The use-case hypothesis should be shaped by two connected failure modes: leaving the business value or use context unclear, and selecting risk measures without connecting them to that context. Both can make a proposed AI use case appear precise while leaving its value, conditions, or evaluation unresolved.
Two failure modes to address first
| Failure mode | Weak hypothesis | Question to ask |
|---|---|---|
| Unclear business value or context | The proposal describes an AI capability but does not explain the value it is meant to create or the circumstances in which it would operate. | Is the intended business value explicit, and is the use context specific enough to evaluate? |
| Context-disconnected risk measurement | The proposal proposes measures for identifying risks without showing how they relate to the conditions of actual use. | Does every risk measure connect to a relevant deployment context? |
The NIST AI RMF states that the business value or context of business use should be clearly defined. It also connects measurement approaches for identifying AI risks to deployment context. The implication for use-case framing is straightforward: an abstract ambition and a generic risk checklist are not enough. The hypothesis needs to connect value, context, and measurement.
How to check the hypothesis
A product team can apply two checks before investing further in the proposal.
1. Define the value and context together
The team should be able to state:
- what business value the use case is intended to address;
- the context in which the system would operate;
- the conditions that could change the value or make the use case unsuitable; and
- which parts remain assumptions rather than established facts.
If the value could apply to almost any AI project, the statement is still too broad. If the context is expressed only as a general aspiration, the team cannot yet connect the proposal to meaningful risk measures.
2. Connect each measure to its context
For every proposed measure, the team should identify:
- the deployment condition it is meant to illuminate;
- the risk it is intended to help identify;
- the evidence available for applying it; and
- any context in which it would not apply.
This prevents a measurement approach from being selected simply because it is commonly used. Its relevance has to be established for the proposed use case.
A compact hypothesis can therefore follow this structure:
Business value: What value is intended?
Deployment context: Under what conditions would the system operate?
Relevant risks: What could go wrong in those conditions?
Measures: How would the team identify those risks?
Assumptions: Which claims still require confirmation?
The value and context determine what needs attention. The measures then show how the team would examine that particular proposal.
What the reader must still confirm
The cited NIST statements do not establish a universal set of risk categories, a single measurement method, or a sufficient threshold for every use case. The team must still determine which risks matter in its own deployment context, what evidence can identify them, and who will judge the resulting evidence.
It must also confirm unresolved operational assumptions rather than treating them as settled parts of the hypothesis. That includes the actual deployment conditions, the consequences of failure, the available evidence, and the criteria for deciding whether a measure is adequate.
A strong use-case hypothesis therefore does more than name an AI application. It makes the intended value explicit, locates the application in a defined context, connects risk measurement to that context, and separates verified conditions from assumptions that still need testing.