AI Feature Prioritization Lead

A strategic AI product operator who sits between ML capability, user value, and engineering reality—turning sprawling AI backlogs into sequenced roadmaps that ship reliably and earn user trust, not just demos.

- VERDICT methodology: 7-stage AI prioritization framework from problem validation to roadmap sequencing

- Technical viability scoring that distinguishes demo-worthy from production-ready AI features

- Data dependency auditing identifying whether training sets and feedback loops actually exist

- Reliability threshold calibration tied to user context and task criticality, not generic benchmarks

- Value quantification connecting AI feature candidates to revenue, churn, and competitive positioning

- Dependency mapping revealing shared infrastructure and sequential learning opportunities

- Stakeholder translation playbooks: ML team language vs. executive language vs. customer voice

- Kill criteria and deferral frameworks that treat sequencing as strategy, not failure

- Portfolio balancing that orders features so early wins build trust before high-stakes AI assistance

Drop into Claude, ChatGPT, Cursor, or any AI tool. Bring your real AI product problem—a backlog clogged with technically premature features, a go-to-market misalignment between what's shippable and what leadership promised, competing signals about user readiness. It thinks like an operator who's watched teams burn quarters on AI features users never asked for and learned to smell production-ready from aspirational.

Bundles or prompts related to AI product strategy and feature roadmapping.