Define the user and the moment of use.
Be specific about who will use the product and when. “An AI assistant for businesses” is too broad to test. “Help a location manager understand an operational exception before a shift” defines a user, a context, and a decision.
Observe the current workflow. What information does the person need? Where do they find it? What is difficult? What do they do when the information is missing or contradictory?
Test the problem before scaling the solution.
Use interviews, workflow walkthroughs, or a small prototype to learn whether the proposed assistance is valuable. These are ways to test an assumption, not proof of demand by themselves.
Separate desirability from feasibility. A useful idea may require data you cannot access, accuracy you cannot yet achieve, or a review process that makes the workflow slower. Identify these constraints early.
Define quality and failure behavior.
Write down what a useful output looks like. Include examples of incorrect, incomplete, or unsafe behavior. Decide when the product should ask a question, hand off to a person, or decline to act.
The NIST AI Risk Management Framework offers a useful structure for considering context, measurement, and ongoing management. Evaluation needs to reflect the product’s real use, not only a collection of polished demonstrations.
Build the smallest useful workflow.
A focused first release is easier to evaluate than a general-purpose platform. Connect the input, the decision or output, and the action the user needs to take. Then test that complete loop with appropriate oversight.
AI is one part of the experience. Data access, interface design, permissions, integrations, and support often determine whether the product is useful in everyday work.