Step 1: Start from a real list, not a wish list
Prioritization only works on a concrete list. Pull together the AI use cases people are actually asking for: the ones in slide decks, the ones a team already prototyped, the ones a vendor pitched last quarter. Write each as a single sentence that names the job, the user, and the outcome. If a candidate cannot be said in one sentence, it is not a use case yet, it is a theme, and themes cannot be sequenced. The goal of this step is a short, honest inventory you can compare like for like, not a catalog of ambitions.
Step 2: Score value where it can be measured
Rank each use case by the return you could actually prove, not the return you could imagine. A narrow, measurable job, cutting the time to answer a support ticket, drafting a first-pass document, triaging a queue, beats a sweeping org-wide transformation that no one can instrument. Ask a blunt question of every candidate: if this worked, what number moves, and could you show it moved because of the AI? Use cases that cannot answer that go to the bottom, however exciting the demo, because ROI you cannot measure is ROI you cannot defend.
Step 3: Weigh feasibility honestly
Value is only half the score. Against it, weigh how hard the use case is to actually deliver: is the data ready and accessible, or scattered and dirty; is the workflow well understood, or a moving target; does it lean on a fragile chain of tool calls, or a single contained task. The pattern that trips teams up is picking the highest-value idea and discovering the data foundation for it does not exist. Feasibility is where a promising use case quietly becomes a twelve-month project, so score it before you commit, not after.
Step 4: Add governability as a third axis
Most prioritization frameworks stop at value and feasibility. For AI, add a third question: can you control and prove this once it is live. A use case you can scope tightly, monitor, and evidence is one you can put in front of a customer or a regulator with confidence. One that touches sensitive data, makes decisions about people, or lets an agent act with little oversight carries a governance cost that belongs in the score, not in a later surprise. The most fundable first use case is rarely the flashiest; it is the one that is valuable, feasible, and governable at the same time.
Step 5: Sequence for a first win, then compound
Rank the list on all three axes and look for the use case that scores well on each. That is your first move, not the single highest-value idea and not the easiest throwaway. A first use case that is measurable, deliverable, and controllable does something a bigger bet cannot: it proves the operating model, earns the trust to fund the next one, and gives you a pattern to reuse. Sequence the rest behind it so each use case builds on the data, the controls, and the credibility the last one established, rather than starting the argument over every time.
Step 6: Revisit the ranking as reality changes
A priority list is a snapshot, and AI moves fast enough that snapshots age. As a use case ships, the data improves and the next candidate that depended on it gets easier. As regulation shifts or a model changes, the governance cost of an idea can rise or fall. Revisit the ranking on a set cadence and whenever something material changes, so the roadmap reflects where you actually are, not where you were when the list was first drawn. A prioritization done once and framed on the wall stops being a plan and becomes a relic.
Frequently asked questions
How should AI use cases be prioritized?
Score each candidate on three axes: the value you could actually measure, how feasible it is to deliver given your data and workflows, and how readily you can control and evidence it once live. The best first use case scores well on all three, not just on value.
Should you start with the highest-value AI use case?
Not necessarily. The highest-value idea is often the hardest to deliver or the riskiest to govern. A first use case that is valuable, feasible, and controllable proves the operating model and earns the trust to fund the bigger bets.
Why include governability in the scoring?
Because a use case you cannot control or evidence becomes a liability the moment it reaches production. Weighing governability alongside value and feasibility keeps a costly governance surprise out of your roadmap.