What it does
Analyses user feedback, effort estimates, and business impact to produce a weighted prioritisation matrix with recommendations.
Why I recommend it
Helps product leaders justify decisions and keep conversations focused on data.
Expected benefits
- Data-driven roadmap
- Clear visibility into trade-offs
- Quicker alignment across stakeholders
- Historical record of decisions
How it works
Collect feature candidates + inputs -> Claude calculates scores (RICE/ICE/custom) -> outputs matrix in Notion/Sheets with narrative summary.
Quick start
Build manual RICE scoring for a few features, then compare with AI-generated output.
Level-up version
Include customer revenue impact, link to Jira epics, and simulate scenarios (if we invest in X, we delay Y).
Tools you can use
Product: Productboard, Jira, Airtable
AI: Claude
Docs: Notion, Coda
Automation: Zapier, Workato
Also works with
Marketing campaign prioritisation, engineering tech debt triage.
Technical implementation solution
- No-code: Airtable backlog -> Zapier -> Claude -> output table + Slack summary.
- API-based: Data warehouse -> AI -> update Coda doc + Jira priorities.
Where it gets tricky
Ensuring accurate input data, preventing AI from over-simplifying nuance, and keeping stakeholders involved in final decisions.
