When a product adds AI, the first instinct is often a chat panel in the corner. Chat is flexible, but it asks people to know what to ask, to type it, and to translate the answer back into the thing they were doing. The most useful AI features tend to meet people inside their existing work instead.
Put AI where the work happens
- Inline suggestions that can be accepted, edited or ignored — a drafted reply, a filled-in field, a suggested category.
- Actions on a selection — summarise these rows, rewrite this paragraph, explain this chart.
- Smart defaults that remove a step entirely, with the reasoning a click away.
Show your working
People trust AI output more when they can check it. Cite the sources an answer was drawn from, link to the records it used, and make it obvious which parts were generated. In dashboards and data products especially, every AI-written insight should lead back to the numbers behind it.
Design for being wrong
Models will sometimes be confidently incorrect. Make output editable rather than final, offer undo, and avoid irreversible actions without confirmation. Where confidence is low, say so, or offer options rather than a single answer.
Make waiting feel short
Generation takes time. Stream results as they arrive, show what the system is doing, and let people keep working or cancel. A progress state that explains itself feels faster than a spinner that doesn't.
Keep people in control
The goal is to make someone faster and more capable, not to take the task away from them. Let people adjust how much the AI does, remember their preferences, and always leave the final decision with them.
Our UI/UX Design and Dashboard & SaaS Design teams design these patterns alongside the models built by our AI & Machine Learning practice.