Generative AI and Operational Decision-Making: Mechanisms for Enhancing Firm Operational Efficiency in the Context of Digital Transformation

Abstract

Generative artificial intelligence has emerged as a pivotal driver of enterprise digital transformation and operational decision-making in contemporary business environments. This study systematically examines how generative AI contributes to firm operational efficiency through organizational mechanisms within the broader context of digital transformation. Leveraging SEC EDGAR 10-K annual reports as an open-source firm-level data source, this research constructs comprehensive measures of generative AI disclosure intensity, digital transformation intensity, knowledge sharing, process optimization, decision intelligence, and operational efficiency. The empirical results demonstrate that generative AI disclosure intensity is positively and significantly associated with subsequent operational efficiency. Mechanism tests further reveal that knowledge sharing, process optimization, and decision intelligence serve as critical mediating channels through which generative AI enhances operational performance. Among these mechanisms, process optimization exhibits a relatively strong effect, suggesting that the operational value of generative AI is closely linked to workflow improvement and coordination efficiency. In addition, digital transformation positively moderates the relationship between generative AI disclosure and operational efficiency, indicating that firms with stronger digital foundations are better positioned to translate generative AI attention into tangible efficiency gains. This study extends existing research on AI-enabled management by shifting the analytical focus from technological application to operational decision-making mechanisms, thereby providing robust empirical evidence on the managerial value of generative AI for firms navigating the digital economy.