Introduction xix Part I Foundational AI/ML Concepts 1 Chapter 1 Artificial Intelligence and Machine Learning: What Every Product Manager Needs to Know 3 AI vs. ml 4 Why This Matters to a PM 4 Key Differences Between AI and ml 5 Common Misconceptions for PMs: Myths vs. Reality 7 Your Glossary as a PM 7 Grounding the Concepts: Real-World AI in Action 10 The AI PM''s Guiding Principles 14 Chapter Summary and Key Takeaways 16 Key Takeaways 16 Onward: Peeking Under the Hood 17 Chapter 2 How Machine Learning Models Learn: A Peek Under the Hood 19 The Learning Process: Training, Validation, and Testing 20 How Models Learn: An Example with k-Nearest Neighbors (k-NN) 22 Applying k-NN (with k=1): 23 Another Example: Testing an Unknown Fruit 26 Evaluating Model Performance 27 The Confusion Matrix: A Foundation for Understanding 27 Key Classification Metrics (and Their PM Implications) 28 The Precision-Recall Trade-Off 29 Choosing the Right Metric 30 Overfitting and Underfitting: Striking the Right Balance for Real-World Performance 31 Overfitting: Memorizing Instead of Learning 31 Underfitting: Missing the Forest for the Trees 32 Visual Analogy: Fitting a Curve 32 Finding the Sweet Spot: Generalization 33 The PM''s Role 33 Human-in-the-Loop: Blending AI Power with Human Expertise 34 What Is Human-in-the-Loop? 34 Why HITL Is Essential for Product Managers (and Their Products) 35 How to Implement HITL (PM Considerations) 37 Chapter Summary and Key Takeaways 38 Key Takeaways 39 Onward: Understanding the Broader Process 39 Chapter 3 The Big Picture: AI, ML, and You 41 Understanding the Relationship Between AI, ML, and Product Goals 41 Types of Machine Learning: Understanding the Spectrum of Learning 44 Supervised Learning: Guiding the Model with Labeled Examples 46 Technical Deep Dive: How Supervised Learning Models Learn from Labeled Data 48 Critical Considerations for Product Managers 54 Unsupervised Learning: Discovering Hidden Patterns in Your Data 55 Technical Deep Dive: How Unsupervised Learning Models Discover Patterns 57 Critical Considerations for Product Managers 60 Reinforcement Learning: Learning Through Trial and Error 61 Technical Deep Dive: How Reinforcement Learning Agents Learn Optimal Policies 63 The Learning Process: Exploration, Exploitation, and Q-Learning 65 Critical Considerations for Product Managers 67 Generative AI: Powering a New Era of Language-Based Applications 67 Technical Deep Dive: How LLMs Understand and Generate Language 69 Critical Considerations for Product Managers 72 The "Gotchas": A PM''s Guide to LLM Limitations and Risks 73 Navigating the Nuances of Generative AI: Understanding GenAI Evaluations-- Ensuring Quality and Trust 75 Prompt Engineering: The Art and Science of Talking to AI 84 Types of Machine Learning: A Recap 89 Introduction to Neural Networks and Deep Learning: The Engines of Complex Pattern Recognition 92 Neural Networks: Mimicking the Brain''s Connections (But Not Really) 92 How Neural Networks Learn: Adjusting the Connections 94 Technical Deep Dive: The Mechanics of Neural Networks and Deep Learning 95 Challenges in Deep Learning 98 Chapter Summary and Key Takeaways 99 Key Takeaways 99 Onward: Mapping the Process 100 Chapter 4 The AI Lifecycle 101 Problem Definition and Business Understanding: The "Why" 102 Data Collection and Exploration: Understanding Your Ingredients 103 Data Preprocessing: Preparing the Ingredients 104 Feature Engineering: Crafting the Inputs for Success 104 Model Selection and Training: Choosing the Right Algorithm 105 Model Evaluation and Tuning: Ensuring Quality 106 Model Deployment and Monitoring: Bringing AI to Life (and Keeping It Healthy) 107 Retraining and Maintenance: Keeping Your Model Up-to-Date 108 Chapter Summary and Key Takeaways 109 Key Takeaways 109 Onward: Exploring the AI PM Roles 110 Part II AI PM Specializations 111 Chapter 5 AI-Experiences PM: Shaping User Interaction with AI 113 Key Responsibilities: Shaping the AI User Experience 114 Day-to-Day Activities 117 Required Skills and Knowledge: The AI-Experiences PM Toolkit 120 Core Product Management Craft and Practices 120 Engineering Foundations for PMs 121 Essential Leadership and Collaboration Skills 122 AI Lifecycle and Operational Awareness 123 Illustrative Example: A Day in the Life of an AI-Experiences PM 124 Challenges and Complexities 127 How