Interoperability Many institutions still rely on legacy systems that struggle to interface with modern business operations. Lack of resources, antiquated infrastructure and outdated regulatory frameworks can also slow the adoption of promising technologies. Interoperability helps ensure that actors from different jurisdictions and regions can benefit from the shared gains of trade. Even if different platforms emerge, aligning protocols can help ensure that legacy systems remain interoperable. Such measures support the inclusion of numerous traders, even when institutions move at varying speeds in incorporating new technologies. Developing common standards can also safeguard interoperability and facilitate the adoption of AI technologies in trade. Areas that benefit from common standards include: –Communication and data protocols, which ensure high-quality data inputs, facilitate data sharing and enable integration with other technologies like IoT, help AI generate accurate and reliable insights, particularly in complex environments like global supply chains. –Cybersecurity benchmarks, which mitigate the risks associated with increased digitalization and AI deployment, ensure transparency and trust in AI systems. –Standard definitions, such as what qualifies as a legally negotiable contract or definitions for “waste materials”, can streamline collaboration. The coordination of standards: –Allows for more consistent adoption of AI through supply chains –Facilitates operations across jurisdictions for logistics providers –Streamlines customs processes –Accelerates trade finance At the same time, AI can be part of the solution by breaking down language barriers to enable communication across diverse cultures and markets, enabling the development of consistent standards, regardless of the language of origin. Trust Verifiability is a cornerstone of both trust and compliance in international trade. Given the complexity of supply chains and the number of inputs involved, parties need assurances that the information they receive (such as documents and identifiers) is correct. Digital ID enables these assurances for humans and AI alike. Verifiability represents a foundational challenge for AI systems, given that the quality of the underlying data is decisive in determining the quality of analysis that AI generates. The concept of “garbage in, garbage out” holds true for AI systems. For AI tools to deliver on efficiency, sustainability and inclusivity, they must be trained on accurate and relevant data. As such, digital ID can help ensure the accuracy of data, improving the ability to generate high-quality datasets for AI training. However, while AI can assist in processing and tracking information at unprecedented scales, traders must coordinate ways to ensure verifiability without compromising sensitive information. Technologies capable of graduated disclosure, such as zero-knowledge proofs (ZKP) and authentic chained data containers (ACDC), are emerging as potential solutions. AI can be part of the solution by breaking down language barriers to enable communication across diverse cultures and markets. Artificial Intelligence for Efficiency, Sustainability and Inclusivity in TradeTech 27

Artificial Intelligence for Efficiency Sustainability and Inclusivity in TradeTech 2025