AI Feature Sprawl: Why Engineering Teams Are Quietly Retiring

Everyone has read the AI failure story by now. MIT’s NANDA initiative found that 95% of enterprise generative AI pilots deliver no measurable P&L impact, with only about 5% reaching production at scale. RAND put a number on the pattern behind that: interviewing 65 data scientists and engineers, it found that more than 80% of AI projects fail, roughly double the failure rate of non-AI IT projects. Gartner has gone a step further and predicted that more than 40% of agentic AI projects will be scrapped by the end of 2027, citing cost, unclear ROI, and “agent washing” as the drivers.

That is the story everyone is telling. It is also, at this point, an old one.

There is a second failure mode nobody is tracking with the same rigor, because it does not show up in pilot-stage statistics at all. It is the AI feature that got past every...

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