Graph Thinking: Network Cognition and Strategic Leadership in AI-Enabled Organizations

Contemporary organizations function as complex networks, yet leadership cognition remains dominated by linear metaphors that assume sequential causality and hierarchical control. This article introduces Graph Thinking as a multi-dimensional leadership capability comprising cognitive, analytical, and behavioral components that enable leaders to perceive, analyze, and deliberately shape organizational network structures. We position Graph Thinking at the intersection of systems thinking, social network analysis, and ecosystem strategy, arguing that it synthesizes these traditions while extending them to address the specific challenges of artificial intelligence deployment. Drawing on network science and strategic management theory, we develop a multi-level framework specifying how Graph Thinking manifests at individual, organizational, and ecosystem levels, with explicit attention to network dynamics and temporal evolution. Through illustrative thought experiments spanning diverse organizational contexts, we demonstrate how network properties function as diagnostic instruments for strategic decision-making. We argue that AI integration creates conditions that may reward explicit network mapping, while acknowledging this relationship is contingent and politically contested. The article contributes to strategic management literature by specifying measurement approaches for future empirical research, addressing power dynamics inherent in network legibility efforts, and providing actionable developmental frameworks. We conclude with boundary conditions, limitations, and directions for empirical validation.