AI for decision-making: exploring the linkage from AI capability to firm performance

Purpose Artificial intelligence (AI) stands as a pivotal, revolutionary force in technologically reshaping industries. Despite extensive research on the potential of AI, the specific mechanisms through which AI capabilities lead to competitive advantages still need delineated. To address this notable gap in literature, we investigate how agility in decision-making, as a focal dynamic capability, is a critical conduit linking AI capabilities for improving organizational outcomes and examine the interplay between a firm's decision-making agility and its internal and external environment, thereby offering a comprehensive understanding of the dynamics of today's complex business ecosystem. Design/methodology/approach The mixed-method approach, combining partial least squares–structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA), was used for analyzing the survey data of 251 firms. Findings The results of PLS-SEM show that two sub-dimensions of AI capability, technical infrastructure and management, affect performance outcomes through decision-making agility. Moreover, environmental dynamism and complexity differently moderate the relationship between decision-making agility and firm performance. Additionally, the results of fsQCA demonstrate how the combination and roles of strategic resources (e.g. AI capabilities and decision-making agility) shift in response to varying organizational and environmental conditions. Originality/value By elucidating these dynamics via a mixed-method approach, our findings not only offer a deeper understanding of the strategic value of AI within the organizational dynamic capability perspective, but also provide practical insights for AI implementation that prioritizes management capability and adaptability to external environments.