AI meeting actions into project systems.

What this workflow does

It turns actions agreed in a meeting into tasks people will actually see. A model reads the transcript or the minute-taker’s notes, separates commitments from discussion, and proposes a task for each with an owner, due date, project and the timestamp it came from. The project lead approves, edits or rejects each proposal, and only approved items are written to the project tool.

The problem is mundane. In a busy consultancy, actions from client meetings live in someone’s notebook until the next meeting reveals they were never done. Our guide to AI for professional services covers where else these firms lose time.

Specification

Inputs

A transcript from Teams, Zoom or Google Meet, or typed notes; the project code; the attendee list with roles; open tasks for that project.

Systems

The meeting platform’s transcript export, a language model returning a fixed task schema, and the project tool (Asana, Jira, monday.com or Microsoft Planner) through its API or MCP server.

Steps

Match the meeting to a project; extract candidate actions with owner, due date, confidence and timestamp; check against open tasks for duplicates; queue the list for approval; create approved tasks and draft minutes.

Human review

The project lead approves the list within one working day. Actions for the client or a subcontractor, or that change scope or fee, are marked for the lead to raise personally.

Outputs

Approved tasks linked to the transcript moment they came from, plus draft minutes for the lead to edit and send.

Measures

Share approved without edits, actions missed (found later by sampling), duplicates created, and hours from meeting end to tasks live.

Risks

Discussion recorded as commitment, wrong owner from speaker errors, tasks in the wrong project, and transcripts kept too long.

Illustrative example: one week at a design practice

Illustrative example: a 40-person Auckland architecture and interiors practice runs about 18 internal and client meetings a week, averaging six actions each. These are synthetic figures. Assumptions: the project lead used to spend 20 minutes writing up each meeting, and reviewing a proposed list takes about 6.

This is the kind of list the lead sees after a client design review (synthetic):

Across the week that is about 108 proposed actions and 4.2 hours of write-up saved (18 meetings × 14 minutes). The number that matters more is actions that previously never reached the tool at all. Measure it in month one by sampling transcripts by hand.

Recording and transcripts under the Privacy Act

A transcript is personal information about everyone who spoke, so the Privacy Act 2020 applies before the first word is captured. Three principles do most of the work:

• IPP3: tell people. When collecting from the people concerned, you must take reasonable steps to make them aware of the collection, its purpose and who will receive it, before collection where practicable. Put it in the invite and say it at the start, especially with clients.
• IPP8: check accuracy before use. An action pinned on the wrong person in client minutes is inaccurate information about that person. The approval step is your reasonable check.
• IPP9: keep it only as long as needed. Set how long raw transcripts live once minutes are approved, and delete on schedule.

Decisions that stay with the project lead

• Whether something said is a commitment. “We should look at that” is not a task until the lead says so.
• Anything assigned to the client, a subcontractor or a council officer. Those become requests in the minutes, raised by a person.
• Actions with fee or scope implications. A client asking for an extra option may be a variation, which is a commercial conversation.
• What goes into minutes sent outside the firm.

How it fails, and the controls that work

• Speaker errors. Transcription credits lines to the wrong person, especially when several people share a meeting-room microphone. Control: owners come from the attendee list with a confidence score, and low confidence is flagged, not guessed.
• Names and te reo Māori. Transcription often mangles personal names and Māori place names. Control: give the model the attendee list and a project glossary, and let the lead correct in the review screen.
• Duplicates in recurring meetings. The same action is re-extracted every fortnight. Control: compare with open tasks before proposing.
• Over-broad access. An assistant with write access to every project can create tasks anywhere. Control: one project per meeting, writes to a staging section only, and permissions set as in our note on agent permissions and approvals.

Connecting it to Asana, Jira or monday.com

Most project tools now offer an API and a Model Context Protocol (MCP) server. As documented on 19 October 2026, Asana’s MCP server can create and manage tasks, and Enterprise+ customers can allow or block individual MCP clients; monday.com hosts a Platform MCP server that includes item creation. For production we usually prefer the plain API with a fixed task schema, which is easier to test and audit than letting a model pick tools. See AI and Asana and AI and Jira.

Cost sits mostly in the review screen and project matching, not model fees. Start with one team and the manual method in turning meeting notes into actions; firm-wide, it becomes AI workflow automation. The same draft-then-approve pattern runs in the property management agent for APL Property, where staff keep the decisions. Related workflows: proposal preparation and research briefing preparation.

Written by the Levy Street team in Wellington. Last updated 19 October 2026.