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1. Not Just for Business Leaders
   The AI Playbook really nails what’s missing in most ML books – a straightforward way to get stuff done written for both Business Leaders and Engineers. Instead of getting lost in theory, the author breaks down ML deployment into bite-sized, practical pieces that you can actually use. The framework is so practical that I’ve made it required reading in my AI Practice’s internal AI Engineering Academy syllabus.Coming from 25 years in tech (though not all in AI/ML), I’ve seen how challenging implementation can be, and this playbook really resonates with reality. The case studies make complex ideas click, like having a mentor walk you through their best projects. Whether you’re just starting out or have been around the block a few times, this playbook delivers and enjoyable easy read and brings real value for both technical and business audiences.

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2. Fantastic book – great information!
   I have worked in personnel research for over 60 years and am still enthusiastically learning. This book and his previous one “Predictive Analytics” are two of the best books I have ever read. He provides very valuable information, both on technical methodology and on the implementation of research results. His information is presented in an easily understood manner and minimizes the technical jargon. I will eagerly buy any of his future books.

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3. A Must-Read for AI Practitioners: Practical and Eye-Opening
   I really loved Eric Siegel’s The AI Playbook: Mastering the Rare Art of Machine Learning Deployment and I have been recommending it to a lot of people. The book is a game-changer for anyone serious about implementing AI in real-world business environments. To me, what sets this book apart is Siegel’s focus on practicality—he doesn’t just discuss AI theory but dives into the nuts and bolts of applying machine learning in ways that genuinely enhance business operations.Throughout the book, Siegel provides clear, actionable strategies that opened my eyes to new possibilities for integrating AI into business processes. Whether you’re new to the field or an experienced practitioner, the insights in this book are invaluable. Siegel’s emphasis on real-world scenarios ensures that the lessons are not only relevant but immediately applicable.Each chapter is a step-by-step guide to overcoming common challenges in AI deployment, making this book an essential resource for anyone looking to leverage AI effectively within their organization.

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4. AI Like All Processes — Needs a Game Plan
   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. I would recommend this book to anyone looking at implementing or deploying ML now or in the future.

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5. Misleading title. Basically a 90’s BPR book sprinkled with ML and AI terminology
   I really wanted to like this book, hoping it would show how AI and operations are being brought together to improve business value in 2024. However, it reminded me of the plethora of business process reengineering (BPR) or IT ROI books that hit the shelves in the nineties. As a “playbook,” it is best in describing how to pitch data modeling projects for business process improvement — using six steps, no less, beginning with “establish the deployment goal.” A sample elevator pitch is thrown in for good measure “lead with the value proposition.” Hardly the cutting-edge AI subject matter one would expect from the title. A few sanitized real world examples are used to good effect. However, the author seems to be chasing the latest AI terminology simply to freshen up his original work in predictive analytics. There is a lot of exciting work to be done in this arena, especially given the latest in AI technology (e.g., generative AI, synthetic data, agent-based models, etc.), but unfortunately this book falls short in key areas. A more honest title would probably be “Selling Data Projects to Management in the Age of AI.”

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6. Really accessible, and very smart!
   The AI Playbook by Eric Siegel is a standout guide for anyone looking to transform machine learning initiatives into successful, value-driven deployments. Siegel breaks down complex AI concepts with a practical, six-step framework, making the book accessible to both business and data professionals. His real-world examples and case studies highlight why many AI initiatives fail and how to ensure success. Siegel’s approachable style makes even semi-technical knowledge easy to grasp, empowering teams to collaborate effectively. 🙏🏼

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7. Excellent book. Definitely a must have! I love the practical real life examples built into explaining the concepts. Look forward to meeting the author one day and to give him a strong handshake!!

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8. This book combines some real world challenges and difficulties.

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