The AI Playbook: Mastering the Rare Art of Machine Learning Deployment

by Eric Siegel

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 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 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.

A note from the author:

What kind of AI does this book cover? The buzzword AI can mean many things, but this book is about machine learning, which is a central basis for—and what many mean by—AI. To be specific, this book covers the most vital use cases of machine learning, those designed to improve a wide range of business operations.

Genres: Artificial Intelligence, Business, Technology, Nonfiction

256 pages, Hardcover

Published February 6, 2024

About the author

Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series and its new sister, Generative AI Applications Summit, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery,” executive editor of The Machine Learning Times, and a frequent keynote speaker. He wrote the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which has been used in courses at hundreds of universities, as well as The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. Eric’s interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award when teaching the graduate computer science courses in ML and AI. Later, he served as a business school professor at UVA Darden. Eric also publishes op-eds on analytics and social justice.

Eric has appeared on Bloomberg TV and Radio, BNN (Canada), Israel National Radio, National Geographic Breakthrough, NPR Marketplace, Radio National (Australia), and TheStreet. Eric and his books have been featured in BBC, Big Think, Businessweek, CBS MoneyWatch, Contagious Magazine, The European Business Review, Fast Company, The Financial Times, Forbes, Fortune, GQ, Harvard Business Review, The Huffington Post, The Los Angeles Times, Luckbox Magazine, MIT Sloan Management Review, The New York Review of Books, The New York Times, Newsweek, Quartz, Salon, The San Francisco Chronicle, Scientific American, The Seattle Post-Intelligencer, Trailblazers with Walter Isaacson, The Wall Street Journal, The Washington Post, and WSJ MarketWatch.

Reviews

[Damon · Rating 3 out of 5]

AI! AI everywhere!

It is extremely hard to read about tech and avoid AI. Between GhatGPT, DeepSeek, any of the open source models, Suno, Grok, there are numerous breathless articles about the latest milestones achieved and within-reaching-distance capabilities.

Enter Eric Siegel. AI Playbook attempts to give a framework for how to deploy machine learning to achieve an identified business goal. The first thing that any reader should be aware of is that his examples focus on machine learning rather than deep learning-based large language models. The framework that he sets out should apply to generative AI, but his examples focus more on established ML approaches rather than applying the latest tools to intangible business problems.

Within his ML focus, Siegel sets out a clear and simple framework for applying ML approaches to improve business outcomes. He draws on his won examples (successes and failures) to show what works, what doesn't, how to strategically avoid the hype of AI, and how to win over skeptics of approaches that overturn conventional thinking. This book is more aimed for the business manager, and almost entirely avoids any technical discussions, making it accessible to those who have never written a line of code before.

For those who want a background on how to approach a conventional application of ML to data-rich environments, this book will work well. The approach falls apart for projects where you lack rich data, or where a clear prediction goal remains unsettled science. As such, this book will help those who are new to the field. Readers who are looking to apply the latest (GenAI) to data-poor environments, however, will need some follow-on material to meet their needs.

[scott j rosenkrans]

I've been in the AI space for the past six years and have faced many of the challenges Eric talks about in this book. My team and I have iterated and gone through many trials and errors to see what would work in getting more successful AI adoption. It wasn't until picking up this book that we were able to see where the gaps were and how we need to position ourselves and our product for a better adoption rate. I've made it mandatory reading for my immediate team and will be leading a book club on this for our larger company of 100+ employees. Thanks for your work on this!

[zoagli · Rating 2 out of 5]

Weak and superficial.

I like the thought of keeping the end goal in mind at all times during an AI/ML project: what do you want to predict, and how will you use the predictions to make business decisions?

I also learned that a yes/no prediction does not have to be whether the customer will buy or not, but e.g. “If sent a brochure, will the customer buy within thirteen business days with a purchase of at least $125 after shipping, and not return the product for a refund within forty-five days?”

After that, the book gets fuzzier. I especially disliked that the author wants to sell us (on) his framework with a supposedly cool name.

[Laura Bahr · Rating 5 out of 5]

I am not a business person or ML professional, I am finding this book extremely helpful just in terms of understanding how and why things work, or DON'T work in this information age. Not only does it help dispel myths and hand-wringing around AI, it shows how extremely helpful successful use of this technology can be if properly guided and implemented. The writing is so sharp, funny, and clear that this book is for any layperson (like myself) who is interested in what's possible that was once impossible.

