AI changes product design because the system can interpret, generate, predict, and act without always producing the same output. Designers must account for uncertainty, user control, data boundaries, and continuous evaluation. The result should feel like a coherent product experience—not a model interface placed beside the real workflow.
Begin with the job the user is trying to complete
Map the current workflow, decisions, information gaps, delays, and moments where expertise is required. Then decide whether AI should explain, draft, recommend, classify, predict, retrieve, or execute.
The interface should make the improved workflow obvious. Users should not need to understand model terminology to receive value.
Design interaction patterns for AI behavior
Editable drafts rather than irreversible generated output
Citations and source previews for knowledge-based answers
Structured choices when business rules require consistency
Progress and status for long-running agentic workflows
Approval, undo, correction, and escalation for consequential actions
Communicate uncertainty without overwhelming users
Confidence scores alone are rarely meaningful. Show why a result was produced, which information was used, what is missing, and what the user can do next.
When the system cannot safely complete a task, refusal and escalation are part of the experience—not technical errors to hide.
Design feedback that improves the system
Capture corrections in the context of the task
Distinguish product usability issues from model-quality issues
Record whether recommendations were accepted and whether outcomes improved
Protect sensitive feedback and prevent uncontrolled reuse as training data
Use evaluation datasets to test changes before release
Bring design, engineering, data, and governance together
AI experience decisions affect architecture, permissions, prompts, retrieval, model selection, analytics, and risk controls. Cross-functional decisions must happen early enough to influence the product.
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