The AI product playbook : strategies, skills, and frameworks for the AI-driven product manager / Marily Nika, Diego Granados.

Format:

Book

Author/Creator:

Nika, Marily, author.

Granados, Diego., author.

Language:

English

Subjects (All):

Product management.

Artificial intelligence.

Physical Description:

1 online resource (339 pages)

Place of Publication:

Newark : John Wiley & Sons, Incorporated, [2026]

Summary:

A comprehensive guide for aspiring and current AI product managers The AI Product Playbook: Strategies, Skills, and Frameworks for the AI-Driven Product Manager, by Dr.Marily Nika and Diego Granados, is a practical resource designed to empower product managers to effectively build, launch, and manage successful AI-powered products.

Contents:

Introduction

Part I Foundational AI/ML Concepts

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)

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

Unsupervised Learning: Discovering Hidden Patterns in Your Data

Reinforcement Learning: Learning Through Trial and Error

Generative AI: Powering a New Era of Language-Based Applications

The "Gotchas": A PM's Guide to LLM Limitations and Risks

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

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

Onward: Exploring the AI PM Roles

Part II AI PM Specializations

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

Onward: Architecting the AI Foundation

Chapter 6 AI-Builder PM: Architecting the Foundation of Intelligent Systems

Key Responsibilities: Building and Managing the AI Foundation

Required Skills and Knowledge: The AI-Builder PM's Technical and Strategic Toolkit

Illustrative Example: A Day in the Life of an AI-Builder PM

Challenges and Complexities

How the AI-Builder PM Interacts with Other Roles

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

Required Skills and Knowledge: The AI-Enhanced PM's Toolkit

Illustrative Example: A Day in the Life of an AI-Enhanced PM

Examples of AI Tools

How the AI-Enhanced PM Interacts with Other Roles

Skill Comparison: AI-Experiences PM, AI-Builder PM, and AI-Enhanced PM

Onward: From Theory to Action

Part III Connecting the Dots Between AI/ML Knowledge and PM Craft

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

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

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

Cultivating an AI-First Mindset

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

Important Considerations.

End-to-End Example-Predicting Churn in a Subscription Service

1. Identify the Business Goal: Defining the "Why"

2. Define the AI/ML Application and Solution

3. Identify Data Sources and Engineer Features: The Raw Materials

4. Select the Metrics: Defining Success

5. Establish Baseline Metrics: Setting the Starting Point

6. Conduct Model Training and Evaluation: Building and Testing the AI

7. Conduct A/B Testing: Measuring Real-World Impact

8. Calculate the Results and ROI: Quantifying the Value

9. Monitor and Maintain the Model for Long-Term Success

A/B Testing for AI and ML Projects: Validating Impact and Optimizing Performance

What Is A/B Testing (in a Nutshell)?

Why Is A/B Testing Especially Important for AI/ML?

How to Conduct A/B Testing for AI and ML: A Step-by-Step Guide

Key Considerations for AI/ML A/B Testing

Onward: From the Lab to a Live Product

Chapter 10 Building and Deploying AI Solutions: From Lab to Live

MLOps: The Key to Reliable and Scalable AI

Key Components of MLOps-The AI Production Line

CI/CD, IaC, and Collaboration: The Foundational Pillars of MLOps

Glossary of Key MLOps Terms

MLOps End-to-End Example: Churn Prediction in a Subscription Service (Product Manager's Perspective)

Onward: Building with Integrity

Chapter 11 Responsible AI and Ethical Considerations: Building AI with Integrity

Understanding AI Bias and Fairness: The Foundation of Responsible AI

Identifying Potential Biases: Where Bias Can Creep In

Mitigating Potential Biases: A Proactive Approach

Protected Classes and AI Fairness-Designing for Inclusion

What are Protected Classes?

Why Focus on Protected Classes? (The Legal and Ethical Imperative).

How Protected Classes Relate to AI Bias: The Mechanisms of Discrimination.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

ISBN:

1394352468

1394335660

9781394335664

OCLC:

1543514118