The AI Product Playbook

Name: The AI Product Playbook
Price: 21 USD
Availability: InStock
ISBN: 9781394335657

Strategies, Skills, and Frameworks for the AI-Driven Product Manager

by Marily Nika, Diego Granados

1st Edition

Publisher: John Wiley & Sons P&T

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Book Details

Print ISBN	9781394335657
eText ISBN	9781394335664
Publisher	John Wiley & Sons P&T
Publishing Year	2025
Edition	1st Edition
Language	English
Pages	336

The AI Product Playbook, 1st Edition, is a textbook focused on artificial intelligence product management. The text provides strategic methods for evaluating machine learning capabilities and aligning technical initiatives with organizational goals.

The material examines fundamental machine learning concepts, spanning supervised learning, unsupervised learning, reinforcement learning, and generative AI. The text guides readers through the process of discovering viable artificial intelligence opportunities, organizing cross-functional collaboration, and calculating financial return on investment for machine learning projects.

To support active learning and implementation, the textbook includes embedded features such as interactive exercises, action plans, checklists, templates, and review quizzes. These built-in tools provide direct practice for readers applying theoretical frameworks to practical artificial intelligence product management tasks.

Chapter 1: Artificial Intelligence and Machine Learning: What Every Product Manager Needs to Know
- • AI vs. ml
- • Why This Matters to a PM
- • Key Differences Between AI and ml
- • Common Misconceptions for PMs: Myths vs. Reality
- • Your Glossary as a PM
- • Grounding the Concepts: Real-World AI in Action
- • The AI PM’s Guiding Principles
- • Chapter Summary and Key Takeaways
- • Key Takeaways
- • Onward: Peeking Under the Hood
Chapter 2: How Machine Learning Models Learn: A Peek Under the Hood
- • The Learning Process: Training, Validation, and Testing
- • How Models Learn: An Example with k-Nearest Neighbors (k-NN)
- • Applying k-NN (with k=1):
- • Another Example: Testing an Unknown Fruit
- • Evaluating Model Performance
- • The Confusion Matrix: A Foundation for Understanding
- • Key Classification Metrics (and Their PM Implications)
- • The Precision-Recall Trade-Off
- • Choosing the Right Metric
- • Overfitting and Underfitting: Striking the Right Balance for Real-World Performance
- • Overfitting: Memorizing Instead of Learning
- • Underfitting: Missing the Forest for the Trees
- • Visual Analogy: Fitting a Curve
- • Finding the Sweet Spot: Generalization
- • The PM’s Role
- • Human-in-the-Loop: Blending AI Power with Human Expertise
- • What Is Human-in-the-Loop?
- • Why HITL Is Essential for Product Managers (and Their Products)
- • How to Implement HITL (PM Considerations)
- • Chapter Summary and Key Takeaways
- • Key Takeaways
- • Onward: Understanding the Broader Process
Chapter 3: The Big Picture: AI, ML, and You
- • Understanding the Relationship Between AI, ML, and Product Goals
- • Types of Machine Learning: Understanding the Spectrum of Learning
- • Supervised Learning: Guiding the Model with Labeled Examples
- • Technical Deep Dive: How Supervised Learning Models Learn from Labeled Data
- • Critical Considerations for Product Managers
- • Unsupervised Learning: Discovering Hidden Patterns in Your Data
- • Technical Deep Dive: How Unsupervised Learning Models Discover Patterns
- • Reinforcement Learning: Learning Through Trial and Error
- • Technical Deep Dive: How Reinforcement Learning Agents Learn Optimal Policies
- • The Learning Process: Exploration, Exploitation, and Q-Learning
- • Generative AI: Powering a New Era of Language-Based Applications
- • Technical Deep Dive: How LLMs Understand and Generate Language
- • The “Gotchas”: A PM’s Guide to LLM Limitations and Risks
- • Navigating the Nuances of Generative AI: Understanding GenAI Evaluations— Ensuring Quality and Trust
- • Prompt Engineering: The Art and Science of Talking to AI
- • Types of Machine Learning: A Recap
- • Introduction to Neural Networks and Deep Learning: The Engines of Complex Pattern Recognition
- • Neural Networks: Mimicking the Brain’s Connections (But Not Really)
- • How Neural Networks Learn: Adjusting the Connections
- • Technical Deep Dive: The Mechanics of Neural Networks and Deep Learning
- • Challenges in Deep Learning
- • Chapter Summary and Key Takeaways
- • Key Takeaways
- • Onward: Mapping the Process
Chapter 4: The AI Lifecycle
- • Problem Definition and Business Understanding: The “Why”
- • Data Collection and Exploration: Understanding Your Ingredients
- • Data Preprocessing: Preparing the Ingredients
- • Feature Engineering: Crafting the Inputs for Success
- • Model Selection and Training: Choosing the Right Algorithm
- • Model Evaluation and Tuning: Ensuring Quality
