skill for managers and employees that interface with AI systems is AI literacy. This calls for knowledge workers to acquire new analytical, data-driven skills that aid in the interpretation of AI-based judgments, algorithmic competencies, and a deeper understanding of their artificial counterparts (Jarrahi & Sutherland, 2019).

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7.3 Understanding New Age Businesses The culture within these organizations is often different from that of more established companies and is often described as fast-paced, dynamic, and collaborations. They are using new methodologies embedded in their environment to drive the office culture positively. There are many start-ups that give preference to employees’ desires and feelings. Every emerging business focuses on empowering the engagement culture in the company, which helps to make employees feel better and more active. They are encouraging personal development sessions to give relaxation. For example, Epam Systems, a software development company, launched employee clubs. In this, they introduced sub-clubs like dance club, trekking club, music club, books club, and more. These clubs conduct events to encourage employees and their talents. Big Indian companies like Tata Consultancy Services (TCS), Infosys, and Wipro and international companies like Walmart, Amazon, Google, and IBM adapted this innovative and flexible approach to increase employee productivity. Nowadays, due to COVID-19, every company has shifted to online mode, where all employees are accepted due to work–life balance. The events are conducted online without increasing online interactions. For example, 5x is a data stack, start-up company that introduced a concept called a “weekly engagement” session, where all employees in a company must gather in an online medium and discuss what activities they have done that week. After that, they introduced the “lunch and learn” concept, wherein online medium employees have their lunch or dinner together. Many new-age businesses are currently focusing on introducing innovative approaches to help companies grow with employees.

7.4 The Concept of KMS KMS is a set of processes, technologies, and tools that help organizations to create, capture, store, and share knowledge. It helps organizations to manage their knowledge effectively, improve their decision-making process, and increase their competitive advantage. KMS comprises two components—explicit knowledge and tacit knowledge. Explicit knowledge is the knowledge that can be documented and stored, such as reports, manuals, procedures, and databases. Tacit knowledge, on the other hand, is the knowledge that is not documented, such as skills, expertise, and experience.

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KMS helps organizations to capture both explicit and tacit knowledge and use it effectively to improve their performance.

7.5 Benefits of KMS in Recruitment KMS can be used as an innovative recruitment strategy to attract, select, and retain the right talent. It offers several benefits in recruitment, such as the following: Attracting the right talent: KMS can be used to create a knowledge-sharing culture within the organization. This culture attracts the right talent who are willing to share their knowledge and expertise. KMS can also be used to create an online community where potential candidates can interact with current employees and learn more about the organization. Retaining the right talent: KMS can be used to create a knowledge-sharing culture that promotes employee engagement and retention. When employees are provided with access to the organization’s knowledge base, they feel valued and motivated to contribute to the organization’s success. Developing the right talent: KMS can be used to provide employees with access to training and development resources. This helps employees to acquire new skills and knowledge, which in turn improves their performance and contributes to the organization’s success.

7.6 How KMS Can be Used in Recruitment KMS can be used in recruitment in several ways, such as the following: 1. Creating an online job portal: KMS can be used to create an online job portal where potential candidates can search for job opportunities, apply for jobs, and upload their resumes. The job portal can be integrated with the organization’s knowledge base, where candidates can learn more about the organization, its culture, and its values. 2. Creating an online community: KMS can be used to create an online community where potential candidates can interact with current employees, learn more about the organization, and ask questions. This creates a sense of community and engagement that attracts the right talent. 3. Using data analytics: KMS can be used to collect and analyze data on potential candidates, such as their skills, experience, and education.

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These data can be used to create a candidate profile that matches the organization’s requirements. Data analytics can also be used to identify potential candidates who are a good fit for the organization based on their skills, experience, and personality. 4. Providing access to training and development resources: KMS can be used to provide employees with access to training and development resources, such as online courses, videos, and tutorials. This helps employees to acquire new skills and knowledge, which in turn improves their performance and contributes to the organization’s success. KMS is critical for businesses as it allows them to capture, store, and share knowledge, ensuring that information is available to employees when they need it. However, the rapid advancements in artificial intelligence (AI) and machine learning (ML) have led to the transformation of KMS into AI-based systems, offering significant advantages for businesses in terms of sustainability. AI-based knowledge management systems are designed to provide more efficient and effective knowledge management. These systems use AI and ML algorithms to analyze large datasets, enabling businesses to better understand their customers, competitors, and market trends, thereby making more informed decisions. For example, AI-based systems can help businesses identify patterns in customer behavior, allowing them to personalize their products and services to meet the needs of their customers. Furthermore, AI-based knowledge management systems can help businesses to reduce their carbon footprint, which is essential for business sustainability. By automating manual processes, AI-based systems can reduce the amount of energy consumed by businesses, resulting in significant cost savings and reducing their impact on the environment. AIbased systems can also help businesses to identify inefficiencies in their operations, enabling them to reduce waste and improve their overall environmental performance. One example of an AI-based knowledge management system is IBM Watson, which uses natural language processing (NLP) and ML to analyze data and provide insights. IBM Watson is widely used in the healthcare industry, where it is used to analyze patient data and provide treatment recommendations. Another example is Salesforce Einstein, an AI-based system that uses ML to analyze customer data and provide insights to businesses, helping them to better understand their customers and improve their products and services.

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KMS and AI-based recruitment strategies are becoming increasingly intertwined in newly emerging businesses. KMS can help businesses manage their knowledge assets, while AI-based recruitment strategies can help businesses find and hire the right people. This integration can help businesses build and maintain a competitive advantage in today’s fast-paced business environment. KMS can help businesses collect and store knowledge about their employees, such as their skills, experience, and performance. This information can be used to identify the knowledge gaps within the organization and to develop targeted training programs to fill those gaps. KMS can also help businesses retain valuable employees by providing opportunities for career development and growth. In this way, KMS can help businesses build a strong knowledge-based culture that is essential for success in today’s knowledgedriven economy. On the other hand, AI-based recruitment strategies can help businesses find the right people to fill their knowledge gaps. AI algorithms can analyze job descriptions and candidate resumes to identify the best fit for the job. AIbased recruitment strategies can also help businesses reduce bias in the recruitment process by removing human subjectivity. This can result in a more diverse and inclusive workplace, which is critical for business success. One example of the integration of KMS and AI-based recruitment strategies is the use of chatbots. Chatbots can help businesses manage their recruitment process by answering candidates’ questions, scheduling interviews, and providing feedback. They can also be used to collect data on candidates, which can be stored in the KMS for future use. One example of a company that has successfully integrated KMS and AIbased recruitment strategies is Unilever, a multinational consumer goods company. Unilever uses a platform called HireVue to manage its recruitment process. HireVue is an AI-powered platform that uses video interviews and assessments to find the best candidates for the job. Unilever’s KMS plays an essential role in its recruitment process. The company uses its KMS to manage its candidate database, which includes information on candidates who have previously applied for jobs at the company. This information can be used to identify candidates who may be a good fit for current job openings, saving time and resources in the recruitment process. The KMS also holds information on Unilever’s employees, including their skills, experience, and performance. This information can be used to find the knowledge gaps within the organization and develop targeted training programs to fill those gaps.

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Unilever’s recruitment process is also supported by HireVue’s AI-powered features. HireVue uses AI algorithms to analyze candidates’ video interviews and assessments, identifying the best fit for the job. The platform can also be used to remove human subjectivity from the recruitment process, reducing the risk of bias in the hiring decisions. Unilever has seen significant benefits from its integration of KMS and AIbased recruitment strategies. The company has reduced its recruitment costs by 90%, saving approximately $1 million per year. The use of HireVue has also improved the candidate’s experience, providing a more engaging and interactive recruitment process that is more in line with the expectations of today’s job seekers. To summarize, Unilever’s integration of KMS and AI-based recruitment strategies is a great example of how these two technologies can be used together to improve recruitment processes and build a strong knowledgebased culture. The use of HireVue has helped Unilever to reduce its recruitment costs, while the company’s KMS has helped it to find knowledge gaps and develop targeted training programs. By using these technologies together, Unilever has built a competitive advantage that has helped it to attract and keep top talent. In conclusion, the link between KMS and AI-based recruitment strategies in newly emerging businesses is crucial for building a competitive advantage. KMS can help businesses manage their knowledge assets, while AI-based recruitment strategies can help businesses find and hire the right people. Together, these strategies can help businesses build a strong knowledgebased culture and a diverse and inclusive workforce, which are essential for long-term success.

7.7 Innovative Approaches to Recruitment Identifying talent for a business is a significant challenge. They use their networking skills to find suitable candidates by attending job fairs, college placements, etc. An original approach started named “Job-A-Thon,” where a group of newly emerging companies come together and hold a test to employ. Based on the results, the organizers send resumes to the companies. Companies will select from the selected candidate list, conduct interviews, and offer positions in their organization. There are many approaches that new-age businesses are focusing on, which are listed below.

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It is being used to automate various recruitment processes such as resume screening, scheduling interviews, and conducting final interviews. It helps employers to get an idea of the attitude and behavior of employees through psychometric tests. Right-fit candidates for the job are found through machine algorithms that also results in speedy process of hiring right candidates at a lower cost. Emerging businesses may participate in industry events, conferences, or meetups to connect with potential candidates and showcase the company culture. Employee referral programs are a way for start-ups to tap into their existing employee network to identify top talent. Start-ups create a referral program and communicate it to their employees. Employees are incentivized to refer their friends, family, or acquaintances by offering a reward for every successful referral. The referred candidates go through the normal recruitment process, but their resumes are flagged as employee referrals. These businesses track the success rate of employee referrals and adjust the program accordingly. The following are examples: 1. There is a referral program and employee brand ambassador program offered by Google. It is designed to help attract top talent to the company by incentivizing current employees to refer qualified candidates and to act as advocates for the company. Under this program, Google offers its employees $2,000 for every referral that is hired and stays with the company for at least 90 days. This incentive serves to encourage employees to refer high-quality candidates who are likely to be a good fit for the company’s culture and values. Employee brand ambassadors are employees who act as advocates for the company and can help to attract top talent by sharing their positive experiences working at the company. These employees are typically identified and selected by emerging businesses within Google. These employees are passionate about the company and are willing to act as brand ambassadors. Once selected, employee brand ambassadors are trained on how to communicate the company’s culture and values to potential candidates. They are given a platform to share their experiences and engage with potential candidates, such as through social media or events. By doing so, they help to create a positive and engaging experience for potential candidates, which can help to attract top talent to the company. The benefits of such programs are manifold. First, employee referrals are typically more reliable and successful than other recruitment methods.

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Current employees have a good understanding of the company culture, and they are likely to refer candidates who are a good fit for the organization. Second, employee brand ambassadors can help to create a positive image of the company and can be powerful advocates for the brand. Finally, such programs can help to build a strong employer brand, which is essential for attracting and retaining top talent. In conclusion, the Google referral program and employee brand ambassador program are effective strategies for attracting and retaining top talent. They serve to incentivize employees to refer high-quality candidates, and they help to create a positive and engaging experience for potential candidates. 2. “Airbnb” has a program called “Community Champions,” which is a group of employee brand ambassadors who represent the company and share their experiences with potential candidates. It is a network of dedicated and passionate individuals who are committed to promoting positive experiences and building strong communities within the Airbnb platform. These champions are volunteers who work closely with Airbnb to support guests and hosts, share their knowledge and expertise, and provide valuable feedback to improve the Airbnb experience for everyone. The community champions program is designed to recognize and reward these individuals for their contributions to the Airbnb community. Champions receive exclusive perks and benefits, such as early access to new features and products, invitations to special events and meetups, and recognition on the Airbnb platform. In order to become a community champion, individuals must first apply to the program and demonstrate their passion for the Airbnb community. This includes a commitment to helping others, a strong knowledge of the Airbnb platform and its features, and an ability to communicate effectively with others. Once accepted into the program, champions are given access to a variety of resources and support, including training materials, mentorship opportunities, and a dedicated community of like-minded individuals. They are also encouraged to share their experiences and insights with others through social media, blog posts, and other online channels. Overall, the Airbnb community champions program is a powerful tool for building strong, supportive communities within the Airbnb platform. By recognizing and rewarding those who go above and beyond to help others, Airbnb can foster a culture of collaboration, empathy, and inclusivity that benefits everyone involved.

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7.7.1 Asynchronous Interviewing In asynchronous interviewing, the interview process is automated while the candidates get to answer the prearranged questions without the interviewer online. The candidates can choose their desired time and location to attend the interview. AI is used to recognize the faces and analyze the candidates’ answers. Here, the candidates are asked to record video responses to a set of predetermined questions by using their mobile devices, laptops, or computers, which can then be reviewed by recruiters. As companies need more employees, an asynchronous interviewing can save time and money for both the company and the candidate, and it also allows for a more diverse pool of applicants since candidates from all over the world can participate in the process regardless of their location. However, it’s important to ensure that the process is fair and inclusive, that the technology used is unbiased, and that there are no conflicts arising out in the recruitment process. Here are some examples of companies that have used asynchronous interviewing: 1. Dell Technologies—Dell Technologies uses asynchronous video interviews to screen candidates for various roles. Candidates are asked to record their responses to pre-set questions and submit the video within a given timeframe. This process helps the company to evaluate candidates’ communication skills, problem-solving abilities, and cultural fit. 2. Hilton—Hilton uses a video interviewing platform to conduct asynchronous interviews for its global recruitment process. Candidates are invited to record their responses to a set of predetermined questions, which are reviewed by the hiring team. The process allows Hilton to assess candidates’ skills and experience more efficiently and provide a better candidate experience. 3. Automatic—Automatic, the company behind WordPress, uses an asynchronous interviewing process to evaluate candidates for various roles. Candidates are asked to complete a series of written exercises and respond to pre-set questions on a collaborative platform. The company values this process as it helps assess candidates’ skills, personalities, and cultural fit while also promoting diversity and inclusion. 4. Unilever—Unilever, the multinational consumer goods company, uses asynchronous interviewing as part of its graduate recruitment process. Candidates are asked to complete a series of online assessments,

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including video interviews, to evaluate their skills and fit for various roles. The process helps Unilever to assess candidates’ potential and identify the best fit for the company. 5. HubSpot—HubSpot, a leading software company, uses a combination of asynchronous video interviews and live interviews to evaluate candidates for various roles. Candidates are asked to record their responses to preset questions and participate in live interviews with the hiring team. The process allows HubSpot to assess candidates’ skills, experience, and cultural fit more efficiently while also providing a positive candidate experience.

7.7.2 Virtual and Augmented Reality Using virtual reality (VR) and augmented reality (AR) technologies in the recruitment process is a dynamic way of using the company’s resources and creating a new trend in the job market. These technologies help the organizations to find the right person for the job and let applicants know if they are fit for the job or not. Rather than going with traditional interviews, VR helps assess candidates by analyzing his/her video interviews anywhere or anytime. The recruiter feels that the candidate is giving an interview in front of him. These technologies are currently being used in several businesses to achieve the best outcome and save time and cost for the company. An example could be a virtual customer service simulation where candidates navigate through different scenarios and the AI assesses their communication and problem-solving skills. Here are some examples of companies that have used VR and AR in their recruitment processes and sustainability efforts: 1. Walmart—Walmart has used VR technology in its recruitment process to simulate real-world scenarios that employees may encounter while working in the company’s stores. The VR training helps to improve employees’ skills and knowledge while also reducing the time and costs associated with traditional training methods. Walmart has also used AR technology in its stores to improve the shopping experience for customers, such as by providing product information and reviews through their smartphones. 2. Nestle—Nestle has used VR technology to educate its employees and stakeholders on sustainable agriculture and the company’s efforts to

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reduce its environmental impact. The VR experience provides an immersive view of the company’s cocoa plantations and the sustainable practices implemented by farmers. Nestle has also used AR technology to promote recycling by allowing customers to scan product packaging with their smartphones and receive information on how to recycle it. 3. PwC—PwC has used VR technology in its recruitment process to provide candidates with a virtual tour of the company’s offices and work environment. The VR experience allows candidates to explore different departments and interact with employees, giving them a better understanding of the company’s culture and values. PwC has also used AR technology to provide customers with virtual consultations and improve the delivery of its services. 4. Siemens—Siemens has used VR technology to improve the safety of its employees by providing them with virtual training on hazardous situations that they may encounter while working in the company’s factories. The VR experience allows employees to practice their responses to emergency scenarios in a safe and controlled environment. Siemens has also used AR technology to improve the maintenance of its machines by providing technicians with virtual overlays of equipment and instructions on how to repair them. 5. Ford—Ford has used VR technology in its sustainability efforts by creating a virtual experience that allows customers to explore the company’s electric and hybrid vehicles. The VR experience provides customers with an immersive view of the vehicles’ features and benefits while also promoting sustainable transportation. Ford has also used AR technology to improve the efficiency of its production line by providing employees with virtual overlays of equipment and instructions on how to assemble them.

7.7.3 Robotics Every single vacancy in a company receives hundreds and thousands of applications. A recruiter can’t check every application manually. To solve this issue, companies developed “Resume Robots” to scan CVs or resumes instantly for essential skills and keywords and cut unmatched candidate profiles to reduce the load on human recruiters. One robot, named “Tengai,” is an interview robot. This robot takes interviews without bias and gives exact results by analyzing candidates’ ways of answering questions.

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Here are some examples of how robots are being used in this area: 1. Recruiting Chatbots—Chatbots are AI-powered robots that can be used to automate the recruitment process. They can interact with candidates through messaging platforms and answer frequently asked questions, schedule interviews, and collect candidate information. For example, Unilever uses a chatbot called U-Partner to engage with candidates and provide a more personalized recruitment experience. 2. Resume Screening Robots—Resume screening robots are used to analyze resumes and identify candidates who meet the job requirements. These robots can scan resumes for keywords, work experience, and education and compare them against job descriptions. For example, Pymetrics uses AI-powered robots to screen resumes and identify candidates who match the company’s culture and values. 3. Video Interviewing Robots—Video interviewing robots are used to conduct interviews remotely and evaluate candidates’ skills, experience, and communication skills. These robots can ask pre-set questions and record candidates’ responses for later review. For example, HireVue uses AI-powered robots to conduct video interviews for companies such as Unilever, Vodafone, and Goldman Sachs. 4. Collaborative Robots—Collaborative robots, or cobots, are used to work alongside human employees and automate repetitive tasks such as data entry and processing. These robots can free up human employees’ time and allow them to focus on more complex tasks. For example, Deutsche Post DHL Group uses cobots to automate tasks in its warehouses, such as picking and packing. 5. Customer Service Robots—Customer service robots are used to interact with customers and provide them with information and assistance. These robots can be used to answer frequently asked questions, provide product information, and process orders. For example, Lowe’s has developed a customer service robot called Lowe Bot that can assist customers in its stores with finding products and answering questions.

7.7.4 Gamification The standard interview process has become ineffective nowadays. So, gamification in recruitment makes the interview process effective. Gamification is a technique where you design games in a digital format. In recruitment, gamification attracts more applicants and helps them highlight their agility

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and competencies. When coupled with recruitment, gamification is titled “Recruiter net.” This process includes games, related behavioral evaluations, challenges, and quizzes. It also allows the hiring team to assess their creative thinking, aptitude, and problem-solving capabilities and helps candidates highlight their job qualifications. Gamification makes the hiring process more fun, enjoyable, and interactive. Companies can use leaderboards to rank candidates based on their performance in different assessments, allowing them to quickly identify the top performers. They can use scenario-based simulations to assess a candidate’s ability to handle different situations, such as customer complaints or emergencies, use role-playing to assess a candidate’s communication and interpersonal skills, as well as their ability to think on their feet as well as use virtual tours to give candidates a sense of the company’s culture and working environment and to help them decide if it’s a good fit for them. Here are some examples of how companies are using gamification in their recruitment process and business sustainability efforts: 1. Deloitte—Deloitte uses a gamified assessment called “Deloitte GameBased Assessment” to evaluate candidates’ cognitive abilities and personality traits. The assessment is designed as a series of minigames that simulate real-world business scenarios, such as managing a team or solving a complex problem. The games are designed to test candidates’ decision-making skills, creativity, and strategic thinking. 2. Marriott International—Marriott International has developed a game called “My Marriott Hotel” that allows players to run a virtual hotel and learn about the hospitality industry. The game provides players with a realistic simulation of running a hotel, including managing staff, ordering supplies, and serving guests. The game is used to attract candidates who are interested in the hospitality industry and provide them with a taste of what it’s like to work in the industry. 3. IBM—IBM uses a gamified learning platform called “IBM Think Academy” to train its employees on new technologies and business practices. The platform provides employees with interactive modules and games that teach them new skills and knowledge. The platform is designed to engage employees and make learning more enjoyable and interactive. 4. Coca-Cola—Coca-Cola has developed a gamified sustainability program called “Water Stewardship Challenge” that encourages employees to reduce their water consumption and promote sustainability in their

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workplace. The program uses a leaderboard system that ranks employees based on their water-saving efforts and provides rewards and recognition for top performers. The program is designed to encourage employees to take ownership of sustainability efforts and promote a culture of sustainability within the company.

