About this book:

This book provides a comprehensive manual for product managers transitioning into the AI-driven landscape. It begins by establishing the role of the PM as a "translator" who converts high-level business objectives, such as reducing churn or increasing engagement, into concrete machine learning problems. The text guides the reader through identifying high-value use cases, building robust data foundations, and establishing a multi-layered metrics framework—comprising North Stars, guardrails, and diagnostics—to ensure that AI features deliver measurable business value without causing unintended harm.

The middle chapters shift toward execution, advocating for a hypothesis-driven development process. The author details various experimentation designs, ranging from online A/B/n tests and multi-armed bandits to offline simulations and switchback tests. It introduces practical strategies for scoping AI MVPs using "thin slices" and human-in-the-loop workflows to de-risk development. Technical decision-making is also addressed, helping PMs navigate the trade-offs between prompting, fine-tuning, and retrieval-augmented generation (RAG) while managing the critical balance of latency, cost, and quality.

The final section focuses on the operational and ethical lifecycle of AI products. It explores the essentials of MLOps, including model versioning, drift monitoring, and incident response, alongside the legalities of privacy and regulatory compliance. The book emphasizes the importance of AI UX—focusing on affordances, transparency, and trust—and provides a strategic approach to roadmapping that prioritizes learning milestones over rigid feature lists. It concludes with an emphasis on cross-functional collaboration and responsible AI practices, ensuring that product decisions are both ethical and sustainable.