A Rundown of What We Automated with AI at Work (Hands-On)
Our team has spent about half a year running AI tools in real work, and I put together what actually sticks. To cut to the chase, full automation is still hard, and using AI as a 'draft generator' is by far the most efficient.
Here's roughly what we're actually using right now.
1. Meeting minute summaries — Drop in the recording and pull out only the action items. The time a person spends re-reading them dropped from 10 minutes to 2.
2. Document drafts — Generate and polish a draft proposal or announcement in 5 minutes. Much faster than writing from scratch.
3. Code review assistance — AI points out minor style issues; people make the structural calls.
4. Data cleanup scripts — Take care of tedious Excel work in one go with Python.
There were failures too. Customer support automation had to be rolled back because a human still had to check it again in the end, and the internal policy Q&A bot lost trust because of hallucinations.
What I took away is that AI has the biggest impact when you attach it not to 'work I don't want to do' but to 'the repetitive stretches of work I'm good at.' At first, everyone thought it was cool and attached it everywhere, then ripped it all out. What's left now is the real gems.
9 answers
Meeting minutes summarization is a total game-changer lol. Our team adopted it too, and the key is just extracting the action items.
No, but honestly, you end up rereading meeting notes anyway. Summaries often lose some important nuance, too. I’m not sure how meaningful the savings really are when you cut something that takes a person 10 minutes down to 2 minutes.
I really relate to the idea that it’s not about the work you hate doing, but about the repetitive parts of the work you’re good at. At first, I also had it do all the tedious stuff, and then I stripped all of that out.
Code review assistance really shines when it comes to automating style feedback. If AI filters out minor stuff like indentation, naming, and unused imports, people can focus only on the logic, so reviews feel about twice as fast. That said, if you don’t train it on your team’s conventions, it produces a lot of nonsense, so we had to redo a fair amount early on. As long as you keep that in mind, it’s worth using.
Got a source?
You wrote that you lost trust because of hallucinations, but isn't the issue not the tool but the fact that RAG wasn't added? For an internal policy bot, just adding search would solve most of it—this feels like a bit of a hasty judgment.
I've been doing something similar for about half a year now, and in the end, document drafts seem to be what sticks the most. If I write from scratch, I lose 30 minutes on the first sentence alone, but if I at least have a draft, I can get started. The only thing that slightly bothers me is that the tone of the outputs all ends up feeling similar.
I really relate to rolling back the customer support automation... I've seen it blow up a few times after deploying without going through QA
Can you go into more detail about the data cleaning script section? I’m most curious about automating the tedious Excel work—what combination of tools did you use? If it’s Python, I’m wondering whether you just wrote it with pandas or used code generated by AI.