AI for meetings: transcripts, summaries, and action items

The most reliable AI productivity win in 2026 is meeting capture. The transcription is good. The summarisation is good. The action-item extraction is good. The cost is low. The setup is one app and one habit.

And yet most people are still typing notes in real time and then losing them, or relying on memory for follow-ups, or holding meetings that produce no written artefact at all. This article is the workflow that fixes that.

The landscape of tools

There are three main categories of AI meeting tool in 2026:

Bot-based services. Otter, Fireflies, Fathom, Read.ai, Avoma. A "meeting bot" joins your video call as a participant, records and transcribes, and afterwards produces a summary and action items. Works in any video tool (Zoom, Meet, Teams). Free tiers exist; paid tiers run €10–€20/month.

Native AI in the meeting platform. Zoom AI Companion, Google Meet Gemini, Microsoft Teams Copilot. Built into the platform; no separate bot to invite. Quality has caught up to standalone bots. Usually requires a paid tier of the underlying platform.

Local recording apps. Granola, Superwhisper, MacWhisper, and others. Run on your laptop, record system audio, and transcribe locally or via API. No bot in the meeting; your colleagues see nothing changed. The privacy story is the strongest here, but you have to remember to start the recording.

For most people, the right answer is one of two:

1. If you live in one platform (Zoom, Teams, Meet), turn on the native AI feature for that platform. Easiest setup, deepest integration.
2. If you switch between platforms, pick one cross-platform tool (Granola for local-first, Otter or Fathom for bot-based) and use it for everything.

Avoid using three different tools. The friction of remembering which one to start eats the benefit.

What AI captures well

Modern meeting AI is reliable for:

• Verbatim transcripts of clearly-spoken English (and increasingly other languages). Vendor benchmarks typically report accuracy in the mid-to-high nineties for clear audio; messy rooms, accents, and overlapping speech bring that down quickly.
• Speaker identification when each person joins with their own audio (i.e., remote meetings). Less reliable when everyone is in a single room on speakerphone.
• High-level summaries of what was discussed. Usually fairly accurate, occasionally generic.
• Action item extraction when people stated actions explicitly ("I'll send the spec by Friday"). Less reliable when actions were implied.
• Decisions when stated unambiguously.

What AI captures badly

Honest list:

• Subtext. The look on someone's face when they disagreed but did not say so. The way a casual "sure, sounds good" actually meant "I do not have time to argue this." AI hears words; people hear meaning.
• Quiet voices, accents, overlapping speech. Especially when everyone is in one room with one mic. Accuracy can drop to 80% or lower in messy audio.
• Implicit owners for action items. "Someone should follow up on X" or "We'll need to figure that out" rarely produce a named owner. The summary may omit the action entirely or assign it to whoever spoke last.
• Technical or specialist vocabulary. Industry-specific terms, internal codenames, product names that sound like common words. AI may transcribe them phonetically. Worth scanning the transcript for known wrong-spellings.
• What was *not* said. A summary cannot flag that a key topic was avoided unless someone explicitly raised it. If the meeting failed to discuss something important, the AI will not notice.

The pattern is consistent: AI does well at literal capture and struggles with everything that requires understanding meaning, context, or absence.

The one-prompt cleanup pass

The default summaries you get from meeting AI are decent but generic. The single biggest quality improvement is to feed the raw transcript through a separate AI conversation with a structured prompt. This takes ninety seconds and produces something materially better.

A reliable cleanup prompt:

Below is a transcript of a meeting. Process it for me as follows.

>

Section 1: Decisions made. Bullet list. For each, quote the line where the decision was confirmed. If a decision was discussed but not finalised, note that and mark with [open].

>

Section 2: Action items. Table with columns: Action | Owner | Due (if stated). Quote the line each comes from. If an action was discussed without an owner or deadline, list it with [unowned] or [no date].

>

Section 3: Open questions. Anything raised that nobody resolved. Quote each.

>

Section 4: Risks or concerns. Anything anyone flagged as a risk, blocker, or worry. Quote each.

