Hyperautomation with Generative AI: Learn how Hyperautomation and Generative AI can help you transform your business 9789355518590

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