AI Readiness: The Infrastructure Gap Encumbering Emerging Economies

March 10, 2026 — Experts warn that many emerging economies are struggling to fully adopt Artificial Intelligence because of a major gap in technological infrastructure, despite growing global interest in AI governance and innovation.

According to Douglas B. Laney, the conversation around artificial intelligence has largely shifted toward regulation, ethics, and safety. However, for many developing nations, the challenge occurs much earlier: they lack the infrastructure necessary to support AI development in the first place.

At the AI for Developing Countries Forum Bangkok Summit, experts highlighted a key issue affecting emerging economies: without sufficient computing power, software access, and institutional support, AI research and deployment cannot progress.

Infrastructure as the Foundation of AI

The problem is not always about talent or interest in AI. Instead, many countries face practical obstacles such as slow procurement processes, limited computing resources, and insufficient funding for advanced research tools.

For example, researchers in Mexico City often experience delays when accessing simulation software needed for research. In some cases, approval processes for funding can take weeks, slowing down academic and government projects that depend on advanced technology.

Technology leader Adriana Vadillo explained that bridging the technology gap between developed and developing nations requires more than policy announcements. It also involves training local experts, securing software licenses, and building strong partnerships between institutions.

Her company, Global Computing, has spent years helping universities and government agencies access advanced computing tools and training.

“My mission was to bridge the gap between more developed countries and my own, ensuring institutions could access leading technology,” Vadillo said.

Latin America’s AI Readiness Challenges

Data shows that the gap between developed and developing countries remains significant. According to the Salesforce Global AI Readiness Index, Mexico scored 15.3 points compared with a global average of 22.1 and a much higher score of 39.7 for the United States.

Across Latin America, only Brazil, Chile, and Uruguay appear among the world’s top 50 countries for AI readiness.

The region faces several major challenges, including limited access to high-performance computing infrastructure and a shortage of advanced AI training programs. In fact, eleven of the nineteen countries studied do not offer doctoral programs focused on artificial intelligence.

The Talent and Investment Gap

Another problem is investment. Although Latin America accounts for about 6.6% of global GDP, it receives only 1.12% of worldwide AI investment, according to international reports.

In addition, most of the region’s high-performance computing resources are concentrated in Brazil, which holds more than 90 percent of the infrastructure capacity. This forces many other countries to rely on foreign technology providers and cloud services.

The shortage of specialized AI professionals also contributes to the problem. For every expert in AI, there are approximately four individuals who possess only basic knowledge of the technology, limiting the ability of institutions to build advanced AI systems locally.

Build or Rent? The Infrastructure Debate

Experts are also debating whether emerging economies should invest billions of dollars in domestic data centers or rely on global cloud providers for AI computing resources.

Building local infrastructure could improve data sovereignty and national independence in technology. However, it requires significant funding and technical expertise. On the other hand, relying on cloud providers allows faster access to AI tools but increases dependency on foreign companies.

Policy leaders argue that governments must take a stronger role in supporting AI development through investments in computing infrastructure, education programs, and research partnerships.

The Future of AI Development

Despite these challenges, experts believe that emerging economies still have the potential to participate in the global AI ecosystem if they focus on long-term investments in infrastructure and talent development.

Vadillo emphasized that responsible regulation, international cooperation, and practical implementation strategies will be essential for ensuring that AI technologies contribute to economic growth and social development.

“Only through decisive action and cooperation can society shape a future where AI drives development and leaves no one behind,” she said.

Ultimately, the future of artificial intelligence may depend not only on breakthroughs in algorithms but also on whether countries build the infrastructure needed to support them.