One mistake buyers make is optimizing for transcription accuracy instead of workflow completion, and it's an understandable one. When AI meeting tools first showed up, transcription was the obvious thing to evaluate: did it capture the words correctly, identify the speakers, miss anything important, and make the conversation searchable later? Those questions still matter, but they aren't the real buying criteria anymore. A perfect transcript isn't the same as a finished ticket, decision log, or customer follow-up.

That sounds obvious until you watch how teams actually evaluate the category. They compare transcripts, inspect summaries, and look for cleaner notes - and then, after the meeting, someone still has to do the real work. As one founder told us, "The transcript was great, but I still had to spend an hour turning it into something my team could use."

Transcription preserves the conversation. Completion moves the work forward.

That's the gap. A transcript can tell you exactly what was said and still leave the questions that matter most unanswered: What did we decide? What changed? Who owns the next step? What needs to go into Linear, what should be sent to the customer, what should be escalated, and what should simply be ignored? Those are workflow questions, not transcription questions. The old way amounted to having a perfect recording of the mess - the information was all there, but the burden of turning it into useful work still sat with the team.

That's why transcription accuracy can be a misleading benchmark. A transcript can be 99% accurate and still produce no leverage. A ticket that's 80% drafted with the right context is worth more than a transcript that caught every word. A customer follow-up that's ready to review beats a beautiful summary. A decision log that preserves the rationale matters more than a searchable archive.

The job isn't to remember every sentence. It's to turn the right context into the right output.

You don't need every word - you need the part that tells engineering what to do next. Buyers should still care about accuracy, but accuracy in service of the workflow, not as the final outcome. Did the tool capture enough context to create a useful artifact? Did it understand the difference between discussion and decision? Did it preserve the tradeoff behind the decision, create something the next person can act on, and reduce the work after the meeting? Those are the better questions, because teams don't adopt AI meeting tools to build a perfect archive. They adopt them to remove the friction between conversation and execution.

A perfect transcript helps you look backward. A completed artifact helps the team move forward.

That's the buyer mistake - optimizing for the transcript instead of the workflow. The best tools won't be judged by whether they captured every word, but by whether the team left with the work already in motion.