- • Model Deployment and Monitoring: Bringing AI to Life (and Keeping It Healthy)
- • Retraining and Maintenance: Keeping Your Model Up-to-Date
- • Chapter Summary and Key Takeaways
- • Key Takeaways
- • Onward: Exploring the AI PM Roles
Chapter 5: AI-Experiences PM: Shaping User Interaction with AI
- • Key Responsibilities: Shaping the AI User Experience
- • Day-to-Day Activities
- • Required Skills and Knowledge: The AI-Experiences PM Toolkit
- • Core Product Management Craft and Practices
- • Engineering Foundations for PMs
- • Essential Leadership and Collaboration Skills
- • AI Lifecycle and Operational Awareness
- • Illustrative Example: A Day in the Life of an AI-Experiences PM
- • Challenges and Complexities
- • How the AI-Experiences PM Interacts with Other Roles
- • Chapter Summary and Key Takeaways
- • Key Takeaways
- • Onward: Architecting the AI Foundation
Chapter 6: AI-Builder PM: Architecting the Foundation of Intelligent Systems
- • Key Responsibilities: Building and Managing the AI Foundation
- • Day-to-Day Activities
- • Required Skills and Knowledge: The AI-Builder PM’s Technical and Strategic Toolkit
- • Core Product Management Craft and Practices
- • Engineering Foundations for PMs
- • Essential Leadership and Collaboration Skills
- • AI Lifecycle and Operational Awareness
- • Illustrative Example: A Day in the Life of an AI-Builder PM
- • Challenges and Complexities
- • How the AI-Builder PM Interacts with Other Roles
- • Chapter Summary and Key Takeaways
- • Key Takeaways
- • Onward: Supercharging the PM Workflow
Chapter 7: AI-Enhanced PM: Supercharging Product Management with AI
- • Key Responsibilities: Augmenting PM Workflows and Decision-Making with AI
- • Day-to-Day Activities
- • Required Skills and Knowledge: The AI-Enhanced PM’s Toolkit
- • Core Product Management Craft and Practices
- • Engineering Foundations for PMs
- • Essential Leadership and Collaboration Skills
- • AI Lifecycle and Operational Awareness
- • Illustrative Example: A Day in the Life of an AI-Enhanced PM
- • Examples of AI Tools
- • Challenges and Complexities
- • How the AI-Enhanced PM Interacts with Other Roles
- • Skill Comparison: AI-Experiences PM, AI-Builder PM, and AI-Enhanced PM
- • Chapter Summary and Key Takeaways
- • Key Takeaways
- • Onward: From Theory to Action
Chapter 8: Identifying and Evaluating AI Opportunities
- • Uncovering Potential Use Cases—Mining Your Product for AI Gold
- • Recognizing Data-Rich Problem Areas
- • Analyzing Existing Data Sources
- • Asking the Right Questions
- • AI/ML Capability Matching: Connecting Problems to Solutions
- • Understanding Your AI/ML Toolkit: Key Capabilities
- • Matching Capabilities to Problems: A Practical Approach
- • Feature: Search Functionality in a Document Management System
- • Feature: Customer Support Chatbot
- • Feature: Reporting Dashboard for Marketing Campaigns
- • Finding AI Opportunities in the User Journey
- • Mapping the User Journey: Charting the Course
- • Identifying Pain Points and Opportunities: The AI Detective Work
- • Applying AI/ML to Enhance Touchpoints: The Transformation
- • Feature Enhancement Through AI/ML— Transforming Existing Functionality
- • Identifying Enhancement Opportunities: Finding the Weak Spots
- • Applying AI/ML to Enhance Features: The Transformation Process
- • Feature: Standard Search Functionality
- • Feature: Data Entry Form
- • Feature: Reporting Dashboard
- • Proactive Product Management—Anticipating User Needs with AI
- • Understanding the Power of Prediction and Automation
- • Key Areas for Predictive and Automation Opportunities
- • Identifying Opportunities: A Practical Approach
- • Responsible AI Foundations—Ethical and Feasibility Considerations
- • Ethical Considerations: The “Do No Harm” Principle
- • Feasibility Considerations: Can We Actually Build This?
- • Practical Ideation Techniques for AI/ML Use Cases—Thinking Like an AI-First Product Manager
- • Ideation Techniques: Unleashing Your AI Creativity
- • “AI Feature Storming”: The Brain Dump
- • “AI Scenario Planning”: Walking in the User’s Shoes
- • “Data Opportunity Mapping”: Leveraging Your Data Assets
- • “AI Capability Alignment”: The Matching Game
- • “AI-Powered Feature Reverse Engineering”: Learning from Others
- • Cultivating an AI-First Mindset
- • Chapter Summary and Key Takeaways
- • Key Takeaways
- • Onward: Measuring the Value of Your Ideas
Chapter 9: ROI Calculation for AI Projects: Measuring the Impact and Demonstrating Value
- • From Model Performance to Business Impact: A PM’s Guide to AI Metrics
- • Defining AI/ML-Specific Metrics: The Foundation for Measuring ROI
- • The Importance of Baselines: Knowing Where You Started
- • Understanding the Confusion Matrix: Decoding Classification Performance
- • Key Performance Metrics for AI/ML Models: Beyond the Confusion Matrix
- • Context Matters: Selecting the Right Metrics for Your AI/ML Application
- • 1. Define Your Business Goals