Comparative analysis of strategic vs. computational thinking in management

The integration of artificial intelligence (AI) into organisational processes is transforming the decision-making dynamics of managerial work. This study examines how AI reshapes managerial roles at the micro level by analysing the interaction between strategic and computational thinking across Mintzberg’s ten managerial roles. Grounded in Peter Senge’s Five Disciplines, the study explores how AI-enabled systems alter managerial routines, including monitoring, sense-making, resource allocation, coordination, and negotiation and how these changes influence human–algorithm decision architectures. A conceptual synthesis approach was used to integrate three theoretical perspectives: (1) Mintzberg’s framework of managerial roles, (2) Senge’s learning disciplines, and (3) contemporary models of computational thinking. Through comparative role mapping and cross-framework analysis, the study identifies how algorithmic logic augments, displaces, or reconfigures cognitive tasks within each managerial role. This synthesis informs the development of a hybrid strategic–computational framework for managerial decision-making in AI-rich environments. Findings indicate that AI adoption differentially affects managerial roles. Roles dependent on relational intelligence, ethical judgment, and influence (leader, liaison, figurehead, negotiator) remain anchored in strategic thinking, though increasingly augmented by predictive and diagnostic analytics. Roles focused on information processing, optimisation, and operational precision (monitor, disseminator, resource allocator) benefit substantially from computational thinking. Entrepreneurial and disturbance-handling roles emerge as hybrid decision zones, requiring managers to integrate AI-driven modelling, simulation, and anomaly detection with contextual interpretation, value-based trade-offs, and principled override decisions. Across roles, AI increases cognitive complexity and introduces new tensions between algorithmic optimisation and systemic, ethical reasoning. The study contributes to AI governance and managerial cognition research by showing how organisational design, regulatory constraints, and decision structures shape micro-level human–AI interaction patterns. For practitioners, including executives, AI steering committees, and governance councils, the proposed framework provides actionable guidance on delineating managerial responsibilities, establishing human-in-the-loop checkpoints, and designing escalation paths that safeguard accountability. The findings underscore the need for balanced upskilling in strategic systems thinking and computational reasoning to ensure responsible, transparent, and legitimate managerial decision-making in AI-enabled workplaces.