Hyperautomation with Generative AI: Learn how Hyperautomation and Generative AI can help you transform your business 9789355518590
Understand how to leverage Hyperautomation and Generative AI to accelerate Business Transformation Key Features ● Explo
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English Pages 812 Year 2023
- Author / Uploaded
- Navdeep Singh Gill
- Dr. Jagreet Kaur
- Suryakant
Table of contents :
Section I: Automation and Its Necessity
1. The Realism of Hyperautomation
Introduction
Structure
Objectives
What is Automation
What is Hyperautomation
Journey of Hyperautomation
High-level plan to automate business processes
Hyperautomation in Information Technology
Hyperautomation in banking
Hyperautomation in Human Resources
Hyperautomation use cases in manufacturing
Hyperautomation use cases in the retail industry
Important points about Hyperautomation
Benefits of Hyperautomation
Conclusion
Key facts
Key terms
Questions
2. Existence of Different Automations
Introduction
Structure
Objectives
Different types of automation
Fixed automation
Programmable automation
Flexible automation
Global and specific automations
Integrated automation
Computer-Aided Manufacturing
Robotics Process Automation
Cognitive intelligence
Conversational automation
Robotic Process Automation
Features of Robotic Process Automation
Why RPA
The problem with humans
Use cases of RPA
Challenges Of RPA
Robots, bots, and cobots
Cobots
Different tools for cobots
Different industries for cobots
Robots
Types of robots
How do robots function
Uses of robots
Bots
How bots work
Types of bots
Advantages of bots
Disadvantages of bots
Coexistence of humans and robots
Why is RPA a boon, and not a curse
The functionality of RPA
RPA in telecom industry
Healthcare
Banking and financial services
Retail sector
Supply chain management
Benefits of RPA
Conclusion
Key facts
Key terms
Questions
3. Fundamentals of RPA Tools and Platforms
Introduction
Structure
Objectives
UiPath - Automation platform
Features of UiPath
UiPath components
UiPath architecture
The client and server side
Three layers
Advance feature of UiPath - AI Fabric
About AI fabric
Key features of AI center
Components of AI Center
Usage guide of UiPath
Building a workflow in UiPath Studio
Applications of UiPath
Sales
Banking
The benefit of UiPath
Automation anywhere with IQ Bots
Benefits of IQ Bots
Solution using IQ Bots
Purchase orders
Insurance
Life sciences
Healthcare
IQ Bots
Usage guide of Automation Anywhere
Setup Automation Anywhere
Create first bot in Automation Anywhere
Use case of IQ Bots
Recruitment process
Invoice processing
Inventory reconciliation process
Blue Prism and Intelligent Robotic Process Automation
What is Blue Prism
RPA Blue Prism: Blue Prism components
Object Studio
Process Studio
Application Modeller
Control room
Features of Blue Prism
Plug and play access
Secure
Work queues
Robust and scalable
Multi-team environment
Execution intelligence
Tesseract OCR
Usage guide of Blue Prism
Advantages of Blue Prism
Case study of Coca-cola
Company objectives
Problems faced by company
Solution
Business impact
Conclusion
Key facts
Key terms
Questions
4. Amalgam of Hyperautomation and RPA
Introduction
Structure
Objectives
Hyperautomation
Key units of Hyperautomation
How does Hyperautomation work
Advantages of Hyperautomation
Challenges in Hyperautomation
Why should businesses implement Hyperautomation
Why is Hyperautomation important
Hyperautomation use cases
Hyperautomation in UiPath
Hyperautomation vs RPA
RPA in different domains
RPA in telecommunications
RPA in healthcare
RPA in insurance
RPA in Information Technology
RPA in banking
RPA in human resources
RPA use cases in manufacturing
RPA use cases in the retail industry
Working on cognitive computing
Why RPA and why cognitive automation
Benefits of cognitive automation
Evolving from Robotic Process Automation (RPA) to Cognitive automation
Why is it necessary
Comparison based on benefits
Comparison based on functionality
Case studies of Hyperautomation
Case studies of RPA
RPA in finance and accounting
Adoption of RPA in industries
Future of Hyperautomation
Hyperautomation vs Intelligent Automation
What is Intelligent Automation
Versatile technologies associated with Intelligent Automation
Why do we need Intelligent Automation
Top barriers to efficient adoption of Intelligent Automation
