OECD examines local conditions for trustworthy AI transition
Regional divides in AI adoption show the need for stronger local skills, infrastructure and policy coordination, according to OECD’s findings.
The OECD is advancing work on AI and the local conditions needed for a trustworthy, ethical, and sustainable transition, focusing on how countries, regions, and cities can develop AI solutions adapted to local needs.
The project, ‘Seizing the full potential of AI: the local factor’, examines how AI is affecting business functions, public governance, jobs, labour markets, and regional economies. The OECD says generative AI has lowered some barriers to adoption by enabling the use of pre-trained models, but uptake remains uneven across places, people, and firms.
The organisation links stronger AI adoption to innovation-leading regions, especially global technology hubs connected to specialised knowledge networks and global value chains. Regions with weaker innovation performance appear to use AI less and adopt it more slowly, while workforce skills act as both an enabler and a barrier to adoption.
The OECD warns that uneven diffusion could affect competitiveness and territorial cohesion, particularly because technology gaps can be difficult to close once they widen. Businesses, regional governments, and cities also face challenges in integrating AI into legacy systems, adapting labour markets, revising skills and employment policies, financing the transition, and managing risks linked to employment, the environment, land use, and natural resources.
The project focuses on place-based AI strategies, local employment and skills needs, regional development policy, and smart and inclusive cities. Its work aims to help national and subnational policymakers assess AI readiness, strengthen stakeholder engagement, and build the policy capacity needed to support broader AI diffusion.
Why does it matter?
The OECD’s work highlights a key risk in AI adoption: technological divides may become territorial divides. If leading innovation hubs move faster while weaker regions lack skills, infrastructure, financing, or institutional capacity, AI could widen gaps in competitiveness, public service quality, and labour market outcomes. Place-based AI strategies can help policymakers tailor adoption, skills, and investment policies to local conditions rather than relying on one-size-fits-all national approaches.
Would you like to learn more about AI, tech, and digital diplomacy? If so, ask our Diplo chatbot!