Skill: AI Leverage Mapping

Capability Domain: Process Engineering
Technology Stack: Opportunity Scoring

Purpose

Say where AI could cut impact, not which agents to build.

File intern fills

streamlit/data/ai_opportunities.yaml columns: id, opportunity_name, stage, ai_can_help (yes/no/unknown), effort_size (S/M/L/XL), expected_gain, plot_x, plot_y.

Quadrant axes: X = effort_size (S–XL), Y = expected_gain. Same id values as swimlane badges. TSY does not re-query Athena for this plot.

Recipe

1. Start from the Cost of Delay Ranking. Ignore low-impact stages.
2. For each remaining box, score independently:
   - AI-can-help? yes / no / unknown (can GenAI shorten touch or wait?)
   - Implementation effort S / M / L / XL
3. Low-hanging fruit = high impact AND AI-yes AND low effort.
4. Plot expected efficiency gain vs effort. Cells are opportunity names (e.g. "PR review wait"), never product names (no Code Review Agent, no Copilot rollout plan).
5. Write AI Opportunity Prioritization Matrix into that YAML. Overlay numbered badges on high-impact AI-yes swimlane cards; gray = no/unknown.

Pitfalls

• Recommending AI on a cheap stage because it is easy.
• Treating this matrix as an agent backlog.