Send the Tape Recorder: Choosing the Right Meeting AI for Your Enterprise

When I think about AI meeting agents, my mind doesn’t go to Silicon Valley or some hot new startup launch.

It goes to a 1985 Val Kilmer movie.

In Real Genius, there’s a running gag where students stop attending a lecture and start sending tape recorders instead. At first, there are one or two devices on the desks. Eventually, the professor walks into a classroom that’s just rows of recorders listening politely on behalf of their owners. In the final beat of the joke, even the professor stops showing up and leaves his own tape recorder.

That scene was meant as an absurdist comedy about checked-out students.

But it's uncomfortably close to how many of us are starting to treat meetings—and the numbers explain why.

According to McKinsey, 61% of executives say that at least half the time they spend making decisions — much of it in meetings — is ineffective. Harvard Business Review found that 71% of senior managers consider meetings unproductive and inefficient. Atlassian's research found that 38% of meeting action items never even get documented in official notes. And ineffective meetings cost U.S. businesses an estimated $37 billion annually in lost productivity.

We don’t always send tape recorders anymore — we send tools like Granola, Fathom, Fireflies, Otter, and increasingly, Notion itself as our AI notetaker.

And that raises a deeper question for enterprises than "Should we use meeting AI?" That line's already been crossed. With 55 million meetings held every week in the U.S. alone and the average knowledge worker spending nearly 400 hours per year in meetings, the question isn't whether to capture this time — it's how.

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Granola vs Fathom vs Fireflies vs Otter—what actually changes when bots start “attending” for us

The real question is: What happens to how we work when more and more of our “attendance” is delegated to machines — and eventually even the humans stop showing up?

To answer that, it helps to understand the different philosophies emerging in meetings that use AI, and how the current set of tools maps to them.

Under the hood, most meeting agents today fall into one of three camps.

In this model:

Classic examples include Fathom, Fireflies, and Otter.

In this model:

Granola Workspace-native notetaker

In this model:

Notion, Productivity Suite Plus Note Taker

The Real Genius lecture hall was full of literal sidecars — recorders that sat in for their humans. But in the enterprise, this architectural choice has real implications.

Perception and friction: bots can make external guests nervous or trigger legal questions (“Are we being recorded? Who owns this?”). Sidecars are invisible to everyone except the person using them. Workspace-native notetakers often rely on humans (or lightweight capture) and feel least intrusive, because “taking notes in Notion” looks like regular work.

Data footprint: bot-based tools often store complete audio recordings and, in some cases, video. Sidecars can store only transcripts and notes or retain additional data on-device. Workspace notetakers tend to store text notes and AI summaries, not raw audio, unless you explicitly pipe recordings in.

Analytics and leverage: bots give you the richest dataset for conversation intelligence — talk-time analytics, libraries of objections, coaching dashboards. Sidecars optimize for individual productivity and privacy. Workspace notetakers optimize for long-term knowledge management: meeting notes linked to projects, docs, tasks, and decisions.

The documentation gap is real: research shows that 63% of meetings are conducted without a predefined agenda, and 54% of workers leave meetings without a clear idea of what to do next. AI notetakers don't just save time — they create a record that actually captures decisions and next steps.

Everything else in this article is basically one big consequence of those architectural choices.

When you choose a meeting agent for your organization, you’re not just picking a vendor. You’re picking a philosophy of what “showing up” means — and where your meeting knowledge lives.

With that frame in mind, here is how five popular tools line up: Granola, Fathom, Fireflies, Otter, and Notion. I’ll also share where I personally land, as someone who uses Fireflies today — and why, despite liking Granola’s approach, I would actually choose Notion as my core workspace over Granola in many cases.

Granola is what happens when you take that Real Genius tape recorder, give it a neural cortex, and tuck it inside your MacBook.

Granola runs as an app on your device. It listens to your system audio and microphone locally rather than joining as a participant. The result is:

Granola positions itself as an AI notepad for teams that want better notes without awkward meeting bots. Its pitch is very clear: “no awkward meeting bots” and “beautiful notes for you and your team.”

Who it is really for:

On the enterprise front, the sidecar architecture gives Granola a strong story: no bots join the meeting, and the company focuses on enhancing notes and transcripts rather than on long-term audio storage. It also integrates with tools like Slack, HubSpot, Notion, and Zapier to push structured notes into existing workflows.

I like Granola’s approach. I like sidecars. I like the privacy implications of not having a bot on every call.

But if I’m being honest about how I work today, I would still choose Notion as my core workspace over Granola for a lot of what Granola is trying to help with.

Notion is an all-in-one AI workspace that already holds so much of my docs, tasks, project spaces, and personal knowledge. When I think about structured notes, templates, and long-term knowledge, my instinct is to centralize that in Notion and then plug AI into that environment — rather than add another dedicated AI note app on top.

So my view:

If you are starting from scratch, Granola is a compelling default. If you are already deeply invested in Notion and Notion AI, I suggest doubling down on Notion and using meeting tools that push structured data into that workspace.

