Every AI assistant will occasionally give a wrong answer. This is not a failure of the technology. It's a property of the technology — one that you can design around, but not design away.
The teams that handle this well treat wrong answers as a normal operational event, not a crisis. The teams that handle it badly treat every error as evidence that the assistant can't be trusted — and slowly stop using it.
Make it visible
The first design decision is transparency. Users need to be able to see, clearly and quickly, when the assistant is uncertain about its answer.
We build explicit uncertainty signals into every assistant: a clear indication when the assistant is drawing from limited information, when it's making an inference rather than citing a fact, and when it recommends verification before acting.
This isn't just about preventing errors. It's about calibrating trust.
Keep humans in the loop for high-stakes outputs
Not all wrong answers cost the same. An assistant that mislabels a ticket priority creates a minor inefficiency. One that sends an incorrect response to a frustrated customer creates a real problem.
For high-stakes outputs, the design should always include a human review step. Design it into the workflow explicitly. If you leave it to users to remember, they won't — especially once the assistant has been reliable long enough that reviewing feels unnecessary.
Build a feedback loop
When a user catches a wrong answer, something should happen. A simple flag — "this answer wasn't right" — attached to the interaction and reviewed weekly is enough to catch patterns. Teams that have a visible feedback loop maintain higher engagement over time than teams where feedback disappears into a void.
Assistants that have a feedback loop improve over time. Assistants that don't drift — slowly becoming less accurate as the world around them changes.
What to say to your team
When you deploy an AI assistant, be honest with your team about what it is and isn't. It will occasionally be wrong. When it is, the right response is to flag it, not to give up on it. Every flag is an improvement opportunity.
Teams that go in expecting perfection are disappointed by the first mistake. Teams that go in expecting occasional errors — and knowing what to do about them — build a healthier and more durable relationship with the assistant.
Design for wrong answers from the start. It's the difference between a system that earns trust over time and one that loses it after the first mistake.