Here’s something that surprised me when I first started looking at this closely: most AI meeting note tools don’t actually understand your meeting. They transcribe it, then summarize the transcript. Those are two very different things, and the gap between them explains most of the complaints people have about these tools.
Let me break down how they actually work, because once you understand the pipeline, the limitations make a lot more sense.
the basic pipeline, step by step
Every major AI meeting notes tool runs roughly the same six-step process:
- Audio capture , usually through a virtual microphone or a bot that joins your Zoom/Meet/Teams call
- Speech-to-text , most tools use OpenAI’s Whisper model or a fine-tuned version of it
- Speaker diarization , figuring out who said what (this is still surprisingly hard and fails more than people expect)
- NLP processing , chunking the transcript into meaningful segments
- Summarization , running those chunks through a language model
- Action item extraction , identifying things that sound like commitments or tasks
Steps 1 and 2 are well-solved. Whisper is genuinely good and the transcription quality at step 2 is usually high. Step 3 is where things start getting messy.
Speaker diarization is one of those problems that looks easy until you try to solve it. In a call with 2 people who have clearly different voices and a stable internet connection, it works well. Add 5 people, someone joining from a noisy coffee shop, and a speaker who tends to talk over others, and the diarization errors compound. A research study from the University of Michigan found that meeting retention drops roughly 40% when people are simultaneously taking notes, which is a compelling argument for these tools. But a tool that attributes 15% of quotes to the wrong speaker creates its own kind of confusion.
what actually makes good AI notes
Good AI notes are not a shorter version of the transcript. That sounds obvious but it’s where a lot of tools fail.
A good summary captures decisions, not just discussion. There’s a meaningful difference between “the team discussed options for the database migration” and “the team decided to use read replicas for the migration and Alex owns the implementation plan by Friday.” Most tools produce something closer to the first version because summarizing decisions requires understanding what was resolved, not just what was said.
Action items are the other thing that separates good tools from mediocre ones. Capturing “someone said they’d look into this” versus “Alex committed to delivering the implementation plan by Friday EOD” requires the model to track ownership and deadlines, which is harder than it sounds when people speak in ambiguous terms in real meetings.
(Side note: the most useful meeting notes I’ve ever seen were from a 47-minute call where someone ran the Craqly Auto Notes feature. The summary was 9 bullet points and every single one had an owner and a date. That’s the bar worth aiming for.)
cloud vs. local processing: the privacy question
This is the thing nobody wants to talk about until after they’ve already used a cloud-based tool for 3 months.
Cloud-based tools (Otter.ai, Fireflies.ai, most of the major ones) send your audio to their servers for processing. That’s how they achieve the accuracy they do , the processing power required for real-time transcription and summarization is significant. For most meetings, this is probably fine. For meetings involving unreleased product roadmaps, legal matters, personnel decisions, or anything else that would be sensitive if it leaked, it’s worth thinking through.
Local processing tools exist. They’re generally slower and the quality is lower. There’s no clean answer here. “It’s in our terms of service” is not the same as “your data is safe,” and different people have different risk tolerances.
A few questions worth asking before committing to any cloud-based tool: does the vendor train their models on your meeting content? What’s their data retention period? Can you opt out of training without losing product features?
where these tools still fall short
AI meeting notes can’t capture what wasn’t said.
The person who’s been conspicuously quiet during every product discussion. The body language when the CEO’s estimate gets challenged. The 30-second pause before someone agrees to a deadline. These signals carry meaning in real meetings that audio transcription can’t recover.
They also struggle with technical vocabulary specific to your domain. If your team has internal codenames, shorthand, or acronyms, early transcriptions will be wrong in ways that propagate through summaries in confusing ways. Most tools have a custom vocabulary feature, but it requires setup and ongoing maintenance.
I’d also be skeptical of the “automatic action item” feature in most tools. It catches the obvious ones. It misses the ones that got agreed to indirectly (“yeah, let’s do that”) or through back-channel follow-up. Treat auto-extracted action items as a starting point, not a complete capture.
getting started without overcomplicating it
Pick one tool and run it for two weeks on all your recurring meetings. Don’t try to evaluate three tools simultaneously. The thing you’re testing for is not accuracy on a sample meeting. It’s whether the summaries are actually useful enough that you refer back to them, and whether the action item capture is reliable enough that you can stop keeping a separate list during calls.
If the summaries sit unread, the tool isn’t working for you regardless of its benchmark scores. That’s the test that matters.
The BLS American Time Use Survey has consistently found that professional workers spend a significant portion of the workday in meetings and meeting-adjacent activities. That’s the time these tools are competing for. The useful question isn’t whether AI meeting notes are better than nothing. It’s whether they’re reliable enough to actually change how you run meetings, and for most people, that’s still a calibration in progress.