AI and organizational leadership: bibliometric review and future trends

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Abstract
Purpose: This article analyses the evolution of scientific literature at the intersection of artificial intelligence (AI) and organizational leadership. It identifies research trends, theoretical frameworks and emerging lines of inquiry, while addressing the practical, ethical and policy implications of AI integration in leadership settings. Design/methodology/approach: A mixed-methods approach was adopted, combining bibliometric techniques with qualitative content analysis. A total of 304 peer-reviewed articles (2014–2025) were retrieved from the Web of Science Core Collection and screened using a PRISMA-inspired procedure. Data were analysed with VOSviewer and thematic synthesis to identify networks, citation patterns, thematic clusters and theoretical foundations. Findings: The study identifies three dominant thematic areas: (1) decision support, (2) evolving leadership roles, and (3) ethics/sustainability. Influential theoretical perspectives include the dynamic capabilities view, social cognitive theory and sociotechnical systems theory. The review also highlights the emergence of diverse leadership styles (transformational, ethical, empowering and digital) shaped by AI's integration into organizational processes. Overall, the field displays both consolidation and fragmentation, underscoring the need for more integrative sociotechnical frameworks. Research limitations/implications: Beyond its diagnostic contribution, this study emphasizes the strategic, ethical and digital competencies required for AI-driven leadership, providing practical insights for organizations seeking to align governance, talent management and innovation strategies with emerging technological challenges. The research offers a systematic and integrative mapping of the AI–leadership field, combining bibliometric indicators with qualitative insights to identify conceptual trends, research gaps and actionable guidance on competencies, hybrid human–AI structures, and governance for responsible and sustainable adoption. Practical implications: The findings offer practical guidance for organizational leaders navigating AI-driven transformation. They identify key leadership competencies such as ethical reasoning, digital literacy and change management, essential for integrating AI effectively. The study also informs the design of leadership development programs, talent strategies, and governance frameworks that promote responsible AI use. By mapping leadership styles suited to AI-mediated environments, it helps organizations align human and algorithmic decision-making, foster trust, and ensure sustainable performance in increasingly digital contexts. Social implications: The integration of AI into leadership practices raises critical social concerns related to fairness, transparency, and accountability. This study highlights the need for inclusive and ethical governance models to address algorithmic bias, protect employee well-being, and ensure equitable access to AI benefits. Leadership plays a key role in mediating these challenges by fostering human-AI collaboration based on trust and ethical alignment. The findings underscore the importance of preparing leaders to navigate complex sociotechnical systems, influence organizational culture, and contribute to shaping public policies that support responsible and sustainable AI adoption. Originality/value: This study offers a systematic and integrative mapping of the academic landscape on AI and leadership. By combining bibliometric indicators with qualitative insights, it identifies conceptual trends and research gaps, while providing actionable guidance for scholars, organizational leaders and policymakers navigating the complexities of digital transformation.

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