Lucia Russo
Jeff Mollins
Bénédicte Rispal
OECD Economic Surveys: Slovenia 2026
Abstract
Slovenia has a growing AI research base and increasing adoption in knowledge-intensive services and industry, especially among larger firms integrated into global value chains such as the automotive sector. However, scaling AI adoption and training remain challenging. Key constraints include skill shortages, regulatory uncertainty, and high electricity costs. Boosting AI uptake requires a balanced policy mix combining targeted advisory support with structural reforms that strengthen competition and incentives for AI adoption. Systematically implementing digital competences and AI skills, including foundational skills, in the education system, and a more comprehensive approach to reskilling are needed. Easing non-EU work permit rules could help tackle AI-related skill shortages.
Artificial intelligence (AI) is increasingly recognised as a transformative technology with strong potential to boost productivity and support economic growth, helping to offset the negative growth effects of an ageing and declining workforce (Calvino, Haerle and Liu, 2025[1]). AI could raise labour productivity growth by 0.2 to 1.3 percentage points annually over the next decade depending on broad and effective adoption of AI (Filippucci et al., 2025[2]).
According to the 2024 OECD.AI Index (OECD, 2026[3]), which compares national AI ecosystems using a composite measure, Slovenia performs below the OECD average (Figure 3.1). The country has comparable strengths in enabling digital infrastructure, including a high share of fibre optic cables and the presence of a national supercomputer, with plans for an additional one optimised for AI. However, weaknesses persist in translating strong per capita AI research publications into commercial outcomes, evidenced by very low scores across AI patents, domestic AI model development, and AI venture capital. Broader adoption needs to be supported to realise the economic benefits promised by AI.
2024 OECD.AI Index scores, from 0 to 1 (highest performance)
Note: The International co-operation component focuses on collaboration in AI development and governance, containing indicators on international AI initiatives, research collaborations, and adherence to international AI standards. Scores are based on a composite relative index with 1 being the highest possible value.
Source: OECD, (2026[3]), OECD.AI Index.
A new national AI strategy was adopted in March 2026, emphasising skills development, ethical considerations, and stronger science-industry linkages. Key priority areas include health and education, supported by increased funding for AI research and innovation, and further development of AI infrastructure such as data centres. The strategy also seeks to address challenges in inter-ministerial coordination and limited AI adoption in the public sector, stemming from gaps in data integration and skills.
Slovenia, starting from low 2023 levels, saw one of the fastest rises in business AI adoption, becoming a leading adopter by 2024 (Kergroach and Héritier, 2025[4]). However, AI uptake remains concentrated in larger, more productive and digitally intensive firms, particularly in information and communication services and selected manufacturing segments such as the automotive industry (Figure 3.2) (Box 3.1). Significant untapped potential remains among small firms in sectors employing large shares of workers and with substantial scope for efficiency gains (Figure 3.3). Only about one-third of medium-sized enterprises and 18% of small firms use AI, a notable gap in Slovenia, where SMEs account for most employment. Smaller firms invest less in AI and face stronger financing constraints, which may limit their ability to adopt advanced technologies (Bank of Slovenia, 2026[5]). Moreover, AI uptake in professional, scientific and technical services is below the EU average, despite this sector’s typically high AI intensity elsewhere. Beyond sectoral factors, broad conditions matter. Firms exposed to international competition, such as those integrated into global value chains, invest more in digital technologies than those in less competitive domestic markets (OECD, 2022[6]). This highlights the need for a balanced policy mix combining targeted measures with structural reforms that strengthen competition, incentives for AI adoption and access to finance for SMEs as discussed in Chapter 4 (OECD, 2025[7]).
% of enterprises with 10 or more employees using at least one AI technology, 2025
Source: OECD (2026[8]), ICT Access and Usage by Businesses (dataset), https://data-explorer.oecd.org/s/42z., accessed on 19 December 2025.
AI diffusion in Slovenia relies mainly on commercial, ready-to-use solutions, especially among smaller firms, while firm-level AI capabilities, such as in production or innovation, remain concentrated in a few large enterprises. Large firms are more likely to develop AI internally, customise existing solutions, or use external providers, whereas SMEs rarely do so (OECD, 2025[9]). This pattern supports initial uptake, but it may constrain longer-term productivity gains, which depend on tailoring AI to specific processes and business models. Key barriers for smaller firms include limited expertise or skill shortages, legal uncertainty and data protection concerns (Figure 3.4). Other key challenges limiting broader adoption include weak governance causing security and compliance risks, lack of evidence on which AI pilots work, hindering investment and learning, and unclear use cases (Competence Centre for Artificial Intelligence Slovenia, 2026[10]). Overcoming these implementation challenges requires stronger firm-level capacity building, including workforce upskilling, managerial awareness, practical support and better access to legal and technical guidance.
