Leadership beyond the Individual Mind: Examining Artificial Intelligence, External Cognitive Influence, Decision-Making, and the Exercise of Power over Others

Abstract

Background: The increasing integration of artificial intelligence (AI), algorithmic systems, advisers, and digital decision-support technologies into organisational leadership challenges conventional assumptions that cognition, judgement, and moral responsibility reside solely within the leader. This study develops the concept of the outside mind to describe the network of external human, technological, informational, and institutional resources incorporated into leadership cognition and decision-making.

Objective: The current study seeks to develop and refine the Outside Mind Theory of Leadership by examining how external cognitive influence shapes leadership judgement, decision-making autonomy, moral agency, organisational power, and potentially oppressive leadership outcomes.

Methods: A PRISMA 2020-guided systematic qualitative review and theory-building synthesis was undertaken. Relevant peer-reviewed studies were identified through systematic database searches using predefined eligibility criteria, screened at title, abstract, and full-text stages, and critically appraised using design-appropriate tools. Extracted findings were analysed through qualitative thematic synthesis, constant comparison, and iterative theoretical integration to identify mechanisms, contextual conditions, and relationships linking cognitive augmentation, cognitive dependency, moral disengagement, employee agency, and organisational power.

Expected contribution: This study proposes that external cognition is not inherently oppressive; rather, harmful outcomes may emerge when cognitive dependency converges with weak accountability, limited transparency, moral disengagement, concentrated power, and reduced employee contestability.

Conclusion: The review provides a systematically derived qualitative foundation for theorising AI-mediated leadership and for informing future empirical testing, leadership development, and responsible AI governance that preserves human judgement, accountability, dignity, autonomy, and employee voice.