AI in strategic management and organizational agility of SMEs: leadership, policy environment, and adaptive capability

This study investigates how artificial intelligence strategic capabilities, transformational leadership, and policy environments collectively influence organizational agility in small and medium-sized enterprises through dynamic capability mechanisms. Employing a mixed-methods design, the research analyzes survey data from 300 SMEs across manufacturing, service, and technology sectors, complemented by qualitative case studies. Structural equation modeling reveals that AI strategic capabilities constitute the strongest predictor of organizational agility (β=0.42, p<0.001), with digital dynamic capabilities mediating 67% of this total effect. Technology-management fit emerges as a critical boundary condition, amplifying AI effectiveness by 123% under high alignment scenarios (β=0.58 versus β=0.26 in low alignment contexts). Transformational leadership exhibits dual mechanisms through direct positive effects on agility (β=0.28, p<0.001) and moderating influences on AI-agility relationships (β=0.21, p<0.01). Notably, AI capabilities demonstrate buffering properties against policy environment uncertainty (β=0.12, p<0.05), transforming institutional constraints into manageable strategic variables. Machine learning analyses reveal nonlinear effects with diminishing returns beyond the 75th percentile of AI adoption. The structural model explains substantial variance in organizational agility (R²=0.64) and firm performance (R²=0.52). These findings extend dynamic capability theory to digital contexts, reconceptualize AI as a strategic capability rather than an operational tool, and illuminate digital leadership dimensions, offering evidence-based guidance for SME managers, technology vendors, and policymakers navigating digital transformation challenges.