AI-driven financial operational Efficiency: A dynamic capabilities perspective on knowledge-oriented leadership and financial flexibility

While artificial intelligence (AI) offers transformative potential for corporate finance, the specific combination of organizational capabilities needed to achieve high operational efficiency remains ambiguous, particularly in knowledge-intensive firms. This study addresses this gap by moving beyond linear-causal models to uncover the synergistic pathways to success. Using a hybrid partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) methodology on survey data from the survey and design industry, we analyze how knowledge-oriented leadership, financial flexibility, trust in AI, and AI utilization combine to drive efficiency. Our findings reveal that no single capability is sufficient. Instead, high financial efficiency is achieved through multiple configurational pathways, all of which require a core foundation of knowledge-oriented leadership and financial flexibility. Critically, we find that trust in AI and the utilization of AI can function as substitutes for one another, demonstrating strategic equifinality. This study's primary contribution is to the Dynamic Capabilities Theory, revealing the complex, configurational, and substitutive nature of the capabilities required for AI-driven transformation and offering a nuanced strategic roadmap for managers.