% of enterprises, 2025
Note: AI adoption rates across countries small (10-49 employees), medium (50-249 employees), and large (250 or more employees) enterprises. “Total” refers to enterprises with 10 or more employees.
Source: OECD (2026[8]), ICT Access and Usage by Businesses (dataset), https://data-explorer.oecd.org/s/42z., accessed on 19 December 2025.
The automotive sector in Slovenia is deeply integrated into European supply chains, with local firms increasingly adopting AI and robotics. Companies manufacturing motor vehicles and other transport equipment report levels of AI adoption above the EU average, with around one-third of firms using AI technologies, including for applications to improve production processes and quality control. The Slovenian manufacturing sector ranked eighth in 2024 for density of industrial robots, above the EU average (306 per 10 000 employees compared with the EU average of 219) and among the highest in the EU. Broader uptake is, however, constrained by limited AI skills, particularly in smaller enterprises. Effective deployment of AI also depends on access to high-quality data and computing infrastructure, which are currently under development.
Since 2016, the Strategic Research and Innovation Partnership “Smart Factories” has supported the adoption of advanced digital technologies, such as robotics, AI, ICT, and Industry 4.0 and 5.0 solutions, to automate and optimise manufacturing processes. Additionally, the Strategic Research and Innovation Partnership for Mobility connects firms, R&D institutions and other partners in the automotive value chain through the Automotive Cluster of Slovenia, a major industrial network that connects Slovenian suppliers, manufacturers, and research institutions in the automotive industry. Its Autonomous Vehicles Living Lab provides a real-world environment for testing autonomous vehicles and developing automated transport and smart mobility solutions.
Source: Eurostat (2025[11]), Artificial intelligence by NACE Rev. 2 activity; International Federation of Robotics (2024[12]); OECD (2025[13]), Progress in Implementing the European Union Coordinated Plan on Artificial Intelligence: Slovenia.
% of enterprises which ever considered using AI, 2024
Note: AI adoption rates across countries small (10-49 employees), medium (50-249 employees), and large (250 or more employees) enterprises. “Total” refers to enterprises with 10 or more employees. Black lines indicate EU levels of adoption for the respective firm sizes.
Source: Eurostat (2025[11]), Artificial intelligence by NACE Rev. 2 activity, https://ec.europa.eu/eurostat/databrowser/view/isoc_eb_ain2__custom_19134595/default/table, accessed in November 2025.
A broad set of support instruments for AI adoption and digitalisation exist, including investment incentives and corporate income tax relief (Table 3.1) (Government of the Republic of Slovenia, 2020[14]). To address legal uncertainty and data protection concerns of enterprises, the Agency for Communication Networks and Services was designated as a one-stop-shop to provide guidance on compliance obligations, especially as key provisions of the EU Artificial Intelligence Act become applicable in December 2027, affecting high-risk AI systems in areas such as hiring and education. The AI Act classifies AI systems by risk-tier. High-risk systems such as those used for recruitment must undergo rigorous conformity assessments, continuous monitoring, and strict data governance protocols. For many SMEs, the cost of legal counsel, compliance auditing, and technical implementation may be high, as may the risk of violating fundamental rights in the event of non-compliance.
The AI Competence Centre, established in February 2026, provides targeted support services to businesses to support AI uptake. It serves as a one-stop shop offering expertise, training, consulting services and connections with solution providers for businesses and the public sector. Led by a consortium including the Jožef Stefan Institute, universities, and business chambers, it supports small and large firms and the public sector, although some targeted support services such as identifying high-impact use cases are only provided to SMEs. However, SMEs, large firms and the public sector require different, specialised assistance, while the Centre’s broad mandate may constrain tailored support for SMEs with the highest need of support. Evaluating the Centre’s impact on broader AI adoption, notably on pockets of low adoption such as SMEs, will therefore be important. Slovenia could also draw on other OECD examples to better target programmes through standardised use cases, regulatory toolkits, and collaboration with sector organisations (Box 3.2). Complementing these measures with structural reforms to boost competition and reduce administrative burdens would further support the entry and scaling of AI-intensive firms, as discussed in Chapter 4.
