Designing AI Experiences for the Moment They Get Things Wrong

What Should Happen When AI Is Wrong?

This UX essay argues that AI products need to be designed for mistakes, not only for smooth success. The author points out that an AI error often looks reasonable, so users may act on it without noticing. The piece sets out four design decisions: explain what went wrong in plain language, let people correct output without starting over, make consequential actions reversible, and show sources so users can check claims. It also warns against overloading people with warnings and confirmations.

For brand builders, product designers and content strategists, this is a practical framework for reviewing AI features beyond the visual polish. The useful part is the set of questions to ask in design reviews, such as whether uncertainty is communicated and whether users can undo a change. Anyone shipping an assistant, dashboard or automated workflow should read it before launch, especially in finance, health or legal contexts where a confident wrong answer does real damage.