AI Use Case Prioritization Matrix

Most organizations identify dozens of potential AI use cases. The challenge is not finding ideas; it is deciding which ones to pursue first. This matrix provides a structured scoring approach to cut through the noise and prioritize based on what actually matters: impact, feasibility, and alignment.

Scoring Dimension 1: Business Impact (1-5)

Rate each use case on its potential to move the needle. Consider revenue impact, cost savings, time savings, risk reduction, and customer experience improvements. A 5 is a game-changer for the business. A 1 is a nice-to-have with marginal value.

• 5:Transformative impact on revenue or costs (>$500K annual value)
• 4:Significant measurable improvement ($100K-$500K)
• 3:Moderate improvement with clear metrics ($25K-$100K)
• 2:Minor efficiency gain (<$25K)
• 1:Exploratory, no clear business case yet

Scoring Dimension 2: Feasibility (1-5)

How realistic is this to implement given your current capabilities? Consider data availability, technical complexity, required integrations, team capacity, and whether proven solutions exist. A 5 means you could start tomorrow. A 1 means significant investment before you can even begin.

Scoring Dimension 3: Strategic Alignment (1-5)

Does this use case support your organization's strategic priorities? Consider alignment with company goals, executive sponsorship, cross-department applicability, and whether success here builds momentum for future AI initiatives.

How to Use the Matrix

Score each use case across all three dimensions. Multiply the scores together to get a composite priority score (max 125). Rank by composite score, then review the top 5 for any practical constraints. Your first AI initiative should score high on all three dimensions, because early wins build organizational confidence in AI.