AI Use Case Prioritization Framework

1. The Prioritization Problem

2. The 4-Dimension Scoring Model

Score each AI use case on 4 dimensions (1-10 each) with defined weights that reflect what actually predicts success:

graph TD
  A[Identify Use Cases<br/>10-30 candidates] --> B[Score Each Use Case<br/>4 Dimensions × 1-10]
  B --> C[Calculate Weighted Score<br/>Impact×35% + Feasibility×30%<br/>+ Data×20% + Fit×15%]
  C --> D{Score Range}
  D -->|7.0+| E[Immediate Priority<br/>Build business case now]
  D -->|5.0-7.0| F[Conditional<br/>After quick-win completion]
  D -->|Below 5.0| G[Deferred<br/>Revisit in 12 months]

3. Scoring Business Impact (35%)

Business Impact is the most heavily weighted dimension because it's the entire point. An AI system that's technically impressive but doesn't move a business metric is a science project, not a business investment.

4. Scoring Technical Feasibility (30%)

Technical feasibility assesses how hard the problem is to build and how likely it is to work. High impact + low feasibility = expensive research project. The weight of 30% reflects that feasibility determines whether impact is ever achieved.

5. Scoring Data Readiness (20%)

Data readiness is weighted 20% but it is often the actual constraint. A perfect impact + feasibility score is worthless if you don't have the data to train or run the model. Data gaps that take 6+ months to close should fundamentally change the priority ranking.

6. Scoring Strategic Fit (15%)

Strategic fit is weighted lowest (15%) because a use case with extraordinary impact, high feasibility, and ready data should be pursued even if it's not perfectly aligned with the current quarter's OKRs. But strategic misalignment creates organizational friction that slows execution.

7. The Prioritization Matrix

Once scored, plot use cases on a 2×2 matrix using the combined Impact score (vertical axis) and Feasibility score (horizontal axis). This visual makes the prioritization conversation concrete and stakeholder-accessible.

quadrantChart
  title AI Use Case Priority Matrix
  x-axis Low Feasibility --> High Feasibility
  y-axis Low Impact --> High Impact
  quadrant-1 Quick Wins
  quadrant-2 Strategic Bets
  quadrant-3 Low Priority
  quadrant-4 Foundation First
  Customer Chatbot: [0.80, 0.72]
  Document Processing: [0.85, 0.65]
  Predictive Maintenance: [0.45, 0.82]
  Dynamic Pricing: [0.38, 0.78]
  Email Classification: [0.90, 0.40]
  Data Lake: [0.70, 0.30]
  Fraud Detection: [0.52, 0.68]
  Autonomous Workflow: [0.22, 0.55]

8. The Portfolio Approach

The prioritization matrix tells you which use cases to pursue - the portfolio approach tells you how many of each type to pursue simultaneously. The 60/30/10 split is based on analysis of enterprise AI programs that successfully scaled.

9. Running a Prioritization Workshop

The scoring model works best when run as a facilitated 2-day workshop with cross-functional stakeholders. Here's the proven agenda: