Adding a chat box to an existing interface is easy. The difficult work is deciding when the system should suggest, when it should explain and when it must return control to the person using it.
Design legible intent, not invisible prediction
Good AI experiences can infer intent without presenting the result as magic. They reveal which information shaped a suggestion, why it appeared and how the user can correct it. Trust comes from explanation quality as much as model accuracy.
Protect the sense of control
Automation should scale with risk. Low-risk, reversible tasks can happen directly. Financial, legal or reputational decisions need clear summaries, approval points and a visible path back.
Design uncertainty, not only empty states
AI systems are sometimes unsure. Instead of turning that moment into an error, show confidence, missing information and the next best action. Honest uncertainty is more useful than false certainty.
Rebuild the definition of success
Clicks and completion rates are no longer enough. Track correction rates, suggestion acceptance, undo behavior and whether people make better decisions with the system.
