13 August 2026
AI meeting notes are only as good as two separate systems working in sequence: the transcription engine that turns speech into text, and the reasoning layer that decides what in that text matters. Most complaints about bad AI notes are actually complaints about the first system, but the more expensive failures happen in the second — where a hedge gets written down as a decision, or a suggestion turns into a commitment nobody agreed to.
Every note-taking model works from a transcript, not from the audio itself. If the transcript is wrong, the notes inherit the error and often amplify it, because the summarization step tends to smooth over garbled text rather than flag it. A name transcribed as a homophone — "Sean" becoming "Shawn," a product name becoming a common word — will propagate into every summary, action item, and follow-up email generated from that meeting.
Browser-based speech recognition, which most video platforms rely on for live transcription, performs unevenly across browsers. It's generally solid in Chrome and Edge and noticeably worse or unsupported in Safari and Firefox, which means the accuracy of your notes can depend on which browser your loudest talker happened to open. Background noise, crosstalk, and speakers with accents underrepresented in training data all degrade word error rate before any AI reasoning even starts.
Getting the words right is necessary but not sufficient. The model also has to know who said them, and that requires reliable speaker diarization — separating the audio stream into distinct speakers before or during transcription. Diarization breaks down in a few predictable ways: two people with similar vocal pitch get merged into one speaker, someone who joins mid-call gets labeled as a new participant, or a quick interjection gets attributed to whoever was speaking before and after it.
Attribution errors are worse than transcription errors because they change the meaning of the record, not just its wording. If "I think we should push the deadline" gets attributed to the project lead instead of the engineer who was pushing back, the note reads as a decision instead of a disagreement. Nobody catches this by skimming the summary — it looks coherent, it's just wrong.
This is where most AI notes tools quietly fail, and it has nothing to do with speech recognition. Meetings are full of language that sounds like a decision but isn't: "maybe we should try X," "I'll look into it," "let's say Friday, tentatively." A model that pattern-matches on action-item phrasing without tracking the surrounding conversation will record all three as firm commitments with owners and dates.
The tell is usually in what happened next in the conversation, not in the sentence itself. If someone floats an idea and the room moves on without agreement, that's a suggestion. If someone floats the same idea and three people confirm it, assign a name, and reference a date, that's a decision. Getting this right requires the model to hold context across the whole meeting rather than scoring each sentence independently — which is a much harder problem than transcription, and it's why note-taking tools that look accurate in a demo start producing wrong action items in longer, messier real meetings.
AVAY's AI participant keeps notes and a running decision log updated as the call happens rather than summarizing after the fact, specifically so it can track whether a topic circled back to a firm agreement or stayed speculative. It still depends on browser speech recognition for live transcription, so accuracy in Chrome and Edge is noticeably better than in Safari, and it will occasionally attribute a quick interjection to the wrong speaker in fast crosstalk — that failure mode isn't solved, just less common than it would be from a summary written after the meeting ends.
Test any meeting notes tool against a recording where you already know what happened, ideally one with overlapping speech and at least one walked-back statement. Check three things specifically: whether names and jargon came through correctly, whether each action item is attributed to the person who actually said it, and whether the tool distinguishes things that were agreed from things that were merely discussed.
Vendors rarely publish word error rates or attribution accuracy, and there isn't an industry-standard benchmark you can compare across tools, so the only reliable test is running your own meeting through it and checking the output against your memory of the call.
Most summarization models score sentences for action-item-like phrasing without tracking whether the room actually agreed to it. A statement like "maybe we push to Friday" contains the same verb structure as a real commitment, so a model without conversational context treats them the same way.
It helps with transcription — clearer audio reduces word error rate — but it does nothing for attribution or decision-tracking errors, which come from how the model reasons about the transcript, not from audio quality.
Chrome and Edge generally have the most reliable built-in speech recognition for browser-based meeting tools. Safari and Firefox support is inconsistent, so if your team is on a mix of browsers, expect uneven transcription quality across participants rather than a single accuracy number.
Some let you edit the transcript or notes manually after the call, which fixes the record but not the underlying error rate. A few, including AVAY, let you search past meetings and cross-reference what was actually said, which is a faster way to catch a wrong attribution than re-reading the whole summary.
AI meeting notes fail in two different places — bad transcription garbles what was said, and bad reasoning misreads what was meant — and the second failure is more common and harder to catch than the first.
Meetings that take their own notes, in the browser: avay.ai.