Public funding for AI and digitalisation lacks systematic ex-post evaluation to assess whether it achieves intended outcomes, as discussed in previous Surveys (OECD, 2022[6]). Business support programmes lack clear key performance indicators to track spending and results, limiting evaluation and benchmarking. Systematic monitoring is needed to refine policies and allocate resources effectively, supported by clear priorities, targets and key performance indicators.
Relatively high AI adoption contrasts with limited AI investment, which may reflect reliance on cloud solutions, small-scale pilots, and limited access to finance for smaller and intangible-intensive firms as discussed in Chapter 4. In 2023, total AI investment per capita, which includes R&D, skills, data, equipment, and intellectual property, was estimated to be the lowest in the EU (Figure 3.5) (Fonteneau et al., 2025[15]). No AI-related venture capital (VC) investment was recorded in 2024 or 2025, pointing to weak private financing of AI. Public support for start-ups is mainly channelled through the Slovenian Enterprise Fund, which provides guarantees, credit and equity lines, as well as start-up lines for new enterprises. However, support for businesses at later growth stages is largely absent.
To address the finance gap, the government has created public VC funds backed by SID Banka, mobilising 0.5% of GDP in public funding since 2017, crowding in 0.4% of GDP in private funding. These schemes could help develop a more vibrant VC ecosystem, including for AI. For example, the French government continues to expand the Tibi investment initiative, a public-private investment vehicle designed to attract large amounts of private capital into French companies with strong growth potential. Strengthening incentives to crowd in private capital, such as allowing buyouts of government stakes in public VC funds, would further support AI investment and scale-up. Other barriers to venture capital growth include unfavourable taxation of employee stock options, complex procedures for capital increases and shareholder change as discussed in Chapter 4. A digitalised government can help reduce such barriers for entrepreneurs and venture capitalists (Box 3.3).
Several OECD countries have implemented targeted measures to accelerate AI adoption in firms by addressing sector-specific challenges. These initiatives focus on practical support mechanisms that lower barriers for SMEs and firms in traditional industries, including shared infrastructure, technical assistance, collaborative projects and access to reusable tools.
The United Kingdom’s BridgeAI supports AI adoption in under-digitised but high-growth sectors such as agrifood, construction, the creative industries, and transport and logistics combining grants, technical mentoring, challenge-led collaborations, and SME-provider matchmaking through rolling competition. Since 2023, GBP 74 million (0.003% of GDP) in grants supported businesses, leveraging GBP 49 million (0.002%) in private investment. The programme funded around 1 000 AI skills courses and promoted AI adoption by integrating British Standards Institution frameworks to help firms address data privacy and AI safety requirements (Innovate UK, 2025[16]).
Spain’s National Tourism Data Space aggregates and standardises data from multiple sources, improving access to high-quality datasets for firms, and enabling applications such as demand forecasting, pricing optimization, and personalised services. The programme started in December 2025, and evaluations have yet to be conducted.
Germany’s Silicon Economy Ecosystem helped logistics companies of all sizes use AI-based tools to automate supply chain processes between 2020 and 2024. It provided open-source data models, interfaces, and software/hardware components free of charge, enabling SMEs to adopt ready-to-use solutions rather than develop systems internally. It provided EUR 34 million to develop an open-source platform supporting collaboration and digitalisation in the logistics sector, such as digital freight documents and AI tools for planning, controlling and automating supply chain activities. By creating open standards, the project has reduced the cost of onboarding new suppliers or digitising standard logistical processes (Fraunhofer, 2025[17]; OECD, 2025[18]).
Korea’s Regional AI Innovation Hubs are equipped with large-scale AI research and demonstration facilities to foster AI transformation and advance AI use within each region’s local specialised industries (OECD, 2025[19]).
Austria’s Automated Transport – Innovation Labs provide physical and digital testing infrastructures for automated road transport where firms can trial AI-enabled mobility solutions, lowering entry barriers for smaller transport operators. Evaluations of the programme have not yet been conducted to date.
Finland’s Future Mobility Finland promotes collaborative pilot projects and ecosystem partnerships that integrate SMEs into innovation networks, supporting capability building through participation in real-world deployments. Evaluations of the programme have not yet been conducted to date.
