Ship AI products without getting embarrassed in the demo — or worse, in production. 18 chapters of field-tested frameworks, real PM stories, and honest failure accounts.
For every PM who stared at a hallucinating model at 2 a.m. and shipped anyway.
And for the data scientists who answered the 6 a.m. Slack.
18 chapters. 2 theory inserts. One cohesive narrative.
Read Parts I–II sequentially to build foundations, then jump to the "Should We Use AI?" framework.
Start with strategies and workflows, then build/buy decisions and the trade-offs that compound.
Case studies plus product sense framework. Skim each chapter's key takeaways section.
Team dynamics, the organizational battle for AI adoption, and where the role is heading.
This isn't a textbook you read front-to-back and shelve. It's a field manual. Dog-ear it. Argue with it. Rip out the frameworks and tape them to your monitor.
Every chapter connects to the AI PM Trinity — Data, Models, and UX. Ignore any pillar and the product collapses. You'll see Trinity callouts throughout.
Each chapter opens with a real PM in a real mess. Named characters, specific numbers, honest failures. The lessons come from the scars, not the slides.
The left margin carries stats, warnings, tips, and cross-references. They're designed to be scanned independently — useful for quick review before a meeting or interview.
Every chapter ends with concrete questions to bring to your data science partners. These are conversation starters, not scripts. Adapt them to your context.
Two short inserts — on fine-tuning and determinism — sit between chapters. They're the minimum technical depth you need to make good decisions without pretending to be a researcher.
Every chapter follows the same structure: opening story → framework → case studies → common mistakes → key takeaways → DS team questions.
Start Reading — Chapter 1 →