The spread of artificial intelligence (AI) in Hungary is more than a technological or simple labour-market issue: it can determine whether the country moves onto a growth path based on higher value-added work. The key question is not only how many jobs disappear, but where the replacement higher-skilled positions are created.
Why this matters particularly for Hungary
Two structural features of the Hungarian economy make this issue especially important. First, location decisions by multinational firms carry large weight: according to the Hungarian Central Statistical Office (KSH), in 2024 foreign-controlled firms accounted for 40.6 percent of value added produced by Hungarian enterprises. If a global company automates roles in Hungary, the decision to create the new higher-value positions may be taken elsewhere.
Second, manufacturing remains a major part of the economy. KSH data from March 2026 show that the vehicle sector (including related industries) represented 26.4 percent of manufacturing output, while manufacturing of electrical equipment accounted for 8.9 percent. In these sectors, robotics, machine vision, predictive maintenance and AI-based production optimisation are becoming increasingly important.
Business service centres are a particular exposure
Business service centres (BSCs, formerly shared services centres) are another exposure point. The Hungarian Investment Promotion Agency (HIPA) identified 245 business service centres in 2025 employing 118,543 people; in 2019 there were about 120 centres and roughly 55,000 employees. Much of their work is information processing, which generative AI can rapidly reshape. The International Labour Organization (ILO) and NASK (2025) analysed nearly 30,000 tasks and concluded that about one in four workers globally are in occupations exposed to generative AI; their finding emphasises transformation of job content rather than wholesale disappearance in most cases.
Firms’ choices: reskill-and-retain or fire-and-hire
When a company automates, two simplified options appear. "Reskill and retain" assesses who can shift into new roles, identifies skill gaps and retrains existing workers. "Fire and hire" lays off affected employees and hires new ones from the market with the required skills. The latter may look cheaper in a corporate financial model, but not all costs show up in the same spreadsheet: severance, recruitment and training are borne by the firm, while unemployment, lower tax revenues and later active labour-market programmes impose costs on the state and society.
In Hungary the matter is even more significant because global firms’ location choices affect the national economy: if the advanced part of the value chain is built elsewhere, Hungary risks losing higher value-added activity.
Short-term transition must happen at the workplace, not only in universities
Although education reform is needed—EU data cited in the European Commission 2026 Country Report show the share of 25–34 year-olds in Hungary with tertiary education is 32.6 percent versus the EU average of 44.8 percent—university reforms are long-term. People whose jobs will change substantially in 2027–2030 are already in the workforce today. Therefore a large part of the adjustment in the next five years must happen on the job: company-level retraining, targeted reskilling and labour-force planning.
International precedents: Australia and Singapore
Past transitions offer lessons. When Ford, Holden and Toyota ended local car production in Australia (2016–2017), federal and state governments together with industry provided over AUD 380 million to support workers and suppliers through retraining, career counselling and job-search assistance. A 2019 evaluation found 82 percent of previously laid-off active car industry workers were re-employed.
Singapore’s Career Conversion Programmes go further and are closer to what an AI-era model may require. The Job Redesign Reskilling scheme enables firms to retrain incumbent employees for new or redesigned roles with growth potential. Under rules effective in 2026, employers can receive wage support during training equal to up to 70 percent of monthly salary (capped at SGD 5,000), and eligible groups—such as citizens and permanent residents aged 40 and over—can receive up to 90 percent support (capped at SGD 7,500). That shifts firms’ incentives by reducing the cost of retraining versus replacement.
What a Hungarian approach could look like
State co-financing in Hungary should not fund ad hoc training purchases but the specific costs of measurable workforce transitions. A company seeking support could be required to submit a strategic workforce plan detailing:
- which jobs and tasks are affected by automation;
- the expected pace of demand changes for those tasks;
- which roles will disappear, transform or expand;
- the new skills needed;
- which current employees can realistically transition;
- the size of the skill gap and retraining costs;
- what would likely happen without support.
The state would not dictate technologies or whom a company must keep. Instead, public funding would lower the firm’s cost of retraining existing staff, conditional on co-financing, auditable assumptions and measurable outcomes.
EU funds and an opportunity for Hungary
The Council of the European Union on 10 July 2026 approved Hungary’s updated recovery and resilience plan, opening the way to around EUR 10 billion of disbursements, including approximately EUR 6.5 billion in grants and EUR 3.5 billion in loans. Legal access to previously disputed funds varies across instruments, but the broader context matters: the European Commission’s 2026 country report notes EUR 5.5 billion in cohesion funding targeted to research and innovation, SME competitiveness and digitalisation, and a mid-term review reallocated roughly EUR 930 million to new strategic priorities, including skills for critical technologies. Within that, EUR 135 million of ESF+ was earmarked for developing specific skills related to STEP critical technologies.
These resources create a rare alignment: a productivity challenge, a technological disruption and available development funding. Linking them through targeted, conditional reskilling programmes could increase the chance that new AI-era, higher value-added jobs are located in Hungary.
Conclusion: competitiveness investment, not corporate charity
The costs of the AI transition need not fall solely on workers or companies. Hungary now has an opportunity to use development funding to not only attract technology and capacity, but to build the workforce able to generate higher value with new technology. Such measures would be investments in national competitiveness rather than social subsidies: in the AI era, an economy’s advantage will depend less on how cheaply it can supply labour and more on how quickly it can transform existing workers into higher value-added roles. When a multinational makes its next ‘‘hire’’ decision, that adaptability may determine how many new jobs actually remain in Hungary.