7.7.5 Chatbots “Chatbots” are computer programs that mimic human conversation and can be integrated with recruitment processes to automate certain tasks. Chatbots can be programmed to answer common questions about the job, the company, and the application process. It can help schedule interviews by collecting candidate availability and coordinating with the hiring team, conducting initial interviews by asking predetermined questions and analyzing the candidate’s responses to identify the most qualified candidates. Chatbots can pre-screen candidates by collecting their qualifications, work experience, and other relevant information and passing it to the human recruiters. They can keep candidates informed of the status of their applications, provide feedback, and help them move through the recruitment process. It provides 24/7 availability for candidates to interact with, which can be helpful for candidates who have busy schedules. Here are some examples of companies that use Chatbots for recruitment: 1. HireVue—HireVue’s AI-powered video interviewing platform uses chatbots to assist with scheduling interviews and providing interview tips and information to candidates. The chatbot interacts with candidates via text or voice and can answer questions about the interview process, provide information about the company and job, and schedule the interview at a convenient time for the candidate. Once the interview is scheduled, the chatbot sends a reminder to the candidate and provides a link to the video interview. 2. Work Fusion—“Work Fusion’s” AI-powered automation platform includes a chatbot that can assist with scheduling interviews, sourcing candidates, and providing information about the company and job. It also helps in screening resumes, identifying the best candidates, and scheduling interviews with them. The chatbot also helps in providing the status of the application and helps to keep track of the candidate’s engagement throughout the recruitment process.

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3. Mya—“Mya” is an AI-powered chatbot that helps companies automate their recruitment process. The chatbot interacts with candidates via text or voice and can answer questions about the job and the company.

7.8 Future Trends in Recruitment The future will be tech-recruitment, where the recruitment process will be changed by using technologies. Recently, Meta introduced metaverse, which is a virtual world. Companies are looking in this area to interact with applicants in the metaverse world and recruit people from this metaverse platform. Many companies are developing AI technologies like chatbots and machine learning applications that help predict applicant status without bias. Instead of following the traditional hiring process, HR managers changed to a social recruitment process. HR managers use social media platforms like LinkedIn and job-hiring platforms in this social recruitment process. Now they are looking into Facebook, Twitter, and Instagram to recruit applicants. This helps them find the perfect and highly qualified applicant for the job profile. Using online platforms embedded with technical specifications gave space to verify candidates and test their skills in online mode. AI will be used to predict which candidates are most likely to be successful in a particular role based on data and analysis of past hires. It will be used to reduce bias and improve diversity, equity, and inclusion in the recruitment process.

7.9 Best Practices for Using KMS in Recruitment Organizations that have successfully used KMS in recruitment have adopted several best practices, such as the following: 1. Creating a culture of knowledge-sharing: Organizations that have a culture of knowledge-sharing attract the right talent who are willing to share their knowledge and expertize. This culture also promotes employee engagement and retention. 2. Providing access to training and development resources: Organizations that provide employees with access to training and development resources create a learning environment that attracts and retains the right talent.

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3. Integrating the job portal with the organization’s knowledge base: Organizations that integrate their job portal with their knowledge base provide potential candidates with a better understanding of the organization’s culture, values, and requirements. 4. Using data analytics to identify potential candidates: Organizations that use data analytics to identify potential candidates who are a good fit for the organization based on their skills, experience, and personality are more likely to attract and retain the right talent.

7.10 Organizations That Have Used KMS in Recruitment Several organizations have successfully used KMS as an innovative recruitment strategy. Some of these organizations are as follows: 1. IBM—IBM has used KMS to create an online community where potential candidates can interact with current employees and learn more about the organization. IBM has also provided employees with access to training and development resources, such as online courses and tutorials. 2. Microsoft—Microsoft has used KMS to create an online job portal that is integrated with the organization’s knowledge base. Potential candidates can search for job opportunities and learn more about the organization’s culture, values, and requirements. 3. Deloitte—Deloitte has used KMS to provide employees with access to training and development resources, such as online courses and videos. Deloitte has also used data analytics to identify potential candidates who are a good fit for the organization based on their skills, experience, and personality.

7.11 Limitations AI-based recruitment systems may perpetuate existing biases and discrimination within the hiring process if they are trained on biased data. For example, if a company’s past hiring decisions were heavily influenced by unconscious bias, an AI-based recruitment system that is trained on that data may continue to make similar biased decisions. Additionally, if the data used to train the system are not diverse or representative of the population, the system may not be effective in recruiting candidates from underrepresented

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groups. If the data used to train the system are not diverse or representative of the population, the system may not be effective in recruiting candidates from underrepresented groups. Implementing and maintaining AI-based recruitment systems can be expensive. AI-based recruitment systems may not be able to fully replicate the judgment and intuition of human recruiters, which could lead to missed opportunities to find the best candidate for the job. Some recruiters and hiring managers feel that the process of recruitment is a human one and that an AI system cannot replicate the human touch that is required for recruiting the best candidate for the job. Ethical issues might arise with lack of transparency and accountability. KMS has become increasingly popular in organizations as it offers many benefits, such as increased productivity, improved decision-making, and enhanced innovation. However, there are also potential demerits of KMS regarding human resource management (HRM). One of the potential demerits of KMS is that it may create a culture of information hoarding. When employees are rewarded for their knowledge, they may become hesitant to share their knowledge with others for fear of losing their competitive advantage. This can create a siloed environment where knowledge is not shared across teams or departments, which can hinder collaboration and innovation. Another potential demerit of KMS is that it may not fully capture tacit knowledge. Tacit knowledge is difficult to articulate and often learned through experience. It is not always easy to transfer tacit knowledge through KMS, as it is often context-specific and relies on personal interactions. Therefore, organizations may miss valuable knowledge if they rely solely on explicit knowledge captured in KMS. Furthermore, KMS may require a significant investment of time and resources to implement and maintain. The process of identifying, capturing, organizing, and sharing knowledge can be time-consuming, and the system may require continuous updates and maintenance to remain effective. This can lead to increased costs and be a burden on HRM resources. In conclusion, while KMS offers many benefits, such as increased productivity and improved decision-making, it also has potential demerits regarding HRM. These demerits include creating a culture of information hoarding, not fully capturing tacit knowledge, and requiring a significant investment of time and resources to implement and maintain. Therefore, organizations should carefully consider these potential demerits when implementing KMS and take steps to mitigate them.

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7.12 Conclusion In this chapter, you learned about the trends and technical approaches used in the recruitment process. The authors have so far covered, the newage business culture promotes employee engagement and innovative methods to harness personal growth. In authors’ opinion, incorporating sustainability into knowledge management practices within AI recruitment can involve several strategies to ensure that the hiring process is socially responsible and environmentally conscious. The recruitment process using AI will be more efficient and inclusive, which will help companies to identify the best talent, regardless of their background. It’s important to note that while AI has the potential to make the recruitment process more efficient, it’s important to ensure that the technology used is unbiased and inclusive and that human recruiters are still involved in the final decisionmaking process. In conclusion, KMS is an innovative recruitment strategy that helps organizations to attract, select, and retain the right talent. KMS can be used in recruitment in several ways, such as creating an online job portal, creating an online community, using data analytics, and providing access to training and development resources. Organizations that have successfully used KMS in recruitment have adopted several best practices, such as creating a culture of knowledge-sharing, providing access to training and development resources, integrating the job portal with the organization’s knowledge base, and using data analytics to identify potential candidates.

7.13 Questions for Class Discussion 1. What are some traditional recruitment strategies, and how do they differ from innovative recruitment strategies that use knowledge management systems? 2. Can the use of VR, robotics, and gamification in recruitment help reduce bias in the hiring process? 3. How can knowledge management systems help businesses sustain their recruitment efforts? 4. What are some advantages of using knowledge management systems in recruitment, and what are some potential drawbacks or challenges? 5. What are some benefits and drawbacks of using virtual reality (VR) in recruitment processes?

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6. How does KMS facilitate knowledge-sharing among employees and candidates during the recruitment process? 7. What ethical considerations need to be taken into account when using VR, robotics, chatbots, and gamification in recruitment? 8. How can companies use knowledge management systems to identify and attract top talent, and what are some effective ways to use these systems for talent management

References Florkowski, G. W., & Olivas‐Luján, M. R. (2006). The diffusion of human‐resource information‐technology innovations in US and non‐US firms. Personnel Review, 35(6), 684–710. Gorman, G. E., & Pauleen, D. J. (2016). The nature and value of personal knowledge management. In Personal knowledge management (pp. 23–38). Routledge. Guenole, N., & Feinzig, S. (2018). The business case for AI in HR. With Insights and Tips on Getting Started. Armonk: IBM Smarter Workforce Institute, IBM Corporation. Jarrahi, M. H., & Sutherland, W. (2019). Algorithmic management and algorithmic competencies: Understanding and appropriating algorithms in gig work. In Information in Contemporary Society: 14th International Conference, iConference 2019, Washington, DC, USA, March 31–April 3, 2019, Proceedings 14 (pp. 578–589). Springer International Publishing. Johansson, J., & Herranen, S. (2019). The application of Artificial Intelligence (AI) in Human Resource Management: Current state of AI and its impact on the traditional recruitment process (Dissertation). Retrieved from http://urn.kb.se/ resolve?urn=urn:nbn:se:hj:diva-44323 Maedche, A., Legner, C., Benlian, A., Berger, B., Gimpel, H., Hess, T., ... & Söllner, M. (2019). AI-based digital assistants: Opportunities, threats, and research perspectives. Business & Information Systems Engineering, 61, 535–544. Pan, Y., Froese, F., Liu, N., Hu, Y., & Ye, M. (2022). The adoption of artificial intelligence in employee recruitment: The influence of contextual factors. The International Journal of Human Resource Management, 33(6), 1125–1147. Rao, A., & Verweij, G. (2017). Sizing the prize: What’s the real value of AI for your business and how can you capitalise. PwC Publication, PwC.

Chapter 8

Human Resources Aakarsh Tattari and Debdutta Choudhury Woxsen University, Hyderabad, India

8.1 Introduction The proliferation of technology in the last two decades has completely transformed the functioning of the industry. The human resource (HR) function has also adopted these rapid technological strides and has imbibed several technological tools to aid in decision-making. Smart Human Resources or SHR 4.0 is a new way of human resource management that uses several technological tools like big data analytics, Internet of Things, social media and artificial intelligence (AI) to create more value for organisations. With millennials and a new-generation workforce, the applications of these smart tools have found strong cooperation and adoption among employees. SHR 4.0 has strong applications in the following HR functions: ■ ■ ■ ■ ■ ■

Managing employee attrition Performance management Compensation management Recruitment HR systems WFM

Major challenges of SHR 4.0 would be selecting the right set of technologies, managing organisation culture to usher in the new tools and managing employee expectations. DOI: 10.4324/9781003358411-8

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In this chapter, we discuss the technological adoption, especially adoption of AI in the context of human resources.

8.2 Employee Attrition Employee attrition is a major concern for businesses worldwide. There are both tangible and intangible impacts of employee turnover directly affecting business results (Porter, 2011). Curtis and Wright (2001) indicated that employee turnover impacts the quality and customer service levels. Employee attrition is becoming a serious problem since there are various career opportunities, and the turnover problem seems to be increasing every year. According to statista.com, the global employee turnover for professional service was 8.3% in 2013 and increased to 13.2% in 2019, reaching a high of 13.9% in 2018. Mishra and Mishra (2013) conducted a bibliometric study to understand the research surrounding employee turnover. According to them, employee turnover is caused by the following factors. Organisational factors consist of organisational policies, justice, support and culture. This is one of the key reasons for employee turnover. Employees are extremely sensitive to the organisational environment that affects their day-to-day functioning. Transparency, perceived justice, support from peers and superiors and overall culture play a strong mediating role in the turnover decisions. HR practices have the strongest influence on employee retention. HR practices include recruitment, training, compensation, performance management and employee engagement practices. Research indicates poor employee perception of multiple HR practices leads to high employee attrition. Other factors like age, gender, location, industry image, etc., also play an important role in employee turnover. With advances in data sciences, machine learning (ML) and AI companies can now analyse enormous amounts of employee-centric data like attendance, leaves, performance ratings and participation in HR events to understand employee engagement and predict attrition. According to Forbes, there are further sophisticated and subtle analyses that can undertake using AI. Felt Accountability: Using AI, companies can understand employee levels of participation in various corporate events by analysing body language, tone, facial expression and words used and thereby predict

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engagement and accountability. This helps companies to identify individuals who are flight risk and help them to create sessions and programmes to re-engage. Manager–Employee Rapport: Companies use sentiment analysis and conversational analytics to gauge employee enthusiasm while interacting with superiors. It is widely acknowledged that direct reporting authority shape employee experience at the workplace and, hence, is key to employee motivation. However, there are serious privacy concerns with these methods, and hence both parties should be aware that their interactions in various workplace tools like Slack may be monitored. Employee Distractions: Today’s workplace requires employees to use multiple tools to perform, like mail clients, Dropbox, Slack, Salesforce, etc. Using these tools plus interactions on phone and in face-to-face meetings continuously creates distractions in functioning and lowers productivity. AI can be used to gauge this context shifting, and concerned employees may be advised to put in do not disturb mode in some of these applications. Fallucchi et al. (2020) created a model to predict employee attrition and tested this model over 1,500 samples of employees in IBM Analytics tools to validate the proposed model. Using Gaussian Naïve Bayes classifier, they have demonstrated that ML tools can be used to support HR systems. They created an ML algorithm and used 70% of their dataset to train the algorithm and sharpen its prediction capability. The remaining 30% of dataset was used to validate the algorithm. The authors then used a Gaussian Naïve Bayes classifier to successfully classify 364 of the 441 in the test dataset. They have been able to predict 51 out of 72 employees who left the company using this ML algorithm. Yedida et al. (2018) used k-nearest neighbours (KNN) algorithm to predict employee attrition with 94.32% accuracy. Alao and Adeyemo (2013) used WEKA (Waikato Environment for Knowledge Analysis) workbench and See5 for Windows to generate decision tree models and rule sets that were used to create predictive models for employee attrition. Several HR analytics tools are used today to predict employee attrition. IBM claims that Watson, its AI platform, can predict employee attrition with 95% accuracy. Python-based tools like Gradient Forest or cloud-based tools like Microsoft Azure or Google Cloud Platform may also be used. According to Analytics India Magazine, companies like IBM and Mastercard are using predictive-based models to understand and reduce employee attrition. The chapter also highlights that social media analytics of

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employees are being used to identify potential employees with a high risk of turnover. According to an article published on the Harvard Business School digital platform, Credit Suisse has used 10–11 characteristics of employees and predicted their leaving in the next 1 year using sophisticated AI-based prediction tools. It is designed to warn managers of potential flight risks and thereby initiate measures to prevent the same. This has a tremendous impact on the financial performance as estimates suggest that a one-point reduction in attrition percentage saves the bank $75 million to $100 million a year.

8.3 Performance Management A performance management system is a process-driven approach to measure employee performance both quantitatively and qualitatively. When done effectively, the organisation can also detect skill and competency gaps in individuals or a population of employees and thereby organise training and mentoring for overall development. In the classical performance management system, an annual exercise has been a 5- or 7-point rating on employee performance and attitude on various parameters marked by the direct supervisor and then validated by a senior functional manager and HR department. One of the limitations of the process is direct supervisor, when unsure, will mark a median grade that may not be reflective of the actual performance. Also, appraisal interviews are a source of discomfort for both the employee and the supervisor when a difficult discussion of employee’s weakness comes up. Also, with the process being annual, the recency factor of employee performance over the last couple of months would be the main decider. With the advent of various digital tools at the turn of the century, many of the paper-based practices have been shifted online, resulting in scalability, faster turnaround and, to a certain extent, avoidance of face-to-face interactions. However, the annual process and the recency effect continued, leading to a certain level of distortion in the entire performance management system. The development of data sciences and AI has addressed some of the concerns of traditional performance management systems. Masson (2020) outlined some of the ways in which an AI-powered system would help. Framework-Driven Approach: This approach makes recognition of employee efforts and performance easier by systematically organising data.

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Natural Language Processing and Sentiment Analysis: It helps to understand employee interactions with others and their reactions to the concerned employee’s efforts. AI-based algorithms continuously look at employee performance in real time and eliminate the annual ritual of appraisal. Algorithms also track employee digital footprints and interactions over various digital tools like mail, calendar, Slack, etc., and integrate relevant information scattered across these applications to analyse performance. Some advantages of an AI-based performance management system are as follows: ■ Elimination of human errors like personal bias, incomplete data compilation and acts of voluntary or involuntary omission and commission. ■ Availability of comprehensive data to judge performance from various scattered sources. ■ Ability to do a real-time analysis of performance on an on-going basis to judge true performance and eliminate the recency bias. ■ Better understanding of skill gaps based on data. Algorithms today can analyse data faster and recommend managers to help in decisionmaking. ■ The real-time analysis also helps employees to understand their limitations and achievements and helps the managers to remove roadblocks to ensure better performance. The deployment of AI system also has certain disadvantages, as follows: ■ It requires substantial investment in terms of cost and time. A wellfunctioning ML algorithm requires substantial time to perfect, and a costbenefit analysis must be done before deployment. ■ The lack of human element in a performance management system using AI is a major limitation. It will take away the elements of empathy, emotional intelligence and cooperation and reduce performance management system as an output of data-driven activity. ■ There are ethical concerns about tracking employee actions in real time over various digital applications. It raises questions of privacy and intrusiveness and, thereby, may erode employee trust and morale. AI systems in performance management should bring in more transparency, aid accurate data-based assessments and remove biases including recency

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bias. However, human touch will always remain the focal point of any performance management system. AI should be an aid in decision-making, not the decision-maker. This would free up time for line managers to make quality decisions and improve the overall performance of the individuals and the team.

8.4 Payroll, Benefits and Incentive Management ML and AI are being used as major tools in payroll management. Companies are not only looking at skill versus compensation at present but are actively trying to model current skills versus future compensation as certain skills become commoditised over time and also make compensation predictions for future skills. According to the Society for Human Resource Management (SHRM), AI can help companies price skills in the following ways: ■ Analyse external and internal datasets. ■ Determine critical skill requirements for today and in future. ■ Pricing these skills based on geography. AI-based vendors like Payscale help companies to determine compensation based on skill levels and geography-based labour costs. However, according to SHRM and the Brookings Institution, biased data fed into the algorithm may bias AI-based compensation recommendations further. The corporates must ensure that they feed complete representative training data to the algorithms; otherwise, that may lead to discriminatory compensation practices if the company develops a high reliance on the compensation prediction algorithms. According to WorldatWork’s 2018 Total Rewards Conference and sightsinplus.com, AI can bring the following benefits to the compensation framework. Fairness: Algorithms can find high-performance workers based on data analytics based on their behaviour and motives and recommend compensation packages that would foster certain outcomes. This would reduce subjectivity and reward employees based on evidence. Personalisation: AI can model and predict individual motivations and customise compensation and benefits to suit individuals. This would improve employee engagement.

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Agile Programmes: According to Deloitte report (Bersin, Deloitte Consulting LLP, High-Impact Total Rewards Research, 2018) and a study by Globoforce and SHRM (SHRM/Globoforce Employee Recognition Report, 2018), compensation modifications are not aligned to employee appraisals. However, more employees are now slowly integrating compensation modification practices that may happen in small increments throughout the year. AI-based algorithms can help companies to achieve the same, resulting in higher employee engagement. Cost Reduction: AI deployment reduces recurring operational costs in compensation management and also has the advantage of quick scalability. According to benefitspro.com, quality assurance in payroll management is a major concerns, and AI can be used to detect payroll anomalies. Alight Solutions has an intelligent assistant, Eloise, who assists payroll team to detect errors and avoid costly mistakes. This technology can be deployed across the payroll life cycle, starting with payroll prediction, inputs and then payroll processing. With the advent of gig economy and work-from-home practices necessitated by the COVID-19 pandemic, flexible work practices have now become a reality. The standard practices of biometric-based attendance systems and their linkage to compensation seem outdated in this milieu. The compensation professionals of today now have to manage most employees remotely and calculate compensation based on their outputs and not their attendance in offices. This is where AI’s computational power and tracking ability can be best leveraged. The AI systems of today can track employee performance on a real-time basis and calculate compensation and rewards accordingly. There is a growing feeling that in future, the entire profession of compensation and benefits will be automated, thereby outliving the importance of manual intervention. According to a poll conducted by https://willrobotstakemyjob.com in 2019, there is a 67% probability that the entire function would be automated in the next two decades. However, the pandemic has accelerated technological adoption, and hence, it seems likely that automation will be the norm in the next few years.

8.5 Intelligent Recruitment and HR Systems 8.5.1 AI in Recruitment Recruitment is one of the fundamental functions of a HR function. It is defined as the process of finding the right candidates and creating a candidate pool

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Figure 8.1

A model for recruitment ( Breaugh, 2008).

against a vacancy (Stoilkovska, IIieva & Gjakovski, 2015). However, due to increased competition for scarce talent, innovations in recruitment are becoming more and more imperative. Selection is the next step towards hiring employees. Traditional methods of selection are pre-interview scanning, interviewing and post-interview processes like reference checks. Breaugh (2008) created a model for recruitment process consisting of five interconnected steps (Figure 8.1). This model describes the entire recruitment process, including recruitment objectives, strategy development, recruitment activities, job applicant variables and recruitment results. From this model, we observe that several variables can either replace human effort or may aid human effort in a significant way through AI deployment. The first step in recruitment is looking at the candidate data and deciding on suitability. AI-based algorithms can rank a candidate’s CV based on training on past data, whereby it learns to score the candidates (Faliagka et al., 2012). Platforms like LinkedIn Recruiter and ZipRecruiter scan millions of candidates on their platforms and rank them on the basis of suitability for the role. Arya, an AI platform, goes a step forward and analyses data in public

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domain, like multiple social media pages and certain databases, to predict employee past antecedents like job and industry shifting, past promotions and responsibilities and then predict a fit in a new role. AI systems like Ideal are doing multiple-factor screening of candidate CVs based on certain traits of previous successful employees, thereby introducing efficiency and thoroughness in the shortlisting process. Automated chatbots using NLP are reaching out to candidates with new positions, responsibilities and compensation even when the person concerned is not actively looking for a job. These chatbots save human time and energy for certain mundane tasks like understanding interest and availability for a certain role and some basic screening for suitability. Another area of AI intervention is in assessment of candidates. A platform like Pymetrics uses cognitive neuroscience-based games to understand candidates cognitive abilities and match them with their predictive algorithm to assess suitability. HireVue has created an AI-based video platform that is being used by companies like Hilton. This platform uses algorithms and facial assessment technologies to understand suitability of a candidate. The algorithm is used to analyse pre-recorded interview videos where candidates have to answer certain standard questions. The answers are graded as well as facial movement and then suitability is assessed. This not only reduces the time to hire but also claims to improve assessment quality compared to human assessment.

