The European Union Coordinated Plan on Artificial Intelligence is a strategic initiative developed by the European Commission and EU Member States to promote development, deployment and use of AI technologies across the European Union. Within that framework, this report examines uptake of artificial intelligence (AI) in four key sectors: agriculture, healthcare, manufacturing and mobility. This chapter first briefly introduces the European Union Coordinated Plan on AI. It then outlines the report’s methodology and, finally, it presents key findings and recommendations pertaining to AI in each of the sectors considered.
Progress in Implementing the European Union Coordinated Plan on Artificial Intelligence (Volume 2)
Uptake in High-Impact Sectors
Abstract
This report corresponds to Output 5 “Progress report on the implementation of the Coordinated Plan” of the project “Monitoring Progress in Implementing the European Union’s Coordinated Plan on Artificial Intelligence”, which is a collaboration between the Organisation for Economic Co‑operation and Development (OECD) and the European Commission. The project aims at analysing uptake of artificial intelligence (AI) technologies across four high-impact sectors, agriculture, healthcare, manufacturing and mobility, in line with the actions presented in the EU Coordinated Plan on AI, which was launched in 2018 and reviewed in 2021 (EC, 2021[1]). These sectors (bar mobility) where also analysed in volume 1 of this publication, which focuses on actions undertaken by Member States to implement the EU Coordinated Plan on AI.
The EU Coordinated Plan on AI is a strategic initiative to promote development, deployment and use of AI technologies across the European Union. It represents a joint commitment between the European Commission and EU Member States to maximise the impact of investments in AI, foster synergies and encourage co‑operation across the European Union. The plan outlines a series of concrete actions to facilitate investment decisions, aligning AI policy in the European Union to remove fragmentation. It also aims to contribute to strengthening the global position of the European Union regarding the development and adoption of human‑centric, sustainable, secure, inclusive and trustworthy AI technologies and applications.
The plan is organised around four key pillars (Figure 1.1), each addressing crucial aspects of AI development and implementation across the European Union. A key target in the plan relates to increasing the combined annual investment in AI by EU public and private sectors to at least EUR 20 billion by 2030. At the EU level, the Digital Europe Programme, Horizon Europe, and the Recovery and Resilience Facility provide funding opportunities to build strategic digital capacities, foster AI research and innovation, and support Member State investments and reforms.
The first pillar focuses on creating the enabling conditions necessary for AI development and adoption across the European Union. It emphasises the importance of building a robust ecosystem that fosters AI innovation and deployment. A key area of action within this pillar involves establishing effective governance and co‑ordination frameworks to facilitate the acquisition, accumulation and sharing of policy insights on AI. The pillar further includes initiatives to improve the availability, sharing and access to high-quality data – essential for training AI systems – as well as efforts to enhance critical computing infrastructure to support advanced AI applications. These efforts also comprise strategic investments in semiconductors to ensure the technological foundation needed for cutting-edge AI development.
The second pillar aims to make the European Union the right place for AI excellence – from laboratory research to market applications. This involves substantial support for research and innovation in AI technologies, including funding mechanisms for promising ideas and solutions. The pillar also emphasises the importance of scaling up AI innovations, particularly supporting start-ups and small and medium-sized enterprises in bringing their AI solutions to market. Furthermore, it includes plans to establish world‑reference testing facilities, enabling rigorous evaluation and refinement of AI technologies before widespread deployment.
The third pillar is centred around ensuring that AI works for people and is a force for good in society. This human-centric approach underscores the commitment of the European Union to developing AI that aligns with EU values and ethical standards. The pillar encompasses initiatives to nurture AI talent and improve relevant skills across the workforce, preparing EU citizens for an AI-driven future. It also involves developing an AI regulatory framework to ensure trust and accountability in AI systems, while promoting the EU’s human-centric approach to AI on the global stage. A notable achievement under this pillar is the adoption of the EU AI Act in June 2024 (Regulation (EU) 2024/1689).
The fourth pillar concentrates on building strategic leadership in high-impact sectors. Recognising the transformative potential of AI across various industries, this pillar targets the application of AI in critical sectors, namely climate and environment, healthcare, robotics, public sector, law enforcement, mobility and agriculture. By focusing on these key areas, the European Union aims to leverage AI to address pressing societal challenges while also strengthening its competitive position in strategically important domains.
