Action items agreed in meetings are forgotten at an alarming rate. Studies suggest that 50% or more of meeting action items are never completed, often because they were never properly recorded or tracked. The gap between “we agreed to do X” and “X is in a tracking system” is where accountability dies.

The Problem

Someone takes notes during the meeting, but the notes are incomplete, ambiguous, or never transferred to a task tracker. “Sarah will look into the vendor issue” becomes a vague memory rather than a tracked deliverable with a due date and owner.

The AI Approach

Meeting transcription services produce full text transcripts. An LLM can parse these transcripts to extract specific action items, identify the responsible person, infer due dates from context, and format them as trackable tasks.

Three-Step Build

Step 1 - Transcribe. Use Amazon Transcribe or a similar service with speaker diarization to produce a transcript with speaker labels.

Step 2 - Extract actions. Pass the transcript to an LLM with a prompt that identifies commitments, assignments, and deadlines. The model returns structured action items with fields: task description, owner, due date, and context (the relevant transcript excerpt).

Step 3 - Create and track. Push extracted action items to your project management tool (Jira, Asana, Linear) via API. Send a summary to meeting attendees for confirmation.

Where It Breaks

Implicit commitments (“I guess I could take a look at that”) are harder to detect than explicit ones. Speaker diarization errors assign actions to the wrong person. Meetings with crosstalk or poor audio produce unreliable transcripts.

The Production Path

Add a confirmation step where attendees can approve, edit, or reject extracted action items before they enter the tracking system. Over time, the system learns which phrases in your organization indicate real commitments versus casual mentions.