When a Kenyan company uses a mobile payment service to sell its crops on the other side of the country, it relies on a digital infrastructure that did not exist fifteen years ago. This type of transformation, multiplied by millions of economic actors, reshapes trade flows, occupations, and power dynamics between nations.
Digital Bottlenecks: What Really Holds Back Economic Transformation
The World Bank points out very concrete barriers to the digital transformation of economies: data fragmentation, insufficient connectivity, and limited cloud capacity. Without these basic building blocks, no software innovation produces measurable economic impact.
Let’s take a simple example. A logistics optimization algorithm is useless if the warehouses it needs to coordinate do not have reliable internet access. The problem is not the algorithm; it’s the bandwidth.
This distinction is important to understand why the productivity gains related to digital technology remain highly concentrated geographically. High-income countries capture most of the benefits because they already have strong infrastructures: high-speed networks, data centers, and suitable regulatory frameworks.
Middle- or low-income economies are lagging behind, not due to a lack of ambition, but due to a lack of technical foundation. An article detailing technological innovations on Claravox illustrates this asymmetry between technological adoption and actual absorption capacity.
Artificial Intelligence and Employment: Augmentation Rather Than Replacement in Developing Economies
You may have noticed that the debate around AI and employment often boils down to a binary question: will it create or destroy jobs? This framing misses the main issue.
The World Bank, in its World Development Report 2026, makes a useful distinction. It separates jobs exposed to automation from jobs likely to benefit from a productive augmentative effect. In simple terms: in many developing countries, the primary risk is not that a robot will replace a farm worker. It’s that this worker will never have access to the tool that could double their output.
This gap explains why the question has shifted. International institutions no longer ask “how many jobs will AI eliminate?” but “who will capture the productivity gains?”. The answer depends less on the technology itself than on the conditions under which it is deployed.
Prerequisites for Effective AI Deployment
The World Bank identifies several prerequisites for AI to translate into real economic gains in developing countries:
- Access to stable electricity, without which no server operates and no terminal functions reliably
- Sufficient internet connectivity to transfer volumes of data usable by learning models
- A base of digital skills within the workforce, not just among engineers but among end users
- Institutions capable of regulating data usage and protecting workers’ rights in the face of rapid changes
Without these foundations, AI remains a theoretical tool that only benefits already equipped economies.
Global Economic Divergence: Is AI Widening the Gap Between Rich and Poor Countries?
Recent work by the IMF highlights a concerning trend. Gains from AI adoption are disproportionately concentrated in high-income economies. Rather than reducing inequalities between nations, the current technological wave could amplify them.
Why? Because AI acts as a multiplier. It amplifies what already exists. A country with strong universities, research laboratories, and a network of innovative businesses reaps immediate benefits from every algorithmic advancement. A country lacking these structures sees the gap widen with each innovation cycle.
Digital as a Lever for Catching Up, Under Conditions
This divergence is not inevitable. Digital transformation also offers shortcuts. Mobile payment in East Africa is a good example: millions of people have accessed financial services without going through the traditional banking network.
The World Bank’s report on AI suggests that a decade of well-managed deployment could yield, in some developing economies, the equivalent of a century of conventional economic progress. The condition: invest first in basic infrastructure rather than in the most sophisticated applications.
Digital Services and Businesses: What is Changing in Business Models
At the business level, digitization is tangibly altering the value chain. Three changes deserve particular attention.
- The dematerialization of services allows small businesses to reach international markets without a physical presence, reducing barriers to entry in global trade
- The automation of administrative tasks (accounting, inventory management, customer relations) frees up time for higher value-added activities, provided that employees have the skills to take on these new roles
- Leveraging customer data allows for real-time adjustments to offerings but requires a data governance framework that many countries have yet to establish
Productivity gains are not decreed; they are prepared. The companies that make the most of digital technology are those that have invested upstream in training their teams and in the quality of their data.
The overall picture that emerges is one of a two-speed global economy. On one side, regions where technological innovation accelerates growth and creates new jobs. On the other, areas where the lack of basic infrastructure prevents any capitalization on these advances. The coming years will tell whether investments in connectivity and training in low-income countries will be sufficient to close this gap.
