When a Prototype Works Because Someone Keeps Rescuing It

A prototype can look reliable because a person is quietly repairing its gaps.

Imagine an AI tool that prepares a project handoff. During the pilot, a teammate fills in missing details, catches mistakes and reminds people what to do next. The task gets done—but that result doesn’t yet show whether the tool itself made the handoff easier.

I’d track those human interventions as part of the test, not hide them as “just support.” Agree who owns each intervention, how much manual help the trial allows, and what would count as success: for example, users completing the handoff with fewer unplanned rescues. If the help keeps expanding, pause and revisit the problem before scaling.

The first trial should reveal not just whether the promise works, but what it takes to keep it.