AI Note Taking Apps Compared 2026: What I Use (and What I Ditched)

The short version: Granola and Fathom are the two I trust with client calls right now, Otter.ai is still fine for straightforward transcription on a budget, and NotebookLM is the one I use for research rather than meetings. None of them replace taking your own notes entirely, and the ones that promise to "never miss a moment" have quietly made a lot of people worse at listening. I'll explain why that matters below, not just which app has the nicer interface.

Why this comparison is different from the others you've read

Most "best AI note taking apps" posts are written by someone who signed up for the free trial of each tool, poked around for twenty minutes, and ranked them by their own marketing pages. I've used five of these tools in actual paid client meetings over the past eighteen months, including one that went badly wrong in a way I'll get into further down. So this isn't a features list copied from each company's pricing page. It's what happened when I put my business, and other people's confidential information, through these tools.

I run an AI consultancy across the UK, the US, and Israel, and I sit in three to six calls a day most weeks. If a note app wastes my time or leaks something it shouldn't, I feel it immediately. That's the lens for everything below.

The apps, what they cost, and where each one falls down

Pricing changes fast in this space, but here's roughly where things sat as of late 2026:

• Granola - £14 to £18 a month for an individual plan. No bot joins your call, it listens locally and turns your rough typed notes into a structured summary afterwards. This is my daily driver for one-to-one client calls. The catch: it only works well if you're on a Mac, and the free tier caps you at a handful of meetings a week.
• Fathom - free for personal use, £15 to £19 a month per seat for team plans with CRM push-through. Joins Zoom, Google Meet, or Teams as a visible bot, records, transcribes, and pulls out action items automatically. Strong on accuracy, weak on subtlety, it will confidently summarise a joke as a decision point.
• Otter.ai - Pro is around £8.99 a month, Business closer to £20 a seat a month billed annually. Solid, cheap, unglamorous. The transcription quality has plateaued for two years while everyone else has improved, and it still struggles badly with strong regional accents and crosstalk.
• Fireflies.ai - Pro sits around £10 a seat a month. Good if your whole team lives in Slack and HubSpot, because the integrations are tidy. On its own, without those integrations, it's unremarkable.
• Microsoft Copilot for Teams meeting notes - bundled into a Copilot licence at roughly £24.70 a user a month on top of your Microsoft 365 plan. If your organisation already pays for Copilot, this is the path of least resistance. If it doesn't, the price on its own doesn't stack up against dedicated tools.
• NotebookLM (Google) - free, with a paid tier through Google One AI Premium. Not really a meeting note app at all, it's a research and synthesis tool that happens to be excellent at turning long transcripts, PDFs, and voice memos into structured summaries and even audio overviews. I use it after the meeting, not during it.
• Plaud Note - a small hardware recorder, around £129 to £159 plus a subscription of roughly £79 a year for unlimited transcription. Useful for in-person meetings and conferences where nobody's laptop is open, but it's one more device to charge and carry.

If you're weighing these up as part of a wider push into building a one-person AI business, your note app is basically your second brain, and it's worth treating the decision with the same seriousness as picking your invoicing software, not as an afterthought you'll sort out later.

The client call that changed how I use these tools

About a year ago I was on a discovery call with a mid-sized retail client in the US, using a bot-based transcription tool (I won't name the exact one, because the mistake was mine as much as the software's). The call ran long, we jumped between three topics, and the transcript came out clean and confident-looking. I skimmed the AI summary afterwards, trusted it, and sent a follow-up email referencing a budget figure the summary had attributed to the client.

They hadn't said that number. The AI had merged two separate sentences from different parts of the call into one tidy, plausible-sounding statement. It wasn't a wild hallucination, it was a small, believable stitch, which is exactly what makes these errors dangerous. The client politely corrected me, I apologised, and it cost me nothing but a bit of pride and twenty minutes of damage control. But it taught me a rule I now stick to without exception: I read the full transcript on any call involving numbers, contracts, or commitments, not just the AI-generated summary. The summary is a starting point, not a source document.

That single habit change, reading the transcript rather than trusting the summary, has caught at least four smaller errors since, none as costly as that first one but all worth catching.

The uncomfortable part nobody selling these apps wants to say out loud

Here's the bit that gets left out of most comparisons. These tools work brilliantly, and that's part of the problem. Once you know the AI is capturing everything, most people stop listening in the way they did before. I've watched it happen in my own meetings and in my own head. You relax, you let your attention drift because "it's all being recorded anyway," and you come out of a sixty minute call with less retained understanding than you'd have had scribbling three bullet points on a notepad.

The research on this isn't new, it's the same effect studied around photography and memory, sometimes called the photo-taking impairment effect, where relying on a device to capture something reduces how well you remember it yourself. Offloading a meeting to a transcription bot is the audio version of that.

