The Values of Value in AI Adoption: Rethinking Efficiency in UX Designers'Workplaces

2026-03-06

AI Summary

This study addresses the tension between organizational expectations of AI-driven efficiency and UX designers’ awareness of its socio-ethical implications, which engenders multi-scalar adoption conflicts. Through a participatory design workshop involving 15 UX designers, the research employs qualitative methods to examine the dynamics of AI adoption across individual, team, and organizational levels. Framing AI adoption as a process of value negotiation, the study uncovers ethical dimensions—such as responsibility allocation, trust formation, and autonomy—that underlie dominant efficiency discourses. It further elucidates how AI reshapes professional roles, interpersonal relationships, and power structures. The findings propose a novel perspective on AI integration centered on redistributing responsibility and enhancing worker agency, offering an empirical foundation for ethically attuned, worker-centered human-AI collaboration strategies.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: User Experience and Usability
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Although organizations increasingly position AI adoption as a pathway to competitiveness and innovation, organizations'perspectives on productivity and efficiency often clash with workers'perspectives on AI's economic and social value. Through design workshops with 15 UX designers, we examine how AI adoption unfolds across individual, team, and organizational scales. At the individual level, designers weighed efficiency, skill development, and professional worth. At the team level, they negotiated collaboration, responsibility, and rigor. At the organizational level, adoption was shaped by compliance requirements and organizational norms. Across these scales, discourses of efficiency carried social and ethical dimensions of responsibility, trust, and autonomy. We view adoption as a site where roles, relationships, and power are reconfigured. We argue that AI adoption should be understood as a process of negotiating values, and call for future work examining how AI systems redistribute responsibility among team members, while understanding how such shifts could strengthen worker agency.

Problem

• AI adoption
• value conflict
• efficiency
• worker agency
• organizational values

Innovation

• value negotiation
• AI adoption
• workplace ethics
• designer agency
• multi-scale analysis