Reasons behind the failure of Automation projects
How intelligent automation empowers enterprises to transform business processes
Best practices to build enterprise automation strategy
Need for Hyperautomation
Intelligent Automation vs. Hyperautomation
Conclusion
Key facts
Key terms
Questions
Section II: Evolution of Automation to Hyperautomation via RPA
5. Devising Hyperautomation Solutions
Introduction
Structure
Objectives
Ingredients of the recipe
First ingredient: Know the problem statement
Second ingredient: Group of manual or semi-automated processes
Third ingredient: A dedicated team
Fourth ingredient: Infrastructure
Fifth ingredient: Technologies
Eco-system of Hyperautomation
The blueprint of Hyperautomation
Steps of the recipe
Road to Hyperautomation
Dedicated workflow process for Hyperautomation
Major steps of Hyperautomation
Identify desired business outcomes
Optimizing the process for scalability
Research for tools
Create a strategy
Build a team
Document everything
Conduct an audit
Set up the right tech stack
Continuous improvement
Key gains using Hyperautomation
Data sharing
Real-time information access
Productivity
Increase work automation
Automated processes
Fosters team collaboration
Increase productivity
Advanced analytics and insights
Increases business agility
Increased employee engagement and satisfaction
Improved data accessibility and storage
Augments ROI
Be future ready
Problems and Hyperautomation as its solution
Fully digitalized processes
Accounts Payable
Claims handling
Customer service operations
Banking customer onboarding
Anti-Money laundering
Redaction for privacy preservation
Processes triggered by incoming documents or email
Use cases: Hyperautomation tech as a solution
Hyperautomation in finance
Hyperautomation in healthcare
Hyperautomation in the E-commerce industry
Hyperautomation in QA industry
Hyperautomation in continuous testing
Challenges of implementing Hyperautomation
Conclusion
Key facts
Key terms
Questions
6. Amalgam of Hyperautomation and Artificial Intelligence
Introduction
Structure
Objectives
Artificial Intelligence
Types of Artificial Intelligence
Reactive AI
Limited memory AI
Theory of mind AI
Self-aware AI
Working of AI
Machine Learning
Deep Learning
Issues in AI
Biases
Control and morality of AI
Privacy
Power balance
Ownership
Environmental impact
Humanity
Applications of Artificial Intelligence
Technologies including AI
Artificial Intelligence: A boon or a curse
Advantages of Artificial Intelligence
Disadvantages of Artificial Intelligence
The past, present, and future of AI
Past of AI
Present of AI
Future of AI
Combination of RPA and AI: Hyperautomation
Applications of AI and RPA
What is Hyperautomation
Benefits of Hyperautomation
Challenges and limitations of Hyperautomation
Why is Hyperautomation important
How Hyperautomation works
Eco-system of Hyperautomation
Conclusion
Key facts
Key terms
Questions
7. Bridging AI with Humans
Introduction
Structure
Objectives
AI and its ethical issues
Addressing ethical issues
Making AI more responsible
The world of AI
Interpretation of responsible AI
Transparent AI
Explainable AI
Configurable AI
The need to make AI responsible
Principles of responsible AI
Implementation and design
Benefits
Use cases for responsible AI
Trust AI and its principles
Problem of trust in AI
What does it take to trust AI
Measuring AI trust
Building trustworthy AI
Explainability
Integrity
Reproducibility
Conscious development
Regulations
Bias and fairness
Transparency
Sustainability
Lack of understanding and ways to bridge the gap
Generating and communicating counterfactuals
Bias mitigation
Uncertainty quantification with explanations
Gaining trust in AI decisions
AI principles
Fairness and bias
Trust and transparency
Accountability
Social benefit
Privacy and security
Built and tested for safety
Maintain high standards of scientific excellence
Conclusion
Key facts
Key terms
Questions
8. Impact of Machine Learning with Hyperautomation
Introduction
Structure
Objectives
Machine Learning
Working of Machine Learning
Different types of Machine Learning
Supervised learning
Unsupervised learning
Advantages of Machine Learning
Point to look out for while implementing ML
Challenges in Machine Learning
Deep learning and its fundamentals
Working of deep learning
Input layer
