Table of contents : Praise for Reimagined About the Authors Dedication Foreword Pioneering AI: Our Adventure from Curiosity to Creation
Part I: Exploring the Landscape of Generative AI 1.1 - The AI Revolution: A Primer 1.11 - What Is Artificial Intelligence (AI) and Generative AI? 1.12 - What Have You Been Getting Wrong About AI? 1.13 - Why Is AI an Old Phenomenon? 1.2 - The Catalysts and Precursors of Generative AI 1.21 - Why Is Now the Right Time for Generative AI? 1.22 - Is Generative AI Really the Future? 1.23 - Why Are We Still Early in the AI Evolution? 1.3 - Generative AI Market Structure and Tech Stack 1.31 - How’s the Generative AI Scene Structured and Who’s Winning? 1.32 - Why Should I Care About the Generative AI Tech Stack? 1.4 - Generative AI Applications 1.41 - What Industries Are Being Revolutionized by Generative AI? 1.42 - What’s Next for the Shared Future of Generative AI and Robotics? 1.5 - Limitations of Present-Day Generative AI 1.51 - What Can Today's Generative AI Technology Truly Achieve? 1.52 - What Are the Areas to Watch Out for When Working with Generative AI?
Part II: Building Generative AI Products 2.1 - Whose Problem Are We Really Solving? 2.11 - Why Do AI Products Often Miss the Mark on Customer Segmentation? What Is the Right Way to Segment Your Customers? How to Choose the Right Segment to Focus? Case in Point: How Synthesia Nailed Segment Selection 2.12 - Problem First or Tech First? The Dilemma in AI Problem Identification Uncovering Jobs-to-Be-Done (JTBD): Rooting AI in Real User Needs Case in Point: How Intercom Transformed Its Go-to-Market Through Jobs-to-Be-Done The Opportunity Statement: Defining the 'Who' and 'What' The Contrarian View: When Prioritizing Tech Can Make Sense How to Determine the Best Use Cases for Generative AI? 2.13 - Validate Problem Assumptions for Generative AI Solutions What Should We Validate? Process and Methods for Assumption Validation Case in Point: Rapid Validation: HeyGen's Lean Approach to $1M ARR in 7 Months 2.2 - How to Design & Build Great Generative AI Products? 2.21 - Why Is It So Hard to Build MVP For an AI Product? Case in Point: The Rocky Road of Neeva’s MVP Search Journey 2.22 - How to Build the Right Generative AI MVP? How to Navigate the Open Source vs. Proprietary LLM Continuum? Case in Point: The AI Battle Royale - Experimenting with LLMs Case in Point: BuzzFeed's Journey in Generative AI Product Development 2.23 - How Will Generative AI Transform Product Design? 2.24 - What Are the Unique Generative AI Product Design Considerations? Characteristics of Generative AI products Product Principles for Generative AI Products Generative AI-UX Interactions & Design Patterns Designing Based on Engagement States The Art of Prompt Design 2.25 - How to Develop Guidelines for Building Responsibly with AI? The Generative AI Trust Framework Case in Point: Crafting Responsible AI with ChatGPT’s Reviewer Guidelines Case in Point: How Instacart Built “Ask Instacart” Employing Red Teaming for Responsible AI Development 2.26 - How Do B2B and B2C Needs Differ When Creating Generative AI Products? 2.27 - How Do You Navigate from MVP to Product-Market Fit? What Are Some Common Myths About Finding Product-Market Fit? How to Tell If You Have (or Don’t Have) Product-Market Fit? Case in Point: How Superhuman Built a Systemized Engine to Measure PMF 2.3 - How to Grow, Measure & Scale Generative AI Products? 2.31 - What Is Your North Star? Besides North Star Metrics, What Else Do I Need to Measure? Unique Generative AI Considerations Case in Point: The Fall of Kite 2.32 - Why Do Promising Products Fail at Go-to-Market (GTM)? Common GTM Challenges How to Do GTM Right? Spotlight: Pricing Challenges for Generative AI Products 2.33 - Choosing the Right Growth Strategy: Product-Led Growth (PLG), Marketing- Led Grow (MLG), or Sales-Led Growth (SLG)? What Is PLG and How to Get Started? Case in Point: PLG in Action at Amplitude When NOT to Use PLG? 2.34 -Putting It All Together: The PLG Iceberg & Canva’s Growth Story 2.4 -What Are Moats and Why Do They Matter? 2.41 -Can Generative AI Companies Have Moats? Red Team Perspective: Generative AI Lacks Defensible Moats Blue Team Perspective: Moats Are Necessary and Achievable What Is Our View?
Part III: Navigating the Product Career in the AI Era
3.1 -How Will Product Managers Evolve in the AI Era? 3.11 - What Does a Product Manager Do? 3.12 - Will AI Take Over Product Management Jobs? 3.13 - What Skills Are Needed for Product Managers to Thrive in the Age of AI? Case in Point: A Speculative Day in the Life of a Product Manager in the AI Era 3.2 - How Can Product Managers Work Well with AI? 3.21 - How May Generative AI Enhance Product Development? 3.22 - How May Generative AI Accelerate PM Career Growth?
Appendix Key Concepts in AI Detailed Process and Methods for Assumption Validation Additional Resources Acknowledgements References