A failed AI experiment can leave useful parts without needing a relaunch

Here is a hypothetical end-of-trial review. An AI assistant produced readable drafts, but checking them took too much effort for the intended workflow. The team decides to stop the trial. What, if anything, should survive?

Sort the artifacts into three groups:

• Keep: useful synthetic examples, a clear task description or a review checklist that someone wants to maintain.
• Change: a smaller task that might deserve a separate experiment, with a fresh question rather than a promise of success.
• Release: outdated drafts, unnecessary copies and assumptions the trial did not support, handled under the team’s retention rules.

For every kept item, name its limitation and a willing future owner. “This helped us find missing conditions” is more honest than “This validates the product.”

Record why the trial stopped so the next person does not repeat it without learning from it. But do not turn the review into an obligation to rebuild. A useful outcome can be a clearer decision to use a simpler tool—or no tool—for now.

What is one artifact worth preserving from an experiment that did not achieve its original goal?