TL;DR
Design the failure path first: show the work, make correction easy, and know when to hand off to a person.
Start with the wrong answer
Every AI feature will sometimes be wrong. The question that shapes your design is what happens to the user when it is.
If the answer is "they lose time and can fix it in a click", you have a lot of freedom. If it's "they send something embarrassing to a customer" or "they make a financial mistake", you need more care.
Four design choices that build trust
Show the work. Say what the answer is based on. Link to the source document, the record or the rule. People trust what they can check.
Make correction cheap. Undo, edit, regenerate. If the user can fix an error in five seconds, they'll tolerate it. If it takes five minutes, they'll stop using the feature.
Be honest about uncertainty. If the system doesn't know, it should say so and offer another route. A confident wrong answer does more damage than "I'm not sure".
Know when to hand off. Decide up front which cases go to a person, such as high value, high risk or low confidence. Build that route before launch.
Scope small
Pick a task that is frequent, bounded and easy to check, and make it very good. Trust compounds. A feature that is reliable at one thing earns permission to attempt the next.
What to do this month
- Write the failure story. For your feature, describe the worst plausible wrong answer and its consequence.
- Add one trust cue. Source links, a "based on" line, or a preview before anything is sent.
- Make undo free. For any action the feature takes.
- Define the handoff rule. What triggers a person, and what they see when it does.
- Measure corrections. How often users change the output is your best quality signal.
Original analysis by Upfinity.
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