Methodological approach
The OECD developed the methodology underpinning the work presented in this report in close co‑operation with the European Commission. While each sector involved its own research and analysis, the work was guided by a common methodological framework. This aimed to ensure consistency in characterising AI-related transformative potential and identifying key AI use-cases, adoption trends and implementation challenges. This methodological approach, including common elements, sector-specific adaptations and key limitations, is presented next.
General research design
To capture both technological developments and on-the-ground implementation experiences, all sector studies employed a mixed-methods approach. The analysis combined two to three core components, depending on the sector:
targeted desk research to map state-of-the-art applications and policy debates
semi-structured expert interviews to gather stakeholder insights and experiences
participation in selected stakeholder workshops for the agriculture, health and mobility sectors
The OECD team conducted research between December 2024 and May 2025, with methodological tools tailored to each sector’s context and stakeholder landscape. Despite minor sectoral differences, all studies aimed to identify priority AI use-cases, capture barriers and enablers of adoption, and derive policy-relevant insights for the EU context. The workshops, organised by the European Commission, served to help the team triangulate findings and engage a broader set of actors.
Desk research and use-case selection
Each sector study began with targeted literature reviews to identify prominent AI applications and map the relevant policy landscape. Sources included academic publications, industry white papers and reports from EU and international institutions.
Based on desk research, three core use-cases were selected per sector in agreement with the European Commission. The selection process was guided by criteria such as relevance to EU priorities, frequency in the literature, availability of evidence and potential for economic and societal impact.
The selected use-cases are outlined in Table 1.1.
Expert interviews
To complement desk research, semi-structured interviews were conducted with key stakeholders in each sector. Interviews aimed to capture operational experiences, perceived benefits and challenges and public policy levers related to AI adoption.
Across the four sectors, the OECD team contacted approximately 250 prospective participants, of whom 45 agreed to be interviewed (Table 1.2). Interviewees were selected to ensure a balanced representation across industry associations and trade bodies; established enterprises and start-ups; AI solution providers and technology developers; and research organisations. Most organisations having participated in interviews are headquartered in the European Union, although several relevant organisations from outside the European Union were also involved.
All interviews were held between December 2024 and April 2025. They followed a consistent semi-structured format, allowing for both comparability and sector-specific exploration. Interview insights informed both the use-case analyses and broader reflections on organisational readiness, infrastructure needs and data availability.
Due to the voluntary nature of participation, stakeholder engagement was uneven across sectors. For example, in the mobility sector, efforts to engage representatives from the automotive industry were unsuccessful, leading to greater reliance on workshop discussions (see Workshop participation below) for the automated driving use-case. The manufacturing interviews, in turn, yielded valuable cross-sectoral insights but provided limited details for use-case analysis.
Workshop participation
In agriculture, healthcare and mobility, the OECD team supplemented interviews with evidence gathered through participation in multi-stakeholder workshops organised by the European Commission, which offered additional practitioner perspectives and broader sectoral reflections.
The following events were attended:
Mobility
“AI in Mobility and Transport: Applications, Opportunities and Barriers” – hosted by the European Commission, 5 February 2025 (European Commission, 2025[2]).
“Stakeholder Workshop on AI in the Automotive Industry” – hosted by the European Commission, 11 April 2025 (European Commission, 2025[2]).
“ERTICO Focus On: Data and AI for Safe and Resilient Intelligent Transport Systems (ITS)” – hosted by ERTICO ITS Europe, 15 April 2025 (ERTICO, 2025[3]).
Agriculture
“AI in Agriculture: Seizing Opportunities, Overcoming Challenges, Mitigating Risks, and Data” – hosted by the European Commission, 27 March 2025 (European Commission, 2025[4]).
Healthcare
“AI in Healthcare: EU Priorities and Ecosystem Synergies” – hosted by the European Commission, 28 March 2025 (European Commission, 2025[5]).
These workshops featured speakers from industry, research and policy communities. They provided valuable insights on AI use-cases, technology-related challenges, and governance and implementation practices across Member States. Related findings were integrated into the respective sector chapters and served to triangulate insights from literature and interviews.
Methodological considerations and caveats
The methodology adopted for the present report was designed to ensure a diversity of perspectives and a multi-layered evidence base, combining desk research, expert interviews and, for three of the sectors, stakeholder workshops. As with any qualitative work of research involving interviews, several considerations should be kept in mind when interpreting the findings:
Scope and representativeness: between 10‑12 interviews were conducted per sector. This sample is, however, not intended to be statistically representative. Rather, the findings reflect insights from experienced practitioners and organisations actively engaged in AI-related developments within their respective sectors.
