Arslan Ali

One of the biggest risks with AI analytics is not when the system says, I don t know.

It is when it gives a confident, polished answer based on the wrong source.

A company may have dozens of tables that could technically answer a question, but only one reflects the definition the business actually trusts.

How are teams helping AI distinguish between data that is available and data that is actually correct for the question?

It doesn't even have to be a big dramatic failures, more the small moments where something clicked and you went from trusting the output by default to double checking everything. For me it was watching an agent confidently rename a function across twelve files, then leave the original function untouched in a thirteenth file it apparently didn't search, with zero indication anything had been missed. It wasn't even a hard case, the file just wasn't in the directory it happened to grep first.
What was your moment? And did it actually change your workflow afterward, or did the trust creep back in after a week like it always seems to for me?

Guys, at the end of March, this problem appeared on ProblemHunt.

Nika @busmark_w_nika, a marketer from Slovakia, came across it. She was dealing with the exact same issue and couldn't find a solution anywhere. In the end, she decided to build a product for herself and even started learning programming. Recently, she published her first Chrome extension that solves her problem. She's now looking for testers. If anyone is interested in her product and would like to help with feedback she would be very grateful. Her Email: businessmarketingwithnika@gmail.com.