The AI Playbook
Mastering the Rare Art of
Machine Learning Deployment
“Eric Siegel delivers a robust primer on machine learning, the key mechanism in AI. A forward-looking, practical book and a must-read for anyone in the information economy.”
—Scott Galloway, NYU Stern Professor of Marketing and bestselling author of The Four
“Eric Siegel, who makes machine learning easy to understand, has delivered an antidote to today's relentless AI hype: A book that explores why some AI initiatives thrive, while others fail, and what it takes for companies and people to succeed.”
—Charles Duhigg, author of bestsellers The Power of Habit and Smarter Faster Better
“This book is the driver's manual for machine learning – every business and analytics professional should read it.”
—Morgan Vawter, Global VP Data & Analytics, Unilever
“The ultimate blueprint for tapping machine learning’s full potential.”
—Andy Gray, Data & Tech Advisory Director, Deloitte
In his bestselling first book, Eric Siegel explained how machine learning works. Now, in The AI Playbook, he shows how to capitalize on it.
The greatest tool is the hardest to use. Machine learning is the world’s most important general-purpose technology – but it’s notoriously difficult to launch. Outside Big Tech and a handful of other leading companies, machine learning initiatives routinely fail to deploy, never realizing value. What’s missing? A specialized business practice suitable for wide adoption. In The AI Playbook, bestselling author Eric Siegel presents the gold-standard, six-step practice for ushering machine learning projects from conception to deployment. He illustrates the practice with stories of success and of failure, including revealing case studies from UPS, FICO, and prominent dot-coms. This disciplined approach serves both sides: It empowers business professionals and it establishes a sorely needed strategic framework for data professionals.
Beyond detailing the practice, this book also upskills business professionals – painlessly. It delivers a vital yet friendly dose of semi-technical background knowledge that all stakeholders need in order to lead or participate in machine learning projects, end to end. This puts business and data professionals on the same page so that they can collaborate deeply, jointly establishing precisely what machine learning is called upon to predict, how well it predicts, and how its predictions are acted upon to improve operations. These essentials make or break each initiative – getting them right paves the way for machine learning’s value-driven deployment.