TaskFlow
Turning meeting capture into trustworthy action items•Designing an AI-assisted workflow that helps teams move from recorded meetings to reviewable, export-ready tasks with more speed, clarity, and trust
Overview
In this concept case study, I designed a fast capture-to-export workflow for a PM or team lead who needs to turn meeting audio into usable follow-through. The scope covered workflow design, interaction model, prototype direction, and an evaluation plan for a record → transcribe → extract → review → export experience optimized for speed and reliability.
- Role: Sole Designer
- Timeline: 2–3 weeks
- Scope: Workflow design, interaction model, prototype, and evaluation plan
- Scenario: A PM or team lead turning meeting audio into follow-through
- Outcome: A record → transcribe → extract → review → export workflow designed for speed and trust
This project frames AI as a drafting partner rather than an autonomous actor: useful for speed, but only if the workflow makes uncertainty visible, supports quick edits, and helps users verify critical details before tasks leave the system.
Problem
After meetings, follow-through often breaks down before work even begins. Action items live in scattered notes, partial transcripts, or memory. While AI can draft tasks quickly, teams cannot trust generated outputs blindly when ownership, dates, or phrasing may be wrong, vague, or missing.
- Action items often are not captured at the source.
- Transcript tools frequently stop at summarization instead of follow-through.
- Generated tasks are fast, but not always trustworthy enough to export blindly.
- Users need quick verification and correction, not just generation.
Why this mattered
- Lost action items create downstream coordination cost before execution even begins.
- The value of AI here is speed, but only if trust is maintained at review time.
- A successful flow has to reduce effort without increasing execution risk.
Goals and success criteria
- Reduce time from recording to a usable task list.
- Make uncertainty visible before export.
- Support quick correction without breaking momentum.
- Create a review step that feels lightweight rather than bureaucratic.
- Time from end of recording to export.
- Percentage of tasks edited before export.
- Number of low-confidence items resolved before export.
- User-rated confidence in the exported task list.
Approach
- Steer without prompting: help users shape output without requiring prompt-writing skill
- Calibrate trust: make confidence and traceability visible at the point of review
- Recover fast: support quick edits, additions, and corrections before tasks leave the system
- Why start with voice capture: capturing at the source reduces the “I’ll clean it up later” failure mode and preserves a reviewable record
- Why review-before-export: the system should accelerate drafting rather than replace user judgment
- Why source-linked trust cues: users need to verify quickly without replaying the whole meeting
- Why lightweight steering controls: users should be able to shape output through visible controls instead of opaque prompt syntax
- Capture: record audio, paste notes, or start from sample input
- Processing: a calm progress state while transcription and extraction run
- Review: inspect transcript and generated tasks side by side
- Refine: correct owners, dates, phrasing, or missing items
- Export: verify critical fields before tasks leave the system
- Used a short review checkpoint instead of full automation.
- Exposed inferred fields with badges rather than hiding uncertainty.
- Linked tasks to transcript lines for traceable verification.
- Prioritized quick-edit chips for common fixes instead of forcing full-form editing.
- Kept progress states calm and low-noise to support review under pressure.
The early direction emphasized fast extraction, but it trusted generated output too heavily. To make the concept feel more responsible and product-ready, I introduced explicit confidence cues, source-linked traceability, and a short review checkpoint before export. The result is still a fast flow, but one that makes judgment easier and more intentional.
Key Screens
UX Motion
Prototype
Interactive vertical slice showing the core workflow: capture meeting input, generate a draft, verify what matters, correct what is unclear, and export with more confidence.
Demo workflow: record a meeting note, skim the transcript, generate tasks, verify anything inferred, apply quick fixes, then export a clean assignable list—optimized for speed, trust, and recovery.
What shipped in the prototype
- Source-linked verification: tasks connect back to transcript lines for quick context checks.
- Confidence cues: inferred owners or dates are surfaced and editable.
- Quick recovery tools: chips and lightweight edits support fast correction.
- Review-before-export: critical fields are checked before tasks leave the system.
Lessons
- Starting with capture at the source reduces drop-off and preserves a reviewable record.
- AI is most useful here as a drafting partner, not an autonomous actor.
- Trust improves when uncertainty is visible and correction is fast.
- Review flows work better when they feel lightweight and integrated into momentum.
Next steps
- Test task verification behavior with real users.
- Refine how low-confidence items are ranked and surfaced.
- Explore integrations with Linear, Notion, or Jira.
- Validate whether review-before-export improves both trust and completion speed.