At work, we recently talked about AI. One use case mentioned (by an AI consulting firm, not us or actually suggested for us) was meeting summaries and extracting TODOs from them.

My stance is that AI could be useful for summaries about topics so you can see what topics were being talked about. But I would never trust it with extracting the or all significant points, TODOs, or agreements. You still need humans to do that, and have explicit agreement and confirmation of the list in or after the meeting.

It can also help to transcribe meetings. It could even translate them. Those things can be useful. But summarization should never be considered factual extraction of the significant points. Especially in a business context, or anything else where you actually care about being able to trust information.

I wouldn't [fully] trust it with transforming facts either. It can work where you can spot inaccuracies (long text, lots of context), or where you don't care about them.

Natural language instructions to machine instructions? I'd certainly be careful with that, and want to both contextualize and test-confirm it works well enough for the use case and context.

we had a plenty of more deterministic tools for parsing human readable text to machine-readable long before llms