A Shift in Global AI Strategy
In its World Development Report 2026, the World Bank outlines a pivotal strategy for developing economies: don't just adopt AI, adapt it. The core message is a departure from the idea that progress comes from simply importing sophisticated, large-scale
AI models from the United States and China. Instead, the institution argues that the greatest benefits lie in creating and nurturing 'small AI'—specialised, efficient models trained on local data to solve specific, regional problems. This approach, termed the 'adopt, adapt, and advance' framework, encourages nations to start by using existing tools, then modify them for local languages and contexts, and over time, build the skills and infrastructure to innovate independently. The goal is to avoid costly attempts at replicating massive AI infrastructure and instead focus on practical applications that deliver immediate value.
Why 'Local AI' Matters
Importing one-size-fits-all AI solutions presents significant risks for developing nations. These models, trained on data from high-income countries, can perpetuate biases, misunderstand local languages and cultural nuances, and fail to address the most pressing local issues. This can lead to what some call 'algorithmic colonisation', where the interests of large tech firms override the needs of local communities. By building AI locally, countries can ensure solutions are relevant. For example, an AI tool that gives farming advice must understand regional soil types and weather patterns, something a generic model cannot do effectively. This focus on local relevance also fosters data sovereignty, ensuring sensitive local data is managed according to national laws and regulations rather than being controlled by external platforms.
The Promise of 'Small AI'
The World Bank places a strong emphasis on 'small AI', which refers to targeted applications designed to work in low-resource environments. These are tools that can run on a basic mobile phone, function with low internet connectivity, and communicate in local languages. The potential applications are vast and transformative. In India, for instance, the AgriConnect scheme uses AI to provide farmers with soil analysis and market information in their own languages, a task that would be impossible to scale using human experts alone. Similar models can be used to accelerate medical screenings, help teachers create better lessons, or provide citizens with access to judicial services. The World Bank's chief economist, Indermit Gill, stated that by adapting such low-cost tools, developing countries could achieve in a decade what might have otherwise taken a century.
Unlocking Economic Opportunity
Beyond solving development challenges, a local AI strategy is a powerful economic engine. It creates a new value chain for local talent, from data annotation and management to developing and maintaining AI models. This can help counter the 'brain drain' of skilled workers to other countries by creating high-value jobs at home. Fostering a local AI ecosystem also stimulates entrepreneurship, giving rise to startups that understand and can cater to market needs that global corporations overlook. By leveraging open-source models and focusing on niche applications, these startups can compete effectively without needing massive capital investment in infrastructure. This approach cultivates technological sovereignty, reduces dependency on foreign suppliers, and allows nations to become creators, not just consumers, of next-generation technology.
The Road Ahead Is Not Without Challenges
While the vision is compelling, the path is steep. A primary obstacle is the lack of foundational infrastructure. The World Bank report notes that many regions in the developing world still lack reliable electricity and consistent internet access, which are prerequisites for any digital transformation. Furthermore, there is a significant scarcity of local AI expertise and investment capital. Governments must invest in digital skills training and create policies that encourage both public and private sector participation. IMF Managing Director Kristalina Georgieva has stressed that for AI adoption to be democratic and successful, governments must first ensure the basics are in place: power, digital infrastructure, and regulatory frameworks that protect against misuse.
