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The Acceleration of Artificial Intelligence: Rethinking Organization and Work in an Era of Rapid Technological Change

Dominic Chalmers et al.

Journal of Management Studies2026https://doi.org/10.1111/joms.70063article
FT50AJG 4ABDC A*
Weight
0.44

Abstract

Artificial intelligence (AI) is transforming the epistemic, interactional, and institutional foundations of contemporary organizations, yet management and organization studies are only beginning to theorise the implications of this shift. Existing research often treats “AI” as a singular construct, despite the fact that predictive, generative, agentic, and embodied systems rely on different logics and produce distinct organizational outcomes. This article interrogates the limits of this conceptual flattening and argues that cumulative theorising requires more precise specification of the technological systems under study. Drawing on developments across the field, we demonstrate how different modes of AI reshape core organizational constructs, including expertise, judgement, coordination, authority, and institutional adaptation. We advance a heuristic framework that differentiates among contemporary AI systems and clarifies their distinct affordances. The article concludes by outlining a research agenda that focusses on the shifting loci of agency, new decision architectures, and the normative and institutional challenges introduced by increasingly powerful AI systems.

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https://doi.org/https://doi.org/10.1111/joms.70063

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@article{dominic2026,
  title        = {{The Acceleration of Artificial Intelligence: Rethinking Organization and Work in an Era of Rapid Technological Change}},
  author       = {Dominic Chalmers et al.},
  journal      = {Journal of Management Studies},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/joms.70063},
}

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Evidence weight

0.44

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

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.57 × 0.15 = 0.09
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.