Survey data from last year showed that roughly seventy percent of enterprise AI pilots were quietly wound down before reaching production. Not because the models did not work — because the organizations around them did not.

The usual suspects

Three failure modes account for most of the casualties:

• No owner. The pilot belongs to an innovation lab, not a P&L line. When budgets tighten, it disappears.
• No baseline. Without a clear “what does it cost today,” there is no way to prove the AI version is better.
• No integration. The demo lives in a sandbox. Connecting it to real systems reveals problems nobody scoped.

The teams that ship start by picking a single painful workflow, instrument it, and then apply AI to move the needle on measurable cost or time.