Someone fixes a number in Excel. A colleague explains an exception on a call. The immediate problem disappears. But where does the learning go?
If it stays in that spreadsheet or with that person, the next team has to solve the same problem again. I’m interested in building systems where that effort leaves something useful behind.
An answer is not enough
AI makes analysis easier to produce. It does not automatically make it consistent or explainable. People need to understand which information, definitions and assumptions shaped a result.
That is why I care about shared context: the data, business definitions and previous decisions a system can draw on. A useful correction should update that context, not disappear when someone closes the conversation. The next relevant question should benefit from what has already been learned.
Make the correction usable
Consider a delivery-planning tool. It recommends accepting an order because the production schedule shows spare capacity. An operator rejects the recommendation: a machine is available, but nobody on that shift is qualified to run it.
A thumbs-down would tell me very little. I would want the product to capture what was missing: the qualification requirement, the shift it applies to and who can confirm it. Then I would check that information against the staffing record before using it in another recommendation.
The distinction matters. “Do not accept this order” is a decision about one situation. “This machine requires a qualified operator” is a constraint that could help with many future decisions. I want the system to preserve the reason, not just repeat the answer.
Remembering is not the same as believing
I would not let every correction become a company-wide fact. People disagree. Temporary workarounds expire. Someone can be right about their own site and wrong about another.
For me, a useful feedback process needs ownership, scope and a way to reverse a change. Who approved this? Where does it apply? When should it be reviewed? If a correction makes recommendations worse, can we identify it and roll it back?
I would test changes against known cases before relying on them more widely. A growing store of context is not, by itself, evidence of improvement.
Did the work actually get better?
In the planning example, I would look for fewer infeasible schedules and fewer last-minute interventions. I would also ask the operators whether the tool had stopped making them explain the same constraint every morning.
Those are the tests I care about. Not how many comments were collected, or how often someone opened the application, but whether a correction made the next decision more useful.
That is the feedback loop I want to build: people can challenge the system, see what changed and judge whether it helped. Their effort should improve the work, not become another inbox somebody has to manage.