8.5.2 Bias in AI Algorithms There is a growing debate about AI systems magnifying human biases manifold in an AI-based recruitment system. Critics argue that the training data used in these algorithms reflect the biases used by human recruiters over the decades. Reuters reported in 2018 that an algorithm created by Amazon favours male candidates over female candidates. This is due to the system being trained on past resumes, which were primarily men. Amazon quickly denied that and argued that the said algorithm was never used due to primitive modelling techniques. Upturn’s report on de-biasing of algorithm notes that such techniques are still not crystallised and hence algorithms are still narrowly focussed on certain characteristics. There is a concern that platforms like HireVue would actively discriminate persons with disabilities. Because of inherent biases, concerns are also raised about job posting on social media, where people of certain groups would even be excluded to view them because of the same inherent biases in algorithm.

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Adina Sterling, a behavioural science professor at Stanford, contends that AI recruitment systems are very good for economies of scale but still not suitable enough for such nuances where inherent suitability of candidate is judged based on their ability and not a certain predetermined characteristic set. She believes companies need to be more vigilant on these counts. The legal and regulatory frameworks of different countries are yet to wake up to this reality. There have been certain rules to stop discriminatory practices in some developed countries, but lawmakers all around the world are still unaware of the full implications of using AI-based recruitment tools.

8.5.3 AI in HR Systems A comprehensive AI-powered HR system is still being deployed at various corporates. Jia et al. (2018) proposed a conceptual framework for AI application in human resource management. The authors contend that with the development of Human Resource Information System (HRIS) and emergence of data-based decision-making in HR, an AI-powered HR system can be developed (Figure 8.2).

Figure 8.2 A conceptual model for artificial intelligence application in human resource management ( Jia et al., 2018).

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HR strategy and planning combined with data mining and AI-based algorithms can create an intelligent decision support system. The entire gamut of HR may be now interlinked and strategic as well as operational decisions can be taken since information asymmetry and opacity can be removed. Also, AI-powered systems may make intelligent recommendations based on past data that would provide intelligent insights for decision-making.

8.6 WFM and Scheduling WFM and scheduling are the critical areas of management today. According to HR Technologist, engaged employees contribute 20% more revenues and are 44% more productive than satisfied employees. Scheduling is one of the techniques to improve employee engagement by ensuring a work–life balance. AI and ML replace or enhance the traditional WFM and scheduling. Adaptive Assignment: AI algorithms can create work schedules and performance management metrices based on individual employee preferences. Policy Assignments: The AI system also looks at customer and organisational requirements and schedule assignments, balancing both employees and organisations. Personalised Fairness: Certain employees have preferences for certain days of the week or certain hours of the day. Some employees volunteer to work on weekends to earn extra benefits. AI-based algorithms look at the history of all these employees and suggest schedules to the manager, taking into account the principle of fairness and also legal requirements. Improve Scheduling Precision During Peak Hours: In industries like retail, managing peak hours is a major challenge. There would be requirements for higher manpower and people with cross-disciplinary expertise. An AI-based system looks at past data and suggests scheduling in such a scenario. Cloud-based AI-driven WFM scheduling solutions ensure efficient and optimised manpower deployment, taking into account availability, rules, skills, holidays, breaks, service levels and budget. Systems like Genesys Cloud Platform use past data and concurrent algorithms to run scheduling competitions to find the most optimised schedules. WFM softwares like Kronos are deployed in the logistics industry to ensure audit of timecard compliance, business volume forecasting and thereby

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improving schedules to match demand and shift management through optimisation, swapping and enhancing employee control to improve engagement. Soft computing tolls like artificial neural network (ANN) are being used in creating simulation models. Research shows ANN can be extensively used in healthcare industry to predict average patient stays in emergency departments (Simeunović et al., 2017). Researchers have used PSO-based algorithms, genetic algorithms and event simulation models to predict workforce scheduling.

8.6.1 Case Study: Using AI in HR – IBM According to Fortune magazine, IBM has saved $1 billion since 2011 by introducing AI-integrated systems in its HR department. The company gets 10,000 job applications every day. ML algorithms help IBM sort and rank these applications based on relevance and help to cut productive time of its employees. IBM’s AI-enabled systems manage employee training, performance management and also compensation. The system tracks employee skills and suggests training programmes to advance professional learning. The topics may vary from communication skills, leadership, management, and also technical skills. IBM has a “proactive retention” programme, which predicts a separation and alerts managers about an intervention requirement to retain the employee. It may be an increment, a bonus, a promotion or a different work schedule. It is estimated that IBM has saved about $300 million in recruitment and lost productivity due to separation through this initiative. IBM has also saved around $700 million by developing certain HR automation applications in-house, thereby saving huge license fees to third-party vendors. Also, due to this automation, a large scale manpower cost is saved in routine and repetitive HR function. IBM now recruits functionaries who can do sophisticated HR roles and create more value for the company.

References Alao, D. A. B. A., & Adeyemo, A. B. (March, 2013). Analyzing employee attrition using decision tree algorithms. Computing, Information Systems & Development Informatics, 4(1), 17–28.

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Breaugh, A. J. (2008). Employee recruitment: Current knowledge and important areas for future research. Human Resource Management Review, 18, 103–118. Curtis, S., & Wright, D. (2001). Retaining employees – the fast track to commitment. Management Research News. 24(8/9), 56–60. 283756441 Employee Attrition Rate of Professional Services Organizations Worldwide From 2013 to 2019. https://www.statista.com/statistics/933710/professional-servicesworldwide-employee-attrition. Accessed on 21.04.2021. Faliagka, E., Ramantas, K., Tsakalidis, A., & Tzimas, G. (2012). Application of machine learning algorithms to an online recruitment system. In Proc. International Conference on Internet and Web Applications and Services. Fallucchi, F., Coladangelo, C., Giuliano, R., & Luca, EWD. (2020). Predicting employee attrition using machine learning techniques. Computers, 2020(9), 86. 10.3390/computers9040086 Jia, Q., Guo, Y., Li, R., Li, Y., & Chen, Y. (2018). A Conceptual Artificial Intelligence Application Framework in Human Resource Management. [ICEB 2018 Proceedings. 91.] Masson, S. (2020). How Smart Analytics and AI Will Redefine the Future Of Performance Appraisals. https://cio.economictimes.indiatimes.com/news/nextgen-technologies/how-smart-analytics-and-ai-will-redefine-the-future-ofperformance-appraisals/77077356. Accessed on 24.04.2021. Mishra, S., & Mishra, D. (2013). Review of literature on factors influencing attrition and retention. International Journal of Organizational Behaviour & Management Perspectives © Pezzottaite Journals. 2(3), 435–444. Porter, J. (2011). Attract and retain top talent. Strategic Finance. 92(12), 56–60. 2373 925461 Silva, W. (2020). Improving the Payroll Process With Artificial Intelligence. https:// www.benefitspro.com/2020/03/30/improving-the-payroll-process-withartificial-intelligence. Accessed on 26.04.21. Simeunović, N.a, Kamenko, I.a, Bugarski, V.a, Jovanović, M.a, & Lalić, B.a (2017). Improving workforce scheduling using artificial neural networks model. Advances in Production Engineering & Management. 12(4), 337–352. 10.14743/ apem2017.4.262 Stoilkovska, A., IIieva, J., & Gjakovski, S. (2015). Equal employment opportunities in the recruitment and selection process of human resources. UTMS Journal of Economics, 6(2), 281–292. Yedida, R., Reddy, R., Vahi, R., Jana, R., GV, A., & Kulkarni, D. (2018). arXiv preprint arXiv:1806.10480

Chapter 9

Evolution of Chatbot in Human Resource Management Mikkilineni Varshini, Dhanekula Rohita, and Hemachandran K Woxsen University, Hyderabad, India

9.1 Introduction Human resource management (HRM) is a crucial function in any organization, encompassing a wide range of activities such as recruitment, training, performance management, and employee engagement. In recent years, chatbot technology has emerged as a promising new tool for enhancing the efficiency and effectiveness of these activities (Bussgang, 2018). The utilization of chatbots in HRM is still a relatively new phenomenon, and their potential benefits and limitations are not yet fully understood (Chatbots Magazine, 2019). Specifically, this study reviews the existing literature on chatbots in HRM, examining how they have been used to date and their impact on organizational outcomes (DeMers, 2018). Overall, this study seeks to contribute to a deeper understanding of the evolution of chatbot technology in HRM and its potential implications for organizations and employees.

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9.1.1 Background Chatbots have already been successfully implemented in various other fields, such as customer service and healthcare, and they have shown significant potential to improve customer satisfaction and service quality, reduce costs, and increase efficiency (Deloitte, 2019). However, their use in HRM is still in its early stages, and research on their efficacy and ethical considerations is limited. The use of chatbots in HRM can benefit organizations in several ways. First, chatbots can automate routine and repetitive HR tasks, such as answering employee queries, processing leave requests, and conducting initial screening interviews (HR Technologist, 2020). This can free up HR personnel to focus on more strategic and value-added activities, such as talent development and succession planning. Chatbots can improve employee engagement by providing real-time feedback and support (Kornblau, 2018). This can help to increase employee satisfaction and motivation, leading to higher productivity and retention rates (Nambiar, 2018). Moreover, chatbots can enable HR departments to gather and analyze data on employee behavior and preferences, enabling them to tailor their programs and policies to better meet employee needs. Despite these potential benefits, there are also several challenges and limitations associated with the use of chatbots in HRM (Shi, Yang & Wu 2020). These include concerns around privacy, security, bias, and the need for ongoing technical maintenance and updates. Additionally, there are questions about how chatbots will affect the human workforce and the need to balance the use of automation with the preservation of human jobs and skills.

9.2 Literature Reviews The use of chatbots in HRM is not a new phenomenon, but recent advancements in artificial intelligence (AI) and natural language processing have increased their capabilities and potential applications. Chatbots can provide personalized and real-time feedback to employees, which can increase their engagement and motivation. They can also automate routine and repetitive HR tasks, such as scheduling interviews, answering frequently asked questions, and providing basic HR information to employees. By freeing up HR personnel to focus on more strategic and value-added activities, chatbots can help organizations to improve their HR efficiency and effectiveness. Several studies have highlighted the potential benefits of chatbots in HRM. For example, a study by Kim et al., (2020) found that chatbots can improve

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employee engagement by providing real-time feedback and support. Similarly, a study by Dehghani et al., (2019) showed that chatbots can increase HR efficiency and reduce the workload of HR personnel. In addition, a study by Saxena et al., (2021) found that chatbots can enhance the candidate experience in recruitment by providing prompt and personalized communication. Similarly, a study by Deloitte (2021) identified chatbots as one of the key HR trends to moniter in the coming years, emphasizing the potential for chatbots to transform HR practices and improve the employee experience. In conclusion, using chatbots in HRM can transform traditional HR practices and create new opportunities for efficiency and innovation. However, some important considerations and challenges must be addressed to ensure the effective and ethical use of chatbots in HRM. This literature review provides a foundation for further research on the evolution of chatbot technology in HRM and its implications for HR practices.

9.2.1 Research Gap One research gap is the limited empirical research available on the use and impact of chatbots in the context of HRM. While there is a growing body of literature on the use of chatbots in other areas, such as customer service and healthcare, there is still relatively little research on how chatbots can be effectively deployed and utilized in HR functions.

9.2.2 Research Question The following are the questions related to research: ■ What is the current state of chatbot technology in HRM? ■ What are the potential benefits and limitations of chatbots in HRM? ■ What are the technical and ethical considerations involved in developing and implementing chatbots in HRM?

9.2.3 Importance of the Study The study of the evolution of chatbot technology in HRM is important since chatbots have the potential to transform traditional HR practices and create new opportunities for efficiency and innovation. By automating routine and repetitive HR tasks, chatbots can free up HR personnel to focus on more strategic and value-added activities, such as talent development and

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succession planning. Additionally, chatbots can improve employee engagement by providing real-time feedback and support, which can help to increase employee satisfaction and motivation.

9.2.4 Research Objectives The objectives of the research are as follows: ■ To systematically review the existing literature on chatbots in HRM, including their use, impact, and evolution over time. ■ To identify the potential benefits and limitations of chatbots in HRM. ■ To investigate the technical and ethical considerations involved in developing and deploying chatbots in HRM.

9.2.5 Scope and Limitation This study aims to investigate the evolution of chatbot technology in HRM and identify its potential benefits and limitations. It focuses on the use of chatbots in various HR functions, including recruitment, onboarding, training, performance management, and employee engagement. However, there are some limitations to this study. First, the study is limited to a review of existing literature and case studies, which may not capture the full range of experiences and perspectives of organizations and employees using chatbots in HR. Second, the study is limited to English-language sources, which may exclude relevant research conducted in other languages.

9.3 Research Methodology This study aims to investigate the evolution of chatbot technology in HRM, including its current state, potential benefits and limitations, and technical and ethical considerations involved in its development and implementation. A quantitative research approach will be used to achieve the research objectives, and data will be collected through an online survey.

9.3.1 Research Method and Design This study employs a mixed-methods research design that combines quantitative and qualitative data collection methods. Participants are recruited

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through various social media platforms and professional networks. The target population includes individuals who have experience working with chatbots and/or HRM.

9.3.1.1 Sampling Technique The study employs a convenience sampling technique where participants are recruited from different levels of the organizational hierarchy, including HR managers, employees, and IT personnel.

9.3.1.2 Data Collection Data are collected through an online survey instrument that will be distributed to the participants via email and various social media platforms. The closeended survey is conducted using a 5-point Likert scale, and the questions are designed to assess the current state of chatbot technology in HRM, potential benefits and limitations, and technical and ethical considerations involved in its development and implementation.

9.3.1.3 Data Analysis The collected data are analyzed using Excel and SPSS. The results are presented in tabular and graphical formats.

9.3.1.4 Ethical Considerations Any identifying information from participants will not be collected for the survey, and the data will only be used for the study. Additionally, the study will comply with ethical guidelines and regulations related to research involving human subjects.

9.3.2 Research Approach The approach involves collecting and analyzing numerical data to answer research questions and test hypotheses. The research approach was chosen because it will allow the study to gather data on the current state of chatbot technology in HRM, the potential benefits and limitations of chatbots in HRM, and the technical and ethical considerations involved in the development and implementation of chatbots in HRM.

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The questionnaires for the survey will be distributed via email, social media, and professional networks. The sampling technique used in the study will be convenience sampling, which means that participants will be recruited through various sources, and anyone who meets the study’s criteria can respond to the survey. The online survey instrument will assess the current state of chatbot technology in HRM, potential benefits and limitations, and technical and ethical considerations involved in its development and implementation.

9.4 Analysis of the Study The study aimed to explore the perceptions and usage of chatbots in HRM. To achieve this objective, a close-ended survey was conducted among HR professionals. The survey contained a total of 15 questions covering various aspects related to chatbots in HRM, such as their adoption rate, benefits, challenges, and future prospects. The demographic analysis of total of 150 HR professional participants is done and shown in the table 9.1–15 and in figure 9.2–16.

9.4.1 Demographic Analysis Table 9.1

Gender

Characteristics

No of respondents

Percentage

Male

69

46

Female

81

54

46% 54%

Male

Female

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Table 9.2

Age

Characteristics

No of respondents

Percentage

Below 25

40

27

26–35

37

25

36–45

28

19

46–55

32

21

Above 55

13

8

8% 27% 21%

25%

19%

Below 25

Table 9.3

26-35

36-45

46-55

Above 55

Occupation

Characteristics

No of respondents

Percentage

HR manager

51

34

IT personal

55

37

Other

44

29

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34%

37%

HR manager

Table 9.4

IT personal

Other (Please state)

Experience

Characteristics

No of respondents

Percentage

Below 5 years

30

20

5–10 years

40

27

10–15 years

37

25

15–20 years

24

16

Above 20 years

19

12

12%

20%

16%

27% 25%

Below 5 years

5-10 years

15-20 years

Above 20 years

10-15 years

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9.4.2 Descriptive Analysis Table 9.5

Do you believe chatbot technology is currently being used in HRM?

Characteristics

No of respondents

Percentage

Strongly agree

35

23

Agree

28

19

Neutral

29

19

Disagree

43

29

Strongly disagree

15

10

10% 23% 29% 19%

19%

Strongly agree

Agree

Disagree

Strongly disagree

Table 9.6

Neutral

Do you believe chatbots can efficiently manage HR tasks?

Characteristics

No of respondents

Percentage

Strongly agree

35

23

Agree

31

21

Neutral

30

20

Disagree

25

17

Strongly disagree

29

19

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▪ 151

23%

17% 21%

20%

Strongly agree

Agree

Neutral

Disagree

Strongly disagree

Table 9.7 Do you believe chatbots can accurately and efficiently respond to HR-related queries? Characteristics

No of respondents

Percentage

Strongly agree

21

14

Agree

38

25

Neutral

27

18

Disagree

31

21

Strongly disagree

33

22

22%

14%

25% 21%

18%

Strongly agree

Agree

Disagree

Strongly disagree

Neutral

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Table 9.8 Do you believe chatbots can provide accurate and timely responses to HR inquiries? Characteristics

No of respondents

Percentage

Strongly agree

33

22

Agree

21

14

Neutral

43

29

Disagree

26

17

Strongly disagree

27

18

18%

22%

17% 14%

29%

Strongly agree

Agree

Disagree

Strongly disagree

Neutral

Table 9.9 Do you believe chatbots can improve employee engagement in HR activities? Characteristics

No of respondents

Percentage

Strongly agree

30

20

Agree

27

18

Neutral

34

22

Disagree

31

21

Strongly disagree

28

19

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19%

▪ 153

20%

21%

18%

22%

Strongly agree

Agree

Disagree

Strongly disagree

Table 9.10 personnel?

Neutral

Do you believe chatbots can help reduce the workload of HR

Characteristics

No of respondents

Percentage

Strongly agree

27

18

Agree

28

19

Neutral

29

19

Disagree

31

21

Strongly disagree

35

23

18%

23%

19% 21%

19%

Strongly agree

Agree

Disagree

Strongly disagree

Neutral

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Table 9.11 future?

How likely are you to use a chatbot for HR-related inquiries in the

Characteristics

No of respondents

Percentage

Strongly agree

31

21

Agree

34

23

Neutral

35

24

Disagree

26

18

Strongly disagree

21

14

14%

21%

18%

23%

24%

Strongly agree

Agree

Disagree

Strongly disagree

Neutral

Table 9.12 Do you believe chatbots can provide a more personalized HR experience compared to traditional methods? Characteristics

No of respondents

Percentage

Strongly agree

27

18

Agree

35

23

Neutral

26

17

Disagree

25

17

Strongly disagree

37

25

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18%

25%

23% 17%

17%

Strongly agree

Agree

Disagree

Strongly disagree

Table 9.13

Neutral

Do you believe chatbots can help reduce HR costs for organizations?

Characteristics

No of respondents

Percentage

Strongly agree

40

27

Agree

27

18

Neutral

23

15

Disagree

34

23

Strongly disagree

26

17

17% 27%

23% 18% 15%

Strongly agree

Agree

Disagree

Strongly disagree

Neutral

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Table 9.14 Do you believe ethical considerations should be taken into account in the development and implementation of chatbots in HR management? Characteristics

No of respondents

Percentage

Strongly agree

25

16

Agree

32

21

Neutral

31

21

Disagree

31

21

Strongly disagree

31

21

16%

21%

21% 21%

21%

Strongly agree

Agree

Disagree

Strongly disagree

Neutral

Table 9.15 Do you believe chatbots can be used in handling confidential HR information? Characteristics

No of respondents

Percentage

Strongly agree

35

23

Agree

25

17

Neutral

24

16

Disagree

38

25

Strongly disagree

28

19

Evolution of Chatbot in Human Resource Management

19%

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23%

25%

17%

16%

Strongly agree

Agree

Disagree

Strongly disagree

Neutral

9.5 Results The survey results indicate that there are varying opinions on the use of chatbot technology in HRM. In response to the question of whether chatbot technology is currently being used in HRM, 29% of respondents were neutral, 29% disagreed, and 10% strongly disagreed. On the other hand, 42% of respondents agreed or strongly agreed that chatbots can efficiently manage HR tasks, while 36% were neutral or disagreed. Similarly, opinions were divided on whether chatbots can accurately and efficiently respond to HRrelated queries. While 39% of respondents agreed or strongly agreed that chatbots can do so, 43% were neutral, disagreed, or strongly disagreed. The same pattern emerged for the question of whether chatbots can provide accurate and timely responses to HR inquiries. Opinions on the potential for chatbots to improve employee engagement in HR activities were also mixed. While 38% of respondents were neutral or disagreed, 38% agreed or strongly agreed. A similar pattern was observed for the question of whether chatbots can help reduce the workload of HR personnel. However, 32% were neutral or disagreed. There was also some uncertainty around whether chatbots can provide a more personalized HR experience compared to traditional methods, with 42% of respondents neutral, disagreeing, or strongly disagreeing. Interestingly, most respondents (45%) agreed or strongly agreed that chatbots can help reduce HR costs for organizations, while 40% were neutral or disagreed. Finally, the survey results indicated that opinions were divided on whether chatbots can be used to handle confidential HR information.

