Serbia’s technology sector is entering what may become the most important structural transition since the country emerged as a regional outsourcing and software-development hub more than a decade ago. The shift now underway is not simply technological. It is economic, infrastructural and strategic.
According to Veselin Jevrosimović, Serbia’s IT industry is gradually moving away from the outsourcing-driven “service programming” model that fueled years of export growth and toward direct development and application of artificial intelligence systems.
That statement reflects a much broader transformation already visible across global technology markets.
For years, Serbia’s IT sector expanded primarily through outsourcing and nearshoring services for foreign clients, particularly from the EU and North America. International companies increasingly relied on Serbian engineering talent for software development, enterprise systems, fintech solutions and digital product support because the country combined relatively competitive labor costs with strong engineering education and technical capability.
This model produced one of Serbia’s fastest-growing export industries.
IT services became a major source of foreign-currency inflows, while cities such as Belgrade and Novi Sad emerged as increasingly important regional technology centers. Thousands of engineers entered the outsourcing ecosystem, and Serbia positioned itself as one of Southeast Europe’s most important software-development markets.
But artificial intelligence is now beginning to fundamentally alter the economics of outsourcing itself.
Generative AI systems, autonomous coding agents and AI-assisted software development are increasingly compressing the amount of labor required for many traditional programming tasks. Activities that once required large engineering teams over multi-year timelines can now be completed dramatically faster with far smaller teams integrating AI-driven development tools.
Jevrosimović illustrated this transformation directly, explaining that projects previously requiring 120 engineers and two years of development can now be completed within several months using only a handful of specialists supported by AI systems.
This changes the entire structure of the outsourcing business model.
Traditional outsourcing economies depend heavily on billable engineering hours, large implementation teams and long-duration development cycles. AI increasingly reduces the value of scale-based programming labor while increasing the importance of higher-level architecture, AI integration, proprietary systems design and infrastructure capability.
In effect, the global IT market is gradually moving from labor-intensive programming toward AI-enhanced engineering ecosystems.
For Serbia, this creates both risk and opportunity simultaneously.
The risk is obvious. Large segments of the regional IT industry remain heavily exposed to outsourcing models dependent on repetitive development processes and scalable engineering labor. Analysts already warn that AI adoption could gradually reduce hiring growth in traditional software outsourcing roles.
Several Serbian technology observers have already noted that companies increasingly freeze hiring or quietly reduce expansion plans as AI systems absorb portions of programming workflows previously performed by junior and mid-level developers.
But the opportunity may ultimately prove much larger.
Serbia possesses one strategic advantage that becomes increasingly valuable in the AI era: engineering density relative to economic size.
The country has built a strong base of highly skilled software engineers, mathematicians, infrastructure specialists and technical universities capable of adapting relatively quickly to AI-oriented development models. Unlike purely low-cost outsourcing destinations, Serbia retains meaningful technical depth that can support movement up the digital value chain.
This is why the transition toward AI development itself matters so much.
Countries that remain dependent entirely on service outsourcing may face increasing pressure as AI automates larger portions of software production. By contrast, jurisdictions capable of building proprietary AI systems, infrastructure integration, advanced engineering platforms and AI-enabled industrial applications may retain much stronger long-term competitiveness.
Serbia increasingly appears to be trying to position itself in the second category.
The country already invested heavily into data-center infrastructure, supercomputing capability and AI-related institutional initiatives over recent years. The government’s AI strategy and growing interest in high-performance computing reflect recognition that the next phase of digital competitiveness will depend not only on programming labor, but also on infrastructure, energy availability and advanced computational ecosystems.
This shift also intersects directly with broader European trends.
The European Union increasingly wants greater digital sovereignty, AI capability and computing infrastructure inside Europe itself. Yet many Western European economies face mounting constraints tied to energy pricing, grid saturation, permitting complexity and infrastructure bottlenecks.
This creates an emerging opening for countries capable of combining:
engineering talent, scalable electricity infrastructure, lower operational friction and proximity to EU markets.
Serbia increasingly fits that profile.
The relationship between AI and energy is becoming especially important.
Modern AI infrastructure consumes enormous amounts of electricity. Hyperscale data centers, AI model training clusters and future autonomous computing systems require stable, scalable power systems increasingly linked to renewable-energy integration and long-term energy planning.
Jevrosimović specifically referenced the scale of future AI energy demand, noting that some AI infrastructure systems already consume electricity volumes comparable to entire national economies.
This is not an exaggeration.
Globally, major AI companies are increasingly investing directly into power infrastructure, renewable generation and advanced energy systems because electricity availability is becoming one of the defining competitive constraints of the AI economy.
For Serbia, this creates a strategic intersection between:
digital infrastructure, renewable energy, engineering education and industrial modernization.
The country’s growing renewable-energy potential, regional grid position and engineering base could theoretically support development of AI-oriented infrastructure ecosystems far beyond traditional outsourcing alone.
The broader transformation also affects labor markets.
Demand for AI specialists in Serbia is already rising rapidly, extending beyond technology companies themselves into banking, insurance, telecoms and industrial sectors.
This reflects another important structural shift.
AI is no longer functioning purely as a technology industry trend. Increasingly, it is becoming embedded into manufacturing, finance, logistics, industrial automation, energy management and infrastructure systems themselves.
As a result, the distinction between “IT sector” and “industrial sector” is gradually blurring.
Serbia’s future competitive advantage may therefore depend not simply on exporting programming services, but on integrating AI into:
industrial systems, energy infrastructure, logistics platforms, manufacturing processes and digital public administration.
This creates opportunities far beyond classical outsourcing.
AI-integrated industrial engineering, smart-energy infrastructure, autonomous logistics systems, digital manufacturing optimization and AI-enabled infrastructure management could all become future growth areas if Serbia successfully combines its engineering talent with broader infrastructure modernization.
The challenge, however, is that the transition requires significant strategic adaptation.
Traditional outsourcing models generated relatively predictable export revenue with limited infrastructure requirements. AI-oriented ecosystems require:
high-performance computing capacity, data infrastructure, energy reliability, advanced education systems, specialized engineering capability and long-term investment frameworks.
Competition is also intensifying globally.
The United States and China remain far ahead in AI scale and infrastructure deployment, while Europe itself still struggles to define a coherent AI industrial strategy. Jevrosimović openly criticized the pace of European regulatory adaptation around AI, arguing that excessive caution risks leaving Europe behind the US and China in practical deployment capability.
That tension increasingly shapes Serbia’s strategic position as well.
The country sits between European regulatory alignment and the need for faster technological adaptation in order to remain competitive inside the emerging AI economy.
The broader implication is that Serbia’s IT sector may be approaching the end of its first developmental era.
The outsourcing-driven expansion model that defined the past decade is unlikely to disappear entirely. But the next phase of growth increasingly appears likely to revolve around:
AI integration, infrastructure systems, advanced engineering capability, digital sovereignty and energy-linked computing ecosystems.
The countries that succeed in the AI era may not necessarily be those with the cheapest programmers. Increasingly, they will be the ones capable of combining engineering talent, energy infrastructure, computational capacity and industrial integration into scalable digital ecosystems.
Serbia appears increasingly aware that this transition has already begun.
