The mental models and vocabulary PMs need to ship AI products, without pretending to be ML engineers.
Chapter 2 Identifying AI Opportunities in Your ProductA practical filter for when AI earns its complexity, and when simpler tools win.
Chapter 3 Data as a Product RequirementHow to treat data readiness as a product decision, not an engineering side quest.
Chapter 4 Building AI Features: What PMs Need to KnowA model that scores well offline can still ship a product users hate. Here's the delivery playbook, evals, rollout, prompts as code, build versus buy, that keeps an AI feature honest end to end.
Chapter 5 Evaluating AI Models and VendorsA model's own /models endpoint is an advertisement, not a contract. Here's the six checks, capability, catalog truth, cost, lock-in, evaluation and kill criteria, that decide whether a vendor bet survives contact with real traffic.
Chapter 6 Designing AI User ExperiencesHow AI reshapes UX: progressive disclosure, expectation-setting, failure design, trust calibration, human-in-the-loop, and why making confidence legible is the core product skill.
Chapter 7 Ethics, Bias, and Responsible AIWhy responsible AI is a PM responsibility: where bias enters the stack, how to audit and govern it, and what guardrails, transparency, and risk management look like in practice.
Chapter 8 AI Strategy: From Feature to PlatformA capstone frame for PMs: AI as bolt-on feature, integrated capability, and platform infrastructure, roadmaps that compound, the data flywheel, org readiness, and what to prepare for next.