Should you use AI to take meeting notes? An honest breakdown.
The honest answer is: yes, but not the way most people are using them.
If you're reading this in 2026, you've probably already tried at least one AI notetaker — Otter, Fireflies, Granola, Fathom, or the AI summary feature built into Zoom or Teams. You've also probably had one of these reactions:
- "This is magic." (The first week.)
- "Why does it keep getting my client's name wrong?" (The second week.)
- "I haven't opened this summary tab in two months." (The third week.)
The pattern is consistent enough that it's worth examining. AI notes are genuinely useful, and most people who try them give up. Both are true at once. Here's why — and what the people who stuck with them are doing differently.
What AI notetakers are actually good at
Three things, with high confidence:
1. Transcription. Modern speech-to-text is shockingly accurate for clear speech in good audio. Even cheap services hit 95%+ word accuracy on a typical 1:1 call.
2. Surfacing action items. AI is good at recognizing imperative language ("I'll send you the doc," "we should schedule a follow-up") and pulling it into a list. Better than most humans, actually.
3. Searchable archives. "What did Sarah and I decide about the brand refresh in February?" — searching across transcripts is the kind of recall task computers excel at.
If those three are your job-to-be-done, AI notes are a no-brainer.
What they're consistently bad at
1. Names of people and companies. Especially names that aren't common English words. "Aoife" becomes "Eva." "Zhao" becomes "Joe." "Anthropic" becomes "anthropic" or worse. You'll be correcting this forever.
2. Sarcasm, tone, and subtext. A line that's clearly a joke in context gets summarized as a commitment. AI is improving here but trust no summary that doesn't include some direct quotes.
3. Sensitive context. Anything where the what matters less than the how it was said — performance feedback, conflict resolution, delicate client conversations — gets flattened by summarization. The nuance is the message.
4. Multi-speaker chaos. Three+ people talking over each other, especially when speakers haven't been trained, produces transcripts where attribution is wrong half the time. Summary quality drops accordingly.
5. Long meetings. Beyond about 45 minutes, summary quality drops sharply. Most tools weight earlier content more heavily — your closing decisions get truncated.
When to use AI notes
Default yes for:
- 1:1 paid client calls (coaching, advisory, consulting).
- Recurring check-ins where continuity across meetings matters.
- Calls where you'd otherwise be splitting attention to take notes.
- Sales calls where action items and follow-ups are the deliverable.
Default no for:
- Performance reviews and feedback conversations.
- Therapy, coaching where the client needs full presence and privacy.
- Sensitive negotiations (legal, M&A, layoffs).
- Calls where one party has explicitly declined recording.
Gray area:
- Internal team meetings (depends on culture).
- Anything with NDAs or regulated data (depends on vendor).
- First call with a new prospect (depends on rapport risk).
The privacy question
This is the part most "should you use AI notes" articles dodge.
When you use Otter, Fireflies, or any of the major notetakers, you're sending audio of your client's voice to a third-party transcription service, which often runs on top of an LLM provider. That data may or may not be used to train future models depending on the vendor's terms. Even if it's not training data, it's being stored on servers you don't control.
For most consulting, coaching, and advisory work, this is fine — but only if you disclose it. The pattern that works:
"I use AI to take notes during our calls so I can be fully present. The recording is stored privately and used to generate a summary I share with you. If you'd prefer I take notes manually, just say so."
Putting this in your booking confirmation email pre-empts 99% of the awkwardness. Clients who object will say so. Most don't object — they appreciate the transparency.
What separates good AI notetakers from bad ones
Five things to look for:
1. Structured summaries, not just bullets. Decisions, action items, open questions, key context — separated. "Summary" with a wall of bullets is lazy.
2. Easy editing. You will need to fix names. The tool should make that easy and stick the fix everywhere.
3. Per-person attachment. Notes that live in a "Recordings" folder are useless. Notes that auto-attach to the person you met with are useful.
4. Selective sharing. You should be able to share with the client without sharing your private notes / impressions.
5. Clear data policy. "We do not use customer data to train models" should be in big letters on the privacy page. If it's buried, that's a signal.
Most major notetakers fail at #3. The notes exist, but in a place you'd never look during prep for next month's meeting.
The pattern that works
The people who get sustained value from AI notes treat them as a CRM substrate, not as a notes tool.
The workflow:
- Call happens. AI transcribes.
- Summary is generated and stored against the contact.
- Next time you meet that person, you see the last three summaries on the way in.
- Searchable archive across all client history.
The 4-step loop is the whole product. Everything else is plumbing. Tools that nail this loop get used for years. Tools that don't get tried and abandoned.
What we built
MeetingWith does AI notes, but structured around the person — not around the recording. Every call attaches to the booking, which attaches to the contact. When that contact books their fourth meeting, you see the first three summaries on the prep view.
That's the part that pays off long-term: AI notes that you actually re-read.
And it's free. Not a tier — the whole product. Claim a handle below.
FAQ
Are AI meeting summaries accurate enough to send to clients? Usually yes for action items and decisions; usually no for nuance. Always review before sending.
What's the most accurate AI notetaker? Quality is converging fast — transcript accuracy is essentially solved. The differentiator is summary structure and where the notes live afterward.
Will my client be weirded out? Some will, most won't. Disclose in the booking confirmation, not at the start of the call. Pre-emption beats apology.
Does Zoom's built-in AI Summary work? Yes, for basic use. It doesn't attach to a CRM, doesn't carry forward across meetings, and doesn't give you a per-client archive. Fine as a free starting point, limited as a system.
Can I self-host an AI notetaker for privacy? Yes, with Whisper for transcription and Llama/Mistral for summarization. Costs more than you'd think once you account for setup and maintenance time. For most solo pros, hosted is the right call — pick a vendor with a clear no-training policy.
If you want notes that survive past next week, MeetingWith is free — claim a handle below.