In his bestselling first book, Eric Siegel explained how machine learning works. In The AI Playbook, he reveals how to actually capitalize on it—why some AI and ML initiatives thrive while so many others stall before deployment and never deliver value.
Machine learning is today’s most important general‑purpose technology, but it’s also one of the hardest to put into production. Outside of Big Tech and a few leading organizations, ML projects routinely fail to move from proof‑of‑concept to real‑world operations. What’s missing, Siegel argues, is a specialized, widely adoptable business practice for ML deployment.
This book presents a gold‑standard, six‑step practice for guiding machine learning projects from conception to deployment. Siegel illustrates each step with candid stories of successes and failures, including case studies from UPS, FICO, and major dot‑coms. The approach:
- Aligns business and data teams around precisely what is being predicted
- Defines how good is good enough for model performance
- Spells out how predictions will drive concrete actions and process changes
- Establishes a repeatable strategic framework for future ML initiatives
Beyond the framework, The AI Playbook “upskills” business professionals with a friendly, semi‑technical introduction to machine learning. It puts business stakeholders and data practitioners on the same page so they can collaborate deeply from end to end on ML projects—defining predictions, evaluating impact, and operationalizing models.
While “AI” can mean many things, this book is specifically about machine learning for improving business operations—what many people really mean when they talk about AI in the enterprise. It’s an antidote to hype, focused on practical deployment and measurable value.
Recommended for executives, product leaders, data scientists, analytics teams, and anyone in the information economy, The AI Playbook is a concrete roadmap for mastering the rare art of machine learning deployment and making AI work in the real world.