The second uncomfortable truth is legal, not psychological. Recording a call and running it through an AI transcription service means you're capturing someone else's voice and words on a third-party server. In two-party consent states in the US, like California, Florida, and Illinois, you're legally required to get explicit consent from everyone on the call before recording, not just a general disclaimer buried in your calendar invite. In the UK and EU, UK GDPR and the EU equivalent require a lawful basis for processing that recording, and "the AI notetaker just joined automatically" isn't one. I now say, out loud, at the start of every recorded call: "I'm using an AI notetaker for this, is that alright with everyone?" It takes four seconds and it has never once gone badly.

Most of the "best AI notetaker" articles you'll find don't mention consent at all. That's a gap worth being uncomfortable about.

How to choose, based on your workflow, not the feature list

Skip the comparison chart mentality and ask three questions instead:

• Are your meetings one-to-one or group calls? Granola-style tools that skip the visible bot work well for one-to-one client work where a recording bot feels intrusive. Bot-based tools like Fathom or Fireflies suit team meetings where everyone already expects a note taker.
• Do you need action items pushed into a CRM or project tool automatically? If yes, look at Fireflies or Fathom's integration list before you look at transcription accuracy, because the integration is what saves you time day to day.
• Is most of your work research and reading rather than meetings? Then a meeting notetaker is the wrong tool entirely. NotebookLM, fed with PDFs, transcripts, and voice memos, will do more for you than any live meeting bot.

I switched three clients off Otter.ai and onto Granola last year, purely because they were solo consultants doing back-to-back discovery calls, and the lack of a visible bot made prospects more relaxed and more talkative. Their close rate on those calls didn't change measurably, but the quality of the notes they got back afterwards, and the time saved not writing anything up manually, was worth the switch on its own.

A five-step setup that works

Whichever tool you pick, here's the setup that has saved me the most time, step by step:

• 1. Turn on auto-join for calendar events only for internal and recurring client meetings, never for first discovery calls, where you want to ask verbally rather than let a bot appear uninvited.
• 2. Set a custom summary template if the tool allows it (Fathom and Fireflies both do), so every summary comes back in the same shape: decisions, action items, open questions. Consistency here is what makes summaries scannable six months later.
• 3. Export transcripts to a single folder, dated and named by client, rather than leaving them scattered inside the app. I use a simple naming convention: date, client name, call type. It takes ten seconds and has saved me hours of searching.
• 4. Read the full transcript, not just the summary, on any call involving money, dates, or commitments. This is the one habit from my earlier mistake that I'd tell anyone to steal.
• 5. Delete recordings you don't need after 90 days. Most of these tools default to keeping everything forever, which is a data liability, not a feature, especially once GDPR or a client NDA gets involved.

What I'd tell someone starting from scratch in 2026

If you only want one tool and you're not fussy: Fathom's free tier is enough for most solo professionals doing under ten meetings a week. If you're on a Mac and do a lot of one-to-one client work, pay for Granola, it's the one tool on this list that changed how my calls feel, not just how my notes look. If your work is more reading and research than meetings, stop looking at meeting notetakers altogether and spend an afternoon learning NotebookLM instead. And whatever you choose, say out loud that you're recording, every single time. It's the cheapest insurance you'll ever buy.

Frequently asked questions

Which AI note taking app is most accurate for strong regional accents?

In my testing, Fathom and Granola both handled Scottish, Irish, and non-native English accents noticeably better than Otter.ai, which still struggles with fast crosstalk and heavy accents in group calls. No tool is perfect on this, so if accuracy on accents matters for your work, test with a real recording of your own voice before committing to an annual plan.

Is it legal to record a client call with an AI notetaker without asking first?

In two-party consent US states such as California, Florida, and Illinois, no, you need explicit consent from everyone on the call, not a passive notice. In the UK and EU, you need a lawful basis under GDPR to process that recording. The safest habit, regardless of jurisdiction, is to ask verbally at the start of the call.

Do AI note taking apps work for in-person meetings, not just video calls?

Yes, but you need a different tool. Hardware recorders like Plaud Note, or simply running Otter.ai or Fireflies from your phone's microphone, work for in-person meetings, though audio quality drops noticeably in noisy rooms compared with a direct video call feed.

Should I trust the AI-generated summary or read the full transcript?

Trust the summary for low-stakes meetings, but read the full transcript for anything involving numbers, dates, contracts, or commitments. AI summaries can merge separate statements into one plausible-sounding sentence that nobody said, and that error is easy to miss if you only skim the summary.

Related reading: Should You Let an AI Notetaker Into Your Client Meetings?.

Related reading: AI Meeting Notes: What They Get Right, What They Miss, and When I Switch Them Off and I Recorded 40 Client Calls With AI Note-Takers. Here's the Bit Nobody Mentions..

Related reading: The AI Notetaker Problem: What Happens When a Client Spots the Bot on Yo.