- Aug 31, 2026
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A Brief History of Why Machine Learning Projects Stall
EDITOR’S NOTE: This is the preface to The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. The paperback edition of this book will drop on October 27, 2026, along with a new, second preface, “Predictive AI Thrives, Despite Generative AI Stealing the Spotlight.”
Special offer: Pre-order the paperback now and receivefree, immediate access to the audiobook.
A note on terminology: Althoughpredictive AIhas recently become the more common term, in this article – and in the book for which it serves as a preface – I refer to it asmachine learning (ML). So remember, when you see “ML,” that means predictive AI.
When promoting breakthrough technology, be careful what you wish for.
Back in the Dark Ages, before data was cool and phones were smart, I networked my way into the swank office of a powerful business executive. Hoping that he would introduce me to—or become—my first client, I declared that I was striking out on my own as a machine learning (ML) consultant. Unfamiliar with ML and disinterested, he looked at me like, “Don’t waste my time,” and I was quickly back on the streets of San Francisco.
This was 2003, right after I’d relocated from the East Coast and ordered new business cards, all in the pursuit of my passion. I had fallen in love with ML a dozen years earlier, first in the research lab and then as a Columbia University professor teaching the graduate-level ML and AI courses. It was the most exciting, potent, and widely applicable kind of technology. Moving west, I vowed to introduce it to the non- academic world. I wanted to see ML deployed.
At that time, a corner of the industrial world was already using ML, but they called it something else: data mining. I thought that term was misleading to the non-data folks, but “machine learning” kept getting me kicked out of offices. So I latched onto a new buzzword that had just started to gain traction, predictive analytics. A rose by any other name.
Unfortunately, my improved vocabulary didn’t immediately land me clients. “You should just take a full-time job,” a senior executive at an established analytics vendor bluntly threw in my financially insecure face.
Instead, I doubled down. Tripled down. I held corporate training seminars. I published articles. I networked like mad.
Clients eventually started coming in, but only enough to keep me busy. I was knee-deep in demand, but I needed it up to my belly button. The world still didn’t get it. I had to evangelize harder. I took a three- pronged approach:
1. Conference. I launched Machine Learning Week (formerly Predictive Analytics World), the first ML conference series outside academic and vendor events. Bolstered by its sister publication, the Machine Learning Times, the conference series has since grown to serve 18,000 attendees internationally.
2. Book. Next, I wrote Predictive Analytics, the first popular book that showed readers of all levels how the algorithms work under the hood. Written to ignite and excite, it ended up becoming a best- seller, winning several awards, landing me 100 keynote speeches at conferences outside my own, and being adopted as course material by hundreds of universities.
3. Music video. I even dropped an educational rap music video called “Predict This!,” which went a bit viral (to watch, go to www.Predict This.org). Surely this proves that I’d literally do anything to spread the gospel of ML.
Whether or not these efforts helped light the fuse, one thing’s for sure: ML exploded in popularity. It grew from a nascent industry to a full-blown commercial movement. It came of age as a core enterprise practice necessary to sustain competitive advantage. Hyperboles reigned as data scientistdethroned firefighterto become “the sexiest job.”
Watching ML become so hot felt both gratifying and surreal. The experience reinforced an age-old lesson: Keep the faith. When you believe in a good idea—such as the notion that learning from data is not only cool but valuable—and stick to your convictions, people will eventually come around.
Unfortunately, ML’s great rise has also taught me another lesson: Be careful what you wish for. The buzz has gone too far. In a way, ML is now too hot for its own good. The problem is, the onslaught of excitement has fed a common misconception that derails many ML projects:
The ML Fallacy: Since ML algorithms work (amazing and true), the models they generate are intrinsically valuable (not true).
The value of ML comes only by launching it to enact organizational change. After generating a model with ML, you capture its potential value only when you deploy it so that it actively improves operations. Until a model is usedto actively reshape how your organization works, it’s use-less—literally. A model doesn’t solve any business problems on its own and it ain’t gonna deploy itself. ML can be the disruptive technology it’s cracked up to be, but only if you disrupt with it.
Most ML projects fail to deploy. I believe this is mainly because most ML leaders neglect to properly plan for the operational change that deployment would bring to fruition. That planning takes more preaching, socializing, cross-disciplinary collaboration, and change- management panache than many, including myself, initially realized.
Far too often, the data scientist delivers a viable model, but the operational team isn’t ready for the pass—and they drop the ball. There are wonderful exceptions and glowing successes, but the generally poor track record we witness today forewarns of broad disillusionment with ML—even a dreaded AI winter. It’s time to tap the brakes and correct course so that ML can deliver on its promise.
So I’ve pivoted from ML cheerleader to wary disciplinarian—albeit an optimistic one—with a new mission: Standardize and broadcast the very particular business discipline needed to get ML launched. Whereas my first book was about how ML works technically, this book is about how to run ML projects so that models not only work in the lab but also successfully deploy.
First things first: Business professionals—who are a primary audience for this book—need some edification. Before those in charge can confidently green-light model deployment, they must gain a concrete understanding of how an ML project works from end to end: What will the model predict? Precisely how will those predictions affect operations? Which metric meaningfully tracks how well it predicts?and What kind of data is needed?
Only when the business leaders—including executives, managers, and decision makers—come up to speed on this semi-technical but straightforward knowledge can we bridge the gap between the tech and business sides and bring model deployment into the realm of possibility.
These days, everything I do is to unite those two worlds, tech and biz. In addition to this book, I’ve taken another three-pronged approach:
1. Conferences focused on deployment.Newer offshoots of my event series, Machine Learning Week, build on the nuts-and-bolts aspect of analytics to also cover industry-specific deployment, including applications in marketing, financial services, industry 4.0, healthcare, and climate technology. The first track devotes itself to the business side—we call it the operationalization and leadershiptrack.
2. Business school professorship. After a twenty-two-year hiatus, I returned to academia to hone the methodology described in this book, serving for one year as the Bodily Professor in Analytics at the Darden School of Business at the University of Virginia. The switch in departments—from computer science years ago to business more recently—reflects my shift in focus: For ML to succeed, we need a business-side vantage.
3. More expansive training.Finally, I’ve launched an online course, “Machine Learning Leadership and Practice: End-to-End Mastery,” to broaden the almost universally narrow focus of today’s ML courses— which typically jump straight to the number crunching, forgoing the extensive business planning that should come first.
If you don’t have time for a three-month course, you might instead just read this book. It covers the disciplined approach required to deploy ML initiatives, formulated as a six-step playbook that I call bizML. Along the way, it gets readers of all backgrounds up to speed on the semi-technical knowledge they need.
Considering the innumerable dollars and resources pumped into ML, how much more potential value could we capture by adopting a universal procedure that facilitates the collaboration and planning needed to reach deployment?
Let’s find out.
Eric Siegel, Ph.D., is a former Columbia University professor who helps companies deploy machine learning. He is the cofounder and CEO of Gooder AI, the founder of the long-running Machine Learning Week conference series, 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. A Forbes contributor, Eric 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. A Forbes contributor, 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, 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.
