JHDD UI UX Report — 2026.08.21

The internal dialogue within a specific AI product, where a side panel shows “Here’s what I heard you say,” “Here’s the thread I’m pulling,” and “Here’s where this is headed,” represents a critical juncture for interface design.

This design choice, alongside the struggle of airport apps to find adoption despite widespread smartphone use, and the broader industry debate around AI’s impact on enterprise software following the SaaSpocalypse, points to a common systemic issue. Product teams continue to build interfaces that either mask crucial operational details or fail to integrate meaningfully into existing user workflows. The current design paradigm often defaults to obscuring complex processes, believing users prefer a clean, “black box” experience, even as user trust erodes and utility remains unproven.

The internal debate within the team building the AI product with the side panel, where “folks who argue against it” believe it is “too much for the user to absorb,” exemplifies a flawed assumption prevalent in product development. This assumption dictates that exposing the “sausage making” process universally detracts from the user experience. However, the true problem lies not in transparency itself, but in undifferentiated transparency. Users do not always want a simplified black box; they desire control, understanding, and accountability, particularly when an AI confidently invents information without disclosing its methodology.

This perspective contradicts the mainstream industry drive towards maximal simplicity, which often equates to maximal opaqueness for AI systems. A more nuanced approach would recognize that critical moments in AI interaction, where certainty is manufactured from mixed information, demand selective clarity. For instance, rather than burdening users with a constant stream of processing data, the interface could offer “explainability on demand” – a collapsed panel that expands only when the user questions an output, or when the system flags its own uncertainty. This interaction pattern, a contextual reveal, integrates robustly into existing design systems as a new component type. It moves beyond abstract “AI ethics” discussions to embedded interaction design for building trust and user agency. Such contextual transparency mechanisms could become standard practice across enterprise software within two years, fundamentally altering how design systems are structured for AI-driven products.

The primary opposing force to this necessary transparency comes from business models and product roadmaps that prioritize rapid feature delivery and a perceived “magical” user experience over genuine user understanding or control. This resistance is compounded by a lack of investment in sophisticated user research methodologies that effectively probe how users build mental models around AI interactions, especially concerning its capacity to invent information, rather than simply measuring task completion. The pressure to quickly recover from events like the SaaSpocalypse can also inadvertently push teams towards less transparent, “black box” solutions in an attempt to demonstrate immediate, impressive results.

A working UI UX professional should this week initiate a targeted user research sprint focusing on “trust thresholds” within existing AI interfaces. Specifically, explore scenarios where AI generates information and prototype micro-interactions that expose varying levels of computational transparency, perhaps through expandable modules or adaptive tooltips. Instead of asking “Do you want to see how the AI works?”, which often elicits a simple “no,” ask “When do you need to know how the AI arrived at this answer, and what specific details would build your confidence or allow you to correct it?” This shifts the conversation from abstract preference to concrete, contextual needs, informing the development of purposeful transparency patterns.

---

Curated References