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.

What You'll Find Inside:

• Learn how to translate business objectives into solvable machine learning problems that align technical capabilities with user needs and strategic goals
• Master experiment design techniques including A/B/n tests, multi-armed bandits, offline evaluation, and switchback tests to validate AI features with scientific rigor
• Discover practical frameworks for prioritizing AI backlogs using RICE, ICE, and 2x2 trade-off matrices that balance impact, feasibility, and uncertainty
• Explore responsible AI launch strategies like shadow mode, canary releases, and staged rollouts that minimize risk while gathering real-world validation
• Gain methods for measuring long-term AI impact through cohort analysis, causal inference, and customer lifetime value to ensure sustainable value creation

Who's It For:

This book is for product managers and adjacent leaders—including founders, designers, analysts, and engineers—who are accountable for product outcomes. It's ideal for those comfortable with experimentation and metrics who want to apply their product management skills to AI features, whether they're experienced product managers recognizing familiar patterns or newcomers to AI seeking a vocabulary and process to lead confidently in AI-driven environments.