How I Turned 90 Minutes Of Meetings Into Actionable Project Boards In Under 3 Minutes Using AI

1. Overview

Meetings generate valuable information, but much of it is lost, forgotten, or requires hours of manual note-taking and cleanup. Across daily Zoom and Google Meet calls, the project manager spent significant time extracting tasks, clarifying next steps, assigning owners, and organizing everything into ClickUp. This process was slow, inconsistent, and highly dependent on human recall.

To fix this, I designed an AI-powered system that automatically converts meeting transcripts into fully structured project boards inside ClickUp. The system extracts tasks, subtasks, owners, deadlines, blockers, dependencies, and action items—then generates a clean, organized project structure without requiring manual notes. This eliminated meeting cleanup time and ensured no action item was ever missed again.

2. Background & Context

The team conducted multiple recurring and ad-hoc meetings each week across:

◉ Client strategy calls

◉ Internal project syncs

◉ Performance reviews

◉ Creative approvals

◉ Cross-functional planning sessions

Each meeting produced 30–90 minutes of discussion and dozens of actionable tasks. Previously, the project manager manually:

◉ Listened through the transcript or recording

◉ Wrote summaries

◉ Extracted action items

◉ Created tasks and subtasks

◉ Assigned team members

◉ Estimated deadlines

◉ Identified blockers or dependencies

This required 1–2 hours after every significant meeting, amounting to 10–12 hours weekly of manual meeting cleanup.

3. Problem Statement

The operation faced several inefficiencies:

1. No consistent method for extracting tasks from meetings

2. Manual meeting cleanup consumed 10–12 hours weekly

3. High risk of forgetting or misinterpreting action items

4. No unified system for converting meetings into execution-ready tasks

5. Delayed follow-ups and slow stakeholder alignment

The team needed an automated, accurate, and scalable method to convert raw meeting conversations into actionable project execution structures.

4. Tools & Automation Stack

◉ Zoom / Google Meet (transcription source)

◉ OpenAI API (task extraction and structuring logic)

◉ Zapier / Make.com (automation orchestration)

◉ ClickUp (task creation and project board generation)

◉ Slack (notifications when meeting boards are ready)

5. Automation Flow

The system followed this structure:

1. A Zoom or Meet recording finishes

2. Transcript becomes available and triggers the workflow

3. Automation fetches the full transcript

4. AI analyzes and extracts action items, owners, blockers, deadlines, dependencies

5. Clean structured task hierarchy is generated

6. ClickUp board is created with tasks and subtasks mapped

7. Slack sends the team a notification that the meeting action board is ready

This created a consistent, automated pipeline that turned every meeting into execution.

Fig. 1: AI-Powered Meeting-to-Execution Workflow for Automatic Project Board Creation

6. Implementation Details

6.1 AI Prompt (The Core Logic)

The system used the following prompt to extract structured action items:

				
					“Analyze the following meeting transcript and extract all actionable items.
For each action item, identify: task name, detailed description, owner/assignee,
priority (low/medium/high), due date (if mentioned), blockers, dependencies, and next steps.

Transcript: {{full_transcript}}

Output Requirements:
- Provide a structured JSON list of tasks.
- Each task must include:
  - title
  - description
  - assignee
  - due_date
  - priority
  - blockers
  - dependencies
  - subtasks (if mentioned)
Only output clean JSON without commentary.”

The AI returns clean, structured task data ready for automation.

6.2 Score Mapping (Interpretation Rules)

Although this workflow didn’t score tasks numerically, it used a classification system similar in intent:

Category	Meaning	Behavior
High Priority	Time-sensitive or crucial tasks	Marked as “High” in ClickUp
Medium Priority	Normal tasks	Standard workflow placement
Low Priority	Non-urgent tasks	Moved to backlog or future sprint
Blockers Identified	Dependencies or issues	Highlighted for escalation

6.3 ClickUp Automations

To ensure structured boards, the following rules were applied within ClickUp:

				
					If task contains blockers → Tag as “Blocked”
If priority = High → Add to “Immediate Action” List
If task includes dependencies → Link tasks automatically
If due date is missing → Assign default follow-up for PM review
If assignee not specified → Assign to project manager for routing

This kept the project board meticulously organized without human intervention.

6.4 Data Extracted for AI Processing

The system evaluated:

◉ Speaker-specific dialogue

◉ Explicit action statements

◉ Deadlines mentioned verbally

◉ Owner names (e.g., “John will handle this”)

◉ Blockers expressed during discussion

◉ Dependencies (“We need X before Y”)

◉ Subtasks (“First do A, then B”)

◉ Risks or concerns raised

This ensured a complete, accurate mapping of meeting content into a project board.

7. Code-to-Business Breakdown

Logic / Code	Business Impact
Transcript ingestion	Eliminates manual note-taking
JSON task extraction	Converts conversations into immediate execution items
Priority tagging	Ensures urgent items rise to visibility
Blocker detection	Accelerates problem-solving and escalation
Auto task creation	Removes administrative burden
Slack notifications	Speeds up team awareness and execution

8. Results & Performance Impact

1. Time Saved

◉ PM saved 10–12 hours weekly by eliminating manual meeting cleanup

◉ Teams acted on meeting outputs immediately, without waiting for notes

2. Execution Speed Improved

◉ Action items identified within minutes of meeting end

◉ More accurate follow-up tasks compared to manual notes

◉ Reduced delays caused by missing or forgotten tasks

3. Team Alignment

◉ Everyone reviewed the same structured task board

◉ Clear ownership and deadlines improved accountability

◉ No more misinterpretation or unclear next steps

4. Scalability

◉ Worked across all meetings: internal, client, strategic, creative

◉ No additional setup required per meeting

◉ Standardized the agency’s entire meeting-to-execution workflow

9. Challenges & How They Were Solved

Challenge: AI struggled with overlapping conversations

Solution: Pre-processed transcripts to separate speakers cleanly

Challenge: Ambiguous owner names in transcripts

Solution: Added a mapping dictionary (e.g., “Mike” = “Michael D.”)

Challenge: Too many tasks generated from long meetings

Solution: Added relevance filtering and topic clustering

10. Lessons for Project Managers

◉ Meetings produce invaluable execution data when properly structured

◉ AI-driven extraction removes the burden of manual note cleanup

◉ Automated boards increase accountability and reduce missed actions

◉ Consistent workflows strengthen cross-team communication

◉ When meeting outcomes become instantly actionable, project velocity increases significantly

11. Conclusion

By integrating transcription analysis with AI task extraction and ClickUp automations, I transformed every meeting into a fully organized project board ready for execution.

The system removed hours of administrative work, ensured no action item was overlooked, and improved clarity and follow-through across all teams.

This case study demonstrates how AI and workflow automation can significantly improve operational efficiency and convert meetings from passive discussions into active execution engines.

How I Turned 90 Minutes of Meetings Into Actionable Project Boards in Under 3 Minutes Using AI