Reimagined: Building Products with Generative AI 9798989966905

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