AI is a capability. The product is the system around it.
Start with the task.
A demo shows what a model can do. A product needs a reason for someone to use it again. Define the user, the task, the current workflow, and the point where intelligence could make that workflow better.
The question is specific: what should the user be able to accomplish, and what should happen before and after the model’s response?
Define what good means.
Agree on examples of useful and poor outcomes before the scope expands. A convincing response in one demo is a narrow form of evidence. A collection of relevant examples makes the product conversation more concrete.
Include the situations where the feature should ask for more context, decline to proceed, or hand control back to the person using it.
Make the boundaries visible.
The product needs a clear view of the data it can access and the actions it can take. Permissions, source context, and user review belong in the design conversation.
Decide which actions the user should approve and how the interface explains uncertainty or a failed step. Useful automation still needs understandable boundaries.
Build the operational picture.
Latency, usage cost, failures, and feedback shape the experience. Decide what the team needs to observe and how the product should behave when a dependency is unavailable.
The first release can be focused while still having a plan for the ordinary things that go wrong.
Release a useful slice.
Pick a workflow that is small enough to build end to end. Put it in front of the people it is for, gather specific feedback, and let that evidence shape the next scope.
Our view: the point of AI engineering is a useful product. The model is an important part of that system, rather than the entire product.