Beyond Human-Centred Leadership Theory: Digital Leadership, Artificial Intelligence and the Governance of Hybrid Human-Machine Organisations

Abstract

Leadership scholarship has developed sophisticated explanations of transformational, transactional, servant, authentic, adaptive, distributed, strategic, and ethical leadership. Yet the accelerating diffusion of artificial intelligence (AI), generative artificial intelligence (GenAI), automation, robotics, algorithmic management, and autonomous systems is altering the technological environment within which leadership occurs. This conceptual study critically examines the contemporary leadership literature and argues that digital leadership should no longer be treated as a peripheral technological extension of conventional leadership. Instead, digital leadership should be conceptualised as an increasingly foundational leadership capability for organisations operating within intelligent, automated, data-intensive, and human-machine environments. Using an integrative and comparative literature-review approach, this paper synthesises classical leadership theories with research on e-leadership, digital transformation, dynamic capabilities, strategic leadership, AI-enabled management, and human-AI collaboration. The current study develops the Digital-Artificial Intelligence Leadership Integration Framework (DAILIF), which conceptualises future leadership through eight interdependent domains: digital vision, technological intelligence, human-AI orchestration, algorithmic judgement, digital ethics and governance, workforce transformation, cyber-resilience, and continuous technological adaptation. Thispaper argues that AI will not simply become another tool available to leaders; it will increasingly participate in analysis, prediction, communication, coordination, evaluation, resource allocation, and organisational decision-making. Leadership, therefore, will progressively involve governing hybrid systems consisting of human workers, intelligent technologies, automated processes, robots, algorithms, and autonomous agents. Comparative analysis indicates that digital leadership requirements differ between advanced and developing economies because technological infrastructure, institutional capacity, digital inequality, organisational resources, and workforce readiness condition AI adoption. The present study concludes that leadership theory must evolve from predominantly human-to-human models towards socio-technical and human-AI models capable of explaining leadership within intelligent organisations beyond 2035.