MIT data shows 95% of generative AI pilots never scale. More broadly, 88% of AI pilots never reach production at all, regardless of company size. RAND puts it even more bluntly: 80.3% of AI projects deliver no measurable business value.
The trend is getting worse, not better. 42% of companies abandoned most of their AI initiatives in 2025, more than double 2024's 17% abandonment rate. The average enterprise scrapped 46% of its AI pilots before they ever reached production.
Here's the part that matters: the underlying technology is rarely why a project fails. The causes are consistently organizational: poor data readiness, misaligned success metrics, and broken workflow integration. Sponsorship evaporates within six months in 56% of failed cases, and 60% of projects without AI-ready data get abandoned.
This is exactly why we start every engagement with diagnosis, not a build. "The AI didn't work" is almost never the real story. The real story is usually that nobody checked whether the data, the metrics, or the integration path were ready before the build started.
A real discovery phase asks three questions before a single model gets trained: Is the data actually ready, or does it just look ready? What does success mean in a number, not a feeling? And where exactly does this plug into a workflow someone already runs today? Skip any of the three, and the pilot has already failed. It just hasn't found out yet.