How to Identify High-Value AI Use Cases: A Prioritization Framework
Executive Summary
- High-value AI use cases sit at the intersection of business impact, feasibility, and strategic alignment
- A systematic discovery process surfaces better opportunities than brainstorming sessions
- This framework uses a 4-step process: discovery, assessment, scoring, and portfolio selection
- Not all good ideas are the right ideas—prioritization requires saying "not now" to viable opportunities
- The best use cases solve real business problems with available (or obtainable) data
- Start with 2-3 prioritized use cases rather than a long list of possibilities
- Revisit prioritization quarterly as capabilities and context change
Why This Matters Now
Every organization has more potential AI use cases than capacity to implement them. Without a systematic prioritization method, organizations either:
- Chase the shiny object: Implementing what's trendy rather than what's valuable
- Spread too thin: Attempting many initiatives with insufficient resources for any
- Pick wrong: Selecting use cases that fail to demonstrate AI value, creating skepticism
The cost of poor prioritization extends beyond wasted investment. Failed pilots create organizational resistance to future AI initiatives. Successful pilots build momentum and capability.
Prioritization isn't about finding the single perfect use case. It's about selecting a portfolio of 2-3 use cases that balance quick wins with strategic capability building.
The 4-Step Prioritization Framework
Step 1: Discovery (Weeks 1-2)
Discovery generates a comprehensive list of potential use cases from across the organization.
Methods:
Business Unit Interviews
- Interview department heads about pain points and opportunities
- Ask: "What takes too long? What's error-prone? What requires expertise you can't scale?"
- Capture both immediate frustrations and strategic aspirations
Process Analysis
- Review key business processes for automation potential
- Identify high-volume, repetitive tasks
- Look for decisions currently made with incomplete information
Data Asset Review
- Inventory data you already collect
- Ask: "What questions could this data answer if analyzed effectively?"
- Identify data that's collected but underutilized
Customer/Employee Feedback
- Review support tickets for patterns
- Analyze employee survey data for operational frustrations
- Identify recurring complaints that AI could address
Competitive Analysis
- Research how competitors use AI
- Identify industry AI applications relevant to your context
- Attend industry events for trend awareness
Output: 15-30 candidate use cases documented with preliminary descriptions.
Step 2: Assessment (Weeks 2-3)
For each candidate use case, gather information needed for scoring.
Use Case Assessment Template:
USE CASE NAME: ________________________________
1. PROBLEM STATEMENT
What specific business problem does this solve?
__________________________________________________
2. BUSINESS IMPACT
- Revenue impact: $_____/year (estimate)
- Cost savings: $_____/year (estimate)
- Quality/satisfaction improvement: ________________
- Strategic alignment: [High / Medium / Low]
3. DATA REQUIREMENTS
- Data needed: _________________________________
- Data available today: [Yes / Partial / No]
- Data quality: [Good / Adequate / Poor / Unknown]
4. TECHNICAL COMPLEXITY
- AI approach type: [ML model / LLM / Automation / Analytics]
- Build vs. buy: [Custom build / Vendor solution / Hybrid]
- Integration requirements: _______________________
- Estimated implementation: [<3 months / 3-6 months / >6 months]
5. RISK PROFILE
- Regulatory/compliance: [High / Medium / Low]
- Reputational: [High / Medium / Low]
- Technical: [High / Medium / Low]
6. DEPENDENCIES
- Prerequisite capabilities: ________________________
- Stakeholder buy-in needed: _____________________
- Resource requirements: _________________________
7. SUCCESS CRITERIA
How will we know this worked?
__________________________________________________
Step 3: Scoring (Week 3)
Score each use case across five dimensions using a consistent framework.
Scoring Dimensions:
| Dimension | Weight | Scoring Criteria |
|---|---|---|
| Business Impact | 30% | Quantifiable value (revenue, cost, quality) |
| Feasibility | 25% | Data availability, technical complexity, timeline |
| Strategic Alignment | 20% | Connection to business priorities, capability building |
| Risk | 15% | Regulatory exposure, reputational risk, technical risk |
| Time to Value | 10% | Months to initial measurable results |
Weighted Score Calculation:
Score = (Impact × 0.30) + (Feasibility × 0.25) + (Strategic × 0.20) + (Risk × 0.15) + (Time × 0.10)
Step 4: Portfolio Selection (Week 4)
Use scores to select a balanced portfolio, not just the highest-ranked options.
Portfolio Composition:
A balanced portfolio typically includes:
1. Quick Win (1 use case)
- High feasibility, moderate impact
- Demonstrates value within 3 months
- Builds organizational confidence in AI
- Lower risk profile
2. Strategic Initiative (1-2 use cases)
- High impact, longer timeline
- Builds important capabilities
- Requires more investment
- Aligned with major business priorities
Decision Tree for Portfolio Selection
Common Failure Modes
1. Picking Technology-Fascinating Cases
AI teams often gravitate toward technically interesting problems that don't solve real business needs.
Fix: Require business sponsors for every use case. If no business owner will champion it, it's not a priority.
2. Ignoring Data Reality
Selecting use cases that assume data exists when it doesn't, or assume quality that's unavailable.
Fix: Validate data availability and quality during assessment, not after selection.
3. Underestimating Change Requirements
Choosing use cases that require significant behavior change without accounting for that in planning.
Fix: Include change management assessment in feasibility scoring.
4. Optimizing for Speed Only
Selecting only quick wins without building strategic capabilities.
Fix: Require portfolio balance—quick wins plus at least one strategic initiative.
5. Analysis Paralysis
Over-analyzing and never selecting, or revisiting decisions constantly.
Fix: Set a deadline for selection. Perfect information doesn't exist; make decisions with available data.
Checklist: Use Case Prioritization
Discovery
- Business unit interviews completed
- Process analysis conducted
- Data assets inventoried
- Customer/employee feedback reviewed
- Competitive analysis completed
- 15-30 candidate use cases documented
Assessment
- Assessment template completed for each candidate
- Business impact estimated with methodology
- Data requirements validated
- Technical approach evaluated
- Risk profile assessed
- Success criteria defined
Scoring
- Scoring criteria agreed across stakeholders
- Consistent scoring applied
- Weighted scores calculated
- Results reviewed for reasonableness
- Outliers investigated
Selection
- Portfolio includes quick win
- Portfolio includes strategic initiative
- Dependencies satisfied
- Resources match requirements
- Stakeholder buy-in secured
Frequently Asked Questions
Next Steps
A systematic prioritization process ensures AI investments target the highest-value opportunities. Start with discovery, be rigorous in assessment, score consistently, and select a balanced portfolio.
For industry-specific use case catalogs, see:
- 15 AI Use Cases for Small and Medium Businesses
- AI Use Cases for Schools: From Admissions to Administration
Book an AI Readiness Audit with Pertama Partners to identify and prioritize AI use cases specific to your organization.
Related Reading
- Building Your First AI Strategy: A Step-by-Step Framework
- 15 AI Use Cases for Small and Medium Businesses
- AI Use Cases for Schools
Frequently Asked Questions
Start with 2-3. One quick win plus one strategic initiative is a solid portfolio. Avoid the temptation to pursue more until you've demonstrated success with your initial choices.
