By Guest Contributor
On March 23, 2026
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The introduction of generative AI into product interfaces has created a paradox: the technology that promises to simplify user experiences is, in the hands of undisciplined product teams, producing some of the most cluttered and confusing interfaces in the history of software.
The culprit is a design philosophy that has not kept pace with the technology: feature-first thinking applied to AI capabilities. Teams are asking "what can we build with this AI?" when they should be asking "what is the user trying to accomplish, and does AI make that simpler or more complex?"
Context-first design is the answer — a discipline that anchors every interface decision in the job-to-be-done and the user's context at the moment of interaction.
When a product team discovers that their AI can summarise documents, generate drafts, answer questions, suggest actions, and predict preferences — all simultaneously — the temptation is to surface all of these capabilities at once. The result: a UI that is technically impressive and practically unusable.
"The best interface is the one that gets out of the user's way. AI does not change this principle — it amplifies it. If AI adds cognitive load instead of reducing it, you have failed."
The patterns that signal feature sprawl over context-first design:
| Anti-Pattern | Symptom | Context-First Alternative | |---|---|---| | AI everywhere | AI suggestions on every screen regardless of relevance | Trigger AI assistance only when task complexity warrants it | | Suggestion overload | 5+ AI-generated options presented simultaneously | Surface the single most contextually relevant suggestion | | Invisible AI | AI acting without user awareness | Clear visual signalling of AI involvement and confidence | | Irreversible AI actions | AI completing actions the user cannot undo | Human confirmation for consequential actions | | One-size-fits-all AI | Same AI behaviour for novice and expert users | Adaptive AI assistance that matches user proficiency |
1. Jobs-to-be-Done Anchoring
Before any AI feature enters a design sprint, the team should articulate the specific job the user is hiring this feature to do. Not "summarise documents" — but "allow a financial analyst to identify the three most relevant clauses in a 200-page contract in under 5 minutes."
The specificity of the job-to-be-done determines the specificity of the interface. Vague jobs produce vague interfaces. Precise jobs produce precise ones.
2. Information Scent
In physical environments, we navigate by scent — visual and spatial cues that tell us we are moving toward our goal. Digital interfaces provide information scent through visual hierarchy, progressive disclosure, and contextual labelling.
AI-augmented interfaces often break information scent by introducing AI-generated content that does not fit the visual vocabulary the user has learned to navigate. Context-first design maintains consistent scent by integrating AI outputs into existing visual patterns rather than creating new ones.
3. Instrumented Drop-Off Analysis
The most powerful feedback loop for context-first design is not user testing (though that matters too) — it is production analytics measuring where users abandon AI-assisted workflows.
High abandonment at an AI suggestion step signals one of three problems: the suggestion is irrelevant, the interaction required to act on it is too high-friction, or the user does not trust the AI output. Each has a different design solution, and you cannot know which applies without measurement.
Before shipping any AI-augmented UI component, ask:
The AI era does not change the fundamentals of good product design — it raises the stakes for getting them right.