Against the backdrop of the deep integration between the digital economy and intelligent manufacturing, artificial intelligence (AI) technology has emerged as a critical driver for enterprises to reduce costs, improve efficiency, and optimize organizational structures. Using a sample of Chinese A-share listed companies during 2011–2023, this study constructs a novel text-mined AI application index through systematic analysis of annual report disclosures. Employing a two-way fixed-effects model (controlling for firm and year fixed effects), we empirically examine the impact mechanism of AI adoption on enterprises’ warehousing and logistics cost control. Our key findings are as follows: (1) AI application significantly reduces corporate logistics costs, and this result remains robust after a series of robustness tests, including alternative variable measurements, exclusion of special years (e.g, 2020 amid the COVID-19 pandemic), and instrumental variable estimation to address potential endogeneity. (2) Mediation analysis reveals three underlying channels: AI technology reduces logistics costs by enhancing the level of specialized division of labor, promoting supply chain diversification, and optimizing inventory management through real-time demand forecasting and predictive analytics. (3) Heterogeneity analysis indicates that the cost-reducing effect of AI application is more pronounced for firms located in eastern China and those operating in technology-intensive industries. This study provides empirical evidence for understanding the micro-level mechanism through which AI influences enterprise operations and cost control, and offers important implications for policymakers formulating digital economy policies and for enterprises implementing intelligent supply chain management. It also contributes to the literatures on operations management and corporate digital transformation by uncovering empirically grounded pathways linking AI deployment to logistics cost performance.
Xiaoyuan Cheng (Fri,) studied this question.