How to Prioritize AI Use Cases Without Guessing
Twelve ideas, budget for three. Here is a defensible way to choose, and the reason the best-scoring one is often not where you should begin.
Erin Moore
Fractional Chief AI Officer
Rank AI use cases on three axes — value, feasibility and reversibility — then start with the highest-scoring one that you can undo cheaply. That last clause is the part most prioritization frameworks omit, and it is the reason so many first projects become permanent embarrassments.
The three axes
Value. Not "strategic importance" — money or time, stated as a number you could check later. If nobody can say what the process costs today, that is your finding: you cannot prioritize what you have not measured, and the ROI calculator exists to force this number into the open.
Feasibility. Volume, rule stability, data quality and exception rate. A process that runs a thousand times a month with stable rules and clean inputs is feasible. One that runs eleven times a month, each time slightly differently, is not — regardless of how annoying it is.
Reversibility. If this fails, how hard is it to go back? A use case that touches an internal workflow is reversible. One that changes how customers are billed, or that you announce publicly, is not. Reversibility is a proxy for the real cost of being wrong.
Score each from one to five. Multiply value by feasibility, then use reversibility as a tie-breaker and a veto.
Why the top score is often the wrong start
The highest value-times-feasibility score is frequently something customer-facing, because customer-facing processes are high volume and the money is easy to name. It is also frequently the least reversible thing on the list.
Starting there means your first AI project is the one where failure is most visible, at exactly the moment your organization has the least experience running these projects. The politically survivable sequence is to start with a high-scoring internal process, learn how your company actually behaves when an AI project underperforms, and spend that credibility on the customer-facing one second.
This is the same reasoning behind the sequencing in the 90-Day AI Playbook: the first quarter is for building the decision muscle, not for the biggest prize.
The disqualifiers
Some use cases should be removed before scoring rather than ranked low:
- No baseline. If you cannot state the current cost or duration, you will never prove the change. Measure first; it is worth doing regardless.
- Nobody owns the outcome. A project with no accountable owner will survive past its usefulness because nobody has standing to end it.
- The process is disputed. If two departments disagree about how the work should be done, automating it multiplies the disagreement. Resolve it on paper first.
Keeping the list honest
Re-score quarterly. Feasibility moves as data improves, value moves as the business changes, and ideas that were unfeasible eighteen months ago are routinely feasible now. A prioritization list that never changes is not being used.
Also keep the rejected list. The most common failure I see is a company re-litigating the same idea three times because nobody wrote down why it was declined. NIST's AI Risk Management Framework makes a similar point in a governance register: the decision and its reasoning are the artifact worth keeping, not the score.
Frequently asked questions
How do I prioritize AI use cases? Score each on value, feasibility and reversibility. Multiply value by feasibility to rank, then use reversibility to decide where to start — the best first project is the highest-scoring one you could undo cheaply if it fails.
What makes a use case feasible? High volume, stable rules, decent data quality and a low exception rate. Low-volume processes with many exceptions rarely repay the effort, however irritating they are to the people doing them.
Should we start with the highest-value use case? Usually not. The highest-value use cases tend to be customer-facing and hard to reverse, which is a poor place to make your first mistakes. Start internal, learn how your organization handles an underperforming project, then go after the big one.
How often should the list be re-scored? Quarterly. Feasibility in particular moves quickly as data improves and tooling changes, so a list left untouched for a year is describing a company that no longer exists.
Further reading
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