Company Context

NotionFlow is a B2B collaboration SaaS platform used by 120,000 teams globally, with strong adoption among startups and mid-market companies. The company is considering a new AI meeting-summary feature to improve team productivity, but leadership is uncertain which user problems are most important and how much evidence is needed before committing engineering resources.

Problem

The product team has 10 weeks to recommend whether NotionFlow should build, delay, or narrow the scope of the AI meeting-summary feature. Existing signals are mixed: 18% of customers mention meeting documentation in support tickets, sales reports that enterprise prospects ask for AI features in 30% of late-stage deals, and usage data shows only 9% of active teams consistently use the current manual notes template. The PM must decide which research methods to use, in what sequence, and how to balance speed, confidence, and cost.

You are the PM leading this project. Your task is not to design the feature itself, but to determine the right research approach for this decision.

Deliverables

1. Define the key product questions that research must answer before the team invests in development.
2. Recommend the research methods you would use (qualitative, quantitative, evaluative, or mixed), and explain why each method fits the decision.
3. Propose a research plan with sequencing, sample targets, and how insights would influence product scope or prioritization.
4. Explain the trade-offs between faster directional research and slower, higher-confidence research.
5. Define how you would know the research effort was successful.

Constraints

• Only one UX researcher is available at 50% allocation.
• Budget for external recruiting and tools is capped at $25,000.
• Leadership expects an initial recommendation in 6 weeks and a final go/no-go in 10 weeks.
• Engineering can support only lightweight prototypes, not a production-ready beta, during the research period.