Why Enterprise AI Pilots Stall, and What Gets Them Moving

The pattern is familiar: a successful pilot, warm internal enthusiasm, then a quiet stop. The cause is rarely the model. It is usually organisational plumbing that was never part of the demo.

The recurring blockers

• Data access. The pilot used a tidy export. Production needs permissions, retention rules and lineage.
• No owner. Pilots run by a working group have no one accountable for production support.
• Undefined success. "It looks good" does not survive a budget review.
• Evaluation drift. No baseline was captured, so nobody can prove improvement.
• Review capacity. Human-in-the-loop designs sound responsible until you calculate the reviewer hours required.

What moves things forward

Pick a workflow with a measurable cost today: hours per document, tickets per week, error rate per thousand. Capture the baseline before touching a model. Give one named owner the authority to ship. And design the review step around available headcount rather than an aspiration.

Scope discipline

The pilots that convert tend to be unglamorous — summarising inbound mail, extracting fields from forms, drafting first responses. Narrow scope makes evaluation possible, and evaluation is what turns a pilot into a line item.