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AI for Product Managers: Leverage Artificial Intelligence to Build Great Products
Paperback
Business GeneralGeneral Computers
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ISBN13: 9798990386631
Publisher: 5d Vision Publishing
Published: Dec 1 2025
Pages: 306
Weight: 0.91
Height: 0.64 Width: 6.00 Depth: 9.00
Language: English
Publisher: 5d Vision Publishing
Published: Dec 1 2025
Pages: 306
Weight: 0.91
Height: 0.64 Width: 6.00 Depth: 9.00
Language: English
AI is reshaping industries and products: This book helps Product Managers understand how to use AI tools to plan, design, and deliver great products. It explains the AI stack and strategic frameworks for integrating AI features into products; how to use GenAI to perform customer discovery, market research, and prototyping; and how the Product Development Life Cycle and the Model Development Life Cycle intersect when creating AI-driven products. This is not a theoretical textbook based on research: it is a practical guide for Product Managers to learn what AI can do and how to implement AI technologies into their products. Various use cases illustrate real-life applications. Practical frameworks are presented to help make decisions about which type of AI to use, what cost structure and business model to adopt, and which key metrics to track to ensure value is delivered to customers. AI does not replace Product Managers: it expands and empowers them. Traditional Product Management practices are still important. Product Managers don't need to become data scientists by building complex data models that drive AI algorithms. With this book they will learn the foundational aspects needed to make informed decisions and work more closely with their engineering teams.List of ChaptersPART I: UNDERSTANDING AI'S FOUNDATIONS
AI Fundamentals for Product Managers
Exploring the AI Stack and Your Strategic Options
The Data Foundation (Why Your Data Strategy Matters More Than Your Model Choice) PART II: USING AI TO ACCELERATE YOUR PM JOB
How AI Transforms the PM Job and the PDLC
GenAI-Powered Customer Discovery
GenAI for Market Research
GenAI for Product Ideation and Validation PART III: THE ART OF BUILDING AI PRODUCTS
AI Product Sense
The AI Product Development Process
Choosing the Right AI Approach
Quality, Testing, and Evals
Metrics for AI Products
Cost Structure of AI Products PART IV: FOSTERING AN AI CULTURE IN THE ORGANIZATION
Transforming the Organization
Driving the AI AdoptionBy the end of this book, the reader will understand: - The different types of AI and how to choose between them
- How to use AI / GenAI to augment and accelerate the PM job
- The differences in building an AI product compared to a traditional software product
- The relationship between the PDLC (Product Development Life Cycle) and the MDLC (Model Development Life Cycle)
- How to drive the AI adoption within the organization using VSM (Value Stream Mapping) and the 21 AI Principles
- How to leverage AI prompts for customer discovery, market research, and solution validation of new product ideasAnd the reader will be able to: - Explain what benefits AI delivers and distinguish between Accelerate, Expand, and Simplify opportunities
- Work more effectively with the data and engineering teams while building an AI-powered product
- Use AI tools to accelerate, expand, and simplify their job
- Use the 3 Dimensions of AI Product to map the benefits of a product
- Save weeks of time by using the right tools at the right moment in the PDLC
Downloadable bonus content: - AI Prompts: All prompts used in the book for Customer Discovery, Market Research, and Ideation / Validation can be downloaded and used on your own to practice and improve
- AI Cost Calculator: Worksheet to evaluate the cost of building + running an AI-powered product
- Case studies and follow-on articles: The book includes links and QR codes to the case studies discussed and deep-dive articles, including the 3 Dimensions of AI Products and the 21 Principles to Drive an AI Adoption
Exploring the AI Stack and Your Strategic Options
The Data Foundation (Why Your Data Strategy Matters More Than Your Model Choice) PART II: USING AI TO ACCELERATE YOUR PM JOB
How AI Transforms the PM Job and the PDLC
GenAI-Powered Customer Discovery
GenAI for Market Research
GenAI for Product Ideation and Validation PART III: THE ART OF BUILDING AI PRODUCTS
AI Product Sense
The AI Product Development Process
Choosing the Right AI Approach
Quality, Testing, and Evals
Metrics for AI Products
Cost Structure of AI Products PART IV: FOSTERING AN AI CULTURE IN THE ORGANIZATION
Transforming the Organization
Driving the AI AdoptionBy the end of this book, the reader will understand: - The different types of AI and how to choose between them
- How to use AI / GenAI to augment and accelerate the PM job
- The differences in building an AI product compared to a traditional software product
- The relationship between the PDLC (Product Development Life Cycle) and the MDLC (Model Development Life Cycle)
- How to drive the AI adoption within the organization using VSM (Value Stream Mapping) and the 21 AI Principles
- How to leverage AI prompts for customer discovery, market research, and solution validation of new product ideasAnd the reader will be able to: - Explain what benefits AI delivers and distinguish between Accelerate, Expand, and Simplify opportunities
- Work more effectively with the data and engineering teams while building an AI-powered product
- Use AI tools to accelerate, expand, and simplify their job
- Use the 3 Dimensions of AI Product to map the benefits of a product
- Save weeks of time by using the right tools at the right moment in the PDLC
Downloadable bonus content: - AI Prompts: All prompts used in the book for Customer Discovery, Market Research, and Ideation / Validation can be downloaded and used on your own to practice and improve
- AI Cost Calculator: Worksheet to evaluate the cost of building + running an AI-powered product
- Case studies and follow-on articles: The book includes links and QR codes to the case studies discussed and deep-dive articles, including the 3 Dimensions of AI Products and the 21 Principles to Drive an AI Adoption
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