Mills, S. orcid.org/0000-0002-6698-0983 and Spencer, D.A. (2025) Efficient Inefficiency: Organisational Challenges of Realising Economic Gains from AI. Journal of Business Research, 189. 115128. ISSN 0148-2963
Abstract
Organisations are increasingly using artificial intelligence (AI). Where AI performs productive tasks more efficiently than humans, organisations will benefit economically through increases in productivity. However, if AI is deployed to undertake unproductive, superfluous tasks, the efficiency benefits will be reduced, even if these tasks are performed more efficiently than a human could, because the said tasks are inefficient to begin with. We call this eventuality ‘efficient inefficiency.’ We outline several reasons why superfluous tasks are created by managers and why they persist in organisations, drawing on an array of behavioural, managerial, and sociological literature. We argue bounded rationality accounts for why managers often fail to identify superfluous tasks, coupled with organisational conflicts which often incentivise their creation. These factors impede the ability of organisations to avoid efficient inefficiency. Restructuring organisations to promote knowledge sharing and align stakeholder incentives may reduce, though not eliminate, the risk of efficient inefficiency.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Crown Copyright © 2024 Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Artificial Intelligence; Bounded Rationality; Organisational Efficiency; Productivity; Superfluous Work |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Economics Division (LUBS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Dec 2024 15:50 |
Last Modified: | 10 Jan 2025 12:08 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.jbusres.2024.115128 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:220508 |