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Roles of Artificial Intelligence in Collaboration with Humans: Automation, Augmentation, and the Future of Work
Citation. Fügner, A., Walzner, D. D., & Gupta, A. (2026). Roles of Artificial Intelligence in Collaboration with Humans: Automation, Augmentation, and the Future of Work. Management Science, 72(1), 538–557. https://doi.org/10.1287/mnsc.2024.05684
Summary. This peer-reviewed paper develops an analytical task-allocation framework for judgment work in which a task can be performed by a human, by AI alone (automation), or by a human using AI advice (augmentation). It distinguishes between-task complementarity, which favors automation, and within-task complementarity, which favors augmentation. An image-classification experiment is used to validate the framework’s predicted work configurations.
Key claims
- Higher between-task complementarity increases the value of automation, whereas higher within-task complementarity increases the value of augmentation.
- With both forms of complementarity, the model and experiment support a differentiated allocation pattern: AI automates relatively easy tasks, augments people on tasks where human and AI performance are similar, and people work unaided on relatively difficult tasks.
- Automation can release human resources for more difficult work, so allocation performance depends on the reallocation of human capacity as well as the accuracy of individual human and AI task performers.
Evidence & limitations
- The paper combines analytical modeling with experimental image-classification data comparing human performance with and without AI support.
- The validation uses an idealized judgment-task setting; it does not establish that the proposed allocation rules transfer unchanged to organizational work with heterogeneous stakes, accountability, worker agency, or evolving expertise.