LEADERSHIP STYLES IN THE AGE OF ARTIFICIAL INTELLIGENCE

ORIGINAL SOURCE
Originally published in INTERNATIONAL CONFERENCE OF NATURAL AND SOCIAL-HUMANITARIAN SCIENCES; Vol. 2 No. 8 (2025): INTERNATIONAL CONFERENCE OF NATURAL AND SOCIAL-HUMANITARIAN SCIENCES; 71-76.

Kurbanbayeva, Malohat (2025) LEADERSHIP STYLES IN THE AGE OF ARTIFICIAL INTELLIGENCE. INTERNATIONAL CONFERENCE OF NATURAL AND SOCIAL-HUMANITARIAN SCIENCES; Vol. 2 No. 8 (2025): INTERNATIONAL CONFERENCE OF NATURAL AND SOCIAL-HUMANITARIAN SCIENCES; 71-76.

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Abstract

The rapid integration of artificial intelligence (AI) into organizational processes is transforming how decisions are made, work is structured, and leadership is practiced. While AI offers significant advantages in efficiency and data-driven insight, it also raises new challenges related to ethics, accountability, trust, and human–machine collaboration. This article examines how leadership styles are evolving in the era of artificial intelligence and identifies the leadership approaches most suitable for AI-enabled organizations. The study adopts a qualitative, theory-driven methodology based on systematic analysis of contemporary academic literature and documented organizational practices. By synthesizing leadership theories and empirical insights, the research highlights the changing role of leaders in AI-driven environments. The findings indicate that transformational, adaptive, digital, and ethical leadership styles are particularly relevant, as they support innovation, flexibility, and responsible use of AI. The results further show that effective leadership in the AI era relies on hybrid decision-making that combines algorithmic insights with human judgment and moral responsibility. The article concludes that artificial intelligence does not reduce the importance of leadership; rather, it increases the need for leaders who can integrate technological competence with emotional intelligence and ethical awareness to ensure sustainable organizational performance.

Item Type: Article
Additional Information: Imported from ICNSHS Conference
SWORD Depositor: Admin User
Depositing User: Admin User
Date Deposited: 23 Sep 2026 22:43
Last Modified: 23 Sep 2026 22:43
URI: https://universalpublishings.uz/id/eprint/7004

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