ai-use-case-scorer

AI use case scoring evaluates potential projects by measuring value, feasibility, and safety. It uses a multi-axis model to generate tiered rankings such as Do Now, Do This Quarter, Park, or Avoid, ensuring you pursue the most valuable and viable projects first.

Evaluating AI project safety and feasibility requires a structured scoring process that weighs data sensitivity, time spent, and team goals. This multi-axis model generates actionable tiers to help you avoid risky or unviable AI initiatives.

To start scoring AI use cases, input your candidate projects, goals, time constraints, and data sensitivity details. The evaluation model then processes these contextual factors to generate a prioritized, tiered ranking for your team's action plan.

You need to provide your candidate use cases, personal or team objectives, time spent, and data sensitivity factors. This contextual information feeds the multi-axis scoring model to accurately assess value, feasibility, and safety.

Yes, AI projects can be categorized into action tiers like Do Now, Do This Quarter, Park, and Avoid. The tiering system translates value, feasibility, and safety scores into actionable prioritization for your team.

You should avoid pursuing an AI use case when the scoring model assigns it to the Avoid tier, indicating low value, poor feasibility, or high safety risks. Evaluating data sensitivity and project goals helps identify these limitations early.