Research on the implementation of Generative Artificial Intelligence (GenAI) in higher education often focuses on strategic goals, overlooking the hidden, and often politically charged, labour required to make it functional. This paper provides an insider's account of the sociotechnical friction that arises when an institutional goal of empowering non-technical staff conflicts with the technical limitations of enterprise Large Language Models (LLMs). Through analytic autoethnography, this study examines a GenAI project pushed to an impasse, focusing on a workaround developed to navigate not only technical constraints but also the combined challenge of organisational territoriality and assertions of positional power. Drawing upon Alter's (2014) theory of workarounds, the analysis interprets "articulation work" as a form of "invisible labour". By engaging with the Information Systems (IS) domains of user innovation and technology-in-practice, this study argues that such user-driven workarounds should be understood not as deviations, but as integral acts of sociotechnical integration. This integration, however, highlights the central paradoxes of modern GenAI where such workarounds for "unfinished" systems can simultaneously create unofficial "shadow" systems and obscure the crucial, yet invisible, sociotechnical labour involved. The findings suggest that the invisible labour required to integrate GenAI within complex organisational politics is an important, rather than peripheral, component of how it becomes functional in practice.

翻译：关于生成式人工智能（GenAI）在高等教育中实施的研究通常聚焦于战略目标，却忽视了使其实际运作所需且常具政治敏感性的隐性劳动。本文通过局内人视角，揭示了当机构赋予非技术人员使用权的目标与企业级大语言模型（LLMs）的技术局限性产生冲突时，所引发的社会技术摩擦。本研究通过分析性自我民族志方法，考察了一个陷入僵局的GenAI项目，重点剖析了为应对技术限制以及组织领地意识与职位权力主张的双重挑战而开发的变通方案。借鉴Alter（2014）的变通理论，分析将"衔接工作"阐释为一种"隐形劳动"形式。通过关联信息系统（IS）领域中用户创新与技术实践的研究脉络，本文主张此类用户驱动的变通方案不应被视为偏离行为，而应理解为社会技术整合的必要实践。然而，这种整合凸显了现代GenAI的核心悖论：针对"未完成"系统的变通方案，既可能催生非官方的"影子"系统，同时也遮蔽了其中关键却隐形的社会技术劳动。研究结果表明，在复杂的组织政治中整合GenAI所需的隐形劳动，并非边缘性因素，而是其在实际中得以运作的重要构成部分。