A structured 4-dimension scoring model for ranking AI use cases by business impact, technical feasibility, data readiness, and strategic fit. Includes the prioritization matrix, portfolio approach, and a complete workshop facilitation guide.
Every organization has more potential AI use cases than capacity to build them. A typical AI discovery workshop generates 15–30 candidates. You can pursue 3–5. The question is: which ones?
Without a structured scoring method, organizations default to one of three bad patterns: HIPPO-driven selection (Highest Paid Person's Opinion), recency bias (whatever was presented last), or technology excitement (the most interesting technically, not the most valuable commercially).
The 4-dimension scoring model replaces opinion with a structured, evidence-based ranking that every stakeholder can inspect and debate. It doesn't eliminate judgment — it structures it.
The most senior person in the room picks their favourite use case. No scoring. High risk of political bias.
Teams build what's technically interesting. Leads to impressive demos that solve the wrong problems.
4-dimension scoring with defined criteria. Transparent, defensible, and improvable over time.
Score each AI use case on 4 dimensions (1–10 each) with defined weights that reflect what actually predicts success:
| Dimension | Weight | Score 1–3 | Score 4–6 | Score 7–10 |
|---|---|---|---|---|
| Business Impact | 35% | Minor efficiency gain; affects <5% of transactions | Meaningful cost or revenue impact; €100K–€1M range | Transformational; €1M+; strategic differentiation |
| Technical Feasibility | 30% | Research-level problem; no proven solutions | Proven approach exists; moderate integration complexity | Solved problem; low complexity; fast to build |
| Data Readiness | 20% | Data doesn't exist; >6 months to acquire | Data exists but needs cleaning/labeling | Clean, labeled, accessible data ready now |
| Strategic Fit | 15% | Tangential to company strategy; regulatory concerns | Supports strategy; moderate stakeholder buy-in | Core to OKRs; executive sponsor committed |
Score = (Impact × 0.35) + (Feasibility × 0.30) + (Data × 0.20) + (Fit × 0.15)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]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.
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.
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.
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.
Does this use case map to at least one company-level OKR? Can you trace a direct line from this AI system to a metric the board cares about?
Is there a named C-level or VP sponsor who will champion adoption, remove blockers, and own the outcome? AI projects without executive sponsors fail 3× more often.
Does this use case fall under EU AI Act high-risk classification? Are there sector-specific regulations that constrain deployment? What's the compliance overhead?
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]
Build immediately. These are your first 1–2 initiatives. They build organizational confidence and fund strategic bets.
Examples: Customer chatbot, document processing, meeting summarization
Plan and invest. These require 12–18 months. Start the data and infrastructure work now while quick wins ship.
Examples: Predictive maintenance, dynamic pricing, autonomous workflows
Build as infrastructure. These enable other use cases and are worth doing, but don't lead with them in executive presentations.
Examples: Data lake, email classification, basic automation
Defer or drop. No compelling reason to pursue these now. Revisit in 12 months when feasibility or impact may have changed.
Examples: Novel research problems, niche tools for small teams
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.
2–3 initiatives. Ship in 3–6 months. Generate measurable savings that fund the strategic bets. Build organizational confidence and AI credibility.
1–2 initiatives. 12–18 months to value. These are the transformational bets. Start data infrastructure and research now while quick wins deliver.
Ongoing. Data platform, MLOps, AI literacy. These don't generate direct ROI but are the prerequisite for everything else. Fund continuously.
The scoring model works best when run as a facilitated 2-day workshop with cross-functional stakeholders. Here's the proven agenda:
We facilitate AI prioritization workshops for enterprise teams — from a 2-hour executive session to a full 2-day cross-functional workshop. Get an objective, scored list of your highest-value AI opportunities.