Hungary’s economy has reached an inflection point. Over the past decade and a half, growth has been driven largely by rising employment and wage growth: The employment rate has now reached 81 percent, while real wages have increased by more than 50 percent since 2008. But this extensive, labor-expansion-based model has now reached the limits of its potential. The next phase of growth can no longer rely primarily on more workers or longer working hours. Instead, the key to further growth will be a meaningful and sustained improvement in productivity.
According to analysis by the McKinsey Global Institute, AI could generate at least 15 billion euros in automation-driven value creation for Hungary by 2030, equivalent to around 6 to 7 percent of GDP. The total impact could be even larger if the country and its companies go beyond making existing processes more efficient, to using AI to build entirely new products, services, and business models, further strengthening the country’s regional competitiveness. The question, therefore, is not whether AI will transform the economy, but whether Hungary will be able to turn this transformation to its own advantage.
Hungary’s economy has solid foundations in several areas, but it continues to face structural challenges. A significant share of value add is generated by a relatively narrow group of large companies, while the productivity of small and medium-size enterprises (SMEs)—which account for a large share of employment—is more than 50 percent below that of large companies. This gap matters because SMEs provide a substantial share of Hungarian jobs. Without their productivity catching up, a lasting productivity turnaround is difficult to imagine. Many domestic companies still invest only to a limited extent in technology, knowledge, international expansion, and innovation, slowing the shift toward higher value-added activities.
The productivity turnaround is constrained not only by the structure of the economy, but also by limitations in human capital, the institutional environment, and innovation capabilities. Skill shortages, low regional mobility, the slow adaptation of the education system, and gaps in health and financial literacy all hamper the economy’s ability to adapt. Although Hungary has strong foundations in several areas—including digital infrastructure and certain elements of e-government—frequent changes in the regulatory environment, limited market competition, inefficient resource allocation, and the partially untapped innovation potential of SMEs may further slow the transition.
AI represents an important breakthrough opportunity. Not only does AI potentially transform individual sectors; it could also alter the functioning of the economy as a whole. It can accelerate decision-making, reduce administrative and coordination burdens, automate repetitive tasks, and make capabilities previously available only to the largest companies accessible to a much broader set of firms. And while AI’s impact may be especially visible in operational areas, the change goes deeper: AI also reshapes the nature of work itself, shifting activity toward problem solving, strategic thinking, collaboration, and value creation.
Yet AI will not solve Hungary’s productivity challenges on its own. Technology creates an opportunity, but the outcome will depend on whether companies, institutions, and workers are able to embed it into everyday operations. Success will be driven less by isolated technology projects than a broader operating-model transformation. In this regard, the role of the state is key. Policy makers can create the right frameworks, data and technology foundations, and incentives. The challenge for companies, meanwhile, will be to use AI not as an add-on tool, but as an engine for redesigning how they operate.
Five strategic priorities emerge for Hungary where AI could deliver tangible productivity impact relatively quickly. In an SME catch-up, AI can make advanced analytical, sales, pricing, inventory management, and administrative capabilities available to smaller firms—capabilities previously accessible only to larger companies. In education and reskilling, the goal should be to build an AI-capable Hungary: a workforce that can not only use new tools but also integrate them into day-to-day workflows. Hungary should also encourage sectoral “AI champions” in areas such as industry, energy, financial services, and telecommunications—sectors in which the country already has strong corporate foundations, data assets, and expertise. In public administration, AI can enable a more integrated, life-event-based model of service delivery, reducing administrative burdens and accelerating processes for both citizens and businesses. And in healthcare, AI can help drive a strengthening of prevention-based care, improved system efficiency, and better access through prescreening, diagnostic decision support, and early intervention.
Successful implementation depends on three core enablers: talent, operating model, and data and technology foundations. On the talent side, Hungary will need more digital specialists, broader AI literacy, and targeted reskilling. In the operating model, AI should not be treated as an isolated pilot, but as a tool for redesigning entire domains, including customer service, administration, decision support, and product development. For data and technology foundations, Hungary will need high-quality, interoperable data, scalable platforms, sufficient computing capacity, and a secure, controlled operating environment. Together, these conditions can ensure AI does not remain an experimental technology but delivers measurable productivity gains at scale.
AI is not merely a technological opportunity for Hungary; it could become one of the most important levers for improving productivity and competitiveness. Those companies that embed AI rapidly and consistently into their operations can build lasting competitive advantage. If Hungary can put the technology at the service of corporate performance, public services, human capital, and innovation, AI can become one of the most important growth engines of the next decade.
