The Global AI Divide: How Southeast Asia, Africa, and South America Are Falling Behind the U.S. and Europe in the AI Revolution
Implications for Recruiters and Recruiting
Apologies to my regular readers. I have not been able to write an article for a few weeks as I have been traveling in Thailand since early January. Life here seems miles away from the constant reminders that artificial intelligence and automation are changing everything. Almost nothing here is automated, and most service functions are still performed by people, often many of them.
Quite different from the changes looming for us in the United States and Europe, where AI is advancing so rapidly that many jobs will disappear or morph into new jobs that are not yet defined.
However, despite AI’s seemingly universal reach, the pace and depth of its adoption vary significantly across regions. Europe and the United States have led much of the early development and integration of AI technologies because of our innovation ecosystems, high-quality infrastructure, and significant capital investment. Leading technology firms and research institutions in these regions have driven machine learning, natural language processing, and robotics breakthroughs. Moreover, capital markets in the U.S. and Europe have consistently supported venture capital and private equity funding for AI startups, allowing rapid prototyping and commercialization.
In contrast, many countries in Southeast Asia, Africa, and South America are progressing slower due to a lack of good infrastructure and financial and regulatory constraints. While select urban centers such as Singapore and São Paulo are hubs of innovation in their respective regions, broad regional disparities remain. Large swathes of the population in many countries still face limited access to high-speed internet, expensive data costs, and insufficient digital skills.
For AI to thrive, various complex requirements must be met. These include investments in infrastructure, education and skill enhancement, government capital funding, and supportive legal frameworks. Implementing all these factors is complex and requires a government that actively promotes AI research with incentives, grants, and favorable legislation.
Below is a snapshot of how several regions are adopting AI.
Southeast Asia
Many countries in the region, including Thailand, Indonesia, and the Philippines, are in the early stages of AI integration. While these nations have made strides in digital adoption—particularly in mobile payments and e-commerce—AI-specific implementations are not yet widespread. Challenges include a shortage of specialized AI talent, inadequate infrastructure in rural areas, and constrained public-sector budgets. For instance, public initiatives to expand high-speed internet access and digital training programs are ongoing. Still, coverage gaps remain, hampering AI-driven solutions that rely on robust connectivity and skilled operators.
Africa
A handful of countries—such as Kenya, Nigeria, Egypt, and South Africa—have begun tech-centric growth. These nations benefit from a dynamic startup ecosystem, mobile innovations (such as mobile banking platforms), and gradually improving infrastructure. Nonetheless, AI-focused investment remains comparatively small. Gaps in broadband coverage, data availability, and specialized talent are evident.
One of the most pressing barriers in Africa is the limited scale of digital infrastructure. Much of the population lacks stable electricity and fast internet connections, which are requirements for sustaining AI-based services.
South America
Countries like Brazil, Chile, and Colombia have started fostering tech ecosystems, with governments providing software development and research incentives. Large banking, agriculture, and mining companies are beginning to invest in AI to become more efficient and competitive. Brazil, in particular, has many AI-driven companies focusing on natural language processing for Portuguese-language applications, and it has introduced policies to encourage innovation in fields like agribusiness tech.
However, widespread AI adoption is hampered by economic volatility and uneven infrastructural development. Currency fluctuations, varying levels of political stability, and inequality in digital access present obstacles. Many smaller firms lack the resources to integrate advanced AI solutions or to train personnel in these new skill sets.
Employment and Workforce Implications
European and U.S. labor markets are experiencing a reshuffling of employment as automation takes hold. Some jobs are being eliminated while others are transforming and require new skills. This has led to the growth of institutions offering advanced training in AI and machine learning, as well as programming and hardware development skills. There has not been any mass unemployment yet, but that may be coming as humanoid robots and AGI advance. It is clear that these regions are leading the adoption of AI, and the new administration in Washington is pushing to drive this even faster,
In Southeast Asia, Africa, and South America, slower AI adoption may temporarily shield some workers from automation. Governments, aware that alternate educational opportunities do not yet exist, may not actively encourage AI development for fear of massive unemployment. However, this also means these regions risk lagging in developing high-value technological competencies.
Over time, a digital skills gap may emerge, disadvantaging local workers in a global labor market that increasingly rewards advanced technical proficiency. Moreover, domestic firms in these regions may struggle to compete internationally if they cannot effectively harness AI, potentially affecting broader economic growth and job creation.
Economic Implications
Economically, countries that can successfully integrate AI into manufacturing, healthcare, and agriculture industries achieve higher productivity gains, attract more foreign investment, and improve GDP growth. The U.S. and many European countries have seen how AI can streamline operations, drive innovation, and create sophisticated service sectors. This means that early adopters accumulate the lion’s share of benefits from advanced technology, and late adopters contend with diminished competitiveness.
For emerging markets, however, there is a strategic opportunity. By investing in digital infrastructure and AI-focused education, countries in Southeast Asia, Africa, and South America can skip certain stages of technological development. Mobile-first solutions have already demonstrated this possibility in African banking, where many countries leapfrogged traditional financial services through mobile money platforms. Similarly, AI-driven applications in telemedicine, precision agriculture, and micro-manufacturing could address local challenges and propel economic diversification.
The contrast between Thailand and the U.S. or Europe is stark - small and even medium-sized stores are heavily staffed. Payment is either in cash or through a banking app. Each bank has its own app for making and receiving payments. Credit cards are accepted only at large stores and restaurants. While educated people may be using ChatGPT, the only connection to technology for most is their smartphone and the banking app. In general, AI and technology are miles away.
We are clearly in a world where technology is the dividing factor for employment, long-term success, and growth.