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Overall, these results suggest that there is a need for further exploration of the potential benefits and challenges of chatbot technology in HRM.

9.5.1 Hypothesis Testing Based on our study questions and goals, our hypotheses will help guide our examination of the survey responses we’ve gathered. HA: The use of chatbot technology in HRM significantly positively impacts the efficiency, effectiveness, and user satisfaction of HR-related tasks and activities. H0: The use of chatbot technology in HRM doesn’t significantly impact the efficiency, effectiveness, and user satisfaction of HR-related tasks and activities. We calculated the significance level using a two-sample t-test to examine the hypothesis. The likelihood of the study hypothesis being correct is considered high if the p-value is less than .05. In contrast, if the p-value is larger than 0.05, we assume that the null hypothesis is correct. After the t-test, the p-value was found to be 0.48, as shown in Table 9.16. Since the significance level is more than 0.05, the research hypothesis is rejected. As a result, the use of chatbot technology in HRM doesn’t significantly impact the efficiency, effectiveness, and user satisfaction of HR-related tasks and activities.

9.5.2 Solutions to Research Questions RQ1: Potential Benefits and Limitations of Chatbots in Human Resource Management

Based on the findings of this study, it can be concluded that chatbots have the potential to bring numerous benefits to HRM. These include increased efficiency, improved accuracy, and faster response times to HR-related queries. Chatbots can also reduce the workload of HR personnel and help organizations save costs. Moreover, chatbots can improve employee engagement in HR activities by providing personalized experiences to each employee. However, there are also limitations to the use of chatbots in HRM. For instance, chatbots may not be able to handle complex HR-related queries or provide the human touch that employees sometimes require. There are also concerns regarding the accuracy and confidentiality of the information provided by chatbots. In addition, there is a risk of chatbots replacing human jobs in HR, which could negatively affect employees.

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t-Test: Paired Two Sample for Means

Mean Variance Observations

Do you believe chatbots can efficiently manage HR tasks?

Do you believe chatbots can improve employee engagement in HR activities?

2.88

3

2.079463087

1.946308725

150

150

Pearson Correlation

−0.086737242

Hypothesized Mean Difference df

0 149

t Stat

−0.702666916

P(T [Accessed 14 June 2020]. [3] Innodata Inc. 2020. How Insurance Companies Are Using Artificial Intelligence To Transform An Industry - Innodata Inc. [online] Available at: https://innodata.com/insurance-data-annotation/ [Accessed 12 June 2020]. [4] KDnuggets. 2020. Big Data For Insurance - Kdnuggets. [online] Available at: https://www.kdnuggets.com/2019/07/big-data-insurance.html [Accessed 12 June 2020]. [5] Mejia, N., 2020. Artificial Intelligence For Digitizing Claims Processing – A Brief Overview | Emerj. [online] Emerj. Available at: < https://emerj.com/aisector-overviews/ai-digitizing-claims-processing/> [Accessed 10 June 2020]. [6] PYMNTS.com. 2020. How AI, ML Are Revitalizing the Insurance Claims Process. [online] Available at: < https://www.pymnts.com/disbursements/ 2019/how-ai-ml-are-revitalizing-the-insurance-claims-process/> [Accessed 14 June 2020]. [7] https://www.cognizant.com/case-studies/ai-claims-automation [8] https://www.cognizant.com/case-studies/ai-analytics-platform-insurancecustomer-service [9] https://www.cognizant.com/case-studies/pdfs/customer-care-done-rightwith-real-time-ai-codex3598.pdf [10] Tzeng, T. 2020. How To Use AI To Optimize Document Understanding | Uipath. [online] Uipath.com. Available at: < https://www.uipath.com/blog/ using-artificial-intelligence-to-optimize-document-understanding> [Accessed 10 June 2020].

Chapter 11

AI in Finance Syed Hasan Jafar1, Parvez Alam1, and Hani EL-Chaarani2 1

Woxsen University, Hyderabad, India Beirut Arab University, Lebanon

2

11.1 Introduction Computer science’s field of artificial intelligence (AI) has been the focus of scientific research and technological development which develops artificially intelligent technology or software that can imitate human behavior. The global financial services sector is being rapidly transformed by AI and its creative uses. The main users of AI in finance were hedge funds and HFT companies, but it has since moved to banking, fintech, and insurance companies as well [1]. AI is used in the financial services sector for robo-advisors, virtual client support, algorithmic trading, portfolio management, and investment advising. The financial services business is currently being transformed by AI in a number of key areas, including banking, fraud detection, algorithmic trading, risk assessment, tax filing, and many more. We now appreciate the content on the internet as a result of the advancement of global technology over the past 10 years. Nowadays, anything can be automated using AI and machine languages, making automation an integral part of our daily life. The process of automation has not spared any sectors, industries, or businesses, and the majority of them are utilizing the fourth generation information and automation process to boost productivity, revenues, and profits.

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11.1.1 Media Getting Automated Through AI Major financial media organizations like Reuters, the Washington Post, the New York Times, Yahoo Finance, NBC, and the Associated Press currently produce stories and reports utilizing natural language. Factual information and news stories are being prepared instantly. Today’s robot reporters may create reports on a variety of subjects, including politics, society, geography, and business. Numerous tools are assisting media organizations in automating repetitive operations and streamlining workflows. Examples of programs include BBC Juicer, Reuters News Tracer, and the Washington Post Knowledge Map.

11.1.2 AI Making Business Easier Programs that use AI and machine learning (ML) gather, organize, analyze, articulate, interpret, and display data to create client-level portfolios and personalized communications that are tailored to meet particular tones, voices, and styles. The same procedures are used by businesses to produce their business, strategy, and intelligence reports. Let’s not forget how strong they are because these programmed reports might include consumers’ names, contact information, locations, rankings, and other statistics. Business applications of AI include chatbots that leverage cognitive computing and customized assistants.

11.1.3 AI and ML Services at Our Fingertips Financial majors can generate automated reports with the aid of businesses like Yseop, United Robots, Automated Insights, and many others. To assist BFSI organizations in their efforts to automate their digital operations, for instance, the French company Yseop recently introduced automated natural language report generation solutions called augmented analyst. Moody’s, BNP Paribas, and Wells Fargo are already utilizing this technology to develop automated goods, services, solutions, and reports that minimize client turnaround times and boost bottom-line results for businesses. Most businesses are kept on their toes by declining commission revenues and rising demand for tailored customer service standards.

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11.1.4 Indian Cos Is Also Racing for AI-Based Services Automated chatbots are being used by Indian financial services firms, including ICICI Bank, HDFC Ergo, Motilal Oswal, IIFL, and others, to provide instant customer care through their mobile apps. With the help of this technology, basic services are provided with just one click, frequent questions are answered, and streamlined reports are made around the clock. Numerous analytics tools have lowered the number of man-hours required due to the automated data points, and the results are assisting users in visualizing concepts, developing business plans, and evaluating performance.

11.2 Credit Scoring and Loan Analysis Financial institutions all across the world are using AI for credit scoring and loan analysis to increase revenues, cut costs, make better decisions, and dominate their markets.

11.2.1 Analysis of Borrower’s Creditworthiness The analysis of people’s and companies’ creditworthiness is greatly aided by AI. Credit information firms, banks, and other financial institutions use the evaluation of borrowers’ creditworthiness as a key aspect in lowering the risk of loan default [2]. When a person or organization applies for a loan but has no credit history, it poses a significant challenge for loan disbursal companies. AI is handling this by examining millions of factors relating to the borrower, including their digital footprint, search habits, educational background, GPA, prior employment history, use of social media, and smartphone data, in order to determine their trustworthiness. Similarly, for companies without a credit history, a number of factors relating to their past performance, the likelihood of demand and consumer satisfaction for their products, an examination of their cash flow, and their likelihood of defaulting on repayments are examined. An “alternate data” analysis could be used to describe this. Lenddo and ZestFinance are two examples of such businesses that examine digital footprint and search history in the market. A business called GiniMachine provides AI services that some European banks employ to evaluate credit histories and behavioral data to estimate risks for specific borrowers, which can save weeks of labor.

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11.2.2 AI for Loan Processing By automating, being accurate, and being skilled at processing huge amounts of customer data, AI is also being utilized to streamline the loan application process [4]. A business named Upstart uses such a study of creditworthiness and loan streamlining as an example.

11.2.3 AI for Service Optimization Self-learning uplift models are also being developed in recent years for analyzing the services of reminding borrowers about debt repayment and recovery by examining their prior responses. For instance, some borrowers are not pursued for repayment because they make their payments on time and without being reminded, while others must constantly be informed of the due dates and the amount to be paid in advance [23].

11.2.4 Analytics Services for Borrowers However, AI is also being used to help borrowers repay their loans more quickly by examining their spending patterns and identifying places where money can be saved. Such end-user data may also be used to recommend lenders to borrowers and identify new clients for lenders. A company in this category is called Personetic.

11.2.5 Case Study 11.2.5.1 The Challenge With operations in Thane, Mumbai, SalaryDost has the goal of giving every Indian access to credit and raising their clients’ salaries. By the beginning of this year, the company had already given out more than 1.5 lakhs in digital loans to more than 2,000 consumers since its establishment. Currently, it seeks to expand its business without hurting the bad rates or approval figures, as well as have a 2–3% reduction in non-performing loans in the upcoming months. The largest problem for lending operations has, however, always been client profile and credit scoring analysis. The loan procedure moves more quickly the faster the profiling operates. Therefore, the business sought to identify strategies to enhance consumer profiling while reducing turnaround time in order to realize its aim of creating a credit line for every Indian. The

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lending business utilizes a vast amount of consumer data that are amassed over time. SalaryDost uses alternative data sources, such as metadata from smartphones, to better understand an applicant’s behavior and, as a result, make wise selections. The lending business utilizes a vast amount of consumer data that are amassed over time. SalaryDost uses alternative data sources, such as metadata from smartphones, to better understand an applicant’s behavior and, as a result, make wise selections [4]. Even though the procedure is automated, as more candidates apply, it becomes slower and takes longer to complete. The present underwriting process’s lack of predictability was another significant obstacle. The organization sought to adopt a robust, cutting-edge solution that would give them access to real-time data insights in order to speed up its operations.

11.2.5.2 The Solution SalaryDost therefore worked with CredoLab, an AI firm based in Singapore, to increase their process efficiency and decrease their time lag. Powerful AI credit scoring technology is smoothly included in the mobile application via CredoLab’s AI-based credit scoring solution, CredoSDK. In order to quickly determine an applicant’s behavioral score, it leverages non-intrusive, nonpersonal smartphone data. Using the best alternative data source—the metadata from smartphone devices—CredoLab creates bank-grade digital scorecards for banks, consumer finance businesses, lenders, insurance providers, and merchants. Prior to turning these patterns into credit scores, their AI-based algorithm analyzes over 18 million characteristics from opt-in smartphone metadata to identify the most prognostic behavioral patterns. These allow any lender to evaluate applicants at the most detailed level feasible. Clients of CredoLab have noticed an increase in new bank customer approvals of 20%, a decrease in non-performing loans of 15%, and a decrease in fraud rates of 22% [3]. SalaryDost can now obtain a comprehensive understanding of the application by fusing the bureau data from their bureau with the customer’s behavioral score, thanks to CredoLab. CredoLab’s AI-based technology analyzes over a million features from opt-in smartphone metadata to uncover predictive delinquent behavioral patterns. The algorithm is built on over 15 million datasets gathered across 50 loan partners in more than 15 countries. Then, CredoSDK employs this priceless alternative data to generate extremely precise credit scores in real time.

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CredoSDK’s adaptable APIs made sure that SalaryDost’s existing IT infrastructure could seamlessly interact with their scoring solution. SalaryDost began utilizing CredoLab’s AI-driven algorithm to transform over 1 million features from opt-in smartphones into credit ratings through an SDK that was simple to apply. SalaryDost was able to rapidly and easily begin using AI credit rating thanks to CredoSDK, which also made sure to keep their risks under control so they could start taking on more clients.

11.2.5.3 The Results SalaryDost transformed into a true digital fintech platform following the launch, from handling leads to disbursement. It uses CredoSDK to build analytical models, calculate client credit scores, and assess the risks associated with certain borrowers. By utilizing the demographic data from the device, the AI-based algorithm also aided the business in having a system for early fraud detection. Alongside, CredoSDK improved credit underwriting based on a dynamic policy of many users and automated the scrutiny process, which in turn improved the consumer profile process. The use of AI in credit scoring reduced the system’s overall cost while also saving the organization time.

11.3 Employee Expense Management An organization’s employee expense management (EEM) may include the following activities: a. b. c. d. e. f. g. h.

preparation of expense reports examination of claims for additional expenses above the policy limits examination of hard copy bills examination of expense reports data entry storage vigilance auditing approval of expenses and processing of the bills.

The aforementioned steps could take a significant amount of time, taking up the time that accountants, HR, and the employees for whom the expenses are incurred are working. This is an unnecessary expense for the businesses that lowers organizational productivity. Despite the aforementioned, AI is about to

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bring about quick changes in the EEM of the businesses, reducing the costs associated with false claims, mistakes, and claims for resources that employees have not used [7]. AI examines spending patterns and trends and anticipates financial and cost patterns for the company through the analysis of big data, which will be used for the organization’s efficient financial decisions for cost reduction, operational efficiency, and profit maximization.

11.3.1 Analysis and Processing of Claims AI is used in the EEM to retrieve information from both printed and electronic receipts and store it in an electronic format for later use. In contrast to manual verification, AI verifies every entry in the process, identifies suspect entries, asks the claimed employee for clarification, and then gets extra confirmation from the approving authority before sending it for bill processing [8]. Employee clarifications are recorded for future use if they are approved by management. In some instances, the claims are denied.

11.3.2 Cost-Benefit Analysis The AI also detects fraud in the form of phony expenses, repeated claims for a single expense, and deviations from Benford’s law. The AI analyzes the historical data of a similar category of employees to identify the significant divergences in the claim patterns, which may not be achievable using the manual approach, in order to limit variations in expenses. According to Travis Andrade, product marketing manager at AppZen, the amount claimed is roughly 33% of the overall claim, even if the bogus claims make up 10% of the total claims [22]. It would be difficult for the auditing teams to audit at a scale that might only result in a sample audit because the majority of organizations are resource constrained. But these obstacles can be overcome using AI for just 20% of the real claim processing costs. AppZen, a provider of SAP Concur Detect, and Wipro are two examples of businesses leveraging the aforementioned AI technologies in EEM (providing Apollo).

11.3.3 Case Study 11.3.3.1 The Challenge BEUMER India is a manufacturing firm with more than 350 employees operating in its Gurgaon and Mumbai offices. It specializes in building

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intralogistics systems for conveying, loading, and distribution processes. Due to the nature of their jobs (remote and on the field), the company’s personnel required routine travel arrangements. Before the procedure was automated, BEUMER India personnel kept track of their travel logs, travel expenses, and reimbursement requests using conventional formats including Excel sheets and paper papers. Such a procedure was time-consuming for the staff and complicated the finance department’s ability to keep track of and analyze data for further business insights. When expenses were managed manually in the past using an Excel spreadsheet and emails, it used to take the finance department a long time to analyze the data. Keshav Dubal, IT Head of BEUMER India, detailed the procedure. The business needed an automated, digital expenditure management solution right now to satisfy their needs and make their lives easier. It became essential for the company to find a travel and spending management system that could increase process efficiency and eliminate any potential errors and hiccups that could arise from manual intervention.

11.3.3.2 The Answer The business needed a cutting-edge travel and expense application that can automate the entire process and give the business access to a single dashboard view of its workers’ travel and expenditure information. BEUMER India decided to collaborate with SAP Concur, a provider of software as a service with a focus on creating tailored expense and travel management solutions for its own clients. The SAP Concur travel and expense management solution was created to let businesses have all of their data in one place to get business insight, automate the reimbursement process, and proactively manage employee spending with accounts payable automation. Travel and expenditure management, according to SAP Concur, is a one-stop solution that unifies client travel and expense processes and offers real-time visibility into various types of business data. With complete information on staff spending, the cloud solution assisted the customer in making a more informed decision. The system not only gives customers data but also highlights policy compliance problems and simplifies budgeting for the business.

11.3.4 Results The company’s automated recording and reporting of trip claims was made possible by the advanced travel and expense system, which also helped the

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finance department save tons of time and effort. Additionally, the majority of human engagement in the act of managing trip expenses was fully eliminated by the solution. In addition to these features, the solution was created to be exceptionally accurate, clever, and user-friendly, all of which contributed to better control and visibility over the company’s travel and cost reporting. Also, the organization might expect a significant decrease in travel expenses within 6 months of deployment. In addition to streamlining the process, the solution had excellent regulatory compliance, which allowed the business to save 2.5% of its expenses. The solution not only made the employee’s time spent filing claims significantly less, but it also offered a single dashboard view for all the pertinent data. Additionally, SAP Concur offered a mobile-based application for the business that was created specifically for field employees and employees who travel frequently and allowed them to submit their claims online immediately. After the implementation, the business saw that operations were much more streamlined and that it was much simpler for administrators and the finance department to review employee information. The solution has allowed the business to save about 30% in productivity and time.

11.4 Fraud Detection and Anti-Money Laundering The technological revolution has made banking more accessible and convenient for all users, but it has also given rise to new types of banking fraud. Banking services and payment gateways that are founded in banking must contend with multi-channel services, which create additional opportunities for fraud. The traditional manual approach to fraud detection is slow and unable to keep up with the problems. Millions of data points that people would miss can be analyzed by ML algorithms. AI will identify fraudulent activities by identifying patterns of fraud, unusual behaviors, and distinctive activity. One of the most frequent scams in the banking sector is credit card fraud and fraud connected to payments. Credit card fraud is typically brought on by the cardholder’s carelessness with his data or by a security hole in a website [15–25]. Here are a few instances: ■ A customer gives his credit card information to strangers. ■ A card is stolen or misplaced, and another person uses it. ■ Mail that belongs to a recipient is stolen and utilized by crooks.

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■ Employees of businesses often duplicate cards or card numbers. ■ Producing fake credit cards.

11.4.1 How AI Assists in Identifying Fraud With the use of algorithms and back testing of previous card usage based on examination of a sizable database of credit card transaction history and trend, AI is used to detect credit card fraud. AI is being used by international exchanges like NASDAQ and NYSE to identify market scams and malicious trading. AI is completely changing how surveillance works. The next generation of surveillance technologies, which are focused on eradicating market abuse, detecting fraud, and preventing insider trading to maintain market integrity, will be developed, and monitored in a significant way with the help of AI and ML.

11.4.2 Case Study Cardlytics is one AI company that offers card-linked marketing software to the financial sector. They assert that they match customers with offers they are likely to purchase by using “purchasegraphics,” or demographics based on purchasing behavior. The business asserts that while still providing effective marketing bargains and customer matches, the protection of sensitive client information was prioritized in the construction of their AI program. An image from Cardlytics’ website that depicts their information flow with safety in mind is provided below:

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The Cardlytics marketing front receives performance reports from the bank’s database, which it subsequently shares with the shops. Then, retailers will utilize these data to create more effective marketing efforts, which they will then send back to Cardlytics for matching to the consumer base. Regarding a case study by Celent on Bank of America’s “BankAmeriDeals” marketing campaign, Cardlytics released a press release. According to the case study, Cardlytics, which uses spending information from around 70% of American families, signed on Bank of America as a client. The study also demonstrated that businesses may dependably spend some of their marketing dollars on card-linked promotion and realize a sizable return on investment. As a result, businesses that wish to profitably target their tech-savvy customers may want to learn more about the applications of AIenabled card-linked marketing.

11.5 Personal Financial Advisor In the field of self-service technologies that are evolving and being used today, the tremendous rise in clients calls for personal investment advice, low-cost accessibility to financial services and investment products, and openness to financial options. Robotic financial advisors are an excellent example of self-service technology that can adapt to changing customer needs. The robo-advisor will advise clients on their portfolios in addition to suggesting the finest bank investments and wealth management services depending on their risk profile. The robo-advisor will primarily concentrate on rebalancing and shifting portfolios based on various investment opportunities offered by banks, as well as cross-selling bank products. The use of robo-advisors can result in time savings, increased control over the customer care process, and a lack of human emotions and avarice when providing client advice [9–11]. Assume you could forecast future investment trends, fund development potential, stock market trends, and trading session behavior, as well as client habits in banks. The banking and financial services industries are likely to be completely changed by this. AI and ML will analyze historical data on customer investments, spending patterns, and savings in order to give banking customers the best guidance possible for managing their wealth and advising them on their investments, insurance plans, and retirement planning while considering their risk tolerance.

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More than 60% of all daily foot traffic in banks is made up of customers making typical, repetitive inquiries. Robot will assist with all your needs and provide a comprehensive response while maintaining an appropriate conversation. By professing to provide customers with an equally high level of service, robo-advisors compete with bank customer care.

11.6 Risk Assessment and Compliance In recent times, AI has been used as one of the important tools for the banking industry in risk and fraud identification. By employing ML to evaluate “big data” in the online retail and commercial banking avenues, AI is used in fraud identification and eradication. It is accurately used to analyze thousands of commercial documents, and the resultant risks save a lot of time on the tedious authentication and verification processes. For example, to identify legal risks, AI is used to analyze and extract important clauses and riskassociated points by decoding the legal documents [12]. The most important application for deploying AI in financial services is risk management. The Ingenico Group’s estimates show that the merchants incur losses to an extent of 1.5% of their annual revenue due to the fraudulent activities. The 2008 financial crisis had pressed for the importance of intelligence on the creditworthiness of the consumers. The lenders during that time mostly relied on simple heuristics to analyze customer financial behavior, which was not sufficient. But the new AI technologies have access to thousands of variables of customer behavioral patterns and need to assess the client’s debt servicing ability. For example, the newly emerged online lenders are using “alternate data” or digital footprints of the users. Using AI they are analyzing the data on the smartphone and social media to analyze the borrower’s risk. However, as the financial sector is governed by strict compliance and regulations on personal data usage, any breach of personal data turns costly to the organizations, which needs to be taken care of with utmost importance.

