Adding an AI feature to a product is not only an engineering problem. Even an accurate model fails if users do not understand what it does, cannot tell when it is wrong, or feel they have lost control. The interface around the AI is where trust is built or lost.

Here are eight principles we use when designing AI features, with practical patterns for each.

1. Set clear expectations

Tell people what the feature can and cannot do, in plain language, at the point where they use it. "Drafts a reply based on this conversation and your help articles" is better than "AI-powered magic". Clear limits reduce disappointment and misuse.

2. Make AI output recognizable

Users should be able to tell what was generated by AI and what came from a person or a system of record. Subtle labels, a distinct style for drafts, or a short note such as "Suggested by AI" all help.

3. Show sources

When AI answers from documents or data, show where the answer came from: links to the source article, the record it used, or highlighted passages. Sources let people verify quickly and make good answers more convincing.

4. Keep people in control of actions

Separate suggestions from actions. Let the AI draft, fill in, or recommend, and let the person confirm, especially for anything that sends, deletes, pays, or changes records. An "Approve" button is a small cost for a large gain in trust.

5. Make editing easy

AI output is rarely perfect. Let people edit inline, regenerate one part, or adjust tone and length without starting over. When editing is easy, people use the feature even when the first draft is only 80 percent right.

6. Handle uncertainty honestly

When the AI is unsure or lacks information, it should say so: "I could not find this in your documents." Design a clear state for "no good answer" instead of forcing an output. An honest "I don't know" builds more trust than a confident wrong answer.

7. Design for failure and slowness

  • Show progress while the AI works, and stream text where it helps.
  • Provide a clear message and a fallback path when the AI service is unavailable.
  • Let people continue their work manually if the AI does not help.

8. Learn from feedback

Give users a quick way to rate or flag an output, such as thumbs up and down or "report a problem". Review that feedback regularly; it shows exactly where the feature falls short.

Example: AI-assisted support replies

In a support tool, an AI could draft a reply to each ticket, labeled as a draft, with links to the help articles it used. The agent edits and sends it, or discards it with one click. When no relevant article is found, the draft area says so instead of producing a guess. This is an illustrative example of the principles above; the right pattern depends on your users and risk level.

A quick design review checklist

  • Does the user know what this feature does and its limits?
  • Can they tell which content is AI-generated?
  • Can they see the sources behind an answer?
  • Does a person confirm every important action?
  • Is editing faster than starting over?
  • What does the interface show when the AI is unsure or unavailable?
  • How do users report a bad result, and who reviews it?

The bottom line

Trust in AI features comes less from the model and more from the experience around it: clear expectations, visible sources, easy editing, honest uncertainty, and people in control of what matters.

If you are designing an AI feature and want help with the interface and user experience, see our digital experiences service or email us.