Selection dynamics: participation in interviews was voluntary and based on stakeholder availability and interest. As a result, the findings may reflect disproportionately the perspectives of more informed or more actively engaged actors.
Sectoral engagement gaps: in some instances, outreach efforts yielded limited responses from certain subsectors. For example, as already indicated, the team was unable to conduct interviews with automotive companies. This gap was partially addressed through participation in targeted workshops that offered valuable insights on automated driving.
Variations in insight depth: in most sectors, interviews supported a detailed analysis by use-case. In manufacturing, however, interview responses offered broader reflections on cross-cutting adoption trends and challenges rather than granular insights related to the three predefined use-cases.
These caveats notwithstanding, the methodology employed enabled the collection of valuable insights across diverse stakeholder groups and sectors.
AI in agriculture
The agriculture sector plays a strategically significant role in the economy of the European Union, serving Europeans with safe and high-quality food and providing millions of jobs. However, the sector is under growing pressure to restructure in response to a decline in smaller farms, an ageing and shrinking agricultural workforce and low levels of professional agricultural training.
Although agriculture contributed just 1.3% to the gross domestic product of the European Union in 2024, it plays a strategically vital role for the region’s food security, rural development and environment. The sector’s total output reached EUR 532.4 billion in 2024, with over half coming from crop production, followed by animal products and a smaller share from services and related activities. Output is concentrated in seven Member States.
The sector faces significant structural issues, including ongoing consolidation, declining farm numbers – particularly among holdings under 5 ha – and an ageing, shrinking workforce. Agricultural employment fell by an average of 2.6% annually between 2008 and 2023, reaching 7.6 million full-time equivalent workers in 2023. Only 6.5% of the agricultural workforce is under 35 years of age, with wide variation across EU Member States. Formal education levels remain low, with many farm managers relying on experience over training. These trends raise concerns about the sector’s long-term resilience, capacity for innovation and the transition to knowledge-intensive agriculture.
AI and related digital technologies are increasingly seen as potential enablers of a more productive, efficient and resilient agricultural sector.
AI as a key enabler of precision agriculture
By integrating and analysing data collected from sensors, satellites, drones and Internet of Things (IoT) devices, AI is a key enabler of precision agriculture. This can improve decision making throughout the agricultural cycle – from land preparation and seeding to crop monitoring and harvest. These technologies offer the promise of increased yields, efficient input use and reduced environmental impact, as well as earlier detection of plant stress or more accurate forecasting of weather-related impacts. Various technologies contribute to the digitalisation of agriculture in the European Union. While it is difficult to fully disentangle the specific contribution of AI from other digital technologies, AI adds particular value through advanced pattern recognition, predictive capabilities and the automation of complex decision-making processes.
Agricultural robotics
Agricultural robotics could revolutionise EU farming by automating labour-intensive tasks. These systems integrate AI with advanced hardware to perform essential functions such as seeding, spraying, weeding, harvesting and managing livestock. Ground-based robots operate with increasing autonomy in complex environments. In livestock farming, robotic systems for milking, feeding and monitoring animal health are improving productivity and animal health, and lessening possible danger to people.
Despite growing interest, adoption of agricultural robotics in the European Union tends to be limited to large farms and high-value crops due to upfront costs, difficulty in navigating and adapting to regulatory requirements and limited platform interoperability. Stakeholders emphasise the need for improved digital connectivity, and tailored support for small and medium-sized enterprises (SMEs) to enable wider uptake.
Predictive analytics
Predictive analytics offers powerful tools to forecast crop yields, anticipate disease outbreaks, optimise input use and support breeding decisions. By analysing vast comprehensive datasets – ranging from satellite imagery and weather records to soil properties and crop performance – machine learning (ML) models can uncover patterns that traditional methods might miss. Crop yield forecasting tools enhance fertiliser and irrigation scheduling, logistics and market planning, while disease prediction tools enable timely interventions to minimise losses. In plant breeding, AI can help identify high-yield, stress-resilient genotypes by analysing genomic and environmental data.
