AI UX Patterns: Show Meaningful User Benefit

Second article in Kathryn Grayson Nanz's series on AI UX patterns (Progress/Telerik developer advocacy), following her piece on user transparency. Her thesis: the differentiator for AI-powered software is no longer model performance but the quality of the experience built around it, and the pattern that matters here is showing users a meaningful, recognizable benefit.

The core argument is that developers systematically overestimate their users' AI literacy. A blank "Ask AI" text box forces users to do prework — figuring out what the tool can solve, how specific to be, what context to supply, and when a response needs refinement — that can exceed the effort of doing the task themselves. The remedies are concrete: examples, templates, suggested prompts, guided workflows, and leveraging existing contextual data so users don't have to type it in.

The second half argues that generation is only the beginning of the user's flow: an output the user can't act on is a novelty. Action buttons, suggested next steps, and direct integrations into the tools users already work in (docs, repos, ticket trackers) are what convert an AI feature into something with lasting use. She closes by tying autonomy and trust to adoption — experiences that strip users of control will simply not be used — and notes that the design/development line is blurring: these are now developer questions, not just designer questions.