Artificial intelligence (AI) is spreading quickly, but not evenly. Organizations in wealthier economies are moving from experiments to everyday use, while many businesses and workers in developing countries are still building the foundations needed to participate.
The AI skills gap is a major part of this divide. For developing economies, closing it could determine whether AI widens existing inequalities or becomes a source of new jobs, productivity, and competitiveness. This article examines the scale of the gap, its causes, and practical ways governments, educators, and businesses can bridge it.
Measuring the Global AI Divide
Microsoft’s AI Economy Institute tracks how generative AI use spreads across economies. Its second-quarter 2026 report found usage at 28.8% of the working-age population in the Global North, compared with 16.2% in the Global South, and noted that the gap continued to widen.
Earlier editions of the report linked slower adoption to infrastructure, language support, and economic barriers. Many widely used AI tools perform best in English and a handful of other major languages, which limits their usefulness for people working in local languages. This affects countries with many local languages, including much of Southeast Asia and Africa.
The divide is not only about access to tools. It also reflects differences in the skills needed to apply AI effectively, from basic digital literacy to data analysis and software engineering. Without these skills, access to AI tools alone produces limited economic benefit.
Why the AI Skills Gap Persists
Several structural factors slow skills development in developing countries. They often reinforce one another:
- Uneven connectivity and power: unreliable internet and electricity limit regular practice with AI tools
- Education systems that lag behind industry: curricula often take years to update, while AI tools change every few months
- Limited access to computing resources and data: training or adapting models requires infrastructure that is expensive to build locally
- Talent migration: skilled professionals often move abroad or work for foreign employers, reducing local capacity
- Cost barriers: subscription fees for advanced AI tools are high relative to local incomes
There are also encouraging signs. Microsoft’s research found that AI use in the Global South leans more toward learning activities than in the Global North, which suggests strong demand for skills when tools are available. The growth of mobile internet in many developing countries also gives more people a practical entry point to AI tools.
Practical Ways to Bridge the AI Skills Gap
Closing the gap requires coordinated action. Governments can invest in connectivity, update curricula, and support programs that teach AI skills alongside critical thinking and data ethics. Partnerships between universities and industry can keep training relevant to real jobs. Targeted programs for teachers are especially valuable, since they multiply skills across many students.
Businesses have a direct role as well. Practical steps include:
- Training existing staff on AI tools relevant to their roles, rather than relying only on new hires
- Offering apprenticeships and internships that build applied AI and software skills
- Using open-source models and tools, which can lower costs and allow adaptation to local languages
- Partnering with experienced technology firms to transfer knowledge through real projects
Countries with large technology and outsourcing workforces, such as the Philippines and India, have a strong base to build on. Upskilling these workers in AI can help them move from routine tasks toward higher-value roles in analysis, development, and AI operations. Doing so also helps these industries remain competitive as AI automates some routine work.
Turning the AI Divide Into Opportunity
The AI divide between developed and developing economies is real and, by current adoption measures, still widening. Investment in infrastructure, education, and practical training can turn AI from a source of inequality into a driver of growth and higher-value work. Organizations in developing markets that want to build AI capability benefit from working with an experienced software development team that combines delivery expertise with knowledge transfer to local talent.
