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

翻译：生成式AI在职场中的应用正迅速加速。然而，谁在采用、谁从中受益、谁被落下以及原因何在等问题仍未得到充分研究。本文以一家从传统人力资源搜索系统向生成式AI支持系统转型的跨国科技公司为背景，分析了搜索日志数据、调查数据（n=25）及十次半结构化访谈，探究这些动态机制。研究结果表明，采用程度取决于生成式AI系统的设计假设与员工的工作位置性（角色、使用语言、任职年限）之间的匹配度。此外，我们发现员工对生成式AI回答的信任是通过源验证、系统间比较以及在存疑时寻求同事或人力资源部门意见而建立的。我们的贡献体现在两方面：第一，我们提供了组织实际转型过程中职场生成式AI采用的实证证据，表明采用受情境适配性、搜索素养和信任校准等因素影响，并进一步由系统的内容质量、员工培训和指导等知识条件所塑造；第二，我们将这些发现转化为面向包容性部署与采用的设计考量，适用于人力资源等高敏感性环境。我们认为，组织机构在设计系统时应考虑其对不同社会群体产生的角色和情境敏感效益，同时需将组织知识基础设施视为人工智能基础设施，以提升生成式AI系统的可问责性与可用性。