>

Section 5: Things worth following up on. Topics that came up briefly and probably need more attention than they got. Use your judgement.

>

Be specific. Quote the actual lines from the transcript. Mark anything you are unsure about with [unclear]. Do not invent owners or due dates.

>

Transcript: [paste]

The output is a clean, structured artefact you can paste into Notion, Slack, or an email. The "quote the line" instruction is critical — it makes the summary auditable. The "do not invent owners" line stops the model from confidently assigning Anna a task she never agreed to.

A useful adaptation for one-on-ones: drop "decisions" and "action items" if those are not the point, and add "things this person mentioned about how they are feeling" or "growth or development topics raised." Adjust the structure to fit the meeting type.

A few specific patterns by meeting type

Status updates. Default summary is usually fine. The cleanup pass adds value by extracting the actions and quote-grounding them.

Decisions / planning meetings. Cleanup pass is essential. The default summary will miss subtle decisions and conflate "we discussed" with "we decided."

Customer calls / interviews. Use a customised prompt that emphasises quotes. "Extract the customer's exact words about pain points. Include the line and the timestamp. Then summarise themes." Quote density matters more than summary in research.

Internal one-on-ones. Ask for "growth topics raised," "things they're excited about," "things they're frustrated by," and "anything they hinted at without saying outright." The last category is where you find the things you should follow up on.

Sales calls. Use BANT (Budget, Authority, Need, Timeline) or MEDDIC structure. Most sales AI tools do this automatically; if yours does not, the cleanup pass with a sales-specific structure is the right move.

Board / leadership meetings. Tightly structured cleanup is worth it. Add "things discussed off-the-record" as a separate section so it does not get pasted into the wrong audience by mistake.

The privacy angle

A meeting AI is recording what people say. That introduces three considerations.

Consent. Most jurisdictions (including most of Europe) require either explicit consent or notification to all participants. Some tools handle this for you (the bot announces itself when it joins). For native AI features, the meeting host usually needs to enable it and the participants get a banner notification. Local recording apps require you to disclose — and you should.

Sensitive content. If the meeting discusses customer data, financial information, legal matters, or other sensitive topics, your meeting AI's data handling matters. Bot-based services typically store transcripts on their servers; enterprise tiers usually offer stronger guarantees. Read the data policy or ask your IT team which tool is approved.

Selective recording. Sometimes it makes sense to not record. Sensitive personal conversations, sensitive performance discussions, legal discussions with counsel, anything where the participants would speak differently if they knew it was being recorded. The simple rule: if you would not write it down in a memo, do not record it.

Setting up the habit

The reason most people fail at meeting AI is not the tool — it is forgetting to use it. Three small habits make it stick.

Pin the bot or feature in your default meeting setup. Most tools have an "auto-record" option that recurs for all meetings on your calendar. Set it once.

Run the cleanup pass before you close the meeting tab. Ninety seconds while the meeting is fresh in your mind is much better than coming back to a raw transcript next week.

File the output somewhere consistent. Notion, Obsidian, a "meetings" Google Doc folder, a Slack channel. Anywhere repeatable. The value compounds when you can search across months of meeting summaries to find "what did we decide about pricing in March."

A few things meeting AI lets you stop doing

Once the habit is in place, several things become unnecessary:

• Real-time note-taking. You can listen instead.
• "Quick recap" emails after the meeting. The structured summary is the artefact.
• "I forgot the action items from Tuesday." The transcript is searchable.
• "Who agreed to what?" Quoted lines settle it.
• "What was the context of that decision?" Open the meeting record and read.

Twenty per cent of every workweek is preparing for, attending, and recovering from meetings. Meeting AI cuts the recovery part to nearly zero. That is real time back.

The takeaway

Pick one tool. Turn it on for every meeting. Run a structured cleanup prompt on every transcript before closing the tab. File the result somewhere consistent.

That is the whole workflow. Less than ninety seconds of work per meeting, no learning curve, and you will look back in three months and not understand how you used to operate without it.