Fathom sits firmly in the bot-in-the-room camp, but with one big strategic move: an unusually generous free tier.

A Fathom bot joins your Zoom, Google Meet, or Microsoft Teams calls, records them, and automatically generates:

These notes can then sync with tools like Slack, Salesforce, HubSpot, Notion, and Asana.

Fathom’s product motion is simple and powerful:

From an enterprise perspective, Fathom is about legitimizing a behavior that is already happening—people quietly installing meeting bots to avoid taking notes—and wrapping it with proper controls later.

Fathom shines in these scenarios:

The main caveat is ensuring the free tier doesn’t become completely unmanaged before IT and security are looped in. You want the organic love, but you don’t want a shadow fleet of bots recording everything with no governance.

Fathom's promise is straightforward: never retake notes, get automatic transcripts and summaries, and keep your attention on the conversation. That last part matters more than it sounds — Microsoft's research on meeting quality found that teams using AI meeting notes report 28% higher engagement when participants can focus on discussion rather than documentation.

On paper, Fireflies looks like another meeting bot. In practice, it is much closer to a conversation-intelligence platform.

A Fireflies bot joins your calls, records them, and then:

This is not just a notetaker. It is a system for turning spoken conversations into structured, queryable data. And that matters: Doodle's State of Meetings report found professionals waste an average of 31 hours per month in unproductive meetings — time that could be reclaimed if those conversations were searchable, analyzable, and actionable.

This is also where I should be explicit: I am a Fireflies user.

I use Fireflies today because I care a lot about the “conversation data” aspect. I want to be able to search across calls, mine them for patterns, and treat them as a dataset that can feed downstream tools and models. The Fireflies approach — plus its desktop app and analytics features — fits that mindset.

Most importantly, Fireflies is especially strong in sales, customer success, and RevOps contexts:

Fireflies markets itself as an AI notetaker that can transcribe, summarize, search, and analyze all your team's conversations. That “analyze all your team conversations” line is the real story.

There is fine print, though: you need to understand pricing tiers, usage caps, and any AI credit systems, so you are not surprised as adoption grows — especially if your team runs lots of 60–90 minute calls. But if your mental model is “a lighter-weight, more accessible conversation-intelligence stack” rather than “a simple notetaker,” Fireflies makes a lot of sense.

Otter is the name many people already know from the early days of AI-driven meeting notes.

It offers:

Otter’s current positioning leans toward an “AI meeting agent” rather than a standalone transcription tool, with features such as live chat, automated summaries, and action items. It is also investing more heavily in agentic functionality and integrations into other systems.

Otter's strengths:

Its weaknesses in 2025:

In many evaluations, Otter now serves as the benchmark:

Notion is not a meeting bot at all — and that’s the point.

Instead of joining calls, Notion wants to be the place where:

In a Notion-first setup, your workflow often looks like:

This is why, even though I like Granola’s sidecar design, I would personally choose Notion as my core notetaker and workspace in many cases:

You can absolutely pair Notion with a bot or sidecar (Fireflies, Fathom, Granola, Otter) to generate raw transcripts and then pipe summaries in. But it’s equally valid to say: “Our system of record is Notion, and meeting AI is just an input.”

If your main priority is long-term knowledge and collaboration, Notion is the most natural home base on this list.

Let’s simplify the choice as an enterprise buyer.

First choice: bot, sidecar, or workspace?

Second choice: What is the actual job to be done?

Here is where it helps to be brutally clear:

Third choice: ask these four questions for every vendor

If you only remember one part of this article, make it these questions.

Those questions will quickly tell you whether a tool is ready for serious enterprise use.

Using Granola, Fathom, Fireflies, Otter, or Notion as your meeting notetaker is step one.

Step two — the part almost nobody does — is actually reusing those transcripts and notes as structured inputs to your broader AI stack.

Once you have transcripts or structured notes, you can:

I previously wrote about connectors and MCP — including how to enable ChatGPT to talk directly to your systems securely — on The AI Enterprise. If you save them to a Google Drive or Microsoft 365, you can then build a searchable database that provides context.

Those pieces go deeper into how to design a system where meeting transcripts and notes are not an end state, but a raw material your agents can reason over.

If we are not careful, we will recreate that empty lecture hall from Real Genius — a world where bots, sidecars, and workspaces faithfully capture every word in meetings where nothing truly needed to be said.

The opportunity for enterprises is not to automate attendance for its own sake.

It is to get specific about:

The ROI is there if you capture it. Productivity research suggests AI meeting tools can save 10-12 hours per person per month by eliminating manual note-taking — and some teams report a 30% increase in meeting efficiency when they standardize on these tools, for a 50-person company where employees average seven meetings per week, that represents roughly 3,500 hours annually spent just on meeting documentation — over $260,000 in salary costs at $75/hour.

Once you are clear on that, the vendor choice becomes almost mechanical:

The hard part isn’t picking a tool. The hard part is deciding what “showing up” should mean in The Artificially Intelligent Enterprise.

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