Last Updated on October 16, 2026 by Jeff Tomas
BANGKOK – AI could help developing nations improve public services, support local businesses, and participate more fully in the digital economy. But those benefits won’t arrive automatically: access to reliable connectivity, affordable computing, relevant data, and practical skills will shape who gains and who gets left behind.
The opportunity isn’t limited to building large AI models or data centers. Countries can start with tools that address local needs, while protecting data sovereignty and investing in home-grown talent. This article looks at practical applications, flexible infrastructure choices, and the steps governments and businesses can take to share AI’s gains more widely.
Key Takeaways
- AI can help teachers, nurses, farmers, and small businesses handle practical tasks, especially when tools reflect local needs and work on everyday devices.
- The gains depend on affordable internet, reliable electricity, access to computing, relevant local-language data, and workers with useful digital skills.
- Countries can choose a mix of domestic infrastructure and international cloud services; building large data centers or frontier models is not the only starting point.
- The World Bank’s AI foundations report highlights connectivity, compute, context, and competency as core investments.
- Adoption alone won’t guarantee broad economic gains, so governments should address unequal access, train workers, and protect local data.
Why AI is a key opportunity for developing nations
AI matters beyond the technology sector because it can help people do more in essential services and everyday businesses. A teacher may use it to prepare lessons, a nurse to track patients, or a small firm to serve customers online. These gains could expand access to useful services and help local companies compete in digital markets, but they depend on who can actually use the tools.
Growing use does not mean equal access
Adoption is rising quickly in middle-income countries. In mid-2025, more than 40% of ChatGPT traffic came from middle-income countries, with Brazil, India, Indonesia, and Viet Nam among the leading sources. Yet traffic share does not show that people in every country can access AI equally.
The gap appears in use among people who are already online. In April 2025, 24% of internet users in high-income countries used ChatGPT, compared with 5.8% in upper-middle-income countries and 4.7% in lower-middle-income countries. The World Bank’s AI adoption figures also show use below 1% in low-income countries.
Reliable internet is only part of the picture. People also need affordable devices, electricity, and services that work well in their language and fit their needs. As a result, rising adoption can coexist with large gaps in who benefits. Without broader access, AI may improve services for connected communities while leaving others further behind.
The opportunity is real, but not guaranteed
AI could help workers handle routine tasks, give people easier access to information and services, and help local firms reach customers beyond their immediate markets. These are practical ways to support development, especially when tools assist workers rather than replace their judgment. However, potential use cases are not proof of economy-wide gains.
The World Bank expects near-term gains in productivity and living standards to be modest overall, with more limited effects in lower-income economies. Its Digital Progress and Trends Report 2025 stresses that benefits depend on foundations such as connectivity, computing access, relevant data, and skills. Countries also need investment that reflects local priorities, rather than adopting tools simply because they are available.
That distinction matters for public agencies and businesses deciding where to start. A language model that performs poorly in local languages, or a service that requires stable broadband many people lack, may have little practical value. Meanwhile, training and access can help workers use AI to improve existing services and strengthen local firms. The opportunity is significant, but broad gains will depend on whether countries build the conditions that let more people participate.
Where AI can improve everyday services and small businesses
AI can make everyday work easier when it handles routine tasks and helps people find useful information. In developing countries, the strongest early uses may be practical tools that support workers and local businesses, rather than expensive projects built around advanced models.
How AI can support health, education, and agriculture
The World Bank identifies examples across Brazil, Egypt, India, Nigeria, the Philippines, and Uzbekistan: teachers can use AI to plan lessons, nurses can track patients, contact-center staff can receive coaching, and farmers can get agronomic advice. These tools can help frontline workers spend less time on routine tasks and respond to local needs more effectively.
For example, a teacher could draft a lesson outline suited to a class’s level, then adapt it to local materials and curriculum. A nurse might use a digital tool to organize follow-up visits or flag missing patient information. Farmers could ask a phone-based assistant about a crop problem, then discuss its suggestions with an extension worker. The World Bank’s examples of AI applications show how these uses can fit into existing services.
However, AI output needs human review, especially when it affects health or education. A nurse should confirm clinical details against approved guidance, and a teacher should check that generated material is accurate, age-appropriate, and aligned with the curriculum. AI can assist professional judgment, but it cannot take responsibility for decisions that affect people.
Why small, practical tools may matter most
Many useful applications can run through a phone, tablet, or basic computer. These “small AI” tools may help a doctor review health data, translate customer messages, draft product descriptions, or answer common questions for a neighborhood business. A shop owner could use a simple assistant to respond to customers in a local language, while a farmer’s cooperative might share crop guidance through a familiar messaging service.
