Turning AI meeting notes into action

01 / Define

The problem to explore

A hypothetical workplace tool automatically records meetings and writes AI summaries, but action items get lost and people don't trust the notes. Explore how attendees can review, correct and assign actions so the summary becomes reliable team memory.

This is an original UXCharan practice scenario, not a commissioned client project or a validated research finding. Treat the problem as a hypothesis. Adapt it when your research points elsewhere.

Who to learn from

Team leads in a small company who run several meetings a day. Recruit consenting adults with relevant experience where possible; document recruitment limits and avoid claiming your sample represents everyone.

02 / Discover

Research before choosing a solution

Questions to investigate

How do teams track decisions and actions after meetings today?
What makes people trust or ignore an automated summary?

Start with a small set of interviews or contextual walkthroughs that fits your access and consent arrangements. Compare two existing approaches to the same task. Separate direct observations, participant statements and your own assumptions. If primary research is not possible, clearly label desk research and list what remains untested.

Map the experience

Trace the task from its trigger through completion and follow-up. Identify where information, responsibility or confidence is missing. Choose one opportunity you can justify with evidence.

03 / Design

A manageable project scope

Review an AI summary
correct or confirm action items
assign owners and due dates

Choose one primary device and a realistic context of use. Explore at least two approaches before refining a flow. Build only the screens and states needed to demonstrate the selected task; a complete production system is outside this brief.

Include the difficult moments

AI assigns an action to the wrong person
sensitive topic discussed in the meeting
attendee joined late

Document loading, empty, error and success states where relevant. Consider keyboard order, text enlargement, contrast, meaningful labels and alternatives to colour-only status. Use synthetic data and a fictional brand; do not collect real medical records, credentials or payment information.

Project-specific artefact

An action-item review pattern that shows what the AI was unsure about.

04 / Test & improve

What would make the design work?

Can a team lead confirm and assign all actions from a meeting in under two minutes, with no wrong owners?

Turn this question into realistic prototype tasks. Record whether participants complete each task, where they hesitate, what they misunderstand and how much help they need. Choose success criteria before testing; report observed counts with sample size, rather than claiming statistically proven improvements.

Prioritise findings by impact on the task. Revise the flow and retest the most consequential change where possible. If you cannot retest, explain that limitation.

05 / Tell the story

Your submission checklist

Problem framing, intended users, scope and your role.
Research approach, anonymised evidence and assumptions that changed.
Journey or flow, alternatives explored and reasons for your decisions.
An action-item review pattern that shows what the AI was unsure about.
Viewable Figma prototype or a shareable document, including key edge cases.
Usability findings, before-and-after revisions and remaining limitations.
A concise portfolio narrative that labels this as a self-initiated concept.

Show what you learned and changed. Do not invent testimonials, interview quotes, launch metrics, employment relationships or business outcomes. Disclose material AI assistance and verify generated content.

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Useful methods for this project

These sources inform the learning process; they do not validate the fictional scenario or endorse UXCharan. Explore more tools in the resource library.