Source: OECD (2025[7]), AI adoption by small and medium-sized enterprises: OECD discussion paper for the G7, https://www.oecd.org/en/publications/2025/12/ai-adoption-by-small-and-medium-sized-enterprises_9c48eae6.html; OECD (2026[20]), Policy Navigator, https://oecd.ai/en/dashboards/national.
Total AI investment per capita, EUR, 2023
Source: Fonteneau, F. et al. (2025[15]), “Advancing the measurement of investments in artificial intelligence”, OECD Artificial Intelligence Papers, No. 47, OECD Publishing, Paris, https://doi.org/10.1787/13e0da2f-en.
A digitalised government can play a key role in fostering a vibrant start-up and venture capital ecosystem. In Estonia, entrepreneurs can start and operate companies entirely online, with near-instant business registration and tax filing. Most interactions with public authorities take place online, reducing administrative burdens and helping attract global talent. The government’s Eesti.ai initiative further supports this ecosystem by integrating private AI solutions into e-government services across public administration, education, and health care (Government Office of the Republic of Estonia, 2026[21]). Venture capital investment is also encouraged through a zero corporate income tax rate on reinvested profits.
The public sector plays a dual role in AI diffusion, both as a regulator and as a significant user and purchaser of AI solutions. The Digital Public Services Strategy 2030 promotes the use of advanced analytics and AI across public administration, while training programmes through the Administration Academy aim to strengthen digital skills of civil servants. Guidelines on innovative public procurement explicitly encourage the consideration of AI-enabled solutions, including from SMEs and start-ups. Sector strategies in health, education, agriculture and environmental policy increasingly reference AI as part of digital transformation agendas and several projects are underway to leverage AI in these sectors (OECD, 2025[22]).
AI use in the public sector is expanding, particularly in health, with additional applications in agriculture, spatial planning, and early chatbots and virtual assistants in digital public services (Box 3.4). However, wider deployment is constrained by legacy IT systems, fragmented data architectures, limited specialised skills, compliance requirements under data protection rules and the EU AI Act, and procurement and budgeting constraints. For instance, legacy IT systems have consequences for public procurement and the selection of suppliers, often creating lock-in. To overcome these challenges, smaller, isolated pilots have been pursued in areas such as health. To realise cost and efficiency benefits of AI, policy should prioritise scaling successful applications in strategic areas such as health, supported by rigorous economic evaluation and reallocating funding from underperforming to high-impact projects.
Such a bottom-up approach could be combined with stronger co-ordination, including a central body with the capacity to provide reusable tools and support to ministries and agencies to scale proven solutions across government. The government is currently preparing an AI Action Plan, setting out responsibilities of different bodies. One way forward could be to designate a central coordinating authority for the development and provision of reusable AI tools, with responsibility for scaling proven solutions across government. Several countries centralise responsibility in dedicated institutions to accelerate public-sector AI deployment, including Norway’s Ministry of Digitalisation and Public Governance, Spain’s State Secretariat for Digitalisation and AI, and the United Kingdom’s Department for Science, Innovation and Technology (OECD, 2025[23]). In Korea, an AI Policy Development Council was set up to coordinate data collection and use across the government (OECD, 2025[19]). At the same time, improving data readiness, through modernising legacy IT systems, strengthening data governance and enabling secure data sharing, is essential.
Smart Digital Public Services project (Ministry of Digital Transformation): EUR 25 million investment to develop an interoperable ecosystem and platform for AI (including generative AI) for use across public services.
Upgrade of the Tray (Pladenj) interoperability platform with machine-learning capabilities and integration with the Once-Only Technical System.
Common Document Management System “Krpan”: integration of AI functionalities such as document categorisation, metadata extraction, text analysis, summarisation and translation.
Common document anonymisation tool to support compliant data processing across public administrations.
AI-based predictive analytics within the BI system “Skrinja” to support public-sector decision-making (e.g. on salaries, tenders and administrative procedures).
Comparative analysis of ICT contracts using AI tools to assess content, service types and unit costs.
Common smart assistant for e-government portals (eUprava, SPOT).
NpUI-funded pilot projects on AI use in cybersecurity and in police work, with a focus on lawful and ethically compliant deployment.
Area Monitoring System (AMS) in agriculture, using AI-based satellite monitoring of agricultural land.
Multiple AI applications in the health sector, supported through Horizon, NpUI and national funding instruments.
