2 posts tagged with "trust"

Most AI product post-mortems focus on the same story: the model was wrong, users noticed, trust eroded. The fix is obvious — improve accuracy. But there is a more insidious failure mode that post-mortems rarely capture because standard accuracy metrics don't surface it: the model was right, but for the wrong reasons, and the power users who checked the reasoning never came back.

Call it the overclaiming trap. It is the failure mode where correct final answers are backed by fabricated, retrofitted, or structurally unsound reasoning chains. It is more dangerous than ordinary wrongness because it looks like success until your most sophisticated users start quietly leaving.

Two product teams at two different companies build the same AI writing assistant. Same model. Similar feature surface. Comparable accuracy numbers. One team celebrates record activation at launch. The other quietly disables the feature after three months of ignored adoption and one scathing internal all-hands question.

The engineering debrief at the struggling company focuses on the obvious variables: latency, accuracy, UX polish. None of them fully explain the gap. The real variable was trust — specifically, whether the AI feature could borrow enough existing trust to earn the right to make mistakes while it proved itself.

Trust transfer is the invisible force that determines whether an AI feature lands or dies. And most teams shipping AI products have never explicitly designed for it.