**Introduction** International organizations can facilitate more equitable access to artificial intelligence (AI) by reducing the structural barriers that shape adoption — digital and data infrastructure, implementation capacity, interoperable governance, and diffusion of practical applications. Current multilateral work increasingly focuses on enabling deployment and broad-based diffusion, rather than concentrating capability in a small number of countries and firms[1][2]. **Mechanisms for equitable AI access** **1.** **Building digital and data infrastructure** Equitable AI access is constrained by the high fixed costs associated with building digital infrastructure at the national level. Multilateral development institutions can support adoption by financing digital public infrastructure (DPI), including connectivity, data-sharing systems, and core digital services that enable AI deployment across sectors[2]. Regional and pooled approaches help reduce duplication and scale disadvantages. Shared platforms, interoperable systems, and coordinated procurement arrangements can improve affordable access to secure cloud services and data infrastructure, supporting public-sector use and participation by smaller firms[2]. **2.** **Strengthening skills and institutional capacity for adoption** Constraints on AI access are frequently institutional rather than technological. Capacity development in data stewardship, public-sector procurement, regulatory oversight, and workforce skills supports feasible adoption and reduces implementation risk[1][3]. The emphasis is on strengthening deployment capability — integrating AI into operations, managing risks, and capturing productivity gains — rather than competing in frontier model development[3]. **3.** **Promoting interoperable governance frameworks** Fragmented regulatory approaches raise compliance costs and limit cross-border deployment of AI-enabled services. Interoperable approaches to trustworthy AI — risk management, transparency, evaluation, and accountability — help reduce these barriers while allowing national adaptation[4][5]. Shared reference frameworks can improve predictability for firms and reduce exclusion risks for smaller economies that depend on access to regional and global digital markets[4]. **4.** **Accelerating diffusion of applied use cases** Equitable access improves when AI deployment prioritizes applied use cases with broad development relevance, including public administration, logistics, agriculture, and small-enterprise productivity. Multilateral initiatives can support diffusion through shared tools, open resources, and cross-country learning mechanisms that lower adoption costs[6]. Data governance remains a binding constraint for AI diffusion, as divergent cross-border data rules and localization requirements restrict service provision and regional scaling. Interoperable and proportionate governance frameworks can reduce these frictions while preserving appropriate safeguards[7]. **Conclusion** International organizations can facilitate more equitable AI access by focusing on four functions: building shared digital and data foundations, strengthening skills and institutional capacity, promoting interoperable governance frameworks, and accelerating the diffusion of applied use cases. Together, these interventions address the structural constraints that concentrate AI capability in a limited number of countries and firms, supporting broader participation in AI-enabled economic activity.
