Imagine an AI assistant turning a meeting into a project plan. The summary reads well. The next steps look reasonable. Then you notice that a task has been assigned to the wrong person.
It’s a small mistake. What happens next tells you a lot about the product.
Can you change the owner directly? Does the correction stay in place when the plan refreshes? Or do you have to explain the entire meeting again and hope the next version gets it right? Good AI design needs an answer to those questions, not just an impressive first result.
Make correction part of the main experience
Correction shouldn’t feel like a special request. If a system can suggest a task owner, the person reviewing that suggestion should have a straightforward way to choose someone else. The control belongs beside the suggestion, where the problem becomes visible.
This is an established design principle. Microsoft Research’s human-AI interaction guidelines recommend making incorrect AI behavior easy to edit, refine, or recover from. They also call for simple ways to dismiss unwanted assistance.
In our hypothetical project plan, editing one assignment should change that assignment without disturbing everything else. Someone who fixes a name shouldn’t have to check whether the deadlines changed too. Keeping the effect of an edit narrow makes it easier to understand what happened.
A VIA perspective: Progress includes the freedom to change direction.
Separate fixing the work from teaching the system
A thumbs-down button can tell a product team that something missed the mark. It doesn’t necessarily fix the task in front of the person using it.
Those are different needs. Someone reviewing the plan wants the right colleague assigned now. They may also want to explain why the original suggestion was wrong, but that explanation shouldn’t be the price of getting their work done.
The interface should be clear about what each action does. Does an edit apply only to this plan? Will a preference be remembered? Is a report going to a support team? Avoid suggesting that feedback immediately retrains a model unless that’s genuinely how the product works.
This matters when the situation changes, too. A colleague covering a task this week isn’t necessarily its permanent owner. Giving people a choice between a one-time correction and a lasting preference can keep a temporary exception from becoming a future assumption.
Design a way forward when the suggestion fails
An apology is a start, not a recovery path. Google’s People + AI Guidebook treats helping people continue their task after a failure as a core part of the experience.
For the project-plan example, that could mean keeping the original meeting notes accessible and letting someone finish the assignments manually. If generating a new version fails, the previous work should still be there. The person shouldn’t lose their progress because the AI couldn’t complete its part.
Reversibility helps, but it has limits. Changing a draft is different from sending an assignment notification to a colleague. A useful review step shows what is about to leave the workspace before it does. Once something has been sent, the interface should explain what can be changed and what recipients may already have seen.
That is a practical expression of human agency: people can intervene at a moment when their choice still matters.
Test the second move, not just the first result
When reviewing an AI workflow, deliberately introduce a believable mistake. Pick the wrong task owner or use an outdated deadline. Then watch someone try to correct it without coaching them.
Notice where they hesitate. Can they find the control? Is it clear whether the change was saved? When the system runs again, does it respect their correction? These questions reveal parts of the experience that a polished demonstration can miss.
Start with one recurring workflow this week. Trace the path from a wrong suggestion to a corrected result, including any messages or actions already triggered. Where that path becomes confusing, you have a concrete design problem to work on.
At VIA Engine, we believe intelligent software should help people move forward with understanding and control. A useful system doesn’t need to pretend it will always be right. It needs to leave people able to put things right when it isn’t.