Some of these cutting-edge applications tend to be adopted by large, tech-savvy farms and specialised operations. Broader adoption faces hurdles such as limited access to harmonised datasets, integration complexity, limited platform interoperability and uncertain returns on investment. Smaller farms encounter additional barriers, including inadequate digital skills, insufficient local data storage and processing capacity, insufficient cloud infrastructure access and resource constraints. In addition, much like some of their larger counterparts, they face the key constraint of limited broadband connectivity. Stakeholders stressed the need for open data, trustworthy AI solutions and targeted incentives to broaden adoption, particularly in lagging regions.
Advanced monitoring technologies
Advanced monitoring technologies powered by AI and computer vision hold great promise for enhancing the accuracy, efficiency and sustainability of agricultural management across crops, soil and livestock. These systems use computer vision, multispectral imaging and environmental sensors to provide real-time insights into plant health, disease symptoms, growth stages, soil conditions and animal health. This enables precise interventions that boost productivity and sustainability. Soil monitoring tools measure variables like moisture, pH and organic matter, while livestock monitoring systems track behaviour and health status.
Adoption is growing but remains uneven, with most uptake in high-value sectors or pilot projects. Barriers include the high cost of hardware, lack of standardised datasets, limited rural connectivity, platform interoperability, and regulatory and market fragmentation. Future developments aim to embed AI models into edge devices and integrate them with digital agronomist tools. To realise the full potential of these plans, stakeholders recommended investment in infrastructure, supporting open datasets, providing farmer training and fostering trusted stakeholder collaboration.
Overcoming barriers to AI adoption in agriculture
Adoption of AI in agriculture remains at early stage and uneven, with significant barriers limiting its broader use. These include limited digital infrastructure in rural areas, fragmented and inaccessible datasets, regulatory complexity and compliance costs, financial barriers and lack of digital skills among farmers. Lack of trust and uncertainty around return on investment further hinder uptake, particularly among small and resource-constrained farms.
Addressing these challenges requires co‑ordinated governance and inclusive policy frameworks that empower farmers to adopt and benefit from AI technologies. Key priorities include as strengthening rural digital infrastructure; facilitating secure data sharing; enabling access of farmers and AI developers and providers to high-quality datasets; ensuring appropriate standards are in place; and targeting support to agri-tech start-ups and SMEs.
Tailored, agriculture-specific regulatory guidance would provide clarity and ease adoption. The European Union is advancing this agenda through legislative tools like the Data Act and Data Governance Act, the Common European Agricultural Data Space (CEADS), and funding instruments such as the Common Agricultural Policy (CAP), Horizon Europe and the European Digital Innovation Hubs. The CAP provides grounds for an integrated, horizontal strategy for agricultural digitalisation at the EU level through the CAP Strategic Plan of each Member State.
Equally important is the adoption of a farmer-centred approach to AI development and deployment. Actively involving farmers in the design, testing, validation and implementation of AI tools can help ensure that solutions are practical, relevant and aligned with real-world farm management needs, as well as.
overcome trust gaps and accelerate adoption by addressing the needs of a broad and diverse range of actors. Capacity building plays a central role in this process. Training should be built around practical education, demonstration and peer learning to support less tech-savvy users. Strengthening advisory services, promoting knowledge exchange and sharing best practices can further support informed adoption.
Key recommendations to enhance AI uptake in agriculture in the European Union based on the findings from the analysis are presented in Box 1.1.
Data availability and access
Invest in open, high-quality datasets: support public collection and dissemination of soil, weather and crop performance data to lower entry barriers and spur innovation, including development of AI models tailored to the European Union’s needs.
Safeguard farmers’ control over agricultural data: provide sectoral-specific guidance on data sharing through advancement of responsible data-sharing frameworks.
Increase awareness of and stakeholder engagement in the Common European Agricultural Data Space (CEADS): promote and disseminate open agricultural data spaces to enhance accessibility of high-quality, field-level data to farmers and start-ups.
Promote standards to reduce fragmentation: encourage use of open data formats, application programming interfaces and protocols across platforms, equipment and systems.
Infrastructure and connectivity
Expand digital infrastructure: improve broadband connectivity, cloud access and affordability, and edge- computing capacity to support real-time AI analytics, particularly in underserved rural areas and by small and medium-sized farms.
Regulatory and policy frameworks
Adopt a comprehensive EU strategy on agricultural digitalisation: integrate funding, regulation, infrastructure and skills development.
