Define the Experience Around Your AI Feature

An AI feature can produce an impressive answer and still leave someone unsure what to do next. The output is one moment in a larger product experience. A product manager needs to define the task before that moment, the decision after it, and the path forward when the result needs correction.

Start with the person’s work. What are they trying to accomplish, and which part of that work should the feature help them complete?

Name the task before the capability

“Add AI to the workspace” leaves the product question open. “Help someone turn a rough project idea into a plan they can review” gives the team a more concrete place to begin.

In that fictional example, the important outcome is a usable plan. Generating a long response may be part of the experience, but length alone will not tell you whether the person can act on it.

Define the information the feature needs, the form of the result, and the decisions that should remain visible to the person using it. This helps the team evaluate the complete interaction.

Explain what the result represents

A person should be able to understand whether they are looking at a suggestion, a summary, a draft, or an action the system has already taken. Those are different states with different implications.

Use specific language in the interface. A draft plan should look and read like something that can be reviewed. If the system has made assumptions, make the relevant ones visible. If an action requires confirmation, put the choice at the point where it matters.

The exact design depends on the task. The product principle is to make the state of the work understandable.

Design the correction path

Consider a plan that contains a task the person does not need. Can they remove it without losing the rest? Can they change a date or add missing context? Can they ask for a revision while preserving a version they want to keep?

Those questions can be investigated before the feature is broadly released. Use realistic sample tasks and observe how people respond when the output is imperfect.

Avoid helping too quickly during the test. If someone needs an explanation of what can be changed, that is part of the evidence about the interface.

Define the boundary

An AI tool may work with documents, connected services, or data. The person using it needs a clear understanding of the relevant limits: what it can access, what it can change, and what happens when a request falls outside those limits.

Sluice, in my product portfolio, is presented as a checkpoint between AI agents and a data warehouse. It provides a useful setting for this product question: how does a boundary become understandable in the experience?

The same question applies to smaller features. When the system cannot complete a request, the explanation should help the person choose a sensible next step.

Measure progress through the task

For the fictional planning feature, an evaluation might examine whether someone can review the suggested plan, correct a deliberate mismatch, and leave with a version they understand. Record both the behavior and the points of confusion.

A favorable reaction to the first output is useful feedback. It is only part of the picture. You also need to investigate whether the result fits the task and whether the person can recover when it does not.

A short product review

Before calling the feature ready, work through these questions:

What task does it help someone complete?
What does the person need to provide?
Can they understand what the result represents?
Can they inspect and correct it?
Are the relevant limits clear?
Can they continue after an unhelpful result?
What behavior would show that the feature is useful?

The answers give the team a more complete definition of the work. They also create a practical starting point for research, design, and release decisions.

Which part of the experience needs more attention in the AI tools you use? Join the discussion at @calixleighreign, or explore the products linked from calix.co.