How to turn call transcripts into project tasks using AI

After client calls, someone needs to write up notes, identify action points, and create tasks in your project management system. This takes time, introduces delays, and often results in missed details or inconsistent formatting.

We found a better approach: record the conversation, then use AI to handle the heavy lifting. Here’s our current workflow.

First, you need a text transcript of your conversation.

Recording options:

• Zoom’s built-in recording – Available on most Zoom plans, automatically creates both video and transcript
• TLDV – External service that works across different meeting platforms
• Your organisation’s approved recording tool

Check what’s signed off by your business before choosing a tool. Most organisations have specific requirements around recording and data handling.

The recording tool will generate a text transcript, usually within a few minutes of the call ending. Download this — you’ll need it for the next step.

Take your transcript and paste it into an AI chat tool (Claude, ChatGPT, or Copilot — whatever your organisation allows).

For general meetings, ask for:

• A summary of what was discussed
• Clear action points with owners
• Any decisions that were made

For scoping calls, ask for:

• A structured project scope
• Key requirements and constraints
• Technical considerations discussed

Example prompt: “This is a transcript from a project scoping call. Can you transform this into a structured scope document with requirements, constraints, and technical considerations?”

The AI will structure the rambling conversation into organised, usable documentation.

Now you have a scope or summary, but you need actual tasks in your project management system.

Grab your task template from Linear (or Jira, ClickUp, Monday — whatever you’re using). This template should include:

• Standard fields you always complete
• Your team’s task structure
• Any required labels or categorisation

Paste this template into the same AI conversation and ask it to create tasks using your format.

Add helpful constraints like:

• “Create tasks that would take roughly half a day to complete”
• “Group related changes into single tasks”
• “Flag any tasks that might need design input”

This gives you consistently sized, well-structured tasks instead of a random mix of 10-minute jobs and week-long epics.

Here’s where it gets efficient: rather than typing corrections, talk through your thoughts.

We use SuperWhisper (a voice-to-text tool), but any voice recording app works. Read through the AI’s output whilst recording yourself and naturally comment on what needs changing:

“This task’s too big, needs splitting… This one should mention the existing dashboard component… These two could be combined…”

Transcribe your rambling feedback, paste it back into the AI, and ask for revisions. You’ll get updated tasks without typing detailed change requests.

Repeat this process until the tasks accurately reflect what needs doing.

Copy the final tasks into Linear (or your project management tool). We’re still doing this manually — there’s probably a way to automate it, but the copy/paste step takes seconds and gives us a final review opportunity.

Our current process works well, but there’s room for improvement:

Pre-load context using AI projects Claude (and other AI tools) let you create projects with persistent context. You could include:

• Your task template
• Information about your business and products
• Technical constraints or standards

This removes repetitive setup and gives more consistent results.

Add codebase context For technical projects, including a summary of your codebase helps the AI write more accurate tasks. It can reference existing components, suggest realistic implementation approaches, and flag potential conflicts.

We haven’t implemented this yet, but it’s on our list to try.

After using this workflow for several months:

• It saves genuine time. What used to take 45 minutes of note-writing now takes about 15 minutes of reviewing and refining AI output.
• Transcripts catch everything. We’ve stopped missing action points or forgetting decisions made at the end of calls.
• Consistency improves. When the AI uses your template every time, tasks end up structured the same way, making them easier to estimate and track.
• It’s not fully automated (yet). We still review everything carefully. AI occasionally misinterprets technical details or combines unrelated points. The human check remains essential.

1. Record your call and get a transcript
2. Ask AI to create a summary or scope
3. Provide your task template
4. Use voice feedback to refine the output
5. Copy final tasks into your project system

This isn’t revolutionary, but it’s practical and works with tools most teams already have access to. If you’re currently spending hours turning meeting notes into tasks, it’s worth trying.