Enhancing top managers' leadership with artificial intelligence: insights from a systematic literature review

Simone Bevilacqua (), Jana Masárová (), Francesco Antonio Perotti () and Alberto Ferraris ()
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Simone Bevilacqua: University of Turin
Jana Masárová: Alexander Dubcek University of Trenčin
Francesco Antonio Perotti: University of Turin
Alberto Ferraris: University of Turin

Review of Managerial Science, 2025, vol. 19, issue 9, No 8, 2899-2935

Abstract: Abstract In the contemporary landscape of digital transformation (DT), the wave of artificial intelligence (AI) is radically restructuring the managerial processes of organizations. As a result, the influence of top managers is emerging as a determining factor in the effectiveness of business strategies related to AI innovation. Academics have provided a large body of literature on this topic, drawing on upper echelons theory, which states that top managers' leadership influences companies' strategic decisions and performance. Leaders have revolutionized their roles and skills to exploit the full potential of AI and integrate it into the business decision-making process effectively. However, given the fragmented nature of existing studies, a systematic literature review is needed to consolidate and clarify how AI impacts top managers' leadership. This paper presents findings involving bibliometric and content analysis tools, examining 63 articles from 31 highly ranked academic journals. Three research clusters emerge: (1) AI-driven skills of top managers' leadership; (2) factors driving top managers' decision to adopt AI in organizations; and (3) the strategic use of AI. The article contributes to upper echelons theory, providing a holistic perspective on top managers' leadership in the AI era and a guidance framework for successfully integrating AI in businesses. Finally, the study offers scholars avenues for future research and provides practical insights for top managers seeking to harness AI technologies to enhance their strategic leadership in organizations.

Keywords: Artificial intelligence; Top managers; Leadership; Upper echelons theory; Digital transformation (search for similar items in EconPapers)
JEL-codes: M12 O32 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11846-025-00836-7

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