Client calls and sales meetings often contain the real work: decisions, promised actions, open questions, risks and deadlines. The problem is that those details get buried in notes, left in a transcript or remembered by one person.
AI works well here as a diligent meeting assistant, not as a generic chatbot. It can organise the messy first draft so the meeting owner can review and send a proper follow-up.
Example workflow: after a project call
After a Teams or Zoom meeting, the transcript is passed to an AI agent along with the calendar invite and basic client or project context. The agent extracts decisions, promised actions, suggested owners, likely deadlines, open questions and risks. It then drafts a follow-up email that the meeting owner can edit before sending.
What the agent would use
- Teams or Zoom transcript
- Typed meeting notes
- Calendar invite
- Client or project context
What it would produce
- Action list
- Owner and deadline suggestions
- CRM or project update
- Draft follow-up email
Where human approval fits
The meeting owner reviews the actions, corrects anything that lacks context and sends the follow-up. Tasks and emails should not be finalised without review.
Likely saving and risk
Likely saving: 20 to 60 minutes per meeting.
Risk level: medium. Transcripts can be wrong and AI can misunderstand nuance, especially around commitments and deadlines.
Practical guardrails
- Use the transcript as source material, not as unquestioned truth
- Mark uncertain actions clearly
- Require review before tasks and emails are created
- Keep a standard follow-up format so outputs are easy to check
A sensible next step
If meetings are creating too much admin, Stuart Cole Consulting can help you design a simple follow-up workflow that fits your current tools rather than adding another complicated system.
