Artificial Intelligence (AI) could become a powerful engine for growth in developing economies, boosting healthcare, education and agricultural productivity even as wealthier nations grapple with fears of job losses and technological competition, according to the World Bank‘s latest development report.
The World Development Report 2026 argues that low- and middle-income countries stand to gain more from AI than advanced economies, with less than 10% of jobs in developing nations vulnerable to automation, compared with more than one-third in high-income countries.
“AI has thrown them a lifeline they should grasp before it slips away,” Indermit Gill, the World Bank Group’s senior vice president and chief economist, wrote in the report’s foreword.
The findings come as developing economies confront slowing growth, mounting debt burdens and a sharp decline in development progress. The World Bank said AI could help reverse that trend by making critical services, including healthcare, legal support and education, accessible to millions through low-cost tools such as text messages and voice applications.
The report estimates that even under pessimistic scenarios, AI adoption would lift potential growth rates in developing countries above the sluggish averages recorded during the first half of the 2020s.
“If handled well, AI could help lift global growth to its strongest pace since the golden years of the 2000s,” Gill said.
Unlike debates in advanced economies, which have centered on whether chatbots will replace white-collar workers or whether countries can dominate the race for artificial general intelligence, the report argues that poorer nations should focus on adapting existing technologies to local needs rather than investing heavily in data centers or developing large language models.
The World Bank found that one in six jobs in developing countries could be enhanced by AI rather than eliminated. Applications include helping judges reduce court backlogs, assisting teachers with lesson preparation, supporting nurses in interpreting medical scans and providing farmers with real-time weather and crop advice.
Examples of AI adoption are already emerging across the developing world. In Bangladesh, AI-generated medical imaging increased the number of patients screened daily for diabetes-related eye diseases by 40%, according to the report. In India’s Telangana state, AI-powered weather forecasts generated savings of up to $560 for small-scale farmers.
Ghana was highlighted as an example of localized innovation. The report cited the Rori AI tutoring system, which was designed to function on basic mobile phones and weak internet networks. Delivered through text messages, the platform generated nearly a year’s worth of mathematics learning for about $5 per student.
The report noted that AI’s benefits are highly dependent on local adaptation. Systems trained in advanced economies may produce poor results when applied elsewhere. In Nigeria, for example, a medical AI model developed using data from wealthier countries recommended more laboratory tests than local conditions required.
Despite the opportunities, the World Bank warned that AI poses significant risks for developing economies, including the loss of entry-level jobs in sectors such as business services and software, growing dependence on foreign technology, rising inequality and the spread of misinformation.
The technology could also worsen electricity and water shortages as demand for computing infrastructure increases.
Rather than pursuing “AI sovereignty” by building every layer of the technology stack domestically, the report recommends that developing countries diversify their suppliers and prioritize interoperability, allowing governments and businesses to switch between AI providers without rebuilding entire systems.
The report pointed to national payment systems such as India’s UPI and Brazil’s Pix as examples of interoperable digital infrastructure that promotes competition and reduces dependence on a single provider.
For policymakers, the World Bank recommended prioritizing small, widely distributed AI applications over costly investments in large language models, arguing that adaptation, rather than technological self-sufficiency, should be the foundation of national AI strategies.
“Today’s developing economies missed the first Industrial Revolution and spent the next two centuries paying the price,” Gill wrote. “They cannot afford to miss this one.”
