The definition that matters
An AI use case is a specific business task where an AI system produces a measurable outcome. It has a defined input, a defined action, and a result you can put a number on. It is not a capability ('we use large language models') and it is not a demo. A real use case names the workflow it changes, the person who owns the outcome, the metric it moves, and what running in production means for it. If you cannot fill in those four blanks, you have an experiment, not a use case.
Where teams get this wrong
Most enterprises are over-investing in pilots and under-investing in the path to production. The reason is usually a scoping problem dressed up as a technology problem. A team demos a model answering questions well, calls it a use case, and then discovers there is no owner, no metric, and no plan for the controls production requires. Operators who have run AI at 300,000-organization scale see the same pattern: the pilot proved the model answers, and no one scoped the business case. The demo is the starting gun, not the finish line.
How to write one that survives contact with production
Start from a workflow that already costs real time or money, not from the model. Name the metric before you name the tool. Give it one owner who is accountable for the outcome, not a committee. Decide up front what controls the use case needs to run in a regulated setting: who can see the data, what gets logged, how you would prove what happened. A use case scoped this way is narrow, and narrow is the point. Value that is measurable and provable in 30 days beats a broad ambition that never ships.
Frequently asked questions
What is the difference between an AI use case and an AI capability?
A capability is something the technology can do, like summarizing text. A use case is a specific business task with an owner and a metric, like cutting the time a claims handler spends triaging a case by a measurable amount. Capabilities are inputs, use cases are outcomes.
What makes an AI use case good?
It is narrow, has a named owner, moves a metric you can measure, and can reach production with the controls a regulated business needs. Good use cases are provable, usually within about 30 days, rather than broad and open-ended.
Why do so many AI use cases fail to reach production?
Because they were scoped as demos, not as business cases. There is no owner, no metric, and no plan for the controls production requires. The model works in the demo and stalls the moment it meets real data, real users, and real accountability.