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

Abstract

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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