Why do 95% of AI Pilots fail

Carlos thank you for sharing this interesting article and MIT report. That stat — “95% of AI pilots fail” — is dramatic but what’s more interesting (and less often highlighted) is why they fail. The MIT report makes clear it isn’t because the technology is inherently broken; rather, it’s how organisations deploy, integrate, and expect from AI that’s going wrong.

One big factor is mismatched expectations. Many companies treat AI pilots like magic bullets. They pick flashy, visible use-cases (sales, marketing), hoping for fast win. But the reality: without embedding the tools into core workflows, without adapting them to internal processes, without committing to iteration and learning, many pilots never translate into actual impact.

Another issue is investment bias — putting more money where the spotlight is instead of where the real payoff might be, like back-office automation, compliance, legal work, and operations.

For the C-suite, this isn’t just an academic concern—it’s a wakeup call. If our organisations are launching AI pilots, success isn’t about hype; it’s about alignment. It's important to define what “success” truly means — measurable revenue, cost savings, improved decision-quality, risk reduction — and tie that to realistic goals. Get the pilots close to real operations: integrate with legacy systems, design for human + AI collaboration, build feedback loops so the system learns and adapts. Also, consider partnerships or buying more mature, purpose-built solutions if internal builds are dragging or full of friction.

In short: The failure of most AI pilots doesn’t mean AI isn’t transformative — it means there’s a big gap between promise and practice. Organisations that succeed will be those that narrow that gap by focusing less on being first or flashy, and more on being grounded, disciplined, and purposeful in how they adopt.

If you lead a company, the question isn’t “Can we afford not to do AI?” but “Can we afford to do AI poorly?”