AI Versus Traditional Product Management
summary
95% of AI projects fail, the claim backed by an MIT stat; success hinges on expectation management not bigger models. Start with an MVP model — Yelp’s car‑ownership heuristic ran an experiment in two weeks, revealed a need for a larger audience and later drove millions, whereas a three‑month partial‑attribution build failed. Use the Three Horizons Framework (aimed 30/30/40 at Yelp), apply timeboxing (one–two month checks), and label work as hype, user problem, or true innovation.
AI-written summary · about
chapters (6)
- 0:00The core thesis of AI PM
- 2:53Defining the MVP for AI
- 6:29Managing success horizons
- 12:06Taming timelines with boxing
- 16:15Innovation, hype, or utility
- 22:42Summary and Q&A
related talks
01h 09mWorkshop: Product development for identifying and leveraging ML opportunities
28mAI at Work
25mProduct Management and AI
29mSteering Your Company Through the AI Revolution
22mHow to operationalize AI
30mAI, Anxiety, and Actual Impact
41mHow to Fail with AI - and How to Succeed?
22mAPI Product Management with an AI tools & mindset