AI and Transformative Leadership: A Systematic Literature Review on Leadership Models and Organisational Performance

The rapid advancement of Artificial Intelligence (AI) and Industry 4.0 technologies is transforming how organisations plan, operate, make decisions, and compete. However, the leadership models required to integrate AI into organisational decision-making effectively remain insufficiently understood. This study presents a Systematic Literature Review (SLR) examining how leadership is evolving in AI-enabled organisational environments and how these changes influence organisational performance globally. This SLR was conducted across four academic databases using a predefined Boolean search strategy. Following a PRISMA-aligned screening process, 63 studies were selected for analysis, including four earlier key studies identified through citation searching. The selected articles were analysed using thematic coding in ATLAS.ti according to six predefined themes: leadership transformation, decision-making attributes, leadership–AI interface, modus operandi, innovation and agility, and organisational performance. These themes were selected to structure the analysis around how AI reshapes leadership roles, decision-making processes, and organisational outcomes, directly aligning with the research intent. The findings suggest that the impact of AI on organisational performance is facilitated primarily through changes in leadership roles and decision structures rather than technological capability alone. AI increasingly reshapes decision-making processes, organisational coordination, and leadership responsibilities, creating hybrid human–AI leadership arrangements. The review identifies a key gap in existing literature, namely the lack of clear and practical frameworks for implementing hybrid human–AI leadership in complex organisational environments. To address this gap, the review synthesises its findings into an integrative Human–AI Leadership Configuration (HALC) framework, a typology of four human–AI leadership configurations, and six testable propositions, providing a structured foundation for both researchers and organisational leaders to better align AI capabilities with effective leadership and improved performance.