Clarify regulatory requirements for the sector: provide specific guidance to facilitate compliance, particularly for start-ups, and small and medium-sized enterprises (SMEs).
Provide guidance on the interplay and application of the EU AI Act and Machinery Regulation: clarify how AI regulations apply to agricultural machinery.
Skills, trust and collaboration
Make AI accessible through user-centred design: develop intuitive interfaces and local language options, especially for older or less tech-savvy farmers.
Share best practices and success stories: leverage multistakeholder platforms and farmers’ associations to strengthen farm advisory services and cooperatives; promote demonstration projects and peer-to-peer learning to build trust among EU farmers and share information on successful use cases.
Invest in digital skills and training: deliver hands-on capacity-building, including workshops, demonstrations and peer learning among farmers. Support “farmer ambassadors” to lead by example.
Provide grants for start-ups and SMEs: develop robotics solutions tailored to European small farms and specialty crop farms.
Prioritise development and adoption of standards: facilitate data sharing and prevent monopolisation by large equipment manufacturers.
AI in healthcare
The healthcare sector is a foundational pillar of the European Union, contributing significantly to both societal well-being and economic resilience. In 2022, healthcare accounted for 10.4% of GDP in the European Union and employed more than 10% of the workforce. Yet, EU health systems face mounting structural pressures: ageing populations and rising chronic disease rates are driving up demand just as workforce shortages constrain service delivery. Meeting these challenges will require both investment in the health workforce and adoption of innovative tools, including AI-enabled ones, to enhance system resilience and operational efficiency. Digital technologies – particularly AI – offer powerful tools to address the pressures facing the healthcare sector. AI can automate administrative tasks, freeing clinicians to focus on care. It can also identify targets, design molecules and streamline clinical trials. In so doing, it can augment diagnostics, support early disease detection, personalise treatments and accelerate drug discovery by cutting both time and cost.
Diagnostics
AI-driven diagnostic tools, especially in radiology, are already increasingly used across the European Union. Machine-learning models, such as convolutional neural networks, help detect tumours, fractures and subtle patterns in scans; prioritise urgent cases; and support more accurate, consistent diagnoses. Evidence suggests that adoption is growing but remains uneven and often limited to specific hospitals or departments. Uptake beyond radiology – for example in dermatology, cardiology or endoscopy – is still relatively limited. Several barriers continue to constrain wider deployment, including challenges integrating AI tools with existing hospital IT systems (such as picture archiving and communication systems), uneven levels of digital maturity across institutions, and the absence of clear reimbursement mechanisms for AI-supported diagnostics.
Operational efficiency
Beyond diagnostics, AI is improving operational efficiency. Hospitals in the European Union are using predictive tools to manage bed occupancy, Intensive Care Unit demand and patient flow. AI also optimises surgical scheduling and automates documentation through speech recognition, natural language processing and generative AI, reducing administrative burdens and freeing time for patient care. While such applications expanded during the COVID-19 pandemic, many remain confined to pilots or isolated use cases. Structural constraints – including limited interoperability between IT systems and lack of real-time data access – continue to hinder scaling.
Drug discovery and development
AI is transforming drug discovery and development by accelerating key stages of research and development. The European Union hosts a vibrant AI-driven drug-discovery ecosystem. Companies, from major pharma firms to biotech start-ups, are using AI to design molecules, predict drug properties, optimise clinical trials and improve manufacturing. These applications reduce costs and timelines, and support more targeted therapies.
Overcoming barriers to AI adoption in healthcare
AI requires large, high-quality, interoperable datasets, yet EU health data are often fragmented. Datasets remain siloed in local or national systems. Data formats vary between institutions and Member States, making it difficult to develop and scale AI models across borders. The proposed European Health Data Space (EHDS) is an important step towards addressing health data interoperability and secure access across EU Member States. By setting out a common legal and technical framework for cross-border data exchange – supporting both primary (care) and secondary (research and innovation) uses – the EHDS could help lay the foundations for training, validating and deploying AI tools at scale. Moreover, licensing and integration expenses can be prohibitive for smaller healthcare institutions. These higher costs can be mitigated by investing in AI technologies in collaboration with other institutions or by investing in those technologies that others have tested and are proven to be fitting for a specific setting and use-case.
