How do we avoid keeping an AI trial alive just because we invested in it?
To keep an AI trial from living on just because you invested in it, write down the result that would justify stopping before you start. Include consequences nobody would accept as well as a benefit too small to matter.
The innovation panel included disagreement about pilots and learning through deployment. Whatever word a team prefers, it needs a standard for deciding that a proposed use shouldn't continue. A workflow can save time and still fail an important requirement.
I'd also decide up front how much effort you're willing to spend on revision. That separates a fixable problem from a reason to reject the approach, and it tells the team when another decision is due.
Later, review the evidence against the original standard. If the standard changes, explain why and who approved the change. Don't redefine the goal after the fact so every result counts as progress.
When the team stops, preserve what it learned and what another team would need to know before trying a similar use. An experiment can end usefully without becoming a permanent workflow, and a clear stopping decision can protect capacity for a better idea.
What to try this week
Take one AI trial you're running or planning and put the stopping result at the top of its plan. Add how much revision effort you'll spend before deciding again.