13 August 2026
AI meeting notes accuracy comes down to two separate problems that get treated as one: whether the system heard the words correctly, and whether it understood which words mattered. A transcript can be 98% correct and still produce notes that are useless, because the errors cluster on names and numbers, or because the model wrote down a suggestion as if it were a decision.
Word error rate is the wrong metric to obsess over, because it averages across the whole transcript. What actually breaks notes is where the errors land. Speech recognition is worst on proper nouns, acronyms, and numbers — exactly the content that shows up in a decision line. A 3% word error rate sounds fine until the 3% is 'ship on the fourteenth' becoming 'ship on the fortieth,' or a client's name getting mangled into three different spellings across one call.
Browser-based speech recognition, which is what most in-browser meeting tools including AVAY rely on, works reliably in Chrome and Edge and is inconsistent or unavailable in other browsers. That's a real constraint worth knowing before you pick a tool for a team that doesn't standardize on Chrome.
Getting the words right doesn't mean getting who-said-it right. On calls with more than three or four people, or on calls where two people have similar voices or accents, attribution errors are common: a note gets assigned to the wrong owner, or a question from one person gets merged into the answer from another. This matters more than it sounds — a decision attributed to the wrong person means the wrong person gets chased for follow-up, or nobody does.
Attribution gets worse specifically at the moments that matter most for notes: when someone interrupts to correct a plan, or when two people talk over each other agreeing on next steps. Those overlaps are exactly where decisions get made, and exactly where the audio is hardest to separate.
This is the failure that actually costs teams time, and it has nothing to do with transcription quality. 'Maybe we could push the deadline to Friday' and 'we're pushing the deadline to Friday' can look almost identical in a transcript, but they are not the same sentence. A note-taking system that can't tell tentative language from a commitment will write both down as action items, and now someone is planning around a decision that was never made.
This is a context problem, not a hearing problem. Distinguishing a passing remark from an actual decision requires tracking who has authority to make that call, whether the room pushed back or went quiet, and whether the same point got revisited and confirmed later in the call. Most transcription tools don't attempt this — they timestamp and label speakers, and leave interpretation to whoever reads the transcript afterward.
AVAY's AI participant keeps notes and decisions live during the call rather than generating them afterward from a static transcript, which means it can use what happens next in the conversation — agreement, silence, someone restating the point — as signal for whether something was actually decided. That doesn't eliminate the ambiguity in genuinely ambiguous moments, but it catches the common case where a suggestion gets confirmed thirty seconds later and should be upgraded to a decision, or challenged and should be dropped.
Bad notes rarely get caught in the meeting. They get caught two weeks later when someone searches for 'what did we decide about the vendor contract' and finds either nothing, or three contradictory summaries from three different calls. The cost of a note-taking error isn't the wrong sentence — it's the time spent reconstructing the truth from memory because the written record can't be trusted.
This is also why search across past meetings matters as much as accuracy within a single one. A single garbled name in one transcript is a minor annoyance. The same garbled name making that meeting unsearchable, so nobody can find the decision six weeks later, is the actual cost.
A few concrete tests reveal more than a vendor's accuracy claims. Run a call with two people who have similar-sounding names and see if the notes attribute correctly. Say something tentative — 'we might want to' — followed by silence, and see if it gets written as an action item. Say a specific date and a specific number in the same sentence and check both against the recording afterward.
It's two problems stacked. Transcription accuracy determines whether the words are right; a separate layer of judgment determines whether the system can tell a decision from a suggestion or a passing remark. A tool can have excellent transcription and still produce notes that misrepresent what happened, because that second layer is harder and less commonly built well.
Speech recognition models are trained on general language data where common words dominate. Proper nouns, especially uncommon names, acronyms specific to your company, and spoken numbers are underrepresented in that training data, so error rates concentrate there rather than spreading evenly across the transcript.
Some can, imperfectly. It requires tracking context beyond the sentence itself — who has authority to make the call, whether the room agreed or went quiet, whether the point got restated later in the call. Tools that generate notes from a static transcript after the call has to infer all of this after the fact; tools that track notes live during the conversation have a better shot because they can use what happens next as a signal.
Yes, if the platform relies on browser-based speech recognition. That technology works reliably in Chrome and Edge and is inconsistent in other browsers, so the same call can produce noticeably different transcription quality depending on what browser people join from.
Test it directly rather than trusting a vendor's claim. Have two similar-sounding speakers talk over each other, say a hedged suggestion followed by silence, and include a specific date or number in a fast sentence — then check the resulting notes against a recording of the call.
AI meeting notes fail for two separate reasons — bad transcription on names and numbers, and no way to tell a suggestion from a decision — and the second one is the more expensive failure because it produces a written record people trust and act on incorrectly.
Meetings that take their own notes, in the browser: avay.ai.