The World Bank is encouraging developing countries to adopt and adapt existing artificial intelligence tools before investing heavily in building their own large AI models. The recommendation focuses on helping countries gain practical economic benefits from AI while developing the skills, infrastructure and research capabilities needed for future innovation.
The institution’s World Development Report 2026, titled The Promise of Artificial Intelligence, examines how AI can support economic growth, improve public services and increase productivity. Its recommendations highlight the importance of matching AI investments to local needs and available resources.
World Bank Group Chief Economist Indermit Gill said: ‘AI has thrown developing economies a lifeline, and they should seize it’. The report argues that countries can begin using AI without first developing large models or constructing expensive computing infrastructure.
## Adopt Existing Technology First
The World Bank outlines a gradual approach to AI development that begins with adopting available tools, adapting them to local conditions and advancing toward domestic innovation. This strategy allows governments and businesses to test practical applications before committing substantial resources to developing new models.
Adoption can involve using existing AI systems for customer service, document processing, education and business operations. Adaptation may include improving support for local languages, incorporating relevant information and adjusting applications to meet national requirements.
The final stage involves building domestic research, technical expertise and specialised AI systems where local needs and capabilities justify the investment. The approach does not exclude developing homegrown models but places practical use before costly infrastructure projects.
## Why Smaller AI Applications Matter
Large AI models can require substantial computing power, investment and technical expertise. These requirements can create barriers for countries with limited digital infrastructure or smaller technology budgets.
Smaller applications may offer a more accessible starting point. Depending on their design, they can operate on ordinary devices or use relatively limited computing resources to perform tasks such as translation, information retrieval and basic assistance.
In agriculture, AI tools could help farmers access relevant information and weather-related guidance. In education, they could support teachers and students, while healthcare applications could assist professionals with routine administrative work and information management.
These uses require careful evaluation. AI systems can produce inaccurate information, and their performance may be weaker when local languages, regional data or specialised requirements are not adequately represented.
## The Global AI Investment Gap
The World Bank’s Digital Progress and Trends Report 2025 examined differences in AI investment, innovation and adoption across economies. Its findings highlighted the concentration of AI development in high-income countries and the challenges facing lower-income nations.
The report attributed 87% of notable AI models, 86% of AI startups and 91% of venture capital funding to high-income countries during the period examined. These figures illustrate the concentration of investment and development capacity within a relatively small group of economies.
At the same time, AI adoption is spreading into middle-income countries. The report found that more than 40% of global ChatGPT traffic came from middle-income countries in mid-2025, demonstrating that the use of AI tools can expand even when domestic model development remains limited.
## Four Foundations for AI Adoption
The World Bank identifies connectivity, computing capacity, locally relevant data and skills as important foundations for AI development. Progress across these areas can help countries move from early experimentation toward broader use in business and public services.
Reliable and affordable internet access allows people and organisations to use online AI services. Computing resources support model training and deployment, while relevant data helps applications respond to local languages, institutions and practical needs.
Skills are equally important. Workers need digital literacy and, for technical roles, training in programming, data analysis, machine learning and AI system evaluation. Without these capabilities, organisations may struggle to translate access to AI tools into measurable improvements.
## Implications for India
India has an established technology sector, a large workforce and a growing digital economy. These factors provide opportunities to apply existing AI systems in industries such as financial services, education, agriculture, healthcare and customer support.
Businesses can begin by integrating existing models into workflows and identifying tasks where AI can improve efficiency. Developers can then customise applications for Indian languages and local requirements, particularly where general-purpose systems do not provide sufficiently accurate results.
Domestic AI research and model development may remain important for strategic independence and specialised applications. The World Bank’s recommendation is to evaluate those investments alongside other priorities, rather than assume that every country must immediately build a large general-purpose model.
## Policy and Infrastructure Challenges
Governments face decisions about how to allocate funding among AI development, education, healthcare and digital infrastructure. Investments in computing alone may have limited value if organisations lack reliable data, trained staff or clear plans for using the technology.
Policies that support digital access, technical education, research partnerships and responsible data use can help create conditions for broader adoption. Governments also need to address privacy, cybersecurity, accountability and the risks of inaccurate AI-generated information.
Open-source models offer another route for adaptation because developers can use available model designs and customise them for particular tasks. However, these projects still require appropriate technical expertise, computing resources and safeguards.
As of October 9, 2026, the World Bank’s recommended approach is to prioritise practical AI adoption and local adaptation before committing to large-scale domestic model development. The strategy aims to help developing economies gain near-term benefits while building the infrastructure, skills and research capacity required for longer-term innovation.