Traditional interface design assumes that a given input produces a known output. A model-backed feature breaks that assumption, and most early AI usability failures come from designers treating a probabilistic component like a button.
The same input can produce different results
A user who submits identical text twice may receive two different answers. Interfaces must make that variability legible, with regenerate controls, version history and language that describes output as a draft rather than a fact.
Latency is part of the experience
Model calls take seconds rather than milliseconds. Streaming tokens, skeleton states and progress narration keep the user oriented, and the design must decide what remains usable while generation runs.
Failure is a normal state
Refusals, truncation, hallucinated details and empty results all happen in ordinary use. Each needs a designed response, not a generic error toast, so that the user knows what to do next.
Action step
Pick one AI feature you use or build. Write down five distinct outcomes a single request could produce, including two failure modes, and sketch how the interface should respond to each.
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