Designing AI Features That Users Actually Stick With

Starting a new AI feature design can feel like facing a blank canvas with no clear path forward. The key is having a systematic way to think about the experience rather than jumping straight into prompt boxes and chat windows. Five areas deserve focused attention: how users express intent, how output is displayed, how results get refined, what tasks AI can perform, and where the AI integrates into existing workflows.

Why Chat Is Fading as the Primary Interface

Traditional chat-alike AI interfaces are losing ground. As Luke Wroblewski noted, when agents can use multiple tools, call other agents, and run in the background, users are increasingly orchestrating AI work rather than having lengthy back-and-forth conversations. Chatbots are rarely an optimal experience paradigm because the burden of articulating intent efficiently falls on the user — something that is remarkably difficult and time-consuming to do well.

Chat is not disappearing, but it is being complemented by task-oriented UIs: temperature controls, knobs, sliders, buttons, semantic spreadsheets, and infinite canvases. In these interfaces, AI provides predefined options, presets, and templates. The focus shifts from the chat input to the work itself — the plan, the tasks, and the outcome. This approach delivers the most value by embedding AI in places where it genuinely helps real users.

Input UX: Helping Users Express Intent

Conversational AI is a very slow way for users to articulate what they want. Usability tests show people often get lost in editing, reviewing, typing, and re-typing prompts — input can take 30-60 seconds. People simply struggle to express their intent clearly. One counterintuitive solution: ask AI to write a prompt to feed itself rather than requiring manual prompt writing.

Several products explore alternatives to free-form typing. Flora AI lets users visualize intent by connecting nodes and sources on a canvas, attaching commands visually instead of explaining a pipeline in prose. Krea.ai abstracts the object users want to manipulate, letting them move it on a canvas for precise AI input. The takeaway: minimize the burden of typing prompts through AI-generated pre-prompts, prompt extensions, query builders, and voice input.

Output UX: Displaying Outcomes for Faster Insights

AI output does not have to be plain text or bullet points. To help users reach insights faster, output can take many forms. Amelia Wattenberger visualized output for her text editor PenPal by adding style lenses that let users explore content across dimensions like sentence length, a Sad — Happy scale, or a Concrete — Abstract scale. An AI GIS analyst can display findings on a map where users toggle individual data layers on and off to explore the underlying information.

Presentations can also use forced ranking and prioritization to suggest best options and prevent choice paralysis, even when a user requests the top 10 recommendations. Results might appear as a data table, dashboard, map visualization, or structured JSON file, depending on what best serves the task.

Refinement UX: Tweaking Output Without Endless Typing

Refinement is often the most painful part of the AI experience. Users typically need to cherry-pick bits from an output, expand on one section, synthesize parts from another, or adjust the result to fit their needs — and too often they must explain all these changes elaborately in text. Traditional UI controls can improve this dramatically: knobs, sliders, and buttons offer granular adjustment, as demonstrated by Adobe Firefly.

Presets and bookmarks also help. Users should be able to highlight specific parts of an outcome they want to change, with contextual prompts acting on just the highlighted section rather than requiring a global prompt rewrite.

AI Actions: Moving From Answers to Task Completion

With AI agents, users can now initiate tasks AI performs on their behalf — scheduling events, planning, deep research, or sorting and filtering results in specific ways. Beyond generating output, features can help users get more from AI results: visualizing the content, making it shareable, transforming between formats, or posting directly to Slack, Jira, and other collaboration tools.

Integration: Meeting Users Where Work Happens

Too many AI interactions are locked inside a dedicated product section. Good AI experiences happen where the actual work happens. It would seem odd to isolate the browser's autocomplete feature in its own tab, yet this is common for AI capabilities. The real productivity gains arrive when AI acts as a co-pilot inside tools people already use daily — Slack, Teams, Jira, GitHub. Dia Browser and Dovetail demonstrate how seamless integration into familiar environments matters more than building a separate AI destination.

The Practical Path Forward

Across all five areas, the goal is minimizing the cost of interaction with a textbox. Users should be able to engage directly with points of interest by tapping, clicking, selecting, highlighting, and bookmarking. Products obsessed with being AI-first often miss the point; being AI-second — focusing on user needs and adding AI value across customer journeys where it fits — yields better outcomes. AI products need not be AI-only. Mapping into mental models users have adopted over years and enhancing them with AI, like autofill in browsers, serves users far better than leaving them facing an omnipresent, intimidating text box.

Further Reading on AI Interface Patterns