AI Use Case Prioritization
AI use case prioritization is the process of comparing potential uses of AI against criteria such as business value, feasibility, risk, and readiness to decide which ones to pursue first.
Most organizations can list far more possible uses of AI than they can fund or support. Prioritization turns that long list into a short, ranked one. Each candidate should be described as a specific workflow and outcome, such as cutting the time it takes to review a contract, rather than a broad goal like "use AI in finance." Teams then score the candidates, often on a simple value and effort grid, and choose a few to test first.
A common misconception is that the most impressive idea should go first. Early projects usually work better when they target a measurable workflow with a clear owner, usable data, and results that are easy to check. Those early wins build trust and show what delivery takes. Prioritization is also not a one-time exercise. As pilots finish, data improves, and tools change, the list should be reviewed and ranked again.
Key parts
Business value. The outcome the use case would improve and how it will be measured.
Feasibility. Whether the needed data, systems, and skills are available.
Risk. The potential for errors, data exposure, or harm, and the oversight that would require.
Workflow fit. How well the use case fits the way people already work.
Ownership. A named person accountable for the result.