The public sector faces a shortage of professionals with the specialised AI skills required for broad adoption of the technology. According to latest available data from 2021, only about 8% of the workforce in ministries were under 35 years old, while the government competes with the private sector for scarce experts (OECD, 2021[24]). The National AI Strategy aims to increase the number of AI-trained public employees, and the government is rolling out training programmes to build AI literacy skills across the public sector. The Ministry of Digital Transformation has also issued comprehensive guidelines for the deployment of AI in the public sector in compliance with the AI Act and the General Data Protection Regulation. Expanding training and providing practical guidance on regulatory compliance, along with templates and advisory support as foreseen by the new Competence Centre for AI, would further enable broader AI use. Broader AI adoption in the public sector will also depend on access to technology and interactions with the private sector.
The research sector is keeping pace with rapid AI developments, as shown by growing AI-related scientific research publications that remain above the OECD average in per capita terms (Figure 3.6). Research is supported by the newly established Competence Centre for AI, grants and a reasonable number of training datasets, although fewer include Slovenian language data compared with major languages, reflecting Slovenia’s smaller speaker base (Figure 3.7).
However, a major weakness is translating research outcomes into innovation. AI-related patent applications remain very limited, reflecting weak knowledge transfer from academia to industry. Many AI innovations are often not patentable and rely on copyright, yet researchers cannot be remunerated for copyright income, unlike patents. University technology transfer offices often face insufficient resources to cover patenting and IP-related costs, particularly in smaller universities, and struggle to attract and retain qualified IP specialists (OECD, 2022[25]). The 2022 Research and Innovation Activities Act allows institutional co-ownership of spin-offs, but approval processes remain complex due to required government consent and legal constraints on capital investments by public institutions, including universities, under public finance rules. A government advisory council working group is currently working on a valuation framework for IP in the public sector, which could support university spin-offs. A more agile system, such as tacit consent after a fixed period, would better support spin-off creation as recommended in the 2022 Survey (OECD, 2022[6]). For instance, Austria’s 2002 university reforms let universities form companies and hold equity without government consent. Since 2010, Finnish universities can start companies without government consent or approval requirements.
Fractional publications per million people
Note: Publications are expressed here as fractional counts, where equal weights are assigned to each publication’s co-author to avoid double-counting of publications.
Source: OECD.AI (2025), data from Elsevier. Please see https://oecd.ai/en/elsevier for more details.
Firms, especially SMEs, make limited use of intellectual property from public research organisations, preferring contracts and collaboration over licensing (Stres and Pal, 2020[26]). As AI expertise is difficult to transfer and requires adaptation to specific applications, the government supports collaboration through innovation vouchers of up to EUR 10 000 for R&D services from universities and research organisations. However, eligibility requires experts to operate through a university or a private company listed in the Digital Innovation Hub Slovenia Catalogue of Experts. Abolishing this requirement would strengthen science-industry linkages, as it excludes researchers and students without a company listed in the Catalogue of Experts, but who may be more affordable and better suited to SMEs’ smaller, applied projects.
Number of open training datasets containing selected languages, thousands
Source OECD.AI (2025), using data from Hugging Face available at: https://oecd.ai/en/data?selectedArea=ai-models-and-datasets.
Despite available data, no large language models (LLMs) are currently credited to Slovenian developers (Epoch AI, 2025[27]; OECD.AI, 2025[28]). Domestic model development can help tailor AI to local needs, but it is resource intensive. Initiatives such as the Adaptive Natural Language Processing with Large Language Models project (PoVeJMo) aim to develop efficient open-access LLMs for academic and industrial use, led by the University of Ljubljana. The government also aims to facilitate secure access to international LLMs adapted for Slovenian users. A tender is currently planned to provide citizens with free access to subscription versions of international LLMs that comply with strict national and EU data protection rules.
Physical infrastructure is essential for AI research and adoption. Local compute infrastructure supports secure processing of sensitive data. However, as of 2024, no major public cloud was located in Slovenia, although proximity to nearby regions with major public clouds limits impacts for many AI applications (OECD.AI, 2025[29]). Furthermore, Slovenia hosts 21 data centres (roughly 10 per million people), above the OECD average of 8 centres per million people, and the VEGA supercomputer supports research requiring high-performance computing (Figure 3.8). Construction of new data centres, an AI factory and supercomputers is underway under the 2023 Action Plan for Open Science and the new national AI strategy for 2030 (OECD, 2025[22]). AI compute infrastructure should be aligned with current and future capacity requirements and other objectives, such as national security and privacy (OECD, 2023[30]). Overall, the current data suggest that private and research computing needs are well-supported.