11.6.1 Case Study 11.6.1.1 The Challenge Even with lower check-processing times due to electronic payments and automated clearinghouse (ACH) transactions, banks must still manually verify

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millions of handwritten checks. Annually, banks risk losing millions as a result of check fraud by counterfeiters. Because a percentage of the funds is made readily available to the depositors, it’s critical to identify counterfeit checks quickly. To reduce the incidence of check fraud, a global bank partnered with Cognizant Digital Business to build a solution based on AI-ML to speed up check verification and lower costs.

11.6.1.2 The Solution To meet the bank’s goals, Cognizant solution needed to identify fraudulent checks in real time, as well as reduce the number of checks requiring manual review. The bank already uses optical character recognition (OCR) and deep learning technology to scan checks, process data, and verify signatures. Cognizant model, based on Google TensorFlow, uses a neural network to parse a historical database of previously scanned checks, including those known to be fraudulent. Cognizant banking AI solutions experts trained the neural network to use a set of comparative algorithms to distinguish good checks from anomalous ones. By automatically comparing various factors on scans of deposited checks to those in the database, the AI model flags potential counterfeits in real time. It assigns a confidence score to each scanned check, flagging it as good, fraudulent, or needing further review. The Cognizant solution is scalable and configurable to meet the client’s evolving needs.

11.6.1.3 The Result Cognizant model forecasts up to $20 million in annual savings on fraud losses while significantly reducing the operational cost of manual check validation. The more checks the system processes, the more accurate it becomes. We also provided the client with advanced analytics and performance tracking, giving the company increased visibility. Fraud is pervasive in financial services, and counterfeiters constantly develop new ways to perpetrate it. The Cognizant solution operates with near-human intelligence to counteract counterfeiters and reduce losses. Every transaction it processes adds to its enormous repository of historical information, which means it can continually learn the habits of habitual fraudsters to defeat them. It’s a win for Cognizant clients and a compelling example of how AI advances data science in financial services.

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11.7 Tax Filing and Processing The major reasons for AI to be implemented in taxation are as follows: a. Identification and prevention of tax default b. Processing volumes of tax filing data c. Providing in-time services to the taxpayers

11.7.1 Applications of AI in Tax Filing and Processing i. By analyzing and monitoring the financial data of an individual or organization using AI, irregularities in the tax payments can be identified and penalized without much human intervention. AI is used to help fight the tax evasion and detect fraud by identifying and analyzing the employment status of a person, whether the person has been audited in the past or not, and whether the person had any illicit sources of income. ii. AI-oriented robots are deployed to automate repetitive tasks which would save time for the tax department to focus more on other challenging tasks. Manual data entry of account numbers and asset ID numbers into the spreadsheets and financial closeouts and reporting can be done by the AI robots themselves. AI is used to process the information of the globally traveling individuals that can be accessed using optical character recognition (OCR). AI and OCR can be used together to search, scan, and upload the documents to a database. iii. Systems like automated tax filings are designed to make it easy for tax filers to reduce the amount of tax-compliant data entry into the tax filing system. iv. For business organizations, data entry into the accounting software and lengthy and complex procedures are done by AI to calculate the tax dues faster, which gives ample time for the companies to go for annual closing. v. AI can further be used to classify the documents’ taxonomy, extract the required data from these classified documents, detect the capital gains and charitable donations, and handle assessment notices. The key information in the above-mentioned documents can be extracted with accuracy and in no time. vi. Companies can use AI systems to accurately forecast the tax using predictive analysis through detection of sales trends frequently,

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i.e., monthly, quarterly, and annual trends. By projecting sales trends, companies would forecast their tax obligations. vii. The AI also uses the previous tax documentation of the organizations and can analyze the yearly changes in the tax laws, making it easier for the businesses to act and save the tax amounts in the required areas.

11.7.2 Case Study 11.7.2.1 The Challenge Panaji Municipal Corporation assesses the property tax based on the unit area of its properties, where the tax is only payable on the per-unit price of the built-up area of the property. The municipal corporation collects three different kinds of taxes in the city depending on the properties—house taxes for residential buildings, trade license fees for commercial ones, and signage taxes for signboards. However, with people from rural areas constantly changing geographies and shifting their base to urban areas in search of job opportunities and a better lifestyle, it became challenging for the municipal corporations to keep track. The ULB soon realized that the dataset used for tax collection was incomplete and prevented the municipality from realizing appropriate revenues for its citizens.

11.7.2.2 The Solution To streamline the process of tax collection for Panaji Municipal Corporation, Transerve Technologies was then appointed to offer a geographic information-based system (GIS) as an online property tax solution [5]. This cloud-based software solution—SmartMu, by Transerve has been designed to smooth the process of tax collection for the municipal corporation of Panaji and to improve the economy for the urban local body. SmartMu is intelligent revenue management that uses data analytics to collect and analyze residential and commercial property data to help municipal corporations in city planning. This flexible platform by Transerve integrates data from different parts of the city using geospatial technology and sensor integration and runs analytics on it to provide realtime updates to authorities. The solution not only enabled timely updates but also completed the database of properties for the corporation of the city of Panaji.

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SmartMu was deployed to digitize the whole process of tax collection and consequently create updated baseline maps and link them with the property tax data. This integrated database covered information like property usage, ownership, and the taxation details along with geo-tagged photographs, which provided complete transparency to municipal corporations in collecting revenues. Without an expensive infrastructure integration, the solution helped the ULB in managing their data, enabling data field validation to capture missing features, and door-to-door property data capture using GPS devices. By integrating this GIS-based tax assessment system, the municipal corporation was also able to have a real-time update of the property owned, and the total tax collected in a single region. This, in turn, can help in replanning the tax amount accordingly to enhance their urban development plan.

11.7.2.3 The Benefits Transerve’s SmartMu platform not only made it easy for the municipal corporations to collect and manage data but also provided transparency for the whole civic department to have a consistent and reliable tax assessment. After the collaboration with Transerve Technologies, the corporation of the city of Panaji was able to locate all the properties of the city—included as well as exempted ones—and assess them smoothly without any lag. By offering the entire geographical data of the region on a single dashboard with the help of the GIS-based property tax module, the civic department was able to digitize their whole department of the tax revenue. The smart system automated the calculation of the property tax and generated reports, which helped the corporation in efficiently managing the properties and their taxation. Its cloud-based services allow authorities to access data anytime and anywhere. After the deployment of the smart technology, the municipal corporation realized that only 79% of the properties in the city were under the CCP house tax net, whereas 21% of the properties were not taxable. Alongside the department also managed to locate 23% of the buildings that weren’t stated in the CCP database and even managed to collect revenues from 61% of businesses that weren’t paying commercial taxes earlier. With these findings, the municipal department managed to increase its revenues of tax collection by ₹5 million in just one ward, which is a significant rise from previous years. Additionally, the solution also helped in collecting those 47% of signage taxes, which weren’t raised by the city for a long time.

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11.8 Algorithmic Trading Strategy Performance Improvement AI has greatly impacted our daily lives, from proposing a favorite YouTube movie to recommending relevant social media updates. Home assistants, self-driving cars, and smart houses are three prominent examples of AIpowered systems in use today. Along with daily life, trading is one of the most fascinating fields where ML and AI have achieved tremendous success. Financial markets and trading are intricate and contain complex models that forecast the performance of economies, businesses, and stock prices. Banks and financial institutions are making significant investments in cutting-edge software and hardware to take advantage of market inefficiencies [9–11].

11.8.1 Ability to Predict Future Accurately anticipating the future movements of the asset classes is essential for successful trading and investing. Complex algorithms are aided by data automation and analysis utilizing ML to speed up trend recognition for trading methods [13–15]. Deep learning, neural networks, social media analytics, sentiment analysis, and AI are all contributing to the performance and optimization of algorithmic trading. The direction of the stock can be predicted by categorizing and screening news headlines, blogs, social media comments, and opinions in seconds. AI and ML are even looking at other traders’ order flows. We can program computers to recognize trends in the past and forecast future price changes. Data on past crises and crashes are supplied to systems, and systems with AI and ML capabilities can evaluate current data to look for signs of similar types of crises and disasters. According to research, more than 1,400 hedge funds are utilizing AI and ML to create trading strategies for a variety of assets, including cryptocurrencies. Rapid changes in micro and macro structures can be used to our advantage with AI, which also maintains us competitive. Powerful computers with ML are capable of quickly analyzing a large number of data points and producing repeated patterns that can provide investors with additional alpha [16–19]. In general, it might take humans months to study those data points. For instance, a CNN story claims that an AI can analyze millions of stock data points in a matter of seconds and can start high-frequency trading on the majority of global exchanges within the first few minutes after a trade. Things now move at breakneck speed thanks to technology, and since every second

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counts, AI can place trades without even phoning the broker or using the trading app [21].

11.8.2 Risk Management Is Also in Control Using AI and ML Few programs separate their methods into three distinct engines: one that monitors the market and searches for new trades; another that generates and sends orders to exchanges; and a final engine that monitors open orders, takes positions, and independently manages risk [6 & 20]. The output of sophisticated trading methods that have been historically back-tested can be provided by AI, which can also evaluate the performance of various techniques. To prevent their rivals from copying or adopting their strategies, which could protect their competitive edge, financial firms utilize sophisticated AI systems.

11.8.3 Global Financial Markets Surveillance AI is being used by international exchanges like NASDAQ and NYSE to identify market scams and malicious trading. AI is completely changing how surveillance works. In order to maintain market integrity, the next generation of surveillance technologies will be developed and monitored with an emphasis on the eradication of market abuse, fraud detection, and insider trading.

11.8.4 Case Study In 2016, Sigmoidal was established in New York. For companies wishing to use AI in the financial, construction, military, and technological sectors, the organization provides advisory services. According to the business, algorithmic trading in stock brokerages uses AI. They assert that their software makes quick choices and transactions in the financial markets using cutting-edge statistical models and ML. Their software could assist companies with: Trade execution algorithms—In order to execute securities deals, stock exchanges must match buy orders, often known as bids, with sell orders. In order to minimize the influence on the stock prices after the trade is performed, Sigmoidal’s software, according to the company, divides trades into smaller orders using statistical approaches. Finding arbitrage possibilities, which is the process by which investment managers could profit from varying prices for the same item (stocks,

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commodities, or securities) in several marketplaces. According to Sigmoidal, their software is capable of doing an autonomous search for these arbitrage opportunities and presenting them to the investor on their dashboard [13–16]. A stock brokerage company or an investment manager could use Sigmoidal’s platform for algorithmic trading applications. When it comes to trade executions, for instance, traders and investment managers at a stock trading firm may have access to information on the most effective manner to carry out their trades through the use of a dashboard. On the company’s current trading platforms, the employees of a stock brokerage firm would indicate the orders they might want to buy or sell. In order to minimize the impact on stock prices, the Sigmoidal platform (connected with the trading systems) would prompt the user in the most effective manner to execute that trade. For instance, the program can advise using an alternative trading system (ATS) rather than the stock exchange to carry out a specific trade in order to improve execution time and reduce the impact on stock price. The trade execution strategy that was suggested on the Sigmoidal dashboard can subsequently be approved by the stock brokers. According to the company, stock brokerage firms may aid their traders or clients in executing trades much more quickly with this feature integration than they could if they did it manually.

11.9 Market Intelligence and Data Analytics for Investment A century ago, we needed more workers for businesses and factories; later, we needed more machines and fewer workers; today, technologies enable us to finish tasks quickly. AI and the automation sector are simplifying life by saving time and effort.

11.9.1 AI and ML for Better Consumer Satisfaction Traditional market research required a lot of time, effort, and resources. Early 1990s market research involved human customer visits; the early 2010s saw a move to survey mail; today, with technology and a wealth of data at our disposal, data collection is simple; but, with the development of customers’ individualized demands, market data analysis is challenging. AI creates individualized solutions for each user by using surveys, social media

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analytics, and behavioral principles to fix the issue. Market research is primarily becoming more affordable and quick thanks to the use of algorithms, AI, and ML. AI is being used by businesses to increase their numbers. Marketers use consumer behavior and sales information to boost sales, and AI is assisting companies in designing tailored sales pitches and boosting revenues. For instance, Amazon, Ola, and Uber use AI to analyze changes in consumer psychology to determine how to adjust prices for their goods and services. The market research can be transformed from back data based on forward-looking insights by adding data on sales, behavioral analytics, messages, and social media information minute by minute. The majority of telecom businesses, for instance, need to upgrade to 5G, which would cost thousands of crores. In the past, they would have used conventional market research to determine which areas needed to be improved. However, automated intelligence can utilize their real-time data to determine whether the network needs to be upgraded.

11.9.2 AI and ML to Analyze the COVID-19 Impact The effect of COVID-19 on the manufacturing sector was examined by Technavio using AI. Additionally, a report titled Global Artificial Intelligence Market in Manufacturing has been made public. According to reports, businesses will be able to take advantage of huge development prospects by using the Internet of Things. Human beings are incapable of performing quantitative analysis or historical data analysis. AI is used by large financial firms like JP Morgan, Barclays, Morgan Stanley, and Goldman Sachs to evaluate data and select investment themes. For instance, the majority of investment companies and rating agencies used to expend time gathering important information or financial statements. It would have taken many days to gather and clear the data necessary to analyze the previous financial performance, particularly about revenues, expenses, debt, and other factors. The full data are now tabulated with the aid of automation and AI so that the analysts may concentrate on the “why” element. Most market and investment research companies are automating and producing 75% of what a typical equal analyst would accomplish utilizing cutting-edge technologies. Robo-advisors are taking into account their clients’ behavioral patterns when it comes to investments, assets, and wealth management in order to determine their financial goals, liquidity needs, risk tolerance, and return expectations in order to recommend an appropriate investment strategy and targeted investment products.

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11.9.3 Creativity and Innovation While behavioral patterns occasionally may not be accurate, AI solutions are dependable and capable of assisting businesses in understanding the end consumers of their products. The basic processes and procedures that most firms wish to automate to increase productivity are searches, surveys, categorizations, and analysis. Innovative jobs and products can be designed with the aid of smart equipment and programs. Finally, cutting-edge technologies can predict which customers will aid in sales and who will withdraw from it in the future. However, this will help businesses focus their financial and human resources on high-quality leads. AI-based research can assist in developing successful business models, customer service, sales strategies, and in-depth consumer analytics.

11.9.4 Important Questions 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19.

What are the main uses of AI in taxation? How does AI determine a person’s tax liability if they go internationally? What are the uses of AI in tax filing for businesses? What are the business uses of AI for tax forecasting? How does AI assist tax departments with data processing? How do commercial banks assess big data to detect and eliminate fraud? AI is used to examine and identify risks in legal documents. False or True. How is AI used to determine the borrowers’ risk? Differentiate between exploiting a user’s digital footprint and a user’s personal data breach (or private data). What duties are associated with an organization’s employee expense management (EEM)? What advantages does AI in EEM offer? Describe how the claims are processed by the AI utilized in the EEM. Which law is applied in cost analysis to discover the dubious expenses? How can AI detect differences in claim patterns, question 14? Which EEM tools and apps based on AI are currently on the market? How has AI been used in credit scoring and loan analysis? What are the advantages of applying AI to loan analysis and credit scoring? How does AI determine the creditworthiness of the borrower? How is AI used in loan processing and service improvement?

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20. How many borrowers benefit from AI analytics? 21. How will AI aid in the production of buy and sell signals in the stock market? 22. How does AI aid in spotting arbitrage opportunities? 23. How does AI affect the stock market? 24. What causes credit card theft, and how might AI assist in spotting this fraud?

References [1] Analytics Insight. 2020. How AI Is Transforming Lending and Loan Management | Analytics Insight. [online] Available at: < https://www. analyticsinsight.net/how-ai-is-transforming-lending-and-loan-management/> [Accessed 14 April 2020]. [2] Archer Software. 2020. How AI Is Changing the Risk Management?[online] Available at: < https://archer-soft.com/blog/how-ai-changing-riskmanagement> [Accessed 14 April 2020]. [3] BasuMallick, C. 2020. How AI Can Prevent Employee Expense Fraud – Technology Signals. [online] Technology-signals.com. Available at: < https:// technology-signals.com/how-ai-can-prevent-employee-expense-fraud/> [Accessed 14 April 2020]. [4] Faggella, D. 2020. Artificial Intelligence Applications for Lending and Loan Management | Emerj. [online] Emerj. Available at: < https://emerj.com/aisector-overviews/artificial-intelligence-applications-lending-loanmanagement/>[Accessed 14 April 2020]. [5] Joshi, N. 2020. How AI and Robotics Can Change Taxation. [online] Forbes. Available at: < https://www.forbes.com/sites/cognitiveworld/2020/01/09/ how-ai-and-robotics-can-change-taxation/#7447a846437b> [Accessed 14 April 2020]. [6] MEDICI. 2020. MEDICI | Risk Management – The Most Important Application of AI in the Financial Sector. [online] Available at: < https://gomedici.com/ risk-management-most-important-application-of-ai-in-financial-sector> [Accessed 14 April 2020]. [7] SutiSoft Blog. 2020. How Artificial Intelligence Can Transform Expense Management—Sutisoft Blog. [online] Available at: < https://www.sutisoft. com/blog/how-artificial-intelligence-can-transform-expense-management/> [Accessed 14 April 2020]. [8] Wipro.com. 2020. Re-Inventing Expense Management Using Artificial Intelligence (AI)—Wipro. [online] Available at: < https://www.wipro.com/enIN/blogs/bhavna-sachar/re-inventing-expense-management-using-artificialintelligence--a/> [Accessed 14 April 2020]. [9] www.yseop.com

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[10] http://unitedrobots.ai/ [11] https://www.forbes.com/sites/bernardmarr/2019/03/29/artificialintelligence-can-now-write-amazing-content-what-does-that-mean-forhumans/#346f875350ab [12] https://www.businesswire.com/news/home/20200206005222/en/YseopLaunches-Augmented-Analyst-Next-Generation-AI-NLG [13] https://www.nasdaq.com/articles/for-the-first-time-nasdaq-is-using-artificialintelligence-to-surveil-u.s.-stock-market [14] https://daytradingz.com/artificial-intelligence-stock-trading-software/ [15] https://emerj.com/ai-podcast-interviews/artificial-intelligence-in-stocktrading-future-trends-and-applications/ [16] https://builtin.com/artificial-intelligence/ai-trading-stock-market-tech [17] https://s3.amazonaws.com/qualtrics-www/assets/wp-content/uploads/2018/ 08/AI-in-MR-Final.pdf [18] https://www.aithority.com/guest-authors/ai-will-change-everything-aboutmarket-research/ [19] https://medium.com/@hbgknowledgehub/future-of-artificial-intelligence-inmarket-research-e0abac35fcd9 [20] https://www.cognizant.com/case-studies/pdfs/ai-driven-solution-reducesfraud-risk-for-bank-codex3688.pdf [21] https://analyticsindiamag.com/case-study-how-the-municipal-corporationof-panaji-city-is-using-geospatial-based-cloud-solution-to-manage-cityrevenue-collection/ [22] https://analyticsindiamag.com/case-study-how-this-manufacturingcompany-simplified-their-travel-expense-management-workflows/ [23] https://analyticsindiamag.com/case-study-how-this-mumbai-based-startupuses-mobile-data-to-assess-credit-scores/ [24] https://spd.group/machine-learning/credit-card-fraud-detection/ [25] https://emerj.com/ai-sector-overviews/artificial-intelligence-credit-cards/

Chapter 12

AI in Legal Shilpi Agarwal, M. Purushottam Naidu, and B. Narasimha Swamy School of Business, Woxsen University, Hyderabad, India

12.1 Automated Report Generation Automated report generation (ARG) is achieved using automation templates to generate automated legal reports. They are generated after taking certain inputs through text, variables, and questionnaires. Using the logical links, the inputs are processed, and a final report is generated. As answering a questionnaire is faster than the manual preparation of the reports, ARG significantly saves time and resources [3]. As the legal know-how is built into the document template, the questionnaire and its interface can be formulated in such a way that even a legal layman can answer them with proper hints that are provided while answering. Of the most important, report automation will help standardize the report presentation and content. It helps in reviewing and managing the legal documents easily.

12.1.1 Applications of ARG Using tools like iSheets by HighQ, automation of legal documents is a simple task. The questionnaires discussed above can be created with a simple plugand-play intuitive technology that can be formulated by anyone [4]. Reports that are required frequently and those that have a high degree of variance and complexity can be automated to save on the resources. The list of documents that can be generated by ARG includes the following: DOI: 10.4324/9781003358411-12

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i. ii. iii. iv. v. vi. vii. viii.

Employment agreements License agreements NDAs Due diligence reports Software agreements Engagement letters Jurisdictional-governed contracts Share purchase agreements

12.1.2 Benefits of ARG With consistency and accuracy ensured within the reports, ARG facilitates faster delivery of contracts with less scope for errors. Automatic entry or presentation of jurisdictional-based clauses is also possible by answering the questions on the “location of the contract made”, etc. The productivity of work increases with ARG as the amount of time minimized by the employees in managing the documents. This will turn cost-effective to the legal firms as there will be a reduction in total billable hours and the employees could focus on more important work aspects. These resultant savings can be passed on to the clients as well to increase their loyalty and satisfaction toward the legal firm. With the advent of AI and machine learning, even the non-legal firms have entered into ARG for affordable services like wills, drafting deeds, and other contracts. Using ARG, lawyers are customizing the templates to serve the individual needs of their clients in specialized services and are building on existing knowledge [5].

