JHDD UI UX Report — 2026.09.27

UX Collective recently published an article questioning why the best resume may be the wrong resume for a design team.

These discussions collectively highlight an evolving friction point: the perceived value of technological advancement, particularly in artificial intelligence, and its practical implications for human interaction, professional development, and ethical design. This is not about AI as a mere feature add-on, but about AI altering foundational assumptions about what a user expects, what a team values, and where the boundaries of a design problem truly lie. The implicit, unnamed pattern connecting these stories is a re-evaluation of human agency and understanding within systems increasingly influenced by non-human intelligence.

The PACED Framework offers guidelines for disclosing AI use, considering audience, context, and the degree of AI involvement. The mainstream industry opinion often maintains that transparency about AI’s role is inherently beneficial and represents the primary goal of ethical design. However, this perspective risks overlooking a deeper shift in user experience: users are not merely evaluating whether AI was used, but how AI fundamentally reshapes their expected interaction patterns and the underlying logic of a system. When an AI generates content, assists a task, or makes a recommendation, the user’s mental model of agency, control, and even the ‘natural’ flow of interaction is challenged, regardless of explicit disclosure. Simply stating “AI was involved” does not inherently resolve the disorientation or altered trust dynamic that can arise when an interaction feels subtly or overtly different from established human-to-human or human-to-tool paradigms. User research needs to move beyond survey questions about trust in AI to observational studies of how altered interaction pacing or unpredictable system responses create friction.

This suggests the industry’s current focus on disclosure strategy risks becoming a superficial remedy for a more profound re-alignment needed in interaction design, and by extension, in our design systems. The critical challenge for designers is not merely to inform users, but to redesign interaction patterns themselves to accommodate the specific feel and implications of AI-driven responses or decisions. For example, an AI chatbot may provide accurate answers, but if its conversational flow is incongruent with typical human rhythm, or if it lacks the capacity for nuanced follow-up, the user experience suffers significantly, independent of any disclosure pop-up. Mainstream advice tends to focus on “how to say it” through text and iconography; the more urgent question for product design is “how to design the interaction so the user intrinsically understands the nature of the agency they are engaging with through behavior and feedback loops.” It is predicted that by mid-2027, leading design systems will begin incorporating specific interaction components and guidelines for “AI-mediated interaction states,” moving beyond simple disclosure alerts to define distinct modalities for co-creative, AI-assisted, and AI-generated outputs that are reflected in core component libraries.

The primary resistance to this re-evaluation comes from organizations that prioritize short-term efficiency gains and feature-driven roadmaps over a fundamental re-assessment of user experience and design system principles. Many design leadership teams, as hinted by the UX Collective piece on “the wrong resume,” remain focused on existing metrics and proven skill sets, rather than investing in the speculative, research-intensive work required to redefine interaction paradigms for AI.

A working UI UX professional should initiate a project this week to map out the current interaction patterns in their key product flows, specifically identifying points where AI integration could subtly or overtly alter user expectations about agency, responsiveness, or outcome reliability. Then, instead of merely planning disclosure elements, prototype entirely new interaction modalities for those identified points that inherently communicate the AI’s role through behavior, pacing, and feedback, rather than relying solely on explicit text labels.

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Curated References