Connectivity that is conducive to AI workloads is available. Slovenia has remained above the OECD average in the percentage of fibre in total fixed broadband subscriptions (Figure 3.9). Fibre connections are ideal for AI workloads due to their low latency and high bandwidth capabilities. Fibre-optic cables are important for training and developing models, but not a necessity for use and adoption of AI. However, the availability of fibre-to-the-premises (FTTHP) connections varies widely across Slovenia, particularly between urban and rural areas. In 2024, only 59% of rural households had access to FTTP, compared with 80% nationwide (Figure 3.10). This suggests that lower access to fibre connections may also be an issue for firms operating in rural areas.
Bridging the territorial connectivity gaps is essential for higher AI uptake. The government should continue to foster competition and investment, while targeted public intervention remains necessary where market forces fall short, notably in rural and remote areas. Currently, public co-financing tenders for rural broadband expansion cover up to 75% of deployment costs in “white areas,” but often fail to reflect the true, location-specific costs of fibre rollout, contributing to some tenders receiving no bids in the past decade. The government is also exploring complementary connectivity solutions, including 5G mobile broadband deployment in underserved areas and satellite broadband for remote regions, reflecting challenges from diverse terrain and dispersed settlements where fixed infrastructure is often uneconomical. Improving tender design through cost-benefit analysis, detailed cost models, and more granular broadband data could help tailor co-financing rates to local conditions and better target state aid to “white areas” as recommended in the 2022 Survey (OECD, 2022[6]).
Data centres per million people
Source: Data Centre Map (2025[31]) and OECD calculations.
With AI adoption increasing and driving energy-intensive computing, investment in energy supply and electricity grids is critical. Household electricity prices have been kept below market prices by government caps during the energy crisis, funded by taxpayers, limiting resources for investment in new energy supply. Network fees were similarly capped in 2025. Without sufficient investment in generation and grid infrastructure, future electricity costs are likely to rise as demand outpaces supply. Reliance on taxpayer-funded compensation highlights the need for a more competitive electricity market to support the energy investment needs of AI as discussed in more detail in Chapter 4.
Municipalities often lack pre-designated zones for data centres, making land-use conversion a lengthy administrative process due to limited municipal permitting capacity. Recent reforms, including the 2025 silence-is-consent rule and a new electronic permitting system, aim to accelerate approvals. Anticipatory planning, i.e., pre-assessing environmental impacts in select industrial zones, as planned by the government, could further reduce project timelines, as seen in Denmark, Ireland, and the Netherlands (Centre on Regulation in Europe, 2025[32]). This approach could be complemented by higher land-use taxes on commercial property or income tax revenue sharing to incentivise faster municipal permitting.
Fibre subscriptions per 100 inhabitants
Note: The OECD average is taken across all subscriptions within OECD countries.
Source: OECD (2025), Broadband Statistics.
% of households (total vs. rural) living in areas where fibre-to-the-premises (FTTP) connections are available
Note: FTTP refers to fibre entering the living unit (FTTH, fibre-to-the-home) and fibre reaching the building, but the last meters inside may use Ethernet or coax (FTTB, fibre-to-the-building).
Source: Eurostat.
Persistent ICT skill shortages and mismatches are slowing AI adoption (OECD, 2025[33]; Lane, Williams and Broecke, 2023[34]; OECD, 2025[9]). The share of ICT specialists reached 4.3% of employment in 2025 but remains below the EU average of 5% (European Commission, 2025[35]). Basic digital skills are weak, with only 46.7% of the population covered in 2023, well below the EU average of 55.6% and the 2030 national target of 80%. At the same time, interest in ICT and STEM education has increased: 6.2% of bachelor graduates studied ICT in 2023, above the OECD average of 5.4% and up by 87% since 2016, while about 28% of bachelor and master graduates held STEM degrees, exceeding the OECD average (OECD, 2025[36]) (Figure 3.11). However, gaps persist by education and age (European Commission, 2025[35]). Only 69% of adults with higher educational attainment have basic digital skills, compared with an EU on average 80%, highlighting underused potential and the need to prioritise re- and upskilling (Eurostat, 2026[37]).
