Use-Case Scoring for CFOs: Impact × Feasibility × Risk
Finance & Banking • ~7 min read • Updated Aug 15, 2025
Context
CFOs are under pressure to back AI initiatives that deliver measurable ROI while controlling risk. Without a structured scoring method, portfolios skew toward hype over value. This framework balances financial discipline with innovation speed.
Core Framework
The scoring model evaluates each AI use-case against three weighted dimensions:
- Impact: Contribution to revenue growth, margin expansion, or cost savings.
- Feasibility: Technical readiness, data availability, and organizational capacity.
- Risk: Regulatory, compliance, operational, and reputational considerations.
Recommended Actions
- Define Scoring Criteria: Establish sub-metrics for each dimension (e.g., ROI %, time-to-value, compliance complexity).
- Assign Weights: Prioritize dimensions based on strategic objectives — for regulated industries, risk may carry more weight.
- Run Portfolio Assessment: Score all candidate use-cases and rank them to guide funding decisions.
- Calibrate & Refresh: Calibrate reviewers on sample cases; refresh scores quarterly to reflect new evidence.
Common Pitfalls
- Overweighting feasibility and missing high-impact but challenging opportunities.
- Subjective scoring without cross-review calibration.
- Not revisiting scores as data quality, regulations, or costs change.
Quick Win Checklist
- Publish a one-page scoring rubric and share it with reviewers.
- Score the top 10 pipeline use-cases before the next capital review.
- Map ranked results to funding tranches and decision rights.
Closing
Adopting a balanced scoring model ensures capital flows to AI initiatives with the best mix of return, viability, and acceptable risk — enabling disciplined innovation at scale.