the AI-Experiences PM Interacts with Other Roles 129 Chapter Summary and Key Takeaways 134 Key Takeaways 134 Onward: Architecting the AI Foundation 135 Chapter 6 AI-Builder PM: Architecting the Foundation of Intelligent Systems 137 Key Responsibilities: Building and Managing the AI Foundation 138 Day-to-Day Activities 141 Required Skills and Knowledge: The AI-Builder PM''s Technical and Strategic Toolkit 144 Core Product Management Craft and Practices 145 Engineering Foundations for PMs 146 Essential Leadership and Collaboration Skills 147 AI Lifecycle and Operational Awareness 148 Illustrative Example: A Day in the Life of an AI-Builder PM 149 Challenges and Complexities 152 How the AI-Builder PM Interacts with Other Roles 154 Chapter Summary and Key Takeaways 156 Key Takeaways 157 Onward: Supercharging the PM Workflow 158 Chapter 7 AI-Enhanced PM: Supercharging Product Management with AI 159 Key Responsibilities: Augmenting PM Workflows and Decision-Making with AI 160 Day-to-Day Activities 162 Required Skills and Knowledge: The AI-Enhanced PM''s Toolkit 165 Core Product Management Craft and Practices 165 Engineering Foundations for PMs 166 Essential Leadership and Collaboration Skills 167 AI Lifecycle and Operational Awareness 168 Illustrative Example: A Day in the Life of an AI-Enhanced PM 169 Examples of AI Tools 172 Challenges and Complexities 173 How the AI-Enhanced PM Interacts with Other Roles 175 Skill Comparison: AI-Experiences PM, AI-Builder PM, and AI-Enhanced PM 177 Chapter Summary and Key Takeaways 184 Key Takeaways 185 Onward: From Theory to Action 185 Part III Connecting the Dots Between AI/ML Knowledge and PM Craft 187 Chapter 8 Identifying and Evaluating AI Opportunities 189 Uncovering Potential Use Cases--Mining Your Product for AI Gold 189 Recognizing Data-Rich Problem Areas 190 Analyzing Existing Data Sources 192 Asking the Right Questions 193 AI/ML Capability Matching: Connecting Problems to Solutions 194 Understanding Your AI/ML Toolkit: Key Capabilities 195 Matching Capabilities to Problems: A Practical Approach 200 Feature: Search Functionality in a Document Management System 200 Feature: Customer Support Chatbot 201 Feature: Reporting Dashboard for Marketing Campaigns 201 Finding AI Opportunities in the User Journey 202 Mapping the User Journey: Charting the Course 202 Identifying Pain Points and Opportunities: The AI Detective Work 204 Applying AI/ML to Enhance Touchpoints: The Transformation 205 Feature Enhancement Through AI/ML-- Transforming Existing Functionality 208 Identifying Enhancement Opportunities: Finding the Weak Spots 209 Applying AI/ML to Enhance Features: The Transformation Process 210 Feature: Standard Search Functionality 212 Feature: Data Entry Form 212 Feature: Reporting Dashboard 212 Proactive Product Management--Anticipating User Needs with AI 213 Understanding the Power of Prediction and Automation 213 Key Areas for Predictive and Automation Opportunities 214 Identifying Opportunities: A Practical Approach 216 Responsible AI Foundations--Ethical and Feasibility Considerations 217 Ethical Considerations: The "Do No Harm" Principle 217 Feasibility Considerations: Can We Actually Build This? 220 Practical Ideation Techniques for AI/ML Use Cases--Thinking Like an AI-First Product Manager 221 Ideation Techniques: Unleashing Your AI Creativity 222 "AI Feature Storming": The Brain Dump 222 "AI Scenario Planning": Walking in the User''s Shoes 223 "Data Opportunity Mapping": Leveraging Your Data Assets 223 "AI Capability Alignment": The Matching Game 224 "AI-Powered Feature Reverse Engineering": Learning from Others 225 Cultivating an AI-First Mindset 226 Chapter Summary and Key Takeaways 226 Key Takeaways 227 Onward: Measuring the Value of Your Ideas 227 Chapter 9 ROI Calculation for AI Projects: Measuring the Impact and Demonstrating Value 229 From Model Performance to Business Impact: A PM''s Guide to AI Metrics 229 Defining AI/ML-Specific Metrics: The Foundation for Measuring ROI 230 The Importance of Baselines: Knowing Where You Started 230 Understanding the Confusion Matrix: Decoding Classification Performance 231 Key Performance Metrics for AI/ML Models: Beyond the Confusion Matrix 233 Context Matters: Selecting the Right Metrics for Your AI/ML Application 237 1. Define Your Business Goals (and Connect Them to User Needs) 237 2. Consider.