[Pat Faulhaber · Rating 5 out of 5]

AI or Machine Learning (ML) as author Eric Siegel refers to it, is broken down into steps for deployment in his new book. He also looks at the strengths of using ML and the weaknesses that center on the lack of communication when planning and deploying machine learning projects. Siegel offers six steps to effective deployment of ML which include Value: Establishing the Deployment Goal; Target: Establish the Prediction Goal; Performance: Establish the Evaluation Metrics; Fuel: Prepare the Data; Algorithm: Train the Model; and Launch: Deploy the Model. Siegel does put his theories to work in case studies which is helpful for the reader. The concepts are fairly technical but Siegel provides a layperson's view of the technology, the language and the usage. The author believes that ML is cool and valuable with its true value being realized when it is launched an it causes organzational change and improves a company's operations. I would recommend this book to anyone looking at implementing or deploying ML now or in the future.

[P · Rating 2 out of 5]

This is a really, really basic book. If you're sort of clueless about how to implement large process/system changes, this book will be helpful. If you've done this sort of thing before, then the introduction of ML and AI isn't much different than past process/system changes, like using new software, hardware, etc. I skimmed through it in about an hour and I still thought I kinda wasted my time. All "DUH" type stuff. That said, I DO think it's got definite value for some people -- I would look into it if you're nervous about proposing and/or implementing any kind of ML/AI solution in your enterprise. For me, it was a dud that might have some use as a reference for others at my company. 3/10

[Joe · Rating 3 out of 5]

Good complement to Siegel's popular book Predictive Analytics . This book, The AI Playbook, is more about the business of using machine learning (implementations), while the other is more about the technology behind it. I liked both. My career is in analytical consulting, a bridge between data science and management consulting. I found that the first book touched on the earlier part of my career: analyzing and building models. This second book is more appropriate for my current mid-level career: convincing people to use analysis and helping to get it implemented.

The book has a few good frameworks, which I'll try to remember when I'm pitching or explaining a machine learning process. The main framework is the author's "bizML" concept. The book is structured around it. It goes from conceptualizing how ML (machine learning) will help the company to deploying the model. The bizML framework seems right; it is consistent with how I've seen successful projects go. The author includes other punchy bullet-point lists along the way, including one in the conclusion on how to write an elevator pitch for a ML project.

The first half of this book was more useful to my current role than the second half. The first half covers the questions you need to answer from the non-technical stakeholders before you even sit down with the data. This part of the book includes nice examples from the author's early career, including some personal failures. It's nice when an author is confident enough to share failures so others can learn from them.

The second half covers some similar ground to the first book, with an overview of the technical steps in the ML process. If you're new to it, this is a fine primer. It's not too technical (the author compared this book's purpose to "driver's ed", not learning how an internal combustion engine works). For those who know most of the technical stuff already, you may find yourself skimming it pretty quickly. I did a lot of skimming but also stopped to appreciate and underline some good examples (e.g., details on UPS's change management process for getting people to use its model, an image recognition that thought it could distinguish wolves from huskies but actually relied too much on the presence of snow in the background of wolf images, etc.).

Recommended for people who use or sell ML, either as consultants or internally. The author would recommend it even more broadly for anyone who feels they need more "data literacy."

[Rommel Guerra · Rating 4 out of 5]

"The AI Playbook" stands out as an essential guide for business leaders aiming to harness the transformative power of AI. Unlike other texts that dwell on the theoretical, this book presents a clear, goal-oriented framework that starts with the business objective in mind. It systematically breaks down the AI implementation process into manageable steps: from defining precise deployment and prediction goals to preparing data, training, deploying, and maintaining models. This structured approach ensures that AI initiatives are directly aligned with enhancing business performance and solving real-world problems, and transforming processes.

What makes this book invaluable is its focus on practicality, offering a roadmap that is both accessible to non-technical business leaders and deeply informative for ML engineers. By emphasizing the importance of starting with the end goal and working backward, "The AI Playbook" equips readers with the tools to not only implement AI solutions effectively but also to integrate them seamlessly into their business strategies. This clarity and focus make it a must-read for anyone serious about leveraging AI to drive their company forward.

[Juan Rivera · Rating 5 out of 5]

The AI Playbook by Eric Siegel is a crisp, highly practical guide for taking ML from idea to impact. It starts with the right question—if AI is everywhere in our personal lives, why is it so hard to implement in enterprises?—and answers it bluntly: we usually begin with the model instead of the business decision and measurable value.

Key takeaways I’ll apply at work:
• Start with value (revenue, savings, risk) and the decision you want to improve.
• Define a quantified objective (what improvement/money are we predicting to move).
• Lock metrics & Go/No-Go thresholds tied to real business outcomes.
• Invest in data preparation/quality (the longest, most neglected step).
• Train once the data is right—much easier and more reliable.
• Deploy with control (A/B, canary, rollback), then monitor and iterate.