Hidden layer
Output layer
Key concepts in deep learning
Types of Neural Networks
Artificial Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Long short-term memory networks
Machine Learning Operation
What is MLOps
Challenges with MLOps
Benefits of MLOps
Working of MLOps
MLOps level 0
MLOps level 1
MLOps level 2
ModelOps and its applications
ModelOps lifecycle management
ModelOps vs MLOps vs DevOps
Why is ModelOps important
Use cases of ModelOps
Applications of ModelOps
ModelOps platforms in the market
Challenges in ModelOps implementation
Future scope for ModelOps
Role of Machine Learning in Hyperautomation
Benefits of Machine Learning in Hyperautomation
Conclusion
Key facts
Key terms
Questions
9. Operationalizing Hyperautomation
Introduction
Structure
Objectives
Hyperautomation as a solution to the busyness of business processes
The need for businesses to scale to Hyperautomation
Assiduity in different business sectors and its solution with Hyperautomation
Manufacturing sector
Banking and finance industry
Insurance industry
BPO and customer service center industry
Healthcare industry
Scaling Hyperautomation solutions
Need to scale Hyperautomation solutions
Assessing readiness for scaling
Analysing the automation’s current state
Finding opportunities for Hyperautomation scale-up
Developing a scalable Hyperautomation strategy
Scaling Robotic Process Automation
Scaling process discovery and mining
Integrating intelligent automation technologies
Measuring and monitoring automation performance
Benefits and challenges of scaling Hyperautomation solutions
Overcoming scalability issues
Architecture of Hyperautomation
Key elements of architecture of Hyperautomation
Hyperautomation frameworks
Challenges for Hyperautomation
Tools for Hyperautomation
Vendors for Hyperautomation
Conclusion
Key facts
Key terms
Questions
10. Successful Use Cases of Hyperautomation
Introduction
Structure
Objectives
Case study 1
Challenge or problem statement
Solution
Diagnostics and monitoring
Configuration, change and auto remediation
Integration of incident management with e-helpline
Collaboration and ChatOps for critical incident management
Business impact
Hyperautomation ecosystems
Delivery approach for Hyperautomation
Case study 2
Organizational overview
The problem
Manual and time-consuming processes
Compliance and regulatory requirements
Customer experience and expectations
Data fragmentation and Silos
The solution
Results and benefits
Case study 3
Hyperautomation in healthcare processes
Transactions
Voice
Key steps for successful implementation of Hyperautomation
Vision
Plan
Evaluate
Support
Track
Results
Impact of automation on workforce
Benefits of leveraging Hyperautomation solutions
Conclusion
Key facts
Key terms
Questions
Section III: Emergence of Generative AI and Its Collaboration with Hyperautomation
11. Generative AI and Hyperautomation
Introduction
Structure
Objectives
Introduction to Generative AI
Difference between Generative AI and Traditional AI
What can Generative AI do
Types of Generative AI models
Text models
Multimodal models
Supervised learning strikes back
Developing Generative AI models
Evaluating Generative AI models
Working of text-based machine learning models
Benefits of Generative AI
Limitations of Generative AI
Output produced by a Generative AI model
Collaboration of Generative AI and Hyperautomation
Content generation and automation
Design and prototyping
Data analysis and decision-making
Workflow optimization and automation
Process automation and optimization
Adaptive learning and continuous improvement
Challenges and considerations
Future considerations
Use case of Generative AI with Hyperautomation
Problem statement
Generative AI with Hyperautomation
Why use Generative AI with Hyperautomation
Solution approach for using Generative AI with Hyper automation for Contact centers
Prerequisites
What a generative AI and Hyperautomation are helping contact centers
Contact centers using Generative AI with Hyperautomation
Considerations for implementing Generative AI with Hyperautomation
Performance and scalability in using Generative AI with Hyperautomation
Collaboration between humans and machines
Business outcome of using Generative AI with Hyperautomation
Conclusion
Key facts
Key terms
Questions
Index
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