Asia AI Divide: Why Developing Economies Risk Falling Behind

Tashkent, Uzbekistan (UzDaily.com) — Generative artificial intelligence is rapidly reshaping work and education across the Asia-Pacific region, but the scale of these changes will depend less on technological capability and more on countries’ ability to close a growing gap in readiness for AI adoption.

This conclusion comes from economists Roshen Fernando and Ed Kiran Reyes of the Asian Development Bank (ADB) in a new study examining growth and employment prospects in the region.

The researchers define AI readiness as an economy’s ability to use artificial intelligence and benefit from it. It is determined by the state of digital infrastructure, workforce skills, innovation capacity, institutional quality, and the structure of the economy — specifically, the share of tasks that can be performed using AI to improve productivity. Across all these dimensions, a significant gap has been identified between developed and developing economies in the region.

The analysis shows that digital infrastructure and skilled labor are the most acute challenges for developing economies.

Weak infrastructure limits access to computing power and cloud services, while a shortage of skilled professionals prevents companies from effectively deploying AI solutions. Labor market data highlights the gap: in developed economies, the share of job vacancies requiring AI-related skills is significantly higher than in developing economies, reflecting differences in business readiness for technological transformation.

Trade in goods essential for AI development — such as electronics, computers, and other digital equipment — also plays an important role. In developed economies, these products account for a much larger share of external trade compared to most developing economies in the region. Firms integrated into relevant global value chains gain broader access to critical components and global knowledge, supporting innovation.

Limited participation in this trade slows technology diffusion and raises the cost of adoption, further deepening existing inequalities.

Model simulations conducted by ADB economists show diverging growth trajectories. In developed Asia-Pacific economies, AI could add between 0.6 and 2.1 percentage points to GDP growth by 2030, before easing to 0.5–1.5 percentage points by 2040. In developing economies, the impact is smaller but more persistent — around 0.2–1.8 percentage points in 2030 and 0.1–1.6 percentage points in 2040.

However, targeted improvements in AI readiness in developing countries could add up to 0.4 percentage points of additional growth, with China showing the greatest potential in this regard.

The impact of AI on labor markets also varies significantly depending on an economy’s level of preparedness. In services, where tasks are most susceptible to automation, adoption is highest.

In Singapore, for example, AI is already used in logistics and port operations, enabling workers to remotely coordinate multiple cranes and vehicles simultaneously. Agriculture and construction remain less affected, although early applications are emerging: farmers are using AI tools to map farmland and forecast rainfall amid changing climate conditions.

In developed economies, AI is driving productivity growth in services and facilitating worker mobility between occupations. In developing countries, lower productivity gains from automation may slow business expansion and lead to job losses, although a smaller share of automatable tasks could cushion short-term impacts and give workers more time to retrain.

ADB economists outline several policy priorities. Investments in digital infrastructure and skills development should be combined with targeted incentives for the services sector, which holds the greatest potential for AI adoption.

Expanding the production of AI-enabling goods and deeper integration into global value chains could simultaneously boost industrial output and employment.

Finally, employment support programs and social protection systems remain essential tools for mitigating short-term labor market disruptions caused by technological transition.