Why 80% of AI Projects Fail—and How to Make Yours Work

Most AI projects fail.

That’s not clickbait—it’s a well-documented reality, with failure rates as high as 90% according to research from Carnegie Mellon, Gartner, and the Wall Street Journal.

Dan Saffer, Associate Director of Outreach at Carnegie Mellon’s Human-Computer Interaction Institute, says the reasons are rarely technical. They’re human: bad data, poor UX, vague goals, and inflated expectations.

In this post, we unpack the real reasons AI projects fall apart—and what successful teams do to avoid the same fate.

Why So Many AI Projects Fail (and Keep Failing)

Dan Saffer has spent years translating academic research into real-world frameworks for building better AI. According to the data—and a whole lot of postmortems—these are the five biggest reasons AI projects tank.

1. Garbage In, Garbage Out

AI runs on data. But if your training sets are mislabeled, incomplete, or irrelevant, the model doesn’t stand a chance. Clean, accessible data isn’t optional—it’s table stakes.

2. Models That Can’t Cut It

Even with solid data, your model still needs to perform. In high-risk domains like medicine or autonomous driving, “good enough” isn’t good enough. If the model can’t meet the bar, don’t ship it.