Three meetings, three AI-generated summaries, and the only thing I actually referenced later was the raw transcript.
The market for AI meeting recorders - Otter.ai, Fireflies, Fathom, Bluedot, Tactiq - is crowded enough now that most tools produce acceptable summaries. That problem is largely solved. What's still genuinely different between tools is everything around the summary: how the tool behaves during the call, how it handles multiple speakers, and what the searchable archive looks like six weeks after the fact.
The Bot-in-the-Room Problem
Most popular meeting recorders join as a named participant. Everyone on the call sees "Otter Notetaker has joined" in the participant list. That's fine for internal standups. It creates real friction on client calls, job interviews, and any meeting where the social dynamic matters.
Bluedot built its product around avoiding exactly that. It captures audio directly from the browser - no bot joins, no notification goes out. Whether that approach is appropriate for a given meeting is a judgment call with legal implications depending on jurisdiction and consent laws. But from a pure workflow standpoint, silent recording changes how the tool fits into professional use.
Where Transcripts Still Fall Short
Speaker diarization - the process of separating and labeling different voices in a recording - is the weakest point across nearly every tool on the market. Most handle two-person calls well. Quality degrades with:
- Three or more speakers on the same call
- Similar-sounding voices
- Heavy accents or non-native speakers
- Crosstalk and people talking over each other
For a tool to be genuinely useful rather than just convenient, it needs to answer: whose words were these? An AI summary that says "the team agreed to extend the deadline" is nearly useless if you can't verify who actually committed to that.
The tools getting consistent adoption in professional settings are the ones that feel invisible during the call and accurate afterward. The AI summary is a nice-to-have. The ability to search a transcript three weeks later and find the exact moment a client agreed to something - that's the feature that creates actual dependency on the tool.