Estimating the task content of work: workforce design for AI-driven human-robot collaboration in intralogistics
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Estimating the task content of work workforce design for AI-driven human-robot collaboration in intralogistics.pdf
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2.39 MB
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Author(s)
Bouquet, Pierre
Bagnoli, Nicolò Piergiovanni
Sheffi, Yossi
Date Issued
March 22, 2026
Journal
International Journal of Production Research
Publisher
Informa UK Limited
Citation
Pierre Bouquet, Nicolò Piergiovanni Bagnoli & Yossi Sheffi (22 Mar 2026): Estimating the task content of work: workforce design for AI-driven humanrobot collaboration in intralogistics, International Journal of Production Research.
Version
Final published version
Abstract
This paper addresses the challenge of strategic workforce planning for AI-driven human-robot collaboration (AI-HRC) in intralogistics. We ask two questions: how can task-level full-time equivalent
(FTE) estimates be constructed from existing labour statistics, and how can these estimates, combined with AI exposure metrics, inform strategic AI-HRC design and workforce planning? Drawing
on U.S. Bureau of Labor Statistics employment data, O∗NET occupational profiles, and task-level AI
exposure scores, we develop a stochastic task-time framework that decomposes occupations into
tasks and models task frequencies as probability vectors on the simplex. A covariance-completion
procedure reconstructs task covariance matrices consistent with survey standard errors, enabling
the translation of occupational data into task-level and detailed work activity (DWA)-level FTE estimates with uncertainty bounds. Applying the framework to the U.S. intralogistics workforce, we find
that approximately 370,000 FTEs (about 17% of workers) are concentrated in the top 15% most AIexposed DWAs. These results provide task-specific insight into AI-driven automation and support
scenario-based workforce planning by linking alternative AI-HRC adoption paths to task-level FTE
impacts, uncertainty bands, and upskilling priorities, thereby offering an analytical foundation for
resilient, human-centered AI-HRC systems.
MIT Department
Massachusetts Institute of Technology. Center for Transportation & Logistics
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
10.1080/00207543.2026.2643477
