Start with the work
An AI model is a capability. A useful product is the experience that turns that capability into a dependable outcome. Between those two things sits a lot of work: context, access, instructions, evaluation, and the decisions about when a person should step in.
Context is a product decision
Before choosing a model, define the task. What does a successful result look like? Which information does the system need? What should happen when that information is incomplete? Those questions shape the product more than a model leaderboard does.
Make the handoffs explicit
A workflow usually crosses boundaries. A brief becomes a plan. A plan becomes an activation. Results become a recommendation. Each handoff needs a clear definition of what is being passed, what has been checked, and who owns the next decision.
Evaluate the outcome
A convincing response is not enough. Evaluation should ask whether the work was completed correctly, whether the evidence supports the recommendation, and whether the result is useful to the person receiving it.
Build for the exception
The happy path is the easy part to demonstrate. Missing data, conflicting instructions, and uncertain results are where a product earns trust. Design those moments as carefully as the successful ones.