**Introduction** Artificial intelligence (AI) is reshaping global value chains (GVCs) by lowering coordination and information costs, increasing the role of data- and knowledge-intensive activities, and enabling faster adjustment of supply chains. These developments improve trade efficiency at the firm level, while also altering patterns of comparative advantage and interacting with resilience- and security-related trade policies, leading to uneven outcomes across economies. **Mechanisms through which AI reshapes global value chains and trade efficiency** **1.** **AI lowers coordination and logistics costs across fragmented value chains** AI improves demand forecasting, inventory management, route planning, and predictive maintenance by enabling firms to process and analyze large volumes of data in real time. These applications reduce delays, inventory imbalances, and idle capacity, improving reliability and lowering the costs of coordinating production and logistics across multiple countries. The adoption of digital and AI-enabled tools in supply-chain management has made reductions in trade costs and improvements in delivery performance, particularly in manufacturing- and transport-intensive sectors[1]. **2.** **AI increases the importance of data- and knowledge-intensive activities in trade** The use of AI raises the relative importance of design, software, data analytics, and after-sales services within global production networks. As a result, a larger share of value in GVCs accrues to activities associated with intangible assets, including data, algorithms, and platforms, rather than to physical assembly alone. This contributes to the growth of digitally deliverable services trade and to the increasing services content of goods trade. Economies with stronger digital infrastructure, data availability, and AI-related capabilities tend to capture a larger share of value added in GVCs, while others remain concentrated in lower-value segments[1][2]. **3.** **AI supports supply-chain adjustment but does not offset the costs of trade fragmentation** AI tools allow firms to map supplier networks, assess exposure to disruptions, and evaluate alternative sourcing or production locations. These capabilities support more timely adjustment of supply chains in response to geopolitical risk, export controls, industrial subsidies, and other policy-induced changes in trade conditions. However, these firm-level efficiency gains do not fully offset the broader economic costs associated with trade fragmentation, including reduced economies of scale, duplicated production capacity, and weaker competitive pressures. As a result, AI improves operational efficiency within firms while aggregate trade efficiency may decline when fragmentation intensifies[3]. **4.** **AI improves trade facilitation and border processes** AI is increasingly applied in customs and border management through automated risk assessment, document processing, and targeting of inspections. These applications reduce clearance times, lower administrative and compliance costs, and improve predictability for traders, directly affecting international trade efficiency. The use of advanced digital technologies, including AI, in trade facilitation plays a central role in reducing trade costs, particularly for time-sensitive goods and for improving the participation of smaller firms in cross-border trade[2][4]. **Conclusion** AI improves international trade efficiency by lowering coordination and logistics costs, increasing the role of data- and knowledge-intensive activities, and supporting more timely adjustment of global value chains. At the same time, it contributes to shifts in comparative advantage and operates within a trade environment increasingly shaped by resilience- and security-related policy objectives. The result is a global trading system in which firms may achieve higher operational efficiency, while aggregate outcomes across economies become more uneven as trade and production patterns adjust.
