Hyperautomation with Generative AI: Learn how Hyperautomation and Generative AI can help you transform your business

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