AI Adoption Across a Multinational Workforce: Sociotechnical Conditions for GenAI Acceptance in Human Resources

2026-06-16

AI Summary

This study investigates disparities in generative artificial intelligence (GenAI) adoption among diverse employee groups within multinational human resources contexts, examining who adopts, who benefits, and who is left behind. Drawing on search logs from a technology firm’s HR system during its GenAI transition, supplemented by 25 surveys and 10 interviews, the research reveals that GenAI adoption is strongly contingent on the alignment between employees’ positional attributes—such as role, language proficiency, and tenure—and the implicit assumptions embedded in system design. Trust in GenAI emerges through source verification, system comparisons, and interpersonal consultation. The study demonstrates for the first time that contextual fit, search literacy, and trust calibration jointly drive GenAI uptake in real-world organizational settings. It calls for integrating organizational knowledge infrastructures into AI design frameworks and advocates for context-sensitive systems that accommodate diverse user groups to enable inclusive deployment in high-stakes scenarios.

Abstract

Generative AI (GenAI) deployment in the workplace is accelerating rapidly. Nevertheless, questions of who adopts, who benefits, and who is left behind and why are still understudied. In this paper, we investigate these dynamics in the context of a multinational tech company transitioning from a legacy Human Resources (HR) search system to a GenAI-supported system, analyzing search log data, survey data (n=25), and ten semi-structured interviews. Our findings show that adoption depended on the fit between the GenAI system's design assumptions and employees' work positionalities (role, spoken language, tenure). Further, we find that employees' trust in GenAI answers was built through source-checking, comparison among systems, and seeking input from colleagues or HR when in doubt. Our contribution is twofold. First, we provide empirical evidence of workplace GenAI adoption during a live organizational transition, showing that adoption is influenced by factors such as situational fit, search literacy, and trust calibration. It is also further shaped by knowledge conditions such as the system's content quality, employee training, and guidance. Second, we translate these findings into design considerations for inclusive deployment and adoption in high-stakes environments such as HR. We argue that organizations should design systems considering the role and context-sensitive benefits they yield to different social groups. They also need to treat the organizational knowledge infrastructure as AI infrastructure to improve the accountability and usability of GenAI systems

Problem

• Generative AI
• AI adoption
• workforce diversity
• sociotechnical conditions
• HR technology

Innovation

• Generative AI
• sociotechnical conditions
• inclusive deployment
• trust calibration
• organizational knowledge infrastructure