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Algorithmic leadership and the game of business

Title: Algorithmic leadership and the game of business
Authors: Spoelstra, Sverre; Butler, Nick
Contributors: Swedish Research Council
Source: Leadership ; ISSN 1742-7150 1742-7169
Publisher Information: SAGE Publications
Publication Year: 2026
Description: Recent advances in artificial intelligence (AI) have renewed debates about the future of leadership, in particular the nature of the relationship between human leaders and intelligent machines. The literature tends to portrays human-machine relations in complementary terms: the machine does the data analysis and the number crunching while the leader makes the decisions, guided by their unique vision, gut-feeling, and inner moral compass. Drawing on the historical metaphor of the Mechanical Turk, the paper complicates this division of labour in two main ways. First, we revisit the influence of game theory on business thinking in order to question whether leadership has ever relied solely on uniquely human faculties such as intuition, vision, or moral judgment. Instead, we suggest that leadership has long been shaped by the algorithmic logic of strategic optimization. Second, we show that AI is becoming adept at simulating human leadership, a capability that makes it tempting to outsource traditional leadership functions to a computerized system. We warn that this dynamic risks producing a reversal of the Mechanical Turk: not a machine concealing human intelligence, but rather a human concealing machine intelligence. We conclude by arguing that AI, understood as a socio-technical system, is currently reshaping what leadership is and how it is enacted in organizations.
Document Type: article in journal/newspaper
Language: English
DOI: 10.1177/17427150261436598
Availability: https://doi.org/10.1177/17427150261436598; https://journals.sagepub.com/doi/pdf/10.1177/17427150261436598; https://journals.sagepub.com/doi/full-xml/10.1177/17427150261436598
Rights: https://creativecommons.org/licenses/by/4.0/ ; https://journals.sagepub.com/page/policies/text-and-data-mining-license
Accession Number: edsbas.99D8B7CB
Database: BASE