This approach can offer value without requiring a country to build a frontier-scale model or a major data center first. The World Bank describes affordable AI applications for everyday devices as one way to expand access to useful services. Its report on practical AI foundations also emphasizes that tools need relevant data, connectivity, and skills to work well.
Local fit matters as much as technical capability. A service should account for the languages people speak, the devices they own, the cost of mobile data, and whether users can access it when connectivity is weak. When a tool works within those limits, it can help a small business serve customers or help a frontline worker reach more people, without replacing the person who understands the local context.
What flexible AI infrastructure developing nations need
The World Bank’s Four Cs offer a practical framework for AI infrastructure: connectivity, compute, context, and competency. Each country should set priorities based on its needs, budget, and existing capacity. That may mean expanding broadband first, improving access to cloud computing, or investing in local-language data and workforce training.
Build reliable connectivity before expecting widespread adoption
AI services depend on more than an internet connection on paper. In low-income countries, 27% of people used the internet, compared with 93% in high-income countries. Even when people can get online, high data prices, slow connections, and frequent outages can make AI tools frustrating or impractical.
Electricity is just as important. A mobile assistant or cloud-based service needs a powered device and a working network; clinics, schools, and businesses also need dependable power to use digital tools throughout the day. When electricity cuts out or broadband is unaffordable, AI cannot reliably reach the people who might benefit from it. Governments can start by widening affordable broadband access and improving power reliability, especially in underserved communities.
Choose compute that fits local needs and budgets
Countries need dependable access to computing power, but they do not all need to build domestic data centers. As of June 2025, low-income countries had less than 0.1% of global co-location data-center capacity, according to the World Bank’s AI foundations overview. That gap can make access to advanced computing harder, but a large facility is not automatically the best first investment.
International cloud services may offer flexible capacity without the high upfront cost of building and operating a data center. However, governments and businesses should weigh ongoing fees, internet reliability, data-control rules, and the risk of depending on a small number of providers. Domestic facilities can offer more local control and may suit specific public services or data needs, but they require capital, skilled staff, reliable power, and enough demand to justify the cost. A mix of local and cloud capacity may fit some countries better than either option alone.
Treat local data and language as core infrastructure
AI tools need relevant, high-quality data to work well in local settings. A model trained mostly on English-language material may misunderstand local terms, overlook regional knowledge, or give weak answers in languages with limited digital content. Data gaps can also affect whether tools reflect local needs in areas such as agriculture, health, and education.
Building context means supporting the collection and responsible use of local data, as well as developing and testing tools in the languages people speak. Competency matters too: people need the skills to assess AI output, protect sensitive information, and adapt tools to their work. Together, these investments help countries choose AI systems that match local priorities rather than simply importing tools that may not fit.
How data sovereignty and home-grown talent can shape AI
AI systems work better when they reflect local languages and needs, and when people can trust how their information is handled. Countries can build that trust while developing the skills to create, adapt, and maintain useful tools.
Protect people’s data while building useful local tools
Data sovereignty means that countries and communities have a meaningful say in how their data is collected, stored, shared, and used. It involves more than choosing where servers sit. Clear local laws and policies should define which data an AI project needs, who can access it, how long it is kept, and what protections apply if information is exposed.
These safeguards matter when data includes health records, school information, financial details, or community knowledge. Public agencies and companies should explain how they use such information in plain language, collect only what they need, and protect it against unauthorized access. The World Bank’s AI foundations report identifies relevant local data and context as part of countries’ ability to benefit from AI.
At the same time, local control should not become a reason to block useful partnerships. Governments can work with universities, local developers, and international providers while setting clear terms for security, data access, and legal accountability.
Depending entirely on outside platforms can leave a country with limited control over costs, service changes, or how data is handled. Community input can help identify those risks early and guide tools toward local languages and real needs. Trust grows when people understand what a system does and have a voice in how it affects them.
Grow skills and keep more talent at home
AI skills begin with basic digital confidence, not just advanced technical training. The World Bank reports that fewer than 5% of people in low-income countries had basic digital skills, compared with 66% in high-income countries. Without those foundations, many people may struggle to use AI tools safely or gain access to jobs that depend on digital systems.
Schools and training programs can teach practical skills alongside core subjects, including how to assess AI-generated information and protect personal data. In workplaces, employers can offer hands-on training tied to specific tasks, such as using translation tools, analyzing records, or supporting customers. Advanced expertise matters too: local universities and research centers need opportunities to work on problems that matter to their countries, including language technology and tools for public services.
