How to check an AI meeting summary before you trust it

AI meeting summaries are good enough that people have stopped reading them carefully, which is exactly when the failures start costing something. They fail in four predictable ways, and because the failures are predictable the review is short. Check conditionals, check attributions, check the numbers, and check what is missing. Two minutes per meeting catches almost everything, and the one that matters most is the first: summarisation compresses, and "we could do that if X" compressing into "agreed" changes a commercial term.

The four checks, in order of cost

01 / Conditionals that lost their condition

The most expensive and the most common. "We might be able to accommodate that timeline" and "we will accommodate that timeline" are one summary apart. Search the transcript for "if", "provided", "assuming" and "once", and check each one survived.

02 / Attribution in a multi-person meeting

Speaker separation degrades with more participants, similar voices and cross-talk. An action item assigned to the wrong person is worse than an unassigned one, because everyone stops looking for an owner.

03 / Numbers and dates

Figures are transcribed like any other word and a plausible wrong number is not visibly wrong. Anything that will be used — a price, a volume, a deadline — is worth checking against the audio rather than reading past.

04 / What is not there

Summarisation is compression, so the question is not only whether what is present is right. Scan for the topics you remember discussing and see which ones did not make it. Omission is invisible by construction, which is why it needs a deliberate pass.

05 / Add what a transcript cannot hold

The read on how something landed, the hesitation before an agreement, the fact that the quiet person had a view they did not voice. That is your professional judgement and no model has access to it. Annotate the summary with it the same day.

Questions

How accurate are AI meeting summaries?

Transcription is reliable for clear speech in ordinary meeting rooms; summarisation is where the risk is, because compressing is its job. The failures are predictable rather than random, which is what makes a short structured review effective.

Can I rely on AI meeting notes professionally?

As a reviewed input, yes — reviewed AI notes are usually more complete than manual ones. As unread output, no. The difference between those two is about two minutes per meeting.

What should I check in an AI meeting summary?

Conditional statements that lost their condition, speaker attribution, any figure or date you will act on, and topics you remember discussing that are not there.

Do AI summaries hallucinate?

Summarisation can smooth an ambiguous passage into something cleaner and more definite than what was said, which is the practically dangerous version of the problem. It looks like a tidy summary rather than an obvious error.

Should I keep the audio?

Yes. A transcript you cannot check against the recording cannot be verified by you or anyone else, and the check is the only thing that makes a quote safe to rely on.

Scriben is recording people know about. You say what the pen is and what it does, they agree, and then it stays out of the way for the rest of the conversation — that second half is the product, and it only works after the first. Recording law varies by jurisdiction and by profession: see recording people lawfully before you start.

Checking an AI meeting summary