I had a team proudly show me how they were feeding their dashboard data into an LLM to predict growth needs, instead of doing basic linear regression or any number of pre-existing basic statistical analysis. The part that depressed me the most on that was it was a bunch of engineers that did it, the people you'd most hope would understand what an LLM is/does.
You can go “here’s a spreadsheet of some data, do a thing to it” and it’ll go through, have it plan out the type of calculations you want to do, and it’ll write you a program to parse your sheet, calculate what you want, then output an result sheet.
The key to good results with minimal effort seems to be the planning modes to me, combined with the /goal command. A little bit of validation before implementation goes a long way.
I'm mostly starting to have some success telling TPMs and engineers to ask chatgpt to give them appropriate code that generates more accurate answers.
However sometimes I think that AI use shows a revealed preference around things. In this case, the actual data just ... maybe never mattered to them at all? People wanted a shiny chart and they got a shiny chart.