Introduction to Prescriptive AI: A Primer for Decision Intelligence Solutioning with Python

Description

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. 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