Generative machine learning in professional work and professional service firms : a research agenda

Faulconbridge, James and Trolle Elmholdt, Kasper and Pemer, Frida and Seepma, Aline and Skjolsvik, Tale and Molyneux, Cara (2025) Generative machine learning in professional work and professional service firms : a research agenda. Journal of Professions and Organization. ISSN 2051-8803 (In Press)

Text (Generative machine learning and professional work - online version)
Generative_machine_learning_and_professional_work_-_online_version.pdf - Accepted Version
Available under License Creative Commons Attribution.
Download (502kB)

Abstract

This paper addresses the need for an approach to theorizing professional work and professional service firms in the generative machine learning age. We develop an approach using insights from existing literature on digital, algorithmic and artificial intelligence technologies. We seek to extend existing theories whilst also responding to the distinctive characteristics of generative machine learning and the implications for how we theorize change. We argue that an approach is needed focused on emerging and future interdependencies between professionals and generative machine learning, something that implies extending but also reimagining theoretical perspectives on expertise, work and organizations.