12.1.3 A Legal Case Study From PWC (PricewaterhouseCoopers) Legaltech 12.1.3.1 Select Case Study: Large Pharmaceutical Company Issue Acquired company contracts have been kept in file cabinets around the world. As the acquisition was mainly for new market access, part of synergies included a 90% reduction in the target company’s workforce. This meant that there was little knowledge of the acquired company’s contracts [10–11]. The client was using a basic in-house contract management system maintained by a contractor. There were multiple instances of system downtime every month. Ability to move to self-service, e-signature, and other capabilities was costprohibitive due to extensive customization required. The client wanted to

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have a workflow segmented by risk profiles of different contract clauses, e.g., indemnification, liability, confidentiality, liquidation damages, etc.

12.1.3.2 Action ■ Templates were designed to capture key contractual information during contract review. ■ PwC set intelligent thresholds whereby contracts that would expire or come up for renewal before the system was implemented were not tagged for data capture. ■ A risk-based workflow was created for approvals whereby standard contract terms required less (lower-cost) review and approval while custom contract clauses would trigger more extensive (expensive) reviews. ■ Industry-leading practices were introduced to simplify and standardize the client’s contract management processes.

12.1.3.3 Impact ■ Actionable reports and dashboards were available to management. ■ Focused user group training ensured that all stakeholders adopted the system. ■ The legal team had assurance that contracts that subjected the company to increased risk were approved by the relevant and competent authority.

12.2 Contract Analysis Contracts define the legal framework of the businesses. While more effort is being put into the negotiations in the business, contracts, after signing, are mostly neglected until something goes wrong. For the businesses to run smoothly and efficiently and to make the most out of the contracts, contract analysis using AI could add value to the bottom line. This would add a strong foundation and transparency to business cooperation.

12.2.1 Errors in Contracts Like the bugs in the software, contracts do have errors that affect the profits of the business. The errors, which affect the profitability of any business, can be

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corrected or avoided with AI-based contract analysis. The possible errors in the contracts include the following: a. b. c. d.

unforeseen edge cases that weren’t resolved, arbitrary or vague clauses that don’t define a specific outcome, lack of terms on dispute resolution, missing or inadequate clauses in case of liabilities and termination of the contract, e. conflicts with the existing contracts, and f. missing time frame or missing defined lifecycle for the execution of the contract.

12.2.2 Mishandling of Contracts Apart from the above, the highest-grossing business enterprises mishandle contract management, which can be avoided using contract analysis. Such mishandling of contracts includes the following: a. b. c. d. e. f. g.

miscommunication of responsibilities, missing deadlines, underestimation of execution costs, deploying additional resources than the agreed, overlooking or failing to renew the contracts, unwanted renewals that had outlived their effectiveness, and the lack of proper risk management.

12.2.3 Contract Analysis Through AI Contract analysis through AI uses natural language processing, smart OCR (Optical Character Recognition), and machine learning to automate processes like data entry, logging, and manual tagging of the contracts. The contracts can be compared using data points such as terms, clauses, or any other of these. Contract analysis software can be taught to tag the new clauses, and new rules can be set for handling these clauses. The new rules can be applied to all the existing and future contracts as well. Using the above features, Evisort, a contract analysis software, is fluent in over 100 contract types, such as client agreements, vendor agreements, NDAs, investment documents, and compliance agreements.

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12.2.4 Key Applications of Contract Analysis Through AI An effective contract analysis using AI can, a. b. c. d. e. f. g.

identify and mitigate the risks, reduce the cost of review and execution, streamline workflows, ease the compliance and protect against legal snares, ease the project planning, simplify the vendor management and procurement, and identify the compliance issues.

Contract analysis software can extract and present the key information of the contract library at any time. Using the contract analysis software, firms can streamline and automate the intensive manual and mundane practices of poor contract management. This reduces the costs of contract management significantly and the resultant costly ramifications due to poor management. As the firms’ KPI (Key Performance Indicator) improves over time using the contract analysis software through their agile analysis, the business firms may go for new commitments and deals with ease. This is possible as the contract library is at the firms’ disposal, helping them to make bolder decisions and increasing efficiency during the negotiation.

12.3 Legal Document Review and Research 12.3.1 Document Review AI is being used in legal document review to increase efficiency, accuracy, and speed of the document analysis. Legal document review contains the task of looking for required or relevant documents. For example, when lawyers use the AI-based legal document review software to flag the relevant documents as per the requirements, the AI learns to identify other documents of a similar type close to the precision. For example, documents containing certain keywords or emails that were sent from Mr A to Ms X regarding project Z during May 2020 are identified. Such kind of learning by AI and updating itself to be accurate for the future tasks is called “predictive coding”. Advantages of predictive coding over the manual documents review are as follows:

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Similar documents are found with just a small sample of documents. Hours of wading through the irrelevant documents volumes are reduced. Statistical verification of the results is generated. Better and faster results are generated than the human review.

12.3.2 Legal Research Every lawsuit, criminal case, legal process, and appeal requires a bit of legal research for a legal precedent or to determine a “case of the first impression” where it lacks a legal precedent. The main purpose of legal research is to find out and know how the courts in the past have decided the cases with a similar pattern of facts and to forecast the chances of winning a case at the courts [6]. Similarly, a corporate lawyer may use legal research to identify whether the new policy brought by the company would make a company liable or vulnerable to any laws, such as regulatory laws and employment laws. Automation is used in U.S. legal research using Lexis or Westlaw. They provide online legal research tools to look up and verify the cases with the current law, saving laborious manual processes and countless hours. For example, using the Ross Intelligence AI tool, a case precedent can be found instantly, which may take about 10 hours for a specialized and experienced lawyer. Using the legal precedents data, AI is also better at predicting the outcomes of the proceedings, legal disputes, and the cost of such proceedings or disputes, giving clients insights for making better decisions.

12.3.3 A Legal Case Study From Freshfields Bruckhaus Deringer 12.3.3.1 The Challenge When a European government passed a law to tackle bribery and corruption in the healthcare sector, we were called in to advise a leading player on its potential exposure. The legislation created two new criminal offenses and extended the scope of criminal liabilities to cover the undue provision of benefits to all healthcare professionals. Our client had 90,000 pages of distribution contracts that needed to be reviewed. Many had “evergreen” provisions, which meant they were automatically renewed each year, and with criminal liabilities potentially attaching to all agreements concluded after the law was passed, it was vital to identify and amend as quickly as possible those that presented a risk [7].

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12.3.3.2 The Solution—Combining Human and AI The sheer volume of the agreements—and the variation in the data they contained (there were multiple different formats and many contained provisions amended by hand)—would have taken too long to review manually. We chose, therefore, to employ an innovative combination of humans and AI. Our lawyers reviewed the contracts to identify the relevant “boilerplate” and individual provisions that might trigger a breach and then used a cuttingedge software platform to extract them into a tailor-made report for individual review by lawyers.

12.3.3.3 Teaching Algorithms in Different Languages Over time, we taught the system’s machine-learning algorithms (initially configured to work only in English) to identify the potentially risky provisions in the relevant language. We also trained the system to distinguish between 20 types of agreements and to extract data such as the parties to each contract and its term length and start date [8].

12.3.4 The Result The review of the first 4,000 contracts took less than 3 months. We were able to categorize and prioritize the unorganized dataset very quickly, allocate contracts to reviewers, and run real-time analytics to provide the client with a clear picture of its exposure [9]. We have applied the same approach to teaching the algorithm to work in other languages. We have also worked with the software’s developers, Kira Systems, to use the outputs from the review to deliver what is effectively a contract management system that will significantly reduce our client’s future risk. The company can now, for example, identify all agreements with potentially problematic provisions, organize and distribute them according to remediation needs, and prioritize the necessary amendments.

12.3.5 Questions 1. Explain the process of ARG. 2. List out which documents can be automated through ARG. 3. What are the benefits of ARG?

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4. What are some of the legal services provided by non-legal firms using ARG? 5. Explore the internet and write down the significant features of iSheets and their uses. 6. What are the errors that might creep into the contracts? 7. How are some contracts mishandled? 8. How is AI applied in contract analysis? 9. What are the key appliances of AI in contract analysis? 10. What are the benefits of using AI in contract analysis? 11. Explain the legal document review. 12. What is “predictive coding” in legal document review? 13. What are the advantages of “predictive coding” in legal document review? 14. Explain legal research. 15. What are the automation tools used for legal research?

References [1] Blog.highq.com. 2020. How Document Automation Can Eliminate Wasted Working Hours. [online] Available at: < https://blog.highq.com//enterprisecollaboration/how-document-automation-can-eliminate-wasted-workinghours> [Accessed 18 June 2020]. [2] Evisort. 2020. Contract Analysis: What It Is And Why It Matters | Evisort. [online] Available at: < https://www.evisort.com/what-is-contract-analysis/> [Accessed 21 June 2020]. [3] Findlaw. 2020. What Is Legal Research?—Findlaw. [online] Available at: < https://hirealawyer.findlaw.com/choosing-the-right-lawyer/legal-research. html#:~:text=Legal%20research%20is%20generally%20the,unregulated%20or %20lacks%20legal%20precedent> [Accessed 22 June 2020]. [4] Harvard Journal of Law & Technology. 2020. A Primer On Using Artificial Intelligence In The Legal Profession. [online] Available at: < https://jolt.law. harvard.edu/digest/a-primer-on-using-artificial-intelligence-in-the-legalprofession> [Accessed 22 June 2020]. [5] LegalUp. 2020. Is The Practice of Law Changing due to AI and Machine Learning? | Legalup. [online] Available at: < https://legalup.me/aimachinelearning/> [Accessed 18 June 2020]. [6] Lawlift.com. 2020. Document Automation Made Easy | Lawlift. [online] Available at: < https://www.lawlift.com/?gclid= CjwKCAjw26H3BRB2EiwAy32zhfgCIH72DPQDpeXIw4bG7X14nTXb8VKpk_ 5nhcCbIGsbE-H-yzVJ_RoCejwQAvD_BwE> [Accessed 18 June 2020]. [7] https://www.freshfields.com/en-gb/what-we-do/case-studies/ai-case-study/

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[8] Marr, B., 2020. How AI and Machine Learning Are Transforming Law Firms and the Legal Sector. Forbes. [online] Available at: < https://www.forbes.com/ sites/bernardmarr/2018/05/23/how-ai-and-machine-learning-aretransforming-law-firms-and-the-legal-sector/#a73cf5432c38> [Accessed 22 June 2020]. [9] Wipro.com. 2020. Contract Analysis Using AI | Contract Intelligence—Wipro. [online] Available at: < https://www.wipro.com/en-IN/holmes/contractintelligence/> [Accessed 21 June 2020]. [10] https://www.pwc.com/gx/en/services/legal/tech/assets/pwc-india-legaltech-solutions.pdf [11] https://www.pwc.com/gx/en/services/legal/tech/case-studies.html

Chapter 13

AI in Supply Chain, Logistics and Manufacturing Debdutta Choudhury and Tanvi Gorantla Woxsen University, Hyderabad, India

13.1 Introduction Artificial intelligence (AI) is playing a key role in the areas of supply chain, logistics and manufacturing. The use of AI and robotics has revolutionised all major areas of operations, especially where they involve repetitive and predictable tasks. Commonly known as Operations 4.0, this modern operation will cover, among many others, the following. ■ ■ ■ ■ ■ ■

Demand forecasting Simultaneous localisation and mapping (SLAM) Satellite imagery for geo-analytics Predictive maintenance Quality management Product life cycle management

In this chapter we will examine the various technology interventions in many areas of operations management that have changed the face of the traditional domain.

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13.2 Demand Forecasting Demand forecasting is one of the major aspects of any product-based organisations. Accurate forecasts lead to production efficiencies and better inventory management and prevent cost overruns. AI-based models have been really instrumental in improving accuracy and transparency of this function. There are two types of models [1–3]. Black box models, where forecasting and the explanation of the model are decoupled. The explanation of the model increases transparency and is hence extremely critical for understanding. The researchers here develop separate algorithms to understand the model. Glass box models where the AI-based algorithm can explain its prediction. Forecasting planners require an explanation of the factors and data that went into each forecast to better understand the soundness of the forecast and thereby have more control. An explanation of a model should have meaningful information and a logical explanation. The model should also have actionability information and may also provide counterfactuals [9]. The technology and algorithm used may have the following features: ■ Provide knowledge about the features of the dataset. ■ Provide the characteristics of the dataset and the ranges. ■ Provide explanations in the correct language and context for the user. Let us now understand why AI and machine learning-based models are preferred.

13.2.1 Traditional Versus Machine Learning-Based Forecasting Techniques Traditional statistical forecasting models are based on historical time series data, and their accuracy is dependent on stable market conditions. However, traditional methods fail to take into account changes in consumer behaviour and disruptions in the marketplace due to innovation and technological breakthroughs. Machine learning algorithms can use internal and external real-time data to create much more relevant and timely forecasts. The Institute of Business Forecasting and Planning outlines certain datasets that modern machine learning algorithms can use. They include sales data, website statistics,

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clickstream data, geolocation, macroeconomic indicators, social media, POS data, third-party syndicated data, etc. Machine learning algorithms use such large datasets and complex interrelationships among various datasets to understand patterns and capture demand. The smart models continue to retrain themselves over time as fresh data come in, thus addressing volatility of the market. This gives much more accurate and reliable forecasts [12]. However, as we discussed earlier in the chapter, it is imperative for a human understanding of the features of the model, the impact of each dataset on the outcome and the rationale for the forecast.

13.2.2 Predictive Analytics Predictive sales analytics is the most common application of machine learning algorithms along with statistical techniques. It helps the company to understand demand and also consumer buying behaviour in certain scenarios. The predictive models combine internal company data with external macroeconomic data and many other external variables to predict demand. It helps an enterprise to take much more informed decisions about consumer buying, product launches and planning for an event [17 & 24]. One of the major drawbacks is that it can at best look at medium-term forecasting because of the complexities of the datasets involved. For a closer understanding of demand, techniques like demand sensing are used.

13.2.3 Demand Sensing Demand sensing algorithms incorporate real-time sales data to throw up short time forecasts as variabilities in buying appear. It extracts daily data from sales, warehouses and other sources to indicate fluctuations in medium-term forecasts. The models also explain the causes of each variability, significance of each factors and offers very short time forecasts for day-to-day functioning.

13.2.4 Popular Deployment of Machine Learning in Demand Forecasting Since machine learning algorithms are costly to deploy and require a huge amount of data and computing power, it is essential to understand how an enterprise can extract the best possible value from this practice. Some best use cases are as follows:

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New product introduction: New product demand forecasting is difficult due to lack of past sales data. Popular methods for such forecasts have been the traditional expert opinions based on experience. However, machine learning algorithms use past sales data of similar products and product life cycle curves to do more accurate and data-based forecasting. This impacts decision-making for production, marketing and supply chain functions of an enterprise [28]. Products with short life cycles: Fashion products are typically seasonal, and the life cycle is about a few months. In such a scenario, demand sensing tools are of great value since they give a sense of predictability and control over the entire process. Seasonal products: Products like winter garments and umbrellas and summer products like cold beverages are dependent on the weather conditions and require extreme short-term forecasts. Machine learning helps enterprises to build various scenarios and determine the effect of each on the demand. PRACTICAL ILLUSTRATIONS Global brand Luxottica, the world’s largest eyewear company, introduces around 2,000 new styles every year. The use of smart forecasting techniques has helped the company to improve its forecasts by 10%, resulting in significant savings and control. The UK National Health System uses ML-based algorithms to understand the blood requirement in hospitals to prevent overstocking and stockouts. Since blood is a critical life-saving item, no stockouts can have very disastrous consequences, and overstocking leads to wastage, it is very important to have accurate and reliable forecasts. This method has reduced overstocking in hospitals by 30%, resulting in less wastage at some hospitals and stockout in others.

13.3 Simultaneous Localisation and Mapping SLAM is a method where autonomous vehicles create a map of a particular territory and simultaneously localising the vehicle on that map. This helps in understanding a particular terrain and addresses issues about path planning and avoiding obstacles [22]. Some important applications of SLAM are parking a self-driven car in a vacant parking slot, arranging materials on a shelf by robots and delivery by drones.

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There are two types of SLAM: visual SLAM (vSLAM) and light imaging detection and ranging (LIDAR) SLAM.

13.3.1 Visual SLAM vSLAM uses cameras and sensors and can provide wide-ranging information that can be used to detect previously measured landmarks. It can be quite cost-effective if relatively inexpensive cameras are used. Monocular vSLAM is where a single camera is used with other sensors and is quite popular. There are two types of vSLAM algorithms. They are the sparse method which matches features and the dense method which matches brightness of images. Some popular vSLAM algorithms are Dense Tracking and Mapping (DTAM), Large‐Scale Direct monocular SLAM (LSD‐SLAM), Direct Sparse Odometry (DSO), Parallel Tracking and Mapping (PTAM), ORB‐SLAM (Oriented FAST and Rotated BRIEF Simultaneous Localization and Mapping) and SVO (Semi‐Direct Visual Odometry).

13.3.2 LIDAR SLAM LIDAR uses lasers that are more precise and used in autonomous vehicles and drones. Due to its high precision, LIDAR is used for distance measurements and, thereby, in construction of a map in both 2D and 3D. 3D LIDAR may be used in junction with global navigation satellite system (GNSS) which helps in identification of measurement objects and also obstacles. For warehouse robots 2D SLAM is generally used, whereas 3D SLAM is commonly used for unmanned aerial vehicles (UAV) and remote car parking [33].

13.3.3 Challenges in SLAM Accumulation of localisation errors leads to a substantial divergence of actual values. For every SLAM activity there will be a small error that becomes a very large problem as it accumulates over time. This leads to substantial deviation in the map to the extent that the robot’s starting and ending points may not coincide. This is called loop closure problem. SLAM technologists need to cancel these errors as they create a map so that errors do not accumulate. SLAM technologies using sensors sometimes fail to recognise the characteristics of a robot’s movement. This may lead to certain sudden unexpected movements forward beyond the robot’s estimated speed.

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This is called localisation failure. Localisation failure can be addressed by using a recovery algorithm or fitting the robot with multiple sensors. Sometimes remembering a key landmark that has been earlier mapped is also used as a frame of reference. Deep learning is used to cancel localisation failure. There is a high computational cost when it comes to implementation of SLAM. The SLAM vehicles are fitted with several microprocessors, and to avoid localisation failures the data received needs to be processed at high frequency on the central servers. Loop closure also requires very high computational power. One way to avoid this is to have parallel processing that analyses different processes parallelly to reduce pressure on one system. Also, a long optimisation processing cycle helps since it only processes smaller packets of data at one period of time.

13.4 LIDAR and RADAR LIDAR uses laser technology to detect objects and their exact size and shape. A LIDAR device emanates laser pulses outwards in several directions till the pulses hit an object and come back to the device that processes the information to record that object [6 & 10]. Since lasers move at the speed of light, the processing is very fast, and several packets of data get received per second. This creates better and more accurate mapping compared to any other technologies like SONAR. LIDAR has several applications like updating maps, measuring stratospheric elements and also in film industry to create a virtual set. Radio detection and ranging (RADAR) uses soundwaves to detect objects and their distance and speed. Since radio waves travel further, RADAR can be used to detect objects at greater distance than LIDAR. Both RADAR and LIDAR use the same principles but different waves. RADAR has applications in military, short-range weather detection and measuring speed of vehicles in traffic. However currently LIDAR has become more preferred technology to detect car speeds in traffic. LIDAR uses shorter waves compared to RADAR and can be used to detect smaller objects and also build a true 3D image of the object. It has a limitation for nighttime and cloudy weather usage. RADAR, on the other hand, can be used 24/7 and is not hindered by cloudy weather. It can also be used to detect objects much farther away. But RADAR cannot detect smaller objects and cannot build a precise 3D image.

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13.5 Satellite Imagery for Geo-Analytics The rise of application of AI algorithms to analyse satellite geospatial data has given rise to a new discipline called geospatial AI. Geospatial AI is dependent on two major factors: characteristics of data and the power of computing.

13.5.1 Characteristics of Data For analysing any particular data and making sense and predictions, the data need to have the following characteristics. Volume: Due to thousands of satellites orbiting the earth and multiplying at a rapid pace, huge amount of high-quality image data is available to analyse. Velocity: Due to high level of satellite density, images are available quickly. Variety: A variety of data can be accessed, including image, temperature, weather, etc. Veracity: Since data are directly captured, there is no manipulation involved. Value: The data are of immense value for several applications like weather forecasting, traffic, supply chain issues, and the like.

13.5.2 Computing Power Supercomputers and the rise of quantum computing have increased computing power that helps to analyse the rich and varied data received from satellites. Cloud technology is being used as a repository for all data saving individual enterprises time, budget and effort to invest in and develop storage servers.

13.5.3 Geospatial AI for Global Sustainable Development Goals The United Nations and the World Bank have jointly declared a global sustainable development goal for improving the quality of life by 2030 across nations. The goal is to improve social and economic conditions of human population along with conservation of natural resources [19]. Geospatial AI plays a major role here with accurate analysis of population clusters, development progress and natural resources to plan and monitor development requirements more accurately. In 2017, the task force for geospatial imagery and data published a report on feasibility of using satellite imagery data to develop

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official statistics that would help to set SDGs. Geospatial data are being utilised to measure agricultural land, forest cover, roads, water availability, etc., to generate statistics that help the planners. Geospatial data are also used for the detection of cropping patterns and quality of forest covers. However, sometimes it is also helpful to have an on-the-ground inspection to validate the data received from geospatial images. Spatial autocorrelation techniques are being used to understand correlation between features observed on the ground, viz., tree cover and water availability. Advanced spatial correlation modelling is being increasingly used to understand and predict both global and local variables. Landslide events in Myanmar, social heat vulnerability, land cover in Kolkata region of India, etc., are some primary examples. Special machine learning models are being used to analyse the data and detect relationships faster and more accurately [23]. The random forest regression kriging model is now used to measure particulate matter concentration in air by simulating nonlinear trends and thereby model particulate concentration dependence on other variables [25]. Mud content on the seabed and water turbidity are some of the phenomena being predicted using these advanced AI models.

