Schulz, F., Valizade, D. orcid.org/0000-0003-3005-2277, Stuart, M. orcid.org/0000-0003-4962-6496 et al. (2 more authors) (2026) Artificial Intelligence Technologies and Employee Pay in the United Kingdom: Evidence From Matched Employer–Employee Data. British Journal of Industrial Relations, 64 (1). pp. 116-129. ISSN: 0007-1080
Abstract
This paper examines the impact of artificial intelligence (AI)-enabled technologies on employee pay in the United Kingdom. We use matched nationally representative data from the Employers’ Digital Practices at Work Survey and an original survey of 6000 UK workers and apply machine learning techniques to uncover relationships between AI technology and employee pay across qualification and occupation skill groups. We find that lower skilled workers were the primary beneficiaries of AI, but this effect was contingent on the extent of worker interaction with AI. Further analysis shows that employee involvement in pay determination facilitates a more equitable distribution of AI-related pay benefits by enabling a significant uplift in pay among lower qualified workers. Overall, while the implications of AI for pay outcomes are broadly positive, the study highlights the need to strengthen workplace voice mechanisms to ensure a more equitable distribution of benefits from the growing use of AI.
Metadata
| Item Type: | Article |
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| Authors/Creators: |
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| Copyright, Publisher and Additional Information: | © 2025 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | artificial intelligence, employee voice, machine learning, pay |
| Dates: |
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| Institution: | The University of Leeds |
| Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Work and Employment Relation Division (Leeds) |
| Funding Information: | Funder Grant number ESRC (Economic and Social Research Council) ES/S012532/1 |
| Date Deposited: | 21 Oct 2025 11:25 |
| Last Modified: | 04 Mar 2026 14:52 |
| Status: | Published |
| Publisher: | Wiley |
| Identification Number: | 10.1111/bjir.70019 |
| Sustainable Development Goals: | |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:233191 |


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