Intelai
A Use Case Is a Decision, Not a Demo
Most AI use cases fail before the model is built. They fail because no one named the decision, the owner, or the cost of being wrong.
Most failed AI projects do not fail at the model. They fail at the use case.
The failure is usually visible before anyone writes code. A team describes a capability, not a decision. It says the model will "improve efficiency" or "surface insights" or "help analysts." None of those is a use case. They are a direction.
A use case is a decision that someone owns, that uses a record you already keep, and that costs something when it is wrong. If you cannot name those three things, you have a hypothesis.
The four questions
What decision changes? A decision has a trigger, an input, and an output. Approve or deny a claim. Route an invoice for review. Prioritize a maintenance ticket. Flag a contract clause. If the model's output does not change one of those, it is a report.
Who owns the decision? A person, not a department. The owner is accountable for the outcome and has the authority to accept or reject the model's recommendation. A use case without an owner becomes a model without a home.
What record does the decision use? The data has to exist as part of the work, not as a one-time extract. If the decision is made in email and the record is reconstructed later, the model is training on a story rather than a process.
What does an error cost? The cost can be money, time, safety, or reputation. The size of the cost determines the review, the controls, and the model tier. A use case with no error cost is a hobby.
The four failure modes
The first is the demo use case. It looks impressive in a conference room and does not change a decision. It dies when the pilot ends because no one can explain what it replaced.
The second is the orphan use case. A model exists, but no one owns the output. When it is wrong, everyone agrees it is interesting and no one fixes it.
The third is the data-free use case. The team wants to predict something the organization does not record. The model is asked to infer a process that was never written down. It produces a plausible answer and no way to check it.
The fourth is the cost-free use case. No one cares enough when the output is wrong, so no one funds the review. The model is cheap to run and impossible to trust.
A one-page test
Before a use case gets a budget, write a one-page memo with six lines.
| Question | Your answer |
|---|---|
| Decision | What changes when the model speaks? |
| Owner | Who is accountable for the outcome? |
| Record | Where does the input data come from? |
| Error cost | What happens if the model is wrong? |
| Review | Who checks the output, and how often? |
| Measure | What number tells you it worked? |
If the memo takes more than a page, the use case is not ready. If a line is blank, that is the next conversation.
Sequence the work
Start where the decision is repeated and the data is already captured. Repetition gives you a test set. A captured record gives you a baseline. A decision with a clear owner gives you a review path.
Do not start with the highest-consequence decision. Start where a human can check the output and the cost of an error is bounded. Build the review habit, then move to the decision that matters more.
Build the review path before the model. If you cannot describe how a person will check the output, you are not ready to deploy. The review is not a brake on the project. It is the evidence that the project works.
The model comes later
The choice of model, and the choice of machine learning type, comes after the decision is clear. The data and the label budget determine the method. The decision determines whether the method is worth using.
That order is uncomfortable for teams that want to start with the technology. It is also the order that produces systems people use.
The model does not create the use case. The decision does. Find a decision that already costs something, and the AI has somewhere to land.