Studying <scp>AI</scp> in the Wild: Reflections from the <scp>AI</scp> @Work Research Group

Abstract

Long before AI entered the public spotlight, the AI@Work research group was already studying how algorithmic technologies change work. When the editors of this special issue invited me to reflect on my experiences, I saw this as a valuable opportunity to articulate our often tacit way of doing research – to make explicit the ontological commitments, methodological choices, and collaborative practices that have shaped how we study Artificial Intelligence (AI) in the wild. I see the AI@Work research group as a distinct school of thought, with a shared ontology, methodology, and epistemic culture that helps us study (and teach) AI in the wild. Since the group is more than just a collection of individual projects, I have chosen not to describe each project in detail. Instead, I use this opportunity to articulate our collective mission: to demystify popular beliefs around AI by analysing the actual changes that occur when AI systems are developed, introduced, and become embedded in everyday work practices. Management and organization scholars have paid insufficient conceptual and empirical attention to AI in the wild. Despite decades of critique, technological determinism has resurged in AI discourse, perpetuating several interrelated problems: treating AI as a pre-given force whose design remains black-boxed; employing methods that abstract work from the practices where it unfolds; and relying on snapshot analyses that miss how AI’s effects emerge over time. These limitations are not merely methodological; they reflect deeper ontological assumptions that leave us poorly equipped to understand AI’s emergent, relational character. Against these dominant approaches, the AI@Work school of thought advances a relational perspective operationalized through relational ethnography. By scrutinizing if, when, how, and why AI reconfigures work and its surrounding social structures, we seek both to advance academic understanding and to support more informed organizational decision-making, before and after adopting (or declining) AI. This essay presents examples from our research on AI development and uses them to illustrate that claim. Central to our perspective is our use of a relational ethnography that helps us reveal and theorize how AI reconfigures knowledge work within specific organizational settings while also generating insights that are transferable across multiple cases. Moreover, how we study AI is deeply intertwined with how we organize our research. In the second part of this essay, I will elaborate on the AI@Work research group’s epistemic culture, one that centres on collectivity and ultimately strives to provide value for academia, industry, and society. I want to stress already at the start of the essay that the AI@Work school of thought is grounded in a larger academic discourse on technology and knowledge work that uses a relational perspective and practice theory to study technology and organizing as mutually shaping phenomena (e.g., Anthony et al., 2023; Bailey et al., 2022; Faraj and Leonardi, 2022; Glaser et al., 2021; Lebovitz et al., 2022; Scott and Orlikowski, 2022, 2025; Suchman, 2023). None of our research that I will discuss in this essay would have been possible without the support of an extensive network of outstanding scholars from around the world – many of whom visited our group and/or hosted our researchers. To understand why technological determinism persists in AI discourse despite decades of critique, we must recognize that the current AI hype is not new – it is the latest iteration of recurring promises to automate knowledge work. Each wave follows a similar pattern: new technologies are marketed as finally capable of capturing expertise, optimizing work, and eliminating reliance on human judgement. Yet these promises consistently fail to materialize as predicted, not because technologies are ineffective, but because they rest on flawed assumptions about the nature of knowledge, work, and change. The rap