13.6 Weather Forecasting Weather predictions have traditionally used statistical models to understand short-term impacts. However, with the development of machine learning and AI algorithms, much more accurate forecasts are being made for short-term and long-term weather conditions [5]. Data collected from various sources like deep space satellites, space observatories, weather balloons and IoT-based sensors are more richer when analysed using AI-based algorithms that can predict longer-term weather patterns. One very popular method is numerical weather prediction (NWP). This model analyses data from several sources to predict short-term weather and long-term climatic conditions. Other methods include artificial neural networks, regression neural networks, genetic algorithms, fuzzy clustering, etc. [17]. Scientists in Spain analyse hard-to-detect cloud formations that are generally associated with cyclone formation later. The model claims to have an accuracy of 99% with a processing frequency of 40 seconds per prediction. IBM’s weather.com uses IBM Watson’s cognitive ability and the power of

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cloud computing to create the IBM Deep Thunder platform. Deep Thunder offers hyperlocal forecasts to its clients at 0.2- to-1.2-mile resolution. Google uses UNET convolutional neural network (CNN) to create an AI forecast tool that accurately forecasts rainfall 6 hours earlier. Monsanto’s Climate Corporation uses satellite images and local weather data and analyses with machine learning models to make accurate weather forecasts for farmers. École Polytechnique Fédérale de Lausanne in Switzerland uses meteorological data and machine learning to build a predictive system that can predict lightning strikes with very close accuracy [24]. Panasonic has created a weather prediction system that successfully predicted Hurricane Irma’s landfall in Florida seven days in advance.

13.7 Human–Robot Collaboration Enhancement Human–robot collaboration is no longer in the realm of science fiction. Increasingly, human–robot teams are being deployed in the areas of space exploration, survivor rescue, clearing hazardous wastes, etc. Robotic systems reduce human workload, fatigue and potential hazards, but there are several challenges to such joint teams, especially in developing a common frame of reference, communication and safety concerns [14]. Human-to-human interactions have several verbal and nonverbal cues. The role of conversation, emotion, attachment, gesture and body language helps human beings to connect and establish a common frame of reference to work as a team. However, the intelligent robots of today still lack the depth to understand the essence of human communication. Still, algorithms are being developed that model the human communication patterns to make the robots understand human commands better. Robots can be used in the following ways. Robot as a tool: Robots can be used as semiautonomous or autonomous tools to perform certain tasks, such as harvesting, removing hazardous materials, urban search and rescue and patrolling in nuclear plants. Two major areas of human–robot coordination must be considered [18 & 27]. The first is adjustable autonomy, where autonomy is adjusted based on the nature of work, and the second is situational awareness, where the human member must be aware of the robot’s workspace to control it better and avoid collisions. Robots as assistants: Robots may be used for guiding, hosting and assisting humans. During the COVID-19 pandemic, we have seen robots assisting

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patients or their escorts at the main hospital entrance, guiding them to the appropriate service floors. This was done to avoid human staff exposure to potential COVID-19 patients at the entry point. Robots are also used as guides in museums, where they display human-like communication skills to hold attention and build rapport. These robots must be highly autonomous since they need to react to human voice, gaze and other body language cues and react appropriately. The main area of thrust for human–robot cooperation is developing normal verbal communication, understanding human body language and responding accordingly, like nodding the head, thus enabling a natural flow of communication [30]. Research has shown that robotic communication improves with the use of dialogue. The robot can understand the physical and informational environment using audio and visual inputs and thus carry out tasks more accurately. A multimodal interface using speech, sensors and other electronic devices helps the robot better spatially reference and discover obstacles or move an object. Robots that can interpret human anxiety from verbal cues and respond appropriately have been developed. A force feedback system between a robot and a human is being used to understand intentions. Here the human and the robot function independently and relay their intent through a force feedback system. This has reduced operational duration and improved efficiencies. Certain autonomous robots work independently till they encounter a problem that they cannot solve [33 & 34]. It is when human operator intervention becomes necessary and the robot’s performance improves with the help of human skill, perception and cognition. Augmented reality (AR) has enhanced human–robot collaboration in the following ways: ■ Enhancing reality. ■ Interaction with real and virtual worlds. ■ Simulating robotic behaviour in the virtual world before deploying in real. ■ Multiple collaboration environments. There are several applications of AR in human–robot collaboration. Some of them are in space applications where the autonomous robot moves through a surface creating and the feedback is received by the astronaut or in the control centre who can create an AR-simulated environment to predict obstacles. Also, UAVs are controlled by using an AR environment through existing data.

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13.7.1 Perceived Safety In human–robot collaboration, there is an issue of perceived safety since many people do not feel safe while working with robots. This is a major problem since this would defeat the entire purpose of robotic usage. Research has shown that three factors are most important in developing perceived safety. They are separation of work areas, team identification and building trust. Researchers had created two work areas: one where humans and robots were separated and one where humans and robots worked side by side. The perceived safety was then measured, and the group that had humans and robots working in separate work areas felt safer. This feeling of relative safety made humans less fearful of robots and more open to working with robots. This would develop trust and dependence leading to lesser feelings of threat. This behaviour, when exhibited over a longer time span, would lead to significant lowering of threat perception from a robot and promote cooperative behaviour.

13.8 Predictive Maintenance Total productive maintenance is a concept developed in the 1960s along with lean production to facilitate less downtime and more efficient usage of machinery. This involved checking and maintaining machines at predetermined intervals to ensure their smooth functioning without any requirement for downtime due to breakdowns. Predictive maintenance takes it a notch further using various data and analytics and then combining the same with AI algorithms to predict the maintenance schedule of each machine. Past maintenance data, sensor data from machines, weather data, etc., are combined to check the current health status of machines and perform maintenance based on requirements. Market Research Future predicts that predictive maintenance will grow at a 25% CAGR to reach $23 million by 2025. In fact, the companies that use predictive maintenance report significant gains in productivity and efficiency. Predictive maintenance uses a large pool of data from IoT sensors about machine condition, acoustics, vibrations, fuel level, coolant level, temperature, power consumption, etc. These data are pooled and analysed using analytics software and AI algorithms to determine the current condition of the machine [31]. The AI algorithms also advise on the maintenance activities to undertake if they are trained on similar historical data. The algorithms needed to be trained in the following ways:

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Error history: The algorithms should be taught on normal functional data and also data when the machine was functioning erroneously leading to failure. Parts-replaced history and maintenance history during each error in each such case improve the accuracy of such predictions. Maintenance history: The entire history of maintenance including parts that were changed due to the nature of failure should be fed into the algorithm to make it more effective. This is a very critical dataset. Machine operations data: Sensor-based data from the machines need to be taken into the algorithm since the algorithm will be able to monitor machine condition and the ageing of parts. Machine feature data: Features of the machine, including manufacturing date, installation date, model, etc., need to be incorporated into the dataset. To have a reliable predictive maintenance system, IoT-based devices and sensor-based devices need to record machine data continuously and feed this into a local data field called the field gateway. The field gateway processes the data and stores it in the cloud gateway. In case of large datasets, the cloud gateway is connected to a Data Warehouse where the data are systematically stored. Machine learning algorithms then can access the data from the Data Warehouse to perform predictive maintenance.

13.9 Product Life Cycle Management Increased computing power and big data capabilities have revolutionised the way companies do business today. This capability has now been extended to product lifecyle management (PLM) where IoT, machine learning, virtual reality and AR technologies are pushing the limits. PLM systems nowadays capture a huge set of data from product design, usage, supply chain and material specifications to create large data warehouses which, coupled with machine learning algorithms, give predictive insights in each stage of the lifecycle. Some of the usages are as follows: Design: Technology has created design interfaces that suggest various options to a user while designing a product. This is also helpful when multiple functions collaborate to create a better-quality design and higher level of design experience [7]. Supply chain: Past data are used by machine learning algorithms to suggest better quality vendors, lower costs and a lower cycle time for the availability of components. This has enhanced the efficiency and output of the entire supply chain.

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Business decisions: Machine learning algorithms have enhanced design, customer feedback mechanisms, cost aspects and overall supply chain efficiencies. This has resulted in companies making better business decisions to produce better products at reduced costs. Digitalisation of PLM must reach a particular state where data should be rich enough to do predictive and prescriptive analysis. In the prescriptive stage the AI system shows various options and roadmaps for businesses and thereby supports agile decision-making. To develop this actionable insight the system should mature through the cycles of descriptive, diagnostic, predictive and prescriptive analytics systems. This level of maturity requires continuous investments in data capture, algorithm development, computing power and human interventions.

13.10 Quality Monitoring One of the most critical areas where AI is entering today is quality control. AI in quality control in the domain of IT and checking quality of code has been in practice for quite some time now and has almost eliminated the role of the testing engineers. However, in manufacturing, AI adoption in quality control has been difficult. This is because the cognitive ability of AI-powered machines and robots is still suboptimal when it comes to checking the finer aspects of a product, like aesthetics and very small defects. IBM Watson has a platform to help the manufacturing industry to detect defects. The algorithm is trained using visual data and uses computer vision to inspect the products and match them with the visual images stored in memory. The system uses ultrasonic camera to monitor the products during the production process and detect scratches and defects as the products move through the production line. The system also learns from human validation of its findings becoming more cognitive in finding defects and thereby assisting in improving productivity. Computer vision along with CNN is being used to find defective products in a production environment. This works very well in both high-mix (continuous changes in products) and low-mix environments. Lights-out factories with complete autonomous manufacturing are slowly becoming a reality, where the entire manufacturing process is run by robots and autonomous machines, reducing human intervention to a minimum. AI systems use sensors to detect settings in manufacturing processes to minimise or completely eliminate defective products.

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However, for minor defects, visual computing is used to detect them as discussed in the earlier paragraphs.

13.11 Supply Chain Optimisation Supply chains are becoming increasingly complex due to newer business paradigms. Demand fluctuations and supply-side uncertainties have resulted in a demand for a resilient supply chain. AI-powered supply chains are an answer to the volatility due to the system’s ability to analyse a large amount of data and create predictive and prescriptive models [4]. The supply chain of today is the focal function linking marketing, production, vendor management, profitability and logistics together. Designing a supply chain is no longer just a functional exercise but a strategic one that is pivotal to business success. Some of the challenges that supply chains must handle today are as follows: ■ Understanding the fluctuations in the demand for multiple products in multiple geographies. ■ Understanding trade-offs between thousands of variables to arrive at an optimal solution. ■ Executing plans in spite of the various apparently constraining variables successfully. Given these challenges, supply chain optimisation using AI is the logical solution. Fortunately, several standalone and integrated solutions like demand forecasting, dynamic inventory management and automation of process flow using AI are already available. However, implementing these requires time and investments, so there are high stakes involved. According to a recent McKinsey survey, only 25% of the respondents said that their objectives and system integrator’s objectives are properly aligned; 35% said that the system fell short of expectations; 45% said the project was not delivered on time and 26% said that there were budget escalations.

13.11.1 Use Cases of AI in Supply Chain Echo, a transport management company, uses AI to optimise the supply chain so as to give their customers the best experience. The services provided are rate negotiation, shipment management, transportation and shipment

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tracking. This helps the customers to find cheaper, quicker and more transparent solutions. Havi uses AI for predictive analytics of the supply chain. From the data received from the supply chain operations, the company can get predictive analytics in warehousing, procuring, freight management and distribution. Symbotic manufactures AI-powered robots that can give fully automated or flexible manual services to its clients [11 & 13]. Uptake uses AI to analyse data and predict downtime for several operational parameters like trucks, cars, railcars and aircraft. Coyote Logistics uses AI and machine learning to understand shipment delivery based on external factors like weather and traffic and generate alternative solutions so that the shipments arrive on time [15, 16 & 20]. Zebra Solutions has created an ecosystem of hardware, software and data analytics to provide smart packing solutions. These solutions optimise space management in containers, quicker delivery and less damage, thereby increasing efficiency and saving cost [21 & 32].

13.12 Video Surveillance The plethora of video cameras and shorter attention span have reduced the efficacy of conventional video surveillance for security and traffic management. In this light, use of AI-based video surveillance has gained ground. AI along with analytics has made the job of video surveillance much easier, much focussed and much more outcome-oriented. With AI-based search engine features, specific instances can be found without looking at hours of footage, reducing time and increasing efficiency [8 & 26]. For example, in the case of a missing person the search feature allows mapping of a person in the traffic security footage against the person’s picture in a matter of minutes. The same is true for stolen vehicles, traffic violations and security transgressions. With the degree of reduction of attention spans and the fact that video surveillance operators have to look at several camera outputs naturally, many instances that are happening in real time might be missed. AI of today can detect “unusual motion” as it happens in real time and set off alerts that can prevent a tragedy or a crime. This is based on pattern recognition and reduces the need to identify through visual scanning. Crime deterrence strategies worldwide are being bolstered by the use of video and IoT technologies along with deep learning algorithms to

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understand, analyse and predict possible crime scenarios that can be monitored in real time and timely and effective measures can be undertaken. Behavioural analytics, an emerging field, predicts human behaviour from past data and helps to understand the body language and intentions of people in various public settings and sensitive places. Another area where AI-based video has widespread application is sports analytics. Extensive video footage aided by analytics helps development of sportspersons and understand opponents’ strengths, weaknesses and game plans. Video surveillance with AI has also found widespread application in smart city management, building management and mapping agricultural land usages, and hence aids in e-governance issues.

13.13 Voice/Speech Recognition Speech recognition is a technology where the computer recognises the voice of a human, translates that to machine language and then acts as per the voice command. According to a survey conducted by Capgemini, 74% of users depend on voice-based commands for official work and also use e-commerce for shopping. Today, we see that all major technology companies like Google, Amazon and Apple have voice-based assistants to make the life of consumers simpler. According to Research and Markets, the global market for voice recognition would cross $18 billion at an annual growth of around 23%. The use of ML-based algorithms along with IoT has made voice recognition technology more accurate and easier to use. IoT-based devices can now act on the basis of voice commands, and simple day-to-day activities like switching off lights and other home devices have become rather common. Natural language processing (NLP) has created these interfaces between man and machine wherein simple voice commands are transcribed by the ML algorithms based on large datasets to respond and act [29]. Today, the speech recognition system has become speaker-independent, i.e., it would respond to any speaker irrespective of their pronunciation and manner of speaking. Other aspects that these algorithms have to deal with are peripheral noises, other speakers, sound of machinery, etc. These acoustic distortions tend to reduce the accuracy and effectiveness of a speech recognition system. Some algorithms that address these concerns are as follows:

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■ Spectral subtraction. ■ MSME techniques to understand the distortion. ■ Spectral equalisation. A common method used is linear predictive coding (LPC), which extracts regression coefficients to match the existing datasets and provides a stable transcription of a user’s speech. Questions 1. What is SLAM? How is SLAM used in actual business scenarios? 2. How are geo-analytics used to manage global sustainability goals? 3. What is predictive maintenance? How is the use of AI making predictive maintenance more accurate and impactful? 4. How would human–robot collaboration enhancement be beneficial for business? 5. Write within 200 words how AI is achieving supply chain optimisation goals.

References [1] AISmartz (2019). Speech Recognition Using Artificial Intelligence. https:// www.aismartz.com/blog/speech-recognition-using-artificial-intelligence/. Accessed on 21. 06. 2021. [2] Ajoudani, A., Zanchettin, A., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., et al. (2017). Progress and Prospects of the Human-Robot Collaboration. Autonomous Robots, Springer Verlag, 2017, pp. 1–17. 10.1007/s10514-0179677-2. hal-01643655 [3] Altexsoft.com (2019). Demand Forecasting Methods: Using Machine Learning and Predictive Analytics to See the Future of Sales. https://www.altexsoft. com/blog/demand-forecasting-methods-using-machine-learning/. Accessed on 13. 05. 2021. [4] Alicke K. et al (2021). Succeeding in the AI Supply-chain Revolution. McKinsey & Company. [5] Anandharajan T.R.V. et al. (2016). Weather Monitoring Using Artificial Intelligence. 2016 International Conference on Computational Intelligence and Networks. [6] Archer-soft.com. Lidar vs Radar: Pros and Cons for Autonomous Driving. https://archer-soft.com/blog/lidar-vs-radar-pros-and-cons-autonomousdriving. Accessed on 20. 05. 2021. [7] Bonoan, J. & Ataev, A. (2020). AI and Speciality Analytics Are Changing Video Surveillance. https://www.securityinfowatch.com/video-surveillance/article/

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21125810/ai-and-specialty-analytics-are-changing-video-surveillance. Accessed on 15. 06. 2021. [8] Boost.ai (2019). Conversational AI and Voice Recognition Technology: An Explainer. https://www.boost.ai/blog/conversational-ai-and-voicerecognition-technology-an-explainer. Accessed on 21. 06. 2021. [9] Brzozowska, E. (2020). Predictive Maintenance: This Is How AI Can Transform Industry 4.0. https://dlabs.ai/blog/predictive-maintenance-this-ishow-ai-can-transform-industry-4-0/. Accessed on 20. 05. 2021. [10] Burgard W. et al (2019). Introduction to Mobile Robotics. [11] Christian M. (Ed.). Artificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains. Proceedings of the Hamburg International Conference of Logistics (HICL), 27, ISBN 978-37502-4947-9, epubli GmbH, Berlin, pp. 413–441, 10.15480/882.2478 [12] Chuprina, R. (2020). The Complete Guide to Predictive Maintenance with Machine Learning. https://spd.group/machine-learning/predictivemaintenance/. Accessed on 20. 05. 2021. [13] Dataiku (2020). AI for Advanced Supply Chain Optimization. https://www. dataiku.com/stories/ai-for-supply-chain-operations. Accessed on 09. 06. 2021. [14] Depaoli, T. (2020). Robot Trucks or Autonomous Vehicles Will Revolutionize the Supply Chain. https://supplychainminded.com/robot-trucks-orautonomous-vehicles-will-revolutionize-the-supply-chain/. Accessed on 20. 05. 2021. [15] Elfadaly, A. & Lasaponara, R. (2019). On the Use of Satellite Imagery and GIS Tools to Detect and Characterize the Urbanization around Heritage Sites: The Case Studies of the Catacombs of Mustafa Kamel in Alexandria, Egypt and the Aragonese Castle in Baia, Italy. Sustainability, 2019(11), 2110; 10.3390/su11 072110 [16] Express Computer (2020). Using AI to Optimize Supply Chains. https://www. expresscomputer.in/artificial-intelligence-ai/using-ai-to-optimize-supplychains/54329/. Accessed on 09. 06. 2021. [17] Gaudin, S. (2020). Why AI Is an Increasingly Important Tool in Weather Prediction. https://www.hpe.com/us/en/insights/articles/why-ai-is-anincreasingly-important-tool-in-weather-prediction-2007.html. Accessed on 20. 05. 2021. [18] Green Scott, A. et al (2008). Human-Robot Collaboration: A Literature Review and Augmented Reality Approach in Design. International Journal of Advanced Robotic Systems, 5(1), pp. 1–18. ISSN 1729-8806. [19] Greenough, P.G. & Nelson, E.L. (2019). Beyond Mapping: A Case for Geospatial Analytics in Humanitarian Health. Conflict and Health. 10.1186/ s13031-019-0234-9 [20] Holloway, J. et al. (2018). Spatial and Machine Learning Methods of Satellite Imagery Analysis for Sustainable Development Goals. 16th Conference of IAOS OECD Headquarters, Paris, France, 19–21 September 2018.

