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The Division of Labor Under Strain: Increasing Task Burdens, Insufficient Coordination Capacity, and the Possible Impacts of Artificial Intelligence

Michelle Jackson et al.

Annual Review of Sociology2026https://doi.org/10.1146/annurev-soc-031524-110734article
AJG 4*ABDC A*
Weight
0.50

Abstract

Classical theories of the division of labor emphasize the overwhelming productivity benefits that arise from specialization. But recent evidence reveals that the division of labor in late industrial societies is under increasing strain. Here, we discuss two types of strain: increasing task burdens and coordination problems. First, worker task burdens are expanding due to the rationalization, bureaucratization, and financialization of late industrial societies. Second, population growth, system complexity, and political attacks on bureaucracy mean that organizational capacity is being overwhelmed. Rising exposure to crises—including pandemics, climate disasters, and geopolitical conflict—further undermines the robustness of the division of labor: Workers are forced to take on unfamiliar tasks and the resilience of existing systems is challenged. We conclude our review by considering the potential of artificial intelligence to relieve or exacerbate these vulnerabilities by reshaping task structures, enhancing coordination, and supporting crisis response. The review integrates fragmented literatures to highlight systemic challenges and possible future trajectories of work and society.

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https://doi.org/https://doi.org/10.1146/annurev-soc-031524-110734

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@article{michelle2026,
  title        = {{The Division of Labor Under Strain: Increasing Task Burdens, Insufficient Coordination Capacity, and the Possible Impacts of Artificial Intelligence}},
  author       = {Michelle Jackson et al.},
  journal      = {Annual Review of Sociology},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1146/annurev-soc-031524-110734},
}

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The Division of Labor Under Strain: Increasing Task Burdens, Insufficient Coordination Capacity, and the Possible Impacts of Artificial Intelligence

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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

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