Distribution of graduates in ICT and STEM fields (% of total graduates)
Note: For more information, please consult Education at a Glance 2024 and the OECD Handbook for Internationally Comparative Education Statistics: Concepts, Standards, Definitions and Classifications. Additional details regarding the methodology used, references to the sources, and specific notes for each country can be found in Education at a Glance 2024 Sources, Methodologies and Technical Notes.
The term STEM (science, technology, engineering and mathematics) refers to the aggregation of the broad fields of natural sciences, mathematics and statistics; information and communication technologies; and engineering, manufacturing and construction.
Source: OECD (2025[36]).
The education system provides the necessary foundational skills needed for AI use, including skills for mathematical reasoning and scientific inquiry. Slovenian students performed above the OECD average in mathematics and science in 2022, although scores declined from 2018 (Figure 3.12, Panels A and B). However, concerns are emerging in reading and creative thinking, skills needed to critically understand and use AI-generated content and data. In 2022, Slovenian students scored below the OECD average in reading and creative thinking (Panels C and D) (OECD, 2023[38]; OECD, 2024[39]). This may signal weaker ability to distinguish fact from opinion or handle abstract, counterintuitive concepts needed for safe use of AI.
Small projects for digital competences and AI skills exist, but they have not been implemented systematically in the education system so far. STEM-specific state scholarships for international students encourage individuals to pursue higher education in Slovenia and to specialise in digital and AI-related skills (The Public Scholarship, Development, Disability and Maintenance Fund of the Republic of Slovenia, 2025[40]). The MCSA COFUND SMASH programme provides AI-focused postdoctoral training with industry collaboration, attracting international PhDs who may remain in Slovenia after completion. The government is currently weaving the acquisition of digital competencies more systematically into study programmes and plans to introduce a digital subject (Box 3.5). However, a systematic approach to providing foundational skill for AI such as computational thinking, logic, and problem solving is currently missing in elementary schools (Borgonovi et al., 2023[41]). A concern is that generative AI is being used without sufficient critical distance and understanding.
Notes: Comparison countries include the six highest-performing countries.
Source: OECD PISA, 2022.
AI is projected to have significant consequences for the labour market (OECD, 2023[42]). In 2023, 12.5% of Slovenian workers faced high automation risk, and 27% performed tasked that could be done by generative AI, both roughly matching OECD averages (OECD, 2024[43]). The workforce is adapting to AI: between 2019 and 2024, 79% of LinkedIn members in Slovenia entering AI roles transitioned from non-AI roles, above the OECD average of 64% (OECD.AI, 2026[44]). But further workforce adaptation is hindered by relatively limited AI-specific skills among workers (Figure 3.13) (OECD.AI, 2025[45]). AI adoption requires both specialised technical skills such as coding and broad literacy to understand and use AI critically. Workers must manage AI-driven workflows, integrate them into operations, and apply foundational skills, such as problem-solving, creativity, and leadership, that AI cannot easily replicate (OECD, 2023[42]; Green, 2024[46]). For those with AI technical skills, formal education plays a central role in skill development: nearly 60% of Slovenian respondents to the Stack Overflow Developer survey learned to code through formal education, above the global average of 49% (Figure 3.14). However, skills necessary to use AI in the workplace have received limited attention so far. As discussed above, the formal education system does not systematically integrate foundational skill for AI in curricula, including computational thinking, logic, and problem solving.
The National Programme for the Promotion of the Development and Use of Artificial Intelligence (NpUI) identifies education system reform as a key pathway, integrating digital and AI-related learning across all educational levels (Government of the Republic of Slovenia, 2020[14]). The Slovenian Digital Education Action Plan 2021-27 supports these developments by promoting AI, digital, and basic computer science skills, particularly in secondary education (Republic of Slovenia, 2021[47]). The National Programme for Education 2023-2033 reinforces this approach by integrating a compulsory subject on informatics and digital technologies in schools and expanding digital teaching materials (OECD, 2025[22]).
AI talent concentration by country, % of LinkedIn members, 2024
Note: This chart shows the share of LinkedIn members with at least two AI engineering skills or who perform an AI occupation per country and in time. Data for OECD member countries Colombia, Japan and Slovakia are missing as they fall below LinkedIn Economic Graph coverage thresholds, please see the methodological note for more information.
Source: OECD.AI (2025[45]), data from LinkedIn Economic Graph, last updated 2025-04-07, accessed on 2026-01-26, https://oecd.ai/.
