When Design Leads: Rethinking Trust, AI, and the Future of Workforce Systems

As artificial intelligence increasingly shapes how organizations evaluate performance, guide conversations, and allocate opportunity, the question facing global institutions is no longer purely technical. It is human. Who decides how AI should behave-and how do people learn to trust systems that quietly influence everyday work? For a growing number of technology leaders, the answer lies not in code alone, but in user experience design.
Zhaoqi Zhong (Nicole), a senior UX designer working in the field of AI-driven inclusive workforce technology, has emerged as a leading voice in this shift. Her work reflects a broader transformation within the UX discipline-from designing usability toward shaping the ethical and social frameworks that govern how AI interacts with people at scale. In complex workforce environments, Zhong argues, UX design is no longer a supporting function. It is a form of leadership.

"AI systems don't exist in isolation," Zhong said in an interview. "They participate in organizational decision-making. UX defines whether that participation is transparent, accountable, and fair-or whether it becomes invisible and unchallengeable."

This perspective represents a departure from earlier generations of enterprise technology, where efficiency and automation often dominated design priorities. In workforce systems, however, AI outputs can influence performance conversations, employee development, and perceptions of fairness. Zhong's research and design approach focuses on how UX can intentionally structure trust-ensuring that AI systems support human judgment rather than replace it.

Central to her thinking is the idea that trust in AI is not an accidental outcome of good performance. It is a designed mechanism. In practice, this means building interfaces that make AI reasoning legible, that clarify boundaries between suggestion and decision, and that preserve human agency even as systems scale. "When users understand what the system is doing and why," Zhong said, "they are more willing to engage critically rather than passively accept outcomes."

Zhong's work has been shaped by the realities of global workforce platforms, where users span regions, languages, and cultural expectations. In such environments, uniform AI behavior can unintentionally reinforce inequity. UX design, she argues, becomes the layer where inclusivity is operationalized-through adaptable interaction models, accessible explanations, and multiple paths to understanding system outputs.

At Amazon, Zhong has contributed to the design of large-scale internal platforms that support employee development, performance-related conversations, and engagement across a global workforce. These systems are used by operational leaders to navigate complex human interactions, often with the assistance of AI-generated insights. Rather than foregrounding automation, Zhong's design frameworks emphasize interpretability and user control, reinforcing trust through transparency.

One defining characteristic of her approach is restraint. AI recommendations are presented as starting points, not conclusions. Users are encouraged to review, refine, and contextualize information before acting. This design choice, subtle on the surface, carries significant implications. It shifts responsibility back to the human user and positions AI as a collaborator rather than an authority.

This philosophy reflects a broader redefinition of leadership within UX. Instead of managing teams or setting visual direction alone, Zhong's leadership is expressed through system-level decisions that shape how organizations behave. Her work influences how managers prepare for conversations, how data informs judgment, and how technology mediates relationships between people.

Before joining Amazon, Zhong worked on enterprise platforms at Oracle, where she designed intelligent workflows across web and mobile systems. That experience exposed her to the challenges of designing for scale, compliance, and organizational complexity-constraints that later informed her thinking about AI governance through design. Earlier research into accessibility further reinforced her belief that design decisions carry ethical weight.

What distinguishes Zhong's current research focus is its societal scope. Workforce technology sits at the intersection of employment, equity, and economic stability. As AI becomes embedded in these systems, design choices can influence not only productivity, but also inclusion and trust in institutions. "When systems affect livelihoods," Zhong said, "designers have a responsibility to think beyond efficiency."

Industry observers note that this shift mirrors a growing recognition that UX designers are uniquely positioned to mediate between technical capability and human values. By translating abstract AI processes into understandable interactions, designers help determine whether systems are perceived as supportive or intrusive. Zhong's work exemplifies this role, positioning UX as a strategic discipline in AI adoption.

Her contributions also highlight a national and global dimension. As countries grapple with how AI reshapes labor markets, internal workforce systems become testing grounds for responsible AI integration. The principles Zhong advances-transparency, agency, inclusivity-align with broader societal goals around ethical technology deployment. In this sense, her work extends beyond any single organization.

Zhong emphasizes that trust must be continually earned. AI systems evolve, data changes, and organizational contexts shift. UX design, she argues, must anticipate uncertainty and design for reflection. "Trust is fragile," she said. "Design should create space for questioning, not just compliance."

As AI continues to redefine how work is organized and evaluated, the role of UX leadership is expanding. Designers like Zhong are shaping not only how systems function, but how they are understood and governed. In doing so, they are influencing how societies navigate the balance between intelligence and humanity.

In an era defined by algorithmic decision-making, Nicole Zhong's work offers a clear reminder: technology alone does not determine the future of work. Design-and the leadership embedded within it-does.

By Matthew R. Collins,
Technology and Society Correspondent

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