I resisted AI meeting notetakers for a long time. Not because I doubted they could transcribe — speech-to-text has been solid for years — but because "here's everything everyone said, searchable!" never sounded like a problem I actually had. I don't need a transcript of a 40-minute call. I need to remember the one thing I agreed to do by Friday.
So I ran an actual test: two weeks, every call, an AI notetaker joining as a silent participant. I expected to end up mildly annoyed by a bot lurking in my calendar. Instead I found the feature I didn't ask for was the one that changed how I close out a workday.
Photo by Mikhail Nilov on Pexels
What I Actually Tested
I used one of the standalone meeting-notetaker apps (the category includes tools like Otter, Fireflies, and Granola, along with notetaking built directly into Zoom and Google Meet now). I'm not naming a single winner here, because the differences between them matter less than how you use the output — more on that in a second. The setup is the same across most of them: the bot joins as a participant, transcribes in real time, and afterward hands you a summary plus a list of what it thinks are action items.
For two weeks I let it run on everything — quick 15-minute syncs, longer planning calls, even a couple of calls where I was mostly listening. I wanted to see where it actually earned its keep versus where it was just noise.
The Transcript Is the Least Useful Part
Here's my honest take, and it's the one place I'll push back on how these tools are usually marketed: the full transcript is basically dead weight for 90% of calls. I opened it maybe three times across two weeks, and only when I needed an exact quote or number someone said. Scrolling a wall of "so yeah I think, um, we could probably" text is not faster than just asking the person again.
What actually got used, every single day, was the short summary and the action-item list sitting at the top. That's the part worth paying attention to if you're evaluating one of these tools — not "how accurate is the transcript" but "how good is the summarization, and does it correctly figure out who owns what."
Where the Action Items Actually Helped
Say you're on a 30-minute call with two colleagues about a project timeline. Six or seven things get decided or half-decided — a deadline gets pushed, someone volunteers to check with a vendor, you agree to send over a doc "later today." In the moment, you're focused on the conversation, not writing it down. By the time the next call starts twenty minutes later, half of that is already fuzzy.
What the AI summary did well was catch the small commitments that don't feel important enough to write down manually but absolutely matter later — "you'll send the doc," "she'll follow up with the vendor by Wednesday." Those are exactly the things that fall through the cracks when you're taking your own notes, because your own notes tend to track the topic, not the todo.
Where it did worse: judgment calls. On one call, someone said "let's not commit to that yet, we'll revisit," and the summary listed it as an open action item anyway. It's picking up on task-shaped language, not actually reading the room. I'd put its accuracy at "genuinely useful first draft," not "trust it blind."
The Habit That Made It Worth Keeping
The thing that made this stick for me wasn't the AI part at all — it was that I started reviewing the summary within an hour of each call and moving real items into my task list immediately. If I let three days of call summaries pile up unread, they were worthless. Old summaries are just old transcripts with extra steps.
That's consistent with something I've noticed across a lot of these AI productivity tools: the tool itself does maybe 40% of the work. The other 60% is whether you build a five-minute habit around using its output. I ran into a version of this same lesson when I built out a [text snippet library](#) for repetitive typing — the tool only pays off once it's part of your actual workflow, not sitting there as a novelty.
Where It Falls Apart
Photo by Yan Krukau on Pexels
A few things I'd flag before you rely on this for anything important:
- Sensitive conversations. Performance reviews, anything with legal or HR implications, anything a participant might not want recorded — don't run a bot on these. Most tools will announce themselves joining, but not everyone notices or reads the notification, and that's a real trust issue, not a minor one.
- Overlapping speakers. Any call with more than three or four people talking over each other gets noticeably worse attribution. It'll sometimes assign a comment to the wrong person.
- Jargon-heavy calls. If your team uses a lot of internal shorthand or acronyms, expect the summary to occasionally garble them. It's not wrong often, but when it is wrong, it's confidently wrong, which is worse than an obvious error.
- Consent and workplace policy. Some companies have explicit rules about recording calls, and some don't have rules simply because nobody's thought about it yet. Check before you turn one of these on for external calls, not just internal ones.
A Smaller Thing I Didn't Expect
The unexpected upside was for calls I couldn't fully attend — the ones where I dial in for the first ten minutes and then have to drop for something else. Instead of asking someone to recap later, I'd read the summary of the part I missed. That's a narrower use case than "replace your notetaking," but it's the one where the time saved felt the most real, probably because the alternative (interrupting a colleague to ask "so what happened after I left") is genuinely annoying for both people.
FAQ
Do I need a paid plan to get useful results?
Most of these tools have a usable free tier, but the free tier usually caps how many minutes or meetings you get per month, and some limit you to shorter calls. If you're testing this out, the free tier is enough to figure out whether the habit sticks before you pay for anything.
Does it work well on calls with people who have accents or speak quickly?
It handles this better than I expected, but not perfectly — I noticed more correction needed on fast cross-talk than on any accent-related issue specifically. The bigger accuracy killer by far was multiple people talking at once, not how any individual person spoke.
Is my data safe with these tools?
That depends entirely on the specific app and your organization's policies, and it's worth actually reading the privacy terms rather than assuming — some tools use call data to train models unless you opt out, and some keep transcripts indefinitely by default. If you're on a work account, check with whoever manages your company's tools before turning this on for client or internal calls.
The Actual Takeaway
I went in expecting to be unimpressed by another "AI does the boring thing for you" pitch, and I was right about the transcript — that part really is overhyped. But the action-item extraction, paired with a quick same-day review habit, quietly fixed a problem I'd been living with for years: promising something on a call and then forgetting I promised it. That's a small, unglamorous win, but it's the kind that actually survives past the first week of novelty, which is more than I can say for most tools I've tried on this blog.
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