Training alone will not retain skilled people if meaningful careers are scarce. Governments, universities, and businesses can support local research, fund early-career roles, and include domestic teams in technology projects. When workers can build products, lead research, and solve local problems at home, they have more reasons to contribute to their own countries’ AI capacity.
How governments and communities can share AI’s benefits
AI adoption should begin with public needs, not the availability of a new tool. Governments can choose a specific service problem, test a solution on a limited scale, and expand only when results show better access, service quality, or cost. Broadband, relevant local-language applications, workforce skills, and safeguards need to develop together so communities can use the technology and help shape it.
Start with public problems and measure what changes
A local agency might test whether an AI assistant helps farmers get timely crop guidance, or whether a tool reduces delays in handling routine service requests. Before a pilot begins, set a baseline and define what success means. Track measures such as response time, cost per case, accuracy, and whether people who previously struggled to access the service can use it.
Then compare the results with the existing service, including the time and support needed to operate the tool. The World Bank describes practical applications such as AI-assisted lesson planning, patient tracking, and agronomic advice in several developing countries. These examples can inform local trials, but each tool still needs testing with the people who will rely on it.
If an error could affect someone’s health, education, income, or access to public benefits, a qualified person should review the output before action is taken.
Set rules that protect people and support innovation
People should know when AI is involved, what information it uses, and how to challenge a decision that affects them. Agencies also need clear responsibility for errors, along with a process to report harmful outputs, investigate bias, and correct problems. The OECD AI Principles support transparency, accountability, and trustworthy AI that respects human rights.
Rules work best when agencies can follow them in practice. Smaller local governments and businesses may not have large legal or technical teams, so guidance should focus on clear duties, basic privacy protections, risk-based review, and usable reporting procedures. Start with safeguards that match the risk of each application, then strengthen them as evidence and capacity grow.
Make sure workers and underserved groups benefit
Productivity gains do not automatically raise workers’ wages or improve their conditions. Employers and governments should involve workers before introducing AI, explain how tasks may change, and provide paid training that helps staff use and question the tools. They should also monitor whether adoption creates job losses or shifts work into lower-paid roles.
Meanwhile, people with limited internet access, less digital experience, or disabilities may face barriers that others do not. Offer non-digital ways to reach essential services, and test tools with communities that are often overlooked. This helps identify problems such as confusing language or poor performance on local devices before a system expands. Governments can then connect adoption to practical investments in connectivity and skills, rather than assuming everyone can benefit equally.
Frequently Asked Questions
AI adoption raises practical questions about cost, control, language, jobs, and what to fund first. The answers depend on local needs, but countries can make informed choices without trying to build every part of the technology themselves.
Can a country benefit from AI without building its own large model?
Yes. A country can adapt existing models or use focused applications for tasks such as translating customer messages, supporting crop advice, or organizing public-service requests. Cloud access can make these tools available without the cost of training a large model, though governments and businesses should weigh service fees, connectivity needs, and data-handling terms. Starting with a specific local problem also helps teams test whether a tool works before expanding its use.
Does data sovereignty mean avoiding foreign AI services?
No. Data sovereignty means having meaningful control over how data is collected, accessed, stored, and used, including when a service comes from another country. A government or business can set limits through procurement rules and contracts, such as restricting sensitive data, defining retention periods, and requiring clear security practices. It should also plan how to move data or switch providers if costs, terms, or service quality change.
How can AI tools work better in local languages?
Developers need language data that reflects how people actually speak and write, including regional terms, spelling variations, and common speech patterns. Native speakers should test tools for accuracy, clarity, and harmful errors, rather than judging them only by technical scores. Communities can help identify useful applications and set expectations for how their language data is collected and used. That involvement can build trust and catch problems before a tool reaches a wider audience.
Will AI take jobs in developing nations?
Some tasks will change, and some roles may shrink, while demand for new work may grow in areas such as AI support, data services, and tool development. The International Labour Organization’s global analysis of generative AI and jobs finds that job transformation is a more likely outcome than full automation for many occupations, though exposure varies. Training, worker consultation, and protections such as fair notice and access to redeployment support can help workers share in productivity gains.
What should a country invest in first?
There is no single starting point for every country. Where internet access or electricity is unreliable, improving those basics may matter more than buying advanced computing; elsewhere, digital skills or safeguards for public-sector data may be the immediate gap. Governments can assess local barriers, test a small number of useful applications, and track who benefits and what the tools cost. That evidence can guide larger investments and help prevent spending on systems that people cannot use.