Job-specific training is another key pathway for acquiring AI skills. Learning from colleagues is particularly important, cited by 30.3% of Slovenian respondents compared with 23.6% globally (OECD.AI, 2025[48]). The government’s Digital Transformation Strategy 2030 promotes retraining and reskilling to enhance digital competences (Republic of Slovenia, 2023[49]). Between 2024 and 2025, over 25 000 adults, aged 30+ participated in non-formal digital skills programmes (Republic of Slovenia, 2024[50]). Other targeted initiatives are supporting these efforts, such as short- and long-term training programmes across a range of ICT roles, including IT support and AI development (OECD, 2025[22]). However, demand for adult training remains low overall. Adult training participation declined from 40.6% in 2016 to 26.5% in 2022, well below the EU average of 39.5%, although labour market surveys indicate a higher training uptake than adult training participation surveys (European Commission: Directorate-General for Education, Youth, Sport and Culture, 2025[51]; IMAD, 2025[52]).
Weak training incentives partly reflect a compressed wage structure that erodes links between pay, productivity and skill demand. Strong seniority-based pay and pathways to early retirement further dampen reskilling incentives. As recommended in previous Surveys, more decentralised wage setting, combining firm-level wage negotiation with central framework agreements, could strengthen incentives for workers to upgrade their skills (OECD, 2022[6]).
Financial incentives such as subsidies for individuals pursuing training programmes or to employers that offer training opportunities for digital skills, may also help address low training demand. However, public spending on training and upskilling employees and the unemployed under active employment policies is significantly lower than in most EU countries (IMAD, 2025[52]). This suggests that a more comprehensive and strategic approach to reskilling is needed to address current challenges. Initiatives in countries such as Czechia, which offers retraining subsidies to individuals, covering 82% of the cost of digital upskilling and reskilling courses, and, Austria, which provides non-repayable grants and other financial resources to SMEs which offer training linked to digital skills, could stimulate demand and further strengthen AI skills of the Slovenian workforce (OECD, 2024[53]).
Coding learning resources among survey respondents based in Slovenia
Notes: Aggregate demographic information from survey respondents is leveraged to build indicators and identify trends related to profession, education, salary and age of AI developers. The 2024 survey included 142 responses from Slovenian-based participants, out of a total of 65,437 respondents across 185 countries (OECD.AI, 2025[48]).
Source: OECD.AI (2025[48]), using data from Stack Overflow available at: https://oecd.ai/data?selectedArea=ai-demographics.
On the supply side, expanding lifelong learning and modular professional qualifications can support the broader diffusion of AI skills. Programmes should integrate AI literacy with role-specific training, including the change-management capabilities needed to embed AI into core business processes. Publicly funded micro-credentials, as foreseen by the newly adopted Higher Education Act, can facilitate rapid technical skill development, while effective implementation relies on close collaboration between workers, government, and social partners (OECD, 2024[54]). In Germany, for instance, the Qualifications Opportunities Act funds reskilling for employees whose jobs face automation or major changes due to AI. The authorities could expand continuous upskilling and reskilling programs for the workforce, prioritising AI skills.
Attracting skilled foreign workers can help address skill shortages. The net inflow of LinkedIn members with AI skills turned positive in 2024, although overall immigration of AI talent remains low (OECD.AI, 2025[55]) (Figure 3.15). Since 2025, temporary residence permits for digital nomads allow remote workers to live in Slovenia for up to 12 months (Ministry of the Interior, 2025[56]). The government also simplified visa procedures for digital-skilled individuals, similar to France (La mission French Tech, 2023[57]). However, working and residence permits for highly skilled non-EU workers remain subject to strict labour market test requirements. The EU Blue Card is Slovenia’s only dedicated pathway for highly skilled migrants. However, Slovenia is one of the few EU countries that applies a labour market test, although without a mandatory advertising period. Decisions are typically issued within five business days. The government could ease these restrictions by removing labour market tests, and automatically granting work permits to non-EU graduates, facilitating their transition from study to employment as recommended in previous Surveys (OECD, 2022[6]).
Between-country AI skills migration, per 10 thousand LinkedIn members, 2024
Note: Net flows are defined as total arrivals minus departures within the given time period. Data for OECD member countries Colombia, Japan and Slovakia are missing as they fall below LinkedIn Economic Graph coverage thresholds, please see the methodological note for more information.
Source: OECD.AI (2025[58]) data from LinkedIn Economic Graph, last updated 2025-04-07, accessed on 2026-01-22, https://oecd.ai/.
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