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

James Faulconbridge et al.

Journal of Professions and Organization2025https://doi.org/10.1093/jpo/joaf018article
AJG 2ABDC A
Weight
0.50

Abstract

This paper addresses the need for an approach to theorizing professional work and professional service firms in the generative machine learning (GML) 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 GML and the implications for how we theorize change. We argue that an approach is needed focused on emerging and future interdependencies between professionals and GML, something that implies extending but also reimagining theoretical perspectives on expertise, work, and organizations.

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https://doi.org/https://doi.org/10.1093/jpo/joaf018

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@article{james2025,
  title        = {{Generative machine learning in professional work and professional service firms: a research agenda}},
  author       = {James Faulconbridge et al.},
  journal      = {Journal of Professions and Organization},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1093/jpo/joaf018},
}

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

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.