What would it take for you to trust an AI-generated lesson from your own past decisions?
AI tools can now read through a team’s history (meeting notes, docs, project data, metrics) and summarize it into lessons: “this approach worked,” “stop doing that.”
I’ve spent over a decade in IT project management, and I’m skeptical. Not because the AI can’t find patterns, but because I’ve seen how easily the record itself misleads. A project can be marked green on schedule and budget while the reasons it was approved have quietly stopped being true. If the AI learns from that record, it learns the wrong lesson with total confidence.
Here’s where I’ve landed so far. I’d need three things before acting on an AI-generated lesson:
The evidence, visible. Which numbers moved, by how much, over what time period. Not just a conclusion.
The context at the time. A call that worked in a growth year might fail in a cost-cutting year. The lesson should say when it was true.
A human sign-off. Someone who was there confirms it before it becomes “how we do things.”
I’m also wary of one trap: a metric improving after a decision doesn’t mean the decision caused it. A lot of “lessons” are really coincidences.
What would you need to see before you’d trust and act on a lesson an AI pulled from your team’s history? And has an AI summary ever led you to the wrong conclusion?
Replies
I think that's the main challenge with letting AI take the reins and make executive decisions like that. I don't think we're completely there yet because there are still human elements missing that may never be able to be replicated. That is the missing key.
My trading research has a good example of a record that misleads. A swing strategy showed +6.17% per trade on the holdout set. It would pass your first two checks: clear number, clear period. Only when I re-ran the parameter grid with the periods swapped did it turn out to be overfit. So I'd add a fourth: the lesson should say what test could have proven it wrong, and whether anyone ran it.