AI Use Case Prioritization Matrix
Weighted decision matrix for ranking AI opportunities. Scores use cases on Strategic Alignment, Business Value, Feasibility, Data Readiness, Risk, and Time to Value. Auto-calculates priority scores, assigns quadrants (Quick Win/Strategic/Fill-In/Deprioritize), and recommendations
Key Insights
Every organization has more AI ideas than resources to implement them. Without structured prioritization, decisions become political—whoever argues loudest wins. A prioritization matrix provides objective criteria, enabling data-driven decisions about where to invest AI resources.
This Excel template provides a complete prioritization framework: weighted scoring across four dimensions (Strategic Alignment, Business Value, Feasibility, Data Readiness), detailed criteria definitions for consistent scoring, quadrant classification for action guidance, and a portfolio dashboard for executive visibility into the AI opportunity pipeline.
Overview
Which AI project should you fund next? The customer service chatbot or the sales forecasting model? Document processing or fraud detection? Without a framework, these decisions become arbitrary—driven by whoever makes the best pitch rather than which investment creates the most value.
This prioritization matrix provides objective criteria for evaluating AI use cases. Score each opportunity consistently, calculate weighted priority scores automatically, and visualize your portfolio to make informed investment decisions.
What's Inside
Sheet 1: Prioritization Matrix
- Use case identification (ID, name, business unit, description)
- Four scoring dimensions with predefined weights:
- Strategic Alignment (25%): How well does this align with company strategy?
- Business Value (30%): What's the potential business impact?
- Feasibility (25%): How technically achievable is this?
- Data Readiness (20%): Do we have the data required?
- 1-5 scoring for each dimension
- Auto-calculated weighted priority score
- Quadrant classification and action recommendation
- Risk assessment and estimated effort
Sheet 2: Scoring Criteria
- Detailed definitions for each score level (1-5) across all dimensions
- Strategic Alignment: From "not aligned" to "core to strategic plan"
- Business Value: From "<$50K value" to ">$1M annual value"
- Feasibility: From "cutting-edge/unproven" to "proven technology"
- Data Readiness: From "no data exists" to "clean, accessible data"
- Ensures consistent scoring across evaluators
Sheet 3: Quadrant Guide
- Quick Win: High value + High feasibility → Proceed immediately
- Strategic: High value + Low feasibility → Invest resources
- Fill-In: Low value + High feasibility → Do if resources available
- Deprioritize: Low value + Low feasibility → Avoid
- Score thresholds: 16-25 (Proceed), 9-15 (Evaluate), <9 (Defer)
Sheet 4: Dashboard
- Portfolio metrics from matrix data
- Total use cases by recommendation
- Quick wins vs. strategic projects count
- Average priority score
- Visual portfolio distribution
Who This Is For
- Chief AI Officers prioritizing AI investments
- Strategy Leaders aligning AI with business objectives
- Business Unit Leaders advocating for AI projects
- Project Portfolio Managers managing AI initiative pipeline
- Finance evaluating AI investment proposals
Why This Resource
Subjective prioritization creates political battles and suboptimal decisions. This framework provides defensible, consistent criteria that can be explained to stakeholders and executives. When someone asks "why did project A get funded over project B?"—you have objective scores to reference.
The quadrant classification provides clear action guidance, not just scores.
FAQ
Q: Who should do the scoring?
A: Cross-functional teams produce better scores than individual evaluators. Include business stakeholders (value), technology (feasibility), and data teams (data readiness). Facilitate sessions to achieve consensus scores.
Q: Can we adjust the weights?
A: Yes. The default weights reflect balanced priorities. If your organization values strategic alignment more heavily, or if data readiness is consistently the blocker, adjust weights accordingly.
Q: How often should we re-prioritize?
A: Quarterly review is typical. Re-score when business strategy changes, new opportunities emerge, or feasibility changes (e.g., new technology becomes available).
What's Inside
Sheet 1: Prioritization Matrix
- Use case identification (ID, name, business unit, description)
- Four scoring dimensions with predefined weights:
- Strategic Alignment (25%): How well does this align with company strategy?
- Business Value (30%): What's the potential business impact?
- Feasibility (25%): How technically achievable is this?
- Data Readiness (20%): Do we have the data required?
- 1-5 scoring for each dimension
- Auto-calculated weighted priority score
- Quadrant classification and action recommendation
- Risk assessment and estimated effort
Sheet 2: Scoring Criteria
- Detailed definitions for each score level (1-5) across all dimensions
- Strategic Alignment: From "not aligned" to "core to strategic plan"
- Business Value: From "<$50K value" to ">$1M annual value"
- Feasibility: From "cutting-edge/unproven" to "proven technology"
- Data Readiness: From "no data exists" to "clean, accessible data"
- Ensures consistent scoring across evaluators
Sheet 3: Quadrant Guide
- Quick Win: High value + High feasibility → Proceed immediately
- Strategic: High value + Low feasibility → Invest resources
- Fill-In: Low value + High feasibility → Do if resources available
- Deprioritize: Low value + Low feasibility → Avoid
- Score thresholds: 16-25 (Proceed), 9-15 (Evaluate), <9 (Defer)
Sheet 4: Dashboard
- Portfolio metrics from matrix data
- Total use cases by recommendation
- Quick wins vs. strategic projects count
- Average priority score
- Visual portfolio distribution
Ready to Get Started?
Sign up for a free Explorer account to download this resource and access more AI governance tools.
Create Free Account