Making AI Work: An Autoethnography of a Workaround in Higher Education

2025-12-24

AI Summary

This study examines sociotechnical friction arising from technical limitations and organizational power tensions when generative AI (GenAI) is deployed in universities—particularly the invisible labor undertaken by non-technical staff to bridge enterprise-level large language model capability gaps and navigate departmental territoriality and positional power conflicts. Method: Drawing on analytical autoethnography, it integrates Alter’s bricolage theory with the practice lens from Information Systems research to theorize “bricolage” as a core sociotechnical integration practice. Contribution/Results: The study reconceptualizes “expressive labor” as central—not peripheral—to GenAI implementation. It reveals the paradox that GenAI systems frequently operate via informal “shadow systems,” demonstrating that invisible labor is constitutive of system functionality rather than incidental. Based on these findings, the paper proposes a novel, politically attuned framework for AI governance design that foregrounds power dynamics and situated practice.

Technology Category

• Philosophy and Ethics of AI: AI & Jobs/Labor
• Humans and AI: Understanding People, Theories, Concepts and Methods
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Examines sociotechnical friction in implementing GenAI in higher education.
• Analyzes workarounds for technical and organizational challenges with enterprise LLMs.
• Highlights invisible labor in integrating GenAI within complex institutional politics.

Innovation

• User-driven workarounds for enterprise LLM technical constraints
• Analytic autoethnography to study sociotechnical friction in GenAI projects
• Workarounds as integral acts of sociotechnical integration in organizations