Too many projects work backward from an algorithm. This book flips the script: design the end decision and its value first, then build toward it. Clear, actionable, and imminently useful for anyone responsible for delivering AI that actually ships and performs. Highly recommended.

[Amitha Harichandran · Rating 5 out of 5]

As a business owner, I know AI is something I should be paying attention to but have always found the way it is discussed or presented in the media to be a bit off-putting. It is hard to take those messages about AI at face value, since business is meant to be more practical, realistic, and value-focused rather than awe-inducing and science fiction sounding. This book really bridged the gap for me, bringing it down to earth without watering it down and without losing the excitement of how novel and exciting the technology is. Ultimately this is an extremely practical book for when it comes to making the use of AI valuable.

[Manuela Epure · Rating 5 out of 5]

Excellent book in any senses.
It was a good reading, easy to understand concepts, simple explained and complemented with insightful examples. The author succeded to blend the data scientist experience with business understanding and the result is perfect for those aiming to deploy ML în real practice.
Most of all, I like teaching-kind of approach that makes easier to understand also for non professional readers.
I enjoyed the first reading and know I decide to read it again în a more sistematic way with notes and May be questions to be send to autor.
Will done!
Prof.Manuela Epure, PhD, MCIM
Bucharest, Romania

[Espen Kristensen · Rating 4 out of 5]

Many good perspectives on the human/business side of making AI projects work. If you’re a technical person, this goes well together with «Reliable Machine Learning» (which covers the technicalities of making, deploying and maintaining an ML solution) - if you’re on the business side, this book probably works really well even on its own (although it’s quite brief in covering ML as a subject, only very roughly dealing with supervised learning).

Main takeaway: something like 80% of ML projects never deploy to production, and it’s often bot down to the technicalities of making ML deploy.

[Vaclav · Rating 5 out of 5]

This book is the Rosetta Stone for translating and facilitating the conversation between business and data teams. The business team provides the context with the objectives for this project. The data team will build the magic technology. Together, they will deliver a successful project.

If you are serious about deploying successful AI project, this book is a must read.

[Tiago Sá · Rating 5 out of 5]

This author has become my go-to for understanding how businesses gain real value with predictive AI. His other book "Predictive Analytics" became a real standard and he's really doubled down on setting the standard for best uses of analytics at companies. This book was eye opening! It's not just about the analytical software but really about how you run a business with analytics.

[Michael Cetta · Rating 5 out of 5]

I'm in healthcare, where AI is a highly relevant daily topic. This book gave me a clear simple view on the AI industry's track record: what's plausible, what works, and where things usually go wrong -- in simple terms that totally resonated with me. I feel much more empowered to work toward AI deployment now that I've read it. Seems like a must read to me!

[James McCaffrey · Rating 5 out of 5]

This book nicely straddles the line between technology and business. Put another way, it's somewhat technical (so there's actual content rather than blah, blah, blah) but at a level that's accessible to anyone. And I found the writing style to be excellent -- the book kept me engaged. Bottom line: Highly recommended.

[Lawrence Ingrassia · Rating 5 out of 5]

Few businesses know how to deploy machine learning for competitive advantage. In The AI Playbook, Eric Siegel takes you inside companies that have succeeded - and failed. This is a must-read for executives at all levels.

[Ji Xiang · Rating 4 out of 5]

Liked the overall structure of the books. The first half of the book has some interesting points regarding business buy-in. The second half is not as useful if you have some data analytics background. A good book if you work in data science/analytics.

[Steven Allen · Rating 4 out of 5]

I am studying data science at the moment, and have been using AI for assistance in SQL, Python, Orange, Power BI, etc. This book had some good ideas, and some new approaches to some of the problems that I am tasked with solving in the classroom.

[K. · Rating 4 out of 5]

A good, down-to-earth overview. I wish it was available when I was starting.

[Joachim · Rating 5 out of 5]

A great, concise, straight to the point explanation how to deploy valuable ML projects. Great for the "business" practitioner & the data scientist.

[Dale · Rating 3 out of 5]

Good quick read that gives a good overview (with opportunities for deep dives) on machine learning.

[David · Rating 3 out of 5]

Review to come...

[Jaume Sués Caula · Rating 3 out of 5]

Practical guide on how to deploy ML in the organizations, with good proposals based on human-in-the-loop and other good protocols. Was too basic for me, though.