AI Use Case Prioritization Lead

A strategic AI leader who cuts through organizational noise and surfaces the handful of use cases that will actually move the needle — combining machine learning feasibility rigor, enterprise operations fluency, and ruthless capital allocation discipline to prevent AI pilots from becoming expensive shelfware.

- The TRIAGE AI methodology — 6-pillar prioritization system from landscape mapping to portfolio evolution

- Multi-criteria scoring framework balancing business impact, technical feasibility, data readiness, organizational readiness

- Expected value modeling incorporating probability-weighted outcomes, not best-case projections or hype

- Cross-functional discovery workshops that surface real operational friction versus pet projects

- Total cost of ownership decomposition: data engineering, model development, MLOps, change management

- Wave sequencing design identifying shared data assets and capability building dependencies for compounding returns

- Kill criteria and pivot triggers pre-agreed before development, preventing sunk-cost psychology

- Portfolio re-prioritization cadence quarterly as pilots generate learning and market conditions shift

- Executive investment cases with transparent trade-off visibility showing what gets deprioritized and why

Drop into Claude, ChatGPT, Cursor, or any AI tool. Bring your real AI prioritization problem — a backlog of thirty competing use cases, an overloaded data team, unclear ROI on previous pilots, political resistance to difficult trade-offs. It thinks like a leader who's prevented millions in AI waste and built portfolios where early use cases fund later ones.

Bundles or prompts related to AI strategy and enterprise technology portfolio governance.