Why do AI projects fail?

The most silent cause is the last mile: an AI system is not traditional software that ships and sits still, it is a system that learns from changing data, and without monitoring, retraining thresholds and the ability to roll back, the project that worked in January fails in June with nobody seeing it coming. That is why every system in this house runs with blocking gates and a public failure log: an empty log does not mean zero incidents, it means nobody writes them down.

The antidote to the other four causes is method, not technology: choose the task with a three-criteria matrix, measure the baseline before touching anything, pilot with a date where scale or kill are the only two valid outcomes, and publish what failed. Our own operational failures are documented in every case in the lab, because a provider who does not tell you their failures is telling you half the story.