Gain a working knowledge of prescriptive AI, its history, and its current and future trends. This book will help you evaluate different AI-driven predictive analytics techniques and help you incorporate decision intelligence into your business workflow through real-world examples.
Author(s): Akshay Kulkarni, Adarsha Shivananda, Avinash Manure
Publisher: Apress
Year: 2023
Language: English
Pages: 200
Table of Contents
About the Authors
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1: Decision Intelligence Overview
Types of AI
Decision Intelligence
Decision Intelligence History
Challenges in AI Adoption
How Can DI Help Bridge the Gap Between AI and Business?
The Need for Decision Intelligence
The Evolution of Decision-Making
Challenges
Applications
Understanding Where Decision Intelligence Fits Within the AI Life Cycle
Decision Intelligence Methodologies
Some Potential Pros and Cons of DI
Examples of How Companies Are Leveraging DI
Conclusion
Chapter 2: Decision Intelligence Requirements
Why Do AI Projects Fail?
DI Requirements Framework
Planning
Approach
Approval Mechanism/Organization Alignment
Key Performance Indicators
Define Clear Metrics
Value
Return on Investment
Value per Decision
Consumption of the AI Predictions
Conclusion
Chapter 3: Decision Intelligence Methodologies
Decision-Making
Types of Decision-Making
Individual vs. Group Decision-Making
Single- vs. Multiple-Criterion Decision-Making
Strategic, Tactical, and Operational Decision-Making
Decision-Making Process
Decision-Making Process Example
Decision-Making Methodologies
Human-Only Decision-Making
Random Decisions
Morality/Ethics Based
Experience Based
Authority Based
Consensus Based
Voting Based
Threshold Based
First Acceptable Match Based
Optimization/Maximization Based
Cognitive Bias Due to Human-Only Decision-Making
Human-Machine Decision-Making
Instruction/Rule-Based Systems
Mathematical Models
Probabilistic Models
AI-Based Models
Machine-Only Decision-Making
Autonomous Systems
Conclusion
Chapter 4: Interpreting Results from Different Methodologies
Decision Intelligence Methodology: Mathematical Models
Linear Models
Nonlinear Models
Decision Intelligence Methodology: Probabilistic Models
Markov Chain
Decision Intelligence Methodology: AI/ML Models
Conclusion
Chapter 5: Augmenting Decision Intelligence Results into the Business Workflow
Challenges
Workflow
Decision Intelligence Apps
How and Why?
User-Friendly Interfaces
Augmenting AI Predictions to Business Workflow
Connect to Business Tools
Map the Data
Conclusion
Chapter 6: Actions, Biases, and Human-in-the-Loop
Key Ethical Considerations in AI
Actions, Biases, and Human-in-the-Loop
Cognitive Biases
Why Is Detecting Bias Important?
Types
What Happens If Bias Is Ignored?
Bias Detection
What Do Bias Tools Do?
Incorporation of Feedback Through Human Intervention
How to Build HITL Systems?
Example: Customer Churn
Conclusion
Chapter 7: Case Studies
Case Study 1: Telecom Customer Churn Management
Case Study 2: Mobile Phone Pricing/Configuration Strategy
Conclusion
Index
