Choosing the Right AI Use Case: Impact vs. Feasibility

Most AI roadmaps stall because ideas are inspirational but ungrounded. A lightweight, transparent scoring model prevents endless debate.

Scoring Dimensions (1–5)

• Impact: Revenue lift, cost reduction, risk mitigation, or experience delta.
• Feasibility: Data readiness, integration complexity, model availability.
• Time-to-Value: Prototype to pilot timeline.
• Differentiation: Strategic moat vs. table stakes automation.

Weighted Score = (Impact * 0.4) + (Feasibility * 0.25) + (Time-to-Value * 0.2) + (Differentiation * 0.15)

Plot candidates on a 2x2 (Impact vs. Feasibility) but keep the weighted score for ranking backlog order.

Red Flags

• “We’ll just get the data later.” (Data debt compounds)
• Undefined success metrics
• Vendor-lock via opaque proprietary pipelines early

Deliverable Pattern

1. Use case brief (problem, success metric, constraints)
2. Data readiness snapshot
3. Risk register (ethical, legal, operational)
4. Prototype scope + evaluation harness
5. Pilot go/no-go criteria

Outcome

Stakeholders can trace why a use case is above or below the cut line. This builds trust and accelerates approvals.