Generative AI and the temporary upskilling of knowledge workers

A randomized controlled trial of 487 management consultants found that workers given access to and training in ChatGPT scored higher than a control group on three data science tasks outside their skill sets, but the treated workers did not match expert data scientists and showed no evidence of better performance on technical questions without ChatGPT after the experiment. The study, published in Nature Human Behaviour, concludes that generative AI acts as an 'exoskeleton' that enhances workers' capabilities only while the technology is in use, with gains that do not translate into knowledge acquisition.

Abstract Upskilling often refers to the process by which workers acquire and expand their skills, enabling them to perform different types of work as market demands change. Here we demonstrate that generative artificial intelligence can act as an ‘exoskeleton’, enhancing workers’ capabilities when attempting new skills, but these gains depend on continued technology use. We run a randomized controlled trial on upskilling by providing management consultants N = 487 access and training in using ChatGPT to solve data science tasks outside their skill sets, which cannot be independently solved by ChatGPT. The treated workers scored higher than the control group on three tasks, although we can reject the hypothesis that they performed as well as expert data scientists. We find no evidence that treated workers are better at answering technical questions without ChatGPT post-experiment, suggesting that their demonstrated technical capabilities do not imply knowledge acquisition. This is a preview of subscription content, access via your institution https://wayf.springernature.com?redirect uri=https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41562-026-02566-6 Access options Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription Learn more https://shop.nature.com/products/plus/?region=ROW Subscribe to this journal Receive 12 digital issues and online access to articles Learn more https://www.nature.com/nathumbehav/subscribe Buy this article - Purchase on SpringerLink - Instant access to the full article PDF. 39,95 € Prices may be subject to local taxes which are calculated during checkout Similar content being viewed by others Data availability The data that support the findings of this study are available from the corresponding authors emma.b.wiles@gmail.com and krayer.lisa@bcg.com . 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Training, wages, and sample selection: estimating sharp bounds on treatment effects. Rev. Econ. Stud. 76 , 1071–1102 2009 . Acknowledgements We thank BCG and the BCG Henderson Institute for facilitating the experiment and providing access to the participant pool. We are grateful to the BCG data scientists who served as benchmark participants, and to the Economic Impacts Research Team at OpenAI for collaboration on task design. Funding This research was supported through the allocation of employee time and internal resources by BCG. No additional external funding was received for this study. Administrative approval was obtained through BCG’s standard internal processes for allocating employee time and company resources. BCG-affiliated authors participated in the conceptualization, design, data collection, analysis, interpretation of results and preparation of the manuscript in their role as co-authors. Decisions regarding manuscript content and submission for publication were made only by the authors. Ethics declarations Competing interests L.K., M.A., U.A., R.K., D.S. and F.C. are or were employees of BCG. P.M. is an employee of OpenAI. E.W. received consulting fees from BCG through the BCG Henderson Institute during the running of the experiment. The study was conducted using employee time and internal resources provided by BCG. Peer review Peer review information Nature Human Behaviour thanks Solene Delecourt and the other, anonymous, reviewer s for their contribution to the peer review of this work. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary information Supplementary Information download PDF https://static-content.springer.com/esm/art%3A10.1038%2Fs41562-026-02566-6/MediaObjects/41562 2026 2566 MOESM1 ESM.pdf Supplementary Sections 1–5, Figs. 1–5 and Tables 1–23. Rights and permissions Springer Nature or its licensor e.g. a society or other partner holds exclusive rights to this article under a publishing agreement with the author s or other rightsholder s ; author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. About this article Cite this article Wiles, E., Krayer, L., Abbadi, M. et al. Generative AI and the temporary upskilling of knowledge workers. Nat Hum Behav 2026 . https://doi.org/10.1038/s41562-026-02566-6 - Received: - Accepted: - Published: - Version of record: - DOI: https://doi.org/10.1038/s41562-026-02566-6