A pilot can hide missing context, weak permissions, and the real cost of running AI in daily work.
Almost every serious buyer has seen a convincing AI demo. A clean dataset. A happy path. A confident answer. Then the same idea meets production: messy systems, real permissions, real customers, real cost.
The gap is familiar. It is also predictable.
What a demo conceals
Demos rarely include the identity model. They rarely include what happens when the retrieval set is wrong. They rarely include the person who must approve an action. They rarely include token cost under real volume.
None of that is a reason to avoid AI. It is a reason to treat the demo as a starting hypothesis, not as evidence that the system is ready.
A better test
Before scaling a pilot, ask: What may it read? What may it write? Who is accountable when it is wrong? How will we know it is drifting? What does it cost at the volume we actually have?
If those answers are not designed, production will invent them under pressure.
