AI tools can now translate natural-language questions into SQL and generate answers from business data. But getting a query to run doesn’t guarantee that the answer is correct.

A question like “Which customer segment is most profitable?” can be interpreted differently depending on how the business defines profit, segments customers, and handles missing data.

For teams experimenting with AI-powered analytics, I’m curious:

• Are you letting AI query production data, or keeping it in a sandbox?

• How do you verify that the numbers and business definitions are correct?

• What’s been the biggest issue so far: SQL errors, missing context, or confidently wrong answers?

I’d be interested to hear what has actually worked in practice.