Overcoming Leadership Barriers in AI Adoption: A Socio-Technical Ambidextrous Approach
Overcoming Leadership Barriers in AI Adoption: A Socio-Technical Ambidextrous Approach

Author(s): Neville Welch
Subject(s): ICT Information and Communications Technologies, Socio-Economic Research
Published by: Scientia Moralitas Research Institute
Keywords: Leadership; Artificial Intelligence (AI); AI Adoption;
Summary/Abstract: Artificial Intelligence (AI) presents significant opportunities for organizational innovation but consistently faces substantial adoption barriers, primarily rooted in complex socio-technical challenges. This research explicitly investigates these barriers through the integrative lens of ambidextrous leadership, emphasizing leaders' dual responsibilities in navigating technological innovation and organizational adaptive dynamics simultaneously. Employing a rigorous Narrative Literature Review (NLR) methodology, this study systematically synthesizes contemporary scholarly insights. The review explicitly identifies critical barriers to AI adoption, notably leadership inadequacies. Findings explicitly highlight ambidextrous leadership as uniquely suited to addressing these challenges, operationalizing the joint optimization imperative of socio-technical theory by harmonizing technological innovation with authentic stakeholder engagement, adaptive organizational learning, and ethical leadership practices. Additionally, explicitly drew, which reinforces the necessity for ethically grounded, adaptively oriented "real leadership," in contrast to traditional coercive or "counterfeit" approaches. Consequently, this study offers substantial theoretical advancements and practical frameworks specifically designed to help organizations effectively navigate the complex adaptive and technical dimensions of AI integration. By explicitly addressing previously identified gaps, this research makes a significant contribution to the scholarly discourse and provides strategic guidance for leadership practice, thereby enhancing sustainable and effective AI adoption processes.

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