Global supply chains are being reshaped by two systemic forces – the rapid uptake of artificial intelligence (AI) and analytics for facilitating safe trade, and a growing wave of environmental requirements linked to market access. These developments are unfolding in an increasingly complex global trade landscape, with important implications for how supply chains operate.
The report Strengthening Supply Chains Through Efficiency, Resilience, AI and Environmental Performance builds on the Organisation for Economic Co-operation and Development’s (OECD) previous work on the policy environment for paperless trade and AI, as well as OECD core databases such as the Trade Facilitation Indicators (TFIs), Digital Services Trade Restrictiveness Index (Digital STRI) and the Index for Digital Trade Integration and Openness (INDIGO). It explores the core building blocks of an enabling environment supporting an effective adoption of AI and broader digital tools for streamlining border processes, which play a pivotal role in improving altogether the resilience, efficiency and environmental performance of supply chains.
Reaping AI benefits in trade facilitation requires building blocks for an operational paperless landscape with interoperability at its core.
At the operational level, digitising trade-related documents into machine-readable formats underpins the application of AI to core tasks, alongside the broader digitalisation of border procedures through systems such as single windows enabling domestic and cross-border co-operation. The integration of complementary technologies, such as big data and distributed ledger systems, can further streamline operations. These technical foundations depend on supportive legal frameworks for electronic transactions and secure cross-border data flows, while interoperability requires overall adherence to international standards for the movement and processing of data and documentation.
The broader trade policy environment needs be conducive to the adoption and diffusion of AI.
The adoption and diffusion of AI for trade facilitation depends on the availability and cross-border movement of key inputs, including AI goods and services, skilled workers, and data. Trade policy plays a central role in determining whether customs and other border agencies can access and deploy AI at scale. For instance, tariffs on information and communication technology (ICT) hardware can increase the cost of physical infrastructure, while restrictions on computer and telecommunications services hinder access to digital inputs such as cloud computing services. Furthermore, as cross-border data flows are the lifeblood of AI, stringent restrictions on data transfers can hamper the aggregation of diverse datasets required for training AI models.
Evolving environmental requirements bring complex new data, traceability, and verification needs across supply chains.
Notifications of environmental-related technical requirements to the World Trade Organization are on the rise, and the OECD’s Inclusive Forum on Carbon Mitigation Approaches (IFCMA) Climate Policy Database currently documents 1 600 individual policy instruments across 38 economies. In complying with such diverse measures where these may be enforced at the border, firms operating across several jurisdictions can face challenges in dealing with divergent data formats, reporting templates, and verification channels. The burden can be heavier for smaller firms and firms operating in lower income economies, which often lack resources, technical capacity, and digital tools. Limited guidance on how environmental requirements interface with border processes adds further costs, creating operational complexity, duplicating verification, and risking bottlenecks that can undermine trade facilitation efficiency.
Co‑ordinated border management, regulatory and technical interoperability are essential to seamlessly integrate new environmental requirements.
Integrating environmental requirements-related data into border processes requires co‑ordination across various actors, including firms, customs administrations, environmental regulatory agencies, accredited verification bodies, and digital system providers. At the core of this effort is the need for both regulatory interoperability (the alignment or mutual recognition of required information) and technical interoperability (standards and interfaces governing how such data moves). Trade facilitation frameworks and emerging technologies, including AI, can offer practical solutions. By adapting existing systems such as Single Windows for trade or risk management systems, environmental-related attributes can be more seamlessly integrated into border workflows without creating duplicative processes.
Korea’s experience showcases how an effective digital ecosystem can enable swift deployment of AI technologies in supply chains.
Building on robust investment in paperless trade facilitation and digital infrastructure, the Korea Customs Service transitioned to AI-driven customs procedure management. This includes applying AI-driven risk profiling, automated X-ray screening to detect illicit goods, and document classification automation. By strengthening the core building blocks of end-to-end digitalised trade processes, Korea has established a policy environment conducive to advanced AI use, demonstrating how they can create the conditions for AI-driven efficiency across supply chains.
Navigating the AI transition and growing environmental requirements along supply chains requires policy actions in key areas.
AI and evolving environmental requirements are two drivers reshaping global supply chains through a shared dependence on digital tools and trusted data. To build an enabling ecosystem, these developments point altogether to several priority areas for policy action:
Advancing interoperability, through common data models, interoperable registries and certification systems, and integration of environmental data into existing trade facilitation architectures.
Enhancing supply chain resilience, by embedding environment-related and compliance information into border processes and digital tools for trade facilitation while preserving predictability and efficiency.
Investing in enabling ecosystems, including digital infrastructure, legal recognition of electronic transactions, and institutional capacity across border agencies. AI can only be scaled up in border settings when documents and processes are digitised, data elements are standardised, and systems interoperate – with legal certainty for electronic transactions and secure cross border data exchange.
Promoting mechanisms for inter-agency co‑ordination, ensuring operational co-operation between customs, environmental authorities, and other regulators to enable the use of emerging technologies such as AI and support coherent implementation of new requirements enforced at the border.
Enhancing international co-operation, including as regards the layers underpinning technical interoperability: the alignment of underlying data elements required, the standardisation of relevant documents, and the compatibility of data systems that enable information to flow reliably between domestic agencies and across borders.