Enacting Large Language Models at Work: Insights for Designing Effective Organizational Training Strategies

Abstract

Organizations often struggle to integrate new digital technologies into everyday work because adoption is shaped not only by technical features but also by routines, roles, and coordination practices. The rapid diffusion of generative artificial intelligence, exemplified by systems such as ChatGPT and Microsoft Copilot, has intensified this challenge while empirical knowledge about its everyday organizational use remains limited. This thesis examined how knowledge workers enact generative AI within work routines and how these enactments and routines evolve over time. Drawing on the practice lens and sociomateriality, the study conceptualized adoption as an ongoing process in which human action and technology co-constitute technology-in-practice. A qualitative longitudinal design was employed in a small German company in electronics manufacturing. Data was collected through two rounds of in-person shadowing and follow-up semi-structured interviews with seven employees across multiple functions. The analysis identified four themes: generative AI enacted as a flexible collaborator for bounded cognitive tasks, reconfiguration of the human-AI division of labor, the emergence and stabilization of prompting and feature routines, and sociomaterial tensions around trust, control, and risk. Overall, generative AI became embedded through layered workflows in which humans framed tasks, delegated execution, and reclaimed control for validation and refinement. The findings suggest that understanding integration requires practice-based perspectives that capture evolving routines, conditional trust, and shifting agency.