Meeting Notes with Speech-to-Text: What to Automate (and What Not To)

Recording and transcribing meetings sounds like the end of note-taking drudgery. Just hit record, let AI do its thing, and enjoy a perfect transcript.

Reality is messier. Raw transcripts are often useless—long, unstructured, and full of filler. The question isn't whether to use speech-to-text for meetings, but how to use it well.

What automation handles well

Some meeting documentation tasks are perfect for STT:

Capturing exact quotes

When someone says something important—a decision, a commitment, a specific number—having the exact words matters. Transcription captures this without relying on someone's scribbled notes.

Creating searchable archives

Once a meeting is transcribed, it becomes searchable. "What did we decide about the pricing change?" becomes a text search instead of a memory test.

Freeing up attention

When someone knows the meeting is being recorded, they can actually listen instead of frantically typing. This often improves the meeting itself.

Covering for absences

Missed the meeting? A transcript (especially with speaker labels) tells you what happened without the "can you summarize?" email thread.

What still needs human work

Automation has limits. Some things require human judgment:

Identifying what matters

A transcript captures everything—including the 10-minute tangent about someone's weekend. Extracting the actually important parts requires understanding context and priorities.

Correcting errors

STT isn't perfect, especially with names, technical terms, and cross-talk. Research comparing STT services shows that even the best systems have meaningful error rates on real-world audio. Someone needs to fix these, particularly for transcripts that will be shared or archived.

Formatting and structure

A wall of text isn't meeting notes. Turning a transcript into something useful—headings, action items, decisions highlighted—takes editorial work.

Protecting sensitive information

Not everything said in a meeting should be documented. Human judgment decides what gets recorded, retained, and shared. The NIST Privacy Framework offers guidance for thinking through data handling.

A practical meeting notes workflow

Here's a workflow that balances automation with necessary human input:

Before the meeting

• Confirm recording consent — Make sure everyone knows and agrees
• Start your transcription tool — Don't scramble to set it up mid-meeting
• Note the attendees — Helps with speaker identification later

During the meeting

• Let the transcript run — Don't try to take parallel notes
• Mark key moments — Many tools let you add timestamps or highlights in real-time
• Note action items as they arise — This is worth capturing separately

After the meeting (same day if possible)

• Skim the transcript — Don't read word-for-word; scan for important sections
• Extract the essentials:
  • Decisions made
  • Action items (who, what, when)
  • Key discussion points
  • Questions to follow up
• Fix obvious errors — Especially names and technical terms
• Store or share — Put it where people can find it

The AI assist

Modern AI can help with post-meeting processing:

• "Summarize this transcript in 5 bullet points"
• "Extract all action items with assigned owners"
• "What decisions were made?"

This isn't fully automated—you're reviewing and editing AI output—but it's much faster than starting from scratch. For a complete capture-to-summary workflow, see our guide on building a voice note workflow.

What to skip

Not every meeting needs documentation:

• Quick syncs with no decisions or action items
• Brainstorms where the value is the thinking, not the transcript
• Sensitive conversations that shouldn't be recorded
• Meetings that could have been emails (a bigger problem)

Recording everything creates noise that makes finding important information harder.

Choosing your tools

For comparisons between different transcription approaches, see our breakdown of voice notes vs. transcription apps. And for evaluating STT providers specifically, our API comparison guide covers accuracy, latency, and cost tradeoffs.