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

A study of a multinational tech company’s shift to a generative AI human-resources system found employee adoption hinged on how well the tool’s design assumptions matched workers’ roles, language, and tenure, according to research posted on arXiv [1]. The paper, submitted to the open-access repository on 16 June 2026, draws on search-log analysis, survey data from 25 employees, and 10 semi-structured interviews [1][2]. The authors report that adoption depended on the fit between the GenAI system’s design assumptions and employees’ work positionalities — specifically their role, spoken language, and tenure [1]. Employees who found the system aligned with their daily tasks were more likely to use it, while those whose circumstances diverged from the design’s implicit expectations often disengaged [1]. Trust in the GenAI answers was not automatic. Workers built confidence by checking sources, comparing outputs across different systems, and consulting colleagues or HR staff when uncertain [1]. The study identifies situational fit, search literacy, and trust calibration as core factors shaping adoption, alongside knowledge conditions such as content quality, employee training, and guidance [1]. The findings arrive as workplace GenAI deployment accelerates, yet questions of who adopts, who benefits, and who is left behind remain understudied [2]. The authors argue that organizations should treat their knowledge infrastructure as AI infrastructure to improve accountability and usability [1]. They also recommend designing systems that account for the role- and context-sensitive benefits different social groups derive from the technology [1]. The paper appears on arXiv, an open-access repository that hosts electronic preprints across disciplines including computer science and statistics [6]. Founded in 1991, arXiv passed the two-million-article milestone by the end of 2021 and now receives roughly 24,000 submissions per month [6]. The study’s abstract page also features arXivLabs, a framework that allows community collaborators to develop experimental tools such as citation explorers and code finders directly on the site [4][5].