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[21] Jacobs, T. (2020). Artificial Intelligence (AI) in Supply Chain & Logistics Supply. https://throughput.world/blog/topic/ai-in-supply-chain-andlogistics/. Accessed on 09. 06. 2021. [22] MathWorks (2020). What Is SLAM (Simultaneous Localization and Mapping) – MATLAB & Simulink - MATLAB & Simulink. https://www.mathworks.com/ discovery/slam.html. Accessed on 20. 05. 2021. [23] Omni Sci (2020). Geospatial Analytics. https://www.omnisci.com/technicalglossary/geospatial-analytics. Accessed on 20. 05. 2021. [24] Preetipadma (2020). How Is AI Empowering the Weatherforecasting Technology? https://www.analyticsinsight.net/ai-empowering-weatherforecasting-technology/. Accessed on 20. 05. 2021. [25] Krishnan, V.B. (2020). The Rise of AI-powered Geospatial Analytics. https:// blog.gramener.com/rise-of-geospatial-analysis-and-ai/. Accessed on 20. 05. 2021. [26] Priyam, R. et al (2013). Artificial Intelligence Applications for Speech Recognition. Conference on Advances in Communication and Control Systems 2013 (CAC2S 2013). [27] Robla-Gómez, S. et al. (2017). Working Together: Review on Safe HumanRobot Collaboration in Industrial Environments. IEEE Access, 5, 2017. 10.11 09/ACCESS.2017.2773127 [28] Rozanec, J.M. & Mladeni, D. (2021). Semantic XAI for Contextualized Demand Forecasting Explanations. Preprint submitted to Information Fusion. [29] Ryan, W. (2020). How Artificial Intelligence (AI) Is Changing Video Surveillance Today. https://www.sourcesecurity.com/insights/artificialintelligence-ai-changing-video-surveillance-co-3126-ga.1536762204.html. Accessed on 15. 06. 2021. [30] Sobalvarro, P. (2020). Here’s Why Human-robot Collaboration Is the Future of Manufacturing. https://www.weforum.org/agenda/2020/08/here-s-howrobots-can-help-us-confront-covid/. Accessed on 02. 06. 2021. [31] Uptake (2018). How AI is Making Predictive Maintenance a Reality for the Industrial IoT. https://www.uptake.com/blog/how-ai-is-making-predictivemaintenance-a-reality-for-the-industrial-iot. Accessed on 20. 05. 2021. [32] Wenzel, H., Smit, D., & Sardesai, S. (2019). A Literature Review on Machine Learning in Supply Chain Management, In: Kersten, Wolfgang Blecker, Thorsten Ringle, [33] Williamson, J. (2019). How Is SLAM Software Powering the Next Generation of Autonomous Industrial Robots? https://www.themanufacturer.com/ articles/slam-software-powering-next-generation-autonomous-industrialrobots/. Accessed on 20. 05. 2021. [34] You, S. et al. (2017). Enhancing Perceived Safety in Human–robot Collaborative Construction Using Immersive Virtual Environments. Automation in Construction, 96 (2018), 161–170. 10.1016/j.autcon. 2018.09.008

Chapter 14

Bayesian Machine Learning Approach for Evaluating the Effectiveness of an Order Fulfillment Reengineering Project in the Downstream Oil and Gas Supply Chain Serdar Semih Coskun İstanbul University, Turkey

14.1 Introduction Oil and gas is a highly mature industry in terms of product life cycles (Nordal & El-Thalji, 2021). In the downstream segment, the marketplaces bring about intense competition over standardized products. Globally, the recent drastic crises of the COVID-19 pandemic and the Russian-Ukraine war aggravate the uncertainty of prices. After the Russian-Ukraine war outbreak on February 24, the Brent oil price exceeded $120 in March 2022, compared to the $20 level that it dropped below in April 2020 in the middle of the pandemic (Statista, 2022a). In such a turbulent environment, the revenues and incomes of oil companies are also fluctuating. For example, the net income of ExxonMobil’s downstream division dropped below $-1,000 million in 2020 (Statista, 2022b). DOI: 10.4324/9781003358411-14

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Downstream business constitutes a crucial part of the whole value chain, considering that revenue is generated in this part. When the marketplace cannot recover the price volatility of crude oil, the profit margins are squeezed. Also, the sustainability-related forces, i.e., the rise of electric vehicles, climate regulation, and emission reduction exert more pressure on the oil companies (Lu et al., 2019). Therefore, to achieve sustainable competitive advantage, oil companies need to improve their value chain by investing the process technology and, thus, creating a cost advantage (Grantt, 1991; Porter, 1991). Here, process technology refers to generating more output from the same set of inputs through operational activities (Acemoglu et al., 2016). From the operational angle, petroleum production is a continuous business in which a limited number of inputs transform into a vast amount of products such as gasoline, diesel fuel, heating oil, and several lubricants. These products are purified from the crude oil in the refineries, where the downstream supply chain begins at one end. Next, oil companies pull these products from refineries by pipelines, rail cars, vessels, or trucks and store them in the terminals. These terminals are the regional distribution centers where intermediary companies, i.e., subcontractors, deliver products to their internal network or gasoline stations owned by independent retailers. Thus, the downstream oil and gas supply chain consists of the marketing and distribution process of ready-to-use petroleum products toward the consumer and industrial markets. On the other hand, inherent characteristics of the downstream business embrace slow-moving operations and lengthy logistics pipelines, which increase the complexity and uncertainty. These obstacles make supply chain coordination more difficult and result in greater costs. Transaction costs theory explains cost factors under direct and indirect cost categories (Cuypers et al., 2021; Williamson, 2008, 1979). Direct costs of the oil and gas products encapsulate unit costs and ordering costs. To improve the unit costs, upstream businesses adopt several technologies such as directional drilling, hydraulic fracturing, and horizontal drilling in the identification and exploration stages of crude oil (He et al., 2019; Inyang & Whidborne, 2019; Lei et al., 2022). For example, Shell reduced exploration expenses to $127 million in 2021, from $5278 million in 2013 (Statista, 2022c). Likewise, new technologies support the downstream business to overcome high ordering costs. For example, BPX Energy uses smart contracts for transactions to reduce invoice processing and field ticket approval efforts (Brett, 2019). Hence, users can view real-time information and take action on the scheduled tickets. Besides direct costs, there may also be indirect costs

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that deteriorate efficiency. For example, downstream oil companies might bear not only the increasing costs of sold goods but also extra workload because of the opportunistic behaviors of the truckers in the conventional order fulfillment when occasionally the real-time data flow disrupts at the point-of-sale (POS). In such cases, information technology (IT) crew must have spent additional endeavor to discern those inappropriate transactions. Opet Oil Company overcomes these indirect costs by investing in system integration to eliminate non-value-adding activities from the processes. With the emerging Industry 4.0, several digital technologies are being developed such as robotic process automation, big data analytics, and the Internet of Things. These technologies pave a promising path toward the era of ‘oil and gas 4.0’ in which better-integrated supply chains generate greater value (Lu et al., 2019). In line with that, oil companies are also eager to realign their value chains by adopting Industry 4.0 technologies. At this stage, companies have to implement BPR projects to embed new technologies in their alive process. However, the success rates of such projects remain questionable. Historically, technical BPR projects disclose high failure rates. Only onethird of organizations can yield from process reengineering efforts, and 89% of applicants have been disappointed (Nair, 2006; Anand et al., 2009). Some other studies also report high failure rates (70%) in BPR projects (AbdEllatif et al., 2018; Ozcelik, 2010). Similarly, the total quality management initiatives do not reveal intended results (Asif et al., 2009; Beer, 2003; Harari, 1993; Kumar et al., 2020). A quality management practice would most probably fail within 18–24 months of the project (Smith et al., 1994). Failure in the project reengineering projects may derive from several adaptation problems such as losing the customer orientation in reengineering programs, managers’ metacognitive biases, lack of formalized strategies for changing plans, weak organizational culture, etc. (Cho & Linderman, 2019; Fundin et al., 2018; Rad, 2006). We can also add increasing complexity and uncertainty when it comes to large-scale supply chains, just as in the energy industry. Shortly, it is urgent to shed light on the project success of oil and gas 4.0 implementations so that future projects can be inspired by these cases. This study introduces an order fulfillment reengineering project that aims to improve the value chain of the downstream oil and gas supply chain in Turkey. The order fulfillment process involves every activity for filling and servicing customer orders (Croxton, 2003). The case company redesigns the gasoline transactions to eliminate certain non-value-adding activities across the supply chain with an in-house project. Therefore, the case company ensures the information integration and improves the supply chain

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performance. The presented project falls under the Industry 4.0 umbrella, considering that system integration is one of the key applications of Oil and Gas 4.0 (Lu et al., 2019). The main contribution of this study is in terms of theory and methodology. First, we define the shadow sale concept which is a new metric for showing the supply chain performance. The shadow sale represents the refueling operations that are operationally completed but do not appear on the system. IT crew reports those mistakes daily as much as they capture. Second, we propose a three-stage and eight-step methodology to decide on the project’s success. This methodology involves a dichotomous Poisson process model. Initially, we assume that the shadow sales trace a Poisson process over a while. The first model describes the general performance intensity across the project timeline. Then the second model investigates if any breakpoint emerges in the time series after the project implementation. A prospective break point overlapping the project’s implementation dates could approve the project’s success since the shadow sales frequencies are expected to diminish significantly due to the actualized project. Results indicate that the implemented project has effectively contributed to the supply chain’s performance. Results also acknowledge that our Bayesian machine learning approach provides a flexible and straightforward ground for evaluating the project’s success in a highly complex environment. The rest of the chapter is structured as follows: Section 2 presents the theoretical background. Section 3 introduces the case company and the realworld project. Next, this section gives details about the mathematical model. Section 4 discusses the analysis results. Section 5 concludes the chapter with important findings, limitations, and suggestions for future research.

14.2 Theoretical Background 14.2.1 Supply Chain System Integration Supply chain management is a strategic and systemic approach that considers the performance of individual organizations and the supply chain as a whole (Mentzer et al., 2001). Just as Christopher (2000) stated earlier, competition takes place between large supply chains rather than stand-alone entities in the energy industry. From refineries to retail stores, downstream supply chain members are bound to each other through the integration of core business

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processes to create value for customers and other stakeholders (Lambert and Cooper, 2000). System integration is one of the key pillars of Industry 4.0. It refers to the technology that enables the various parts of the oil and gas supply chain to better coordinate with each other (Lu et al., 2019). There are two types of integration within the Industry 4.0 context, namely, horizontal and vertical integration (Yu et al., 2020). Vertical integration remains at the factory level, where different IT systems from various automation hierarchies harmonically work. Whereas horizontal integration is a supply chain-level integration that facilitates the coordination between companies so that materials can more fluently flow across the value stream (Fang et al., 2022; Julia AcevedoUrquiaga et al., 2021; Sun et al., 2020). Improved risk management, increased flexibility, improved responsiveness, and reduced logistics costs are among the potential benefits of horizontal integration (Govindan et al., 2022). To achieve horizontal integration and better coordinate the supply chains, companies must equally utilize hardware and software technologies (Govindan et al., 2022). Arshinder et al. (2008) suggest that IT is one of the main coordination mechanisms in supply chains. Incompatible and old information systems may cause difficulties in coordination and result in weak performance and waste (Arshinder et al., 2008; Fisher et al., 1994). Similarly, Power (2005) reviews IT systems as a principal element of supply chain integration. Integration refers to combining an integral whole, a vital element of coordination (Arshinder et al., 2008). IT systems enable information integration, referring to sharing critical information with the supply chain network (Prajogo & Olhager, 2012). Hence, organizations between the distributor–vendor interfaces can link with each other in oil and gas supply chains, based on real-time data. Conventionally, automation systems, enterprise resource planning (ERP), and electronic data interchange (EDI) are the pervasive IT systems that integrate the supply chain members. Prospective distortions in the real-time information flows from the downstream oil retailers to the upstream distributor may imperil the information integration and entail extra outlays. In such situations, competitive forces compel the focal companies to look again at how their supply chain processes are restructured to better manage the material, information, and money flows.

14.2.2 Supply Chain Order Fulfillment Process The Global Supply Chain Forum identifies eight key processes that an overall supply chain embraces: customer relationship management, customer

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services management, demand management, order fulfillment, manufacturing flow management, supplier relationship management, and product development and commercialization (Croxton et al., 2001). Among these processes, order fulfillment aims to fill and service customer orders effectively and efficiently. Although it is mostly viewed as a logistics activity, order fulfillment has strong links with other processes. For example, it feeds the demand management process with the flow of information designed earlier. Similarly, customer relationship management provides inputs for order fulfillment by identifying critical customers. At the strategic level, the order fulfillment process involves designing the metrics for measuring the effects of order fulfillment on the financial performance of the firm. Croxton (2003) suggests two important metrics: order-to-cash cycle time and customer cycle time. However, with the increase in complexity in the supply chain and the use of new technologies, different metrics and indicators can be necessary to better evaluate the order fulfillment performance. For example, this study conceptualizes the shadow sale phenomena to evaluate the order fulfillment performance of the oil and gas distribution network. At the operational level, order fulfillment begins with generating customer orders. Subsequent activities range from editing orders, checking inventory, and preparing documents to receiving payments and measuring process performance (Croxton, 2003). Growing technologies help companies achieve a streamlined and cost-effective cash cycle. For example, blockchain technology provides a peer-to-peer (P2P) network in which the transactions are managed in a transparent and trusted environment (Lu et al., 2019). On the other hand, technological tools and systems are also costly undertakings. Purchasing new equipment, hiring new personnel, and training employees for their new roles can be counted among such monetary costs (Ozcelik, 2010). BPR literature reports several failures in technology projects. The order fulfillment team needs to evaluate investment costs against the value added by the technology. Additionally, failure risks are the paramount factor that needs to be considered before technology investments. The next section elaborates on BPR projects and their roles in supply chain performance.

14.2.3 Process Reengineering for Improving Supply Chain Performance Supply chain performance can be measured with various factors such as cost, quality, time, flexibility, responsiveness, agility, adaptability, and alignment (Beamon, 1999; Chen et al., 2004; Gunasekaran et al., 2004; Whitten et al., 2012).

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Among these variables, the cost is the most critical one for showing how efficiently companies manage their resources. Companies aim to minimize resource use by reducing operational and inventory costs. Personnel requirements, equipment utilization, energy usage, and inventory levels provide measures to evaluate supply chain performance in terms of costs (Beamon, 1999). In the oil and gas distribution domain, we would neglect the transaction costs if the consumer market were the case. The consumer market is a perfectly competitive market where the quality of goods is almost identical for every oil brand and sellers are not powerful enough to affect the competitive equilibrium price (Acemoglu et al., 2019). However, in the industrial markets, customers’ willingness to pay may vary according to the conditions that sellers offer. For a more efficient trade, gas distributors do not leave the pricing mechanism to be determined by the “invisible hand,” but rather they regularize it within the supply chain hierarchy. Therefore, transaction cost theory explains the differentiation of profitability in the industrial markets. Companies convey efficiency improvements within the supply chain operations. In fact, there might be a vast amount of opportunities to reduce transaction costs considering the complex operations in oil and gas distribution. Naturally, customers are only interested in the value-adding activities that contribute to the finished goods or services. Past research reports that nonvalue-adding activities can be up to 40% of a business process (Mohamed & Tucker, 1996). BPR projects aim to eliminate those non-value-adding activities to reduce costs. It requires a radical design of the business process to improve efficiency (Hammer and Champy, 1993). Kiran (2017) views BPR as a total quality management technique. In the application of this technique, various steps range from developing a vision and objectives to identifying, implementing, sustaining, and evaluating new processes (Kiran, 2017). Organization, technology, strategy, and humans are the four major areas subject to change with the BPR projects (Davenport, 1993). Among those areas, IT plays a crucial role as a change catalyzer in oil and gas distribution. IT can not only support reengineering processes but also be the reengineered process itself since it automates human-based processes and replaces an existing legacy system (AbdEllatif et al., 2018; Chan, 2000). Computer-based systems for ensuring supply chain traceability are developed with the BPR approaches. For example, Bevilacqua, Ciarapica, and Giacchetta (2009) developed a BPR to set up a computerized product traceability system in the food supply chain. Therefore, IT can also be the new product or process replacing manual operations and enables process innovation and collaboration within and between organizations (Chan, 2000; Kiran, 2017).

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On the other hand, IT projects are subject to a fair amount of failure risk. It is important to present new metrics and methods to evaluate project success. For example, Sharma and Chanda (2017) developed a risk quantification model based on Bayes networks to evaluate the risk exposure of a research and development project. It might become more difficult to overcome the uncertainty while measuring the project’s success, particularly in large-scale supply chains such as oil and gas distribution networks. The next section introduces the Bayesian approach that we implement to evaluate the project’s success in such a complex environment.

14.2.4 Poisson Processes for Evaluating the Project Success The Bayesian approach provides quite powerful business analytics tools to overcome the uncertainty phenomena existing in the decision environment. For example, Liu et al. (2021) apply a perfect Bayesian equilibrium and find that contract unobservability leads to opportunistic behavior of competing manufacturers in supply chains. The Poisson process is also a Bayesian nonparametric model that allows for probabilistically representing the functional relations between time and outputs received as count data. In this model, the rate parameter of the Poisson distribution is set by the Gaussian process (GP), an infinite-dimensional generalization of the Gaussian distribution (Martin, 2018). By defining a GP prior, the number of parameters increases as much as the data points. Therefore, these models can avoid overfitting problems by generating a family of distributions indexed by time instead of a single distribution to represent the sample space. Recently, the Poisson distribution has become substantially useful in supply chain studies for modeling lost sales, customer demand, hazard rates, delivery time quotations, replenishment lead time, etc. (Alvarez & Van Der Heijden, 2014; Fathi et al., 2021; Hammami et al., 2022; Zhou et al., 2017). Poisson processes are also used for defining an imperfect production system where defective items are detected by an inspection system (Aghsami et al., 2021). Although supply chain demand management literature has well studied the Poisson processes, there is still little or no attention paid to Bayesian machine learning approaches in various branches of supply chain studies (Babai et al., 2021). For instance, Filom et al. (2022) report the applications of machine learning methods in port operations. Results showed that Bayesian approaches have not found any room in business analytics with descriptive, predictive, or prescriptive components yet. In our study, to the best of our knowledge, this was the first time we employed probabilistic

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programming with a Bayesian Poisson process model to evaluate the effectiveness of a BPR project from a descriptive angle.

14.3 Case Study 14.3.1 The Company This study has been conducted in the Opet Oil Company, the second-largest onshore energy company in Turkey. The distribution network consists of 1,764 gas stations for land vehicles. It also has five terminals with a 1,083,955 m3 oil stock capacity. Opet facilitates not only essential fuel products but also caters to enriched products and services to its customers. Smart fleet management systems are one of those enriched product categories that enable firms to trace their vehicle fleets’ gasoline consumption. These technologies are both integrated into the focal company’s and customer firms’ ERP systems. Hence, they ensure long-term relationships with customers based on a trustworthy partnership (Opet, 2022). This project’s scope is delimited by reengineering the process of selling operations through smart fleet management systems.

14.3.2 Project Description As the focal company, Opet commits to a vast amount of interactions with industrial customers through the order fulfillment process. Selling operations are the backbone of these interactions which focus on executing the actual sales to customers. These operations are also very labor-intensive and open to failure. Our “in-house” project aims for a streamlined order-to-cash cycle in gasoline-filling transactions. Figure 14.1 summarizes the problem situation. In the standard procedure, real-time sell information flows to the focal company when the sales forces conduct the operation. Successful implications of these operations require a high level of integration between the main distributor and sub-distributors. The critical point is the process visibility along with the downstream echelons. However, several handicaps cause disruptions in the flows, such as human errors, internet outages, and device failures. When such disruptions occur, the system does not automatically update the customer limits. Thus the focal company cannot account for those sales unless the IT crew captures them later or the related firm declares the situation.

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Figure 14.1

Supply chain interfaces.

Figure 14.2

Order fulfillment reengineering project.

Figure 14.2 gives rise to the details of the process of reengineering. Traditionally, POS devices take payments after supplying the gasoline to the vehicle. The new process, however, redesigns the selling operation by eliminating the use of POS devices. Prior to gasoline supply, pump machines can recognize the vehicle and automatically transmit the limit updates to the automation system if the vehicle license is verified. After the refueling is completed, the cash register pulls out the receipt and approves the payment. Hence, payments are made more quickly thanks to bypassing the traditional and very manual order-to-cash cycle. Implementation of the new process requires the training of sales forces. To avoid the unintended consequences of the change, the focal company keeps the former process active simultaneously with the novel process for a particular time. The managerial board of the Opet Oil Company expects promising reductions in the transaction costs along with the downstream supply chain with this IT-centric BPR project. However, excess trust in technology solutions may lead to failure unless people properly engage with the new technology. Therefore, process monitoring is essential to evaluate the success of

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the relevant project. The following sections elaborate on the rest of the evaluation methodology.

14.3.3 Research Model This study employs a top-down approach to evaluate the BPR project’s success. Figure 14.3 depicts the flow of eight steps across three stages and

Figure 14.3

Research flowchart.

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relevant tools and sources to build up the model flow. The methodology is mainly composed of Poisson processes. We implement the sequential Monte Carlo (SMC) approach for conducting the inference operations as well as calculating the marginal likelihoods of the dichotomous models. PyMC3 and ArviZ are the main probabilistic programming libraries we use to conduct the analysis (Kumar et al., 2019; Salvatier et al., 2016). The codes are written in Python 3 environment through the Linux Ubuntu 21.04 operating system. The research flow starts with the problem definition. This study addresses the problem of evaluating the effectiveness of a BPR project in the oil and gas supply chain. While the above sections introduce the case company and give rise to the project details, the below sections present the rest of the research flow.

14.3.3.1 Conceptualization In the conceptualization, we develop the shadow sale phenomenon as the main performance metric of the case project, based on the transaction costs theory. Shadow sale refers to the failure of automatic data flows in the selling operations that are already completed in the downstream supply chains. It leads to direct and indirect operational costs as transaction cost theory explains. The conceptualization phase is comprised of qualitative and quantitative data collection endeavors. First, we conducted a semi-structured interview with two experts from the case company who have taken part in the project. We have solicited these experts to respond to nine open-ended questions to help resolve the project details. Then, the IT crew prepared the time series dataset involving the daily shadow sale frequencies collected from the countrywide gasoline stations. Assuming that s is an observed variable of the shadow sales and t is the time, we obtained the training set

D = {(ti , si ); i

, 0 1 means that Model 1 is more favorable than Model 2. Table 14.1 shows the intervals to interpret the ratios (Martin, 2018). To calculate BF, we are proportioning the relevant marginal likelihoods. Indeed, sampling from the full posterior of marginal likelihoods can be difficult because of the multiple minima problem. Sampling algorithms might focus on a unique peak but overlook the global extremes when regions of very low probabilities separate those peaks from each other (Martin, 2018). To avoid this problem, SMC starts sampling from the prior distribution and gradually increases the weight of steps drawn from the marginal likelihood. This technique is known as the tempering operation. Recall 1 = {r } and 2 = {b , e , l } , then BF is calculated as Eq. (14.13), where is the tempering parameter that the SMC algorithm automatically specifies. Here, {0 =

i0

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