Limited digital and AI literacy are another important hurdle: healthcare professionals need an understanding of AI to interpret and trust AI outputs. Training for the healthcare workforce, public communication, and co-development with healthcare professionals and patients can help ensure that AI tools align with real-world clinical needs and societal expectations
Additional clarity regarding regulatory frameworks and their interplay, notably the Medical Device Regulation, the General Data Protection Regulation (GDPR) and the EU AI Act, , may also help increase uptake - especially among start-ups or academic research teams that may lack the legal and regulatory resources of larger firms.
Realising the potential of AI in healthcare will require parallel investments in infrastructure and technical capacity. At the same time, public-private collaboration will be essential to foster development of secure, open foundation models adapted to the EU context. An EU-wide registry of AI tools that meet essential standards (CE‑marked) can support transparency and market uptake. Institutionalising benchmarking protocols, such as tests of generalisability across external datasets, would promote best practices, ensure accountability and support regulatory confidence.
Key recommendations to enhance AI uptake in healthcare in the European Union based on the findings from the analysis are presented in Box 1.2.
Data availability and access
Accelerate implementation of the EHDS: fast-track the EHDS roll-out with a clear governance model that ensures seamless, responsible access to health data for research and innovation. Build on federated architecture principles to preserve privacy, maintain intellectual property protections and enable statistical power through distributed access, while encouraging use of shared semantic standards for full interoperability.
Improve health data quality and representativeness: address critical issues related to the availability of high-quality, comprehensive health datasets to support AI development. This includes adopting common data quality standards, documenting data sources rigorously, ensuring broad population coverage and implementing systematic statistical bias identification and mitigation practices.
Strengthen comprehensive health data governance frameworks within and across Member States: develop robust governance structures that ensure timely data availability, quality and interoperability, while facilitating secure cross-sector exchange among public institutions, private entities and research organisations. These frameworks should combine technical infrastructures (such as the EHDS) with clear access conditions, standardised privacy and security protocols, interoperable data-sharing rules and co-ordinated oversight across Member States.
Support open access knowledge bases: support the creation of public probabilistic medical knowledge bases to foster innovation and ensure cross-border AI compatibility. These could include structured, open medical repositories that underpin tools like probabilistic symptom checkers or AI-powered triage systems. Encourage contributors to document data provenance, population coverage and mitigation steps for any identified biases, thereby enhancing trust and usability across diverse clinical contexts.
Infrastructure
Expand AI factories: strengthen dedicated healthcare-focused AI compute and development centres with secure access to specialised datasets and high-performance computing resources.
Invest in cross-border collaboration: support collaborative research through dedicated funding.
AI tools and software
Foster public-private foundations for AI models: establish partnerships to develop foundation models in healthcare that are open, secure and designed for European contexts.
Establish an EU registry of AI approved solutions: create an EU-wide platform listing CE-marked healthcare AI systems, modelled on the Food and Drug Administration device listing in the United States. This should include training data summaries, clinical validation and post-market surveillance to improve transparency, market confidence and clinical uptake.
Institutionalise benchmarking protocols: develop EU-wide “gold standard” model evaluation tests, such as generalisability to external laboratory data. Ensure that models can perform reliably across diverse clinical settings and are not limited to their original development environment. Publish results via public leaderboards to promote transparency and reproducibility.
Regulatory frameworks
Clarify the applicability of the research exemption in the AI Act, particularly regarding pharmaceutical research and development, and internal use of AI models.
Ensure clear pathways for compliance: develop consolidated guidance to streamline requirements across the GDPR, Medical Device Regulation and AI Act, and simplify navigation for developers and healthcare institutions. Share notified‑body expertise, harmonised guidance and, where feasible, a single technical dossier.
Ensure consistent interpretation across the European Union: promote uniform application of AI-related rules through centralised guidance and regulatory co-ordination mechanisms.
Enable regulatory sandboxes: support experimental spaces where start-ups and researchers can test AI systems under regulatory supervision, including sandboxes under the joint oversight of medical device and AI regulators, while maintaining robust risk management.
Skills, trust and collaboration
Develop and retain AI talent in the healthcare sector: scale EU-wide AI education and training programmes, talent attraction schemes and reskilling initiatives to strengthen joint health and AI competences.
Support eHealth training across Member States: create transferable digital skillsets and develop European-level quality indicators to support continuing medical education and cross-border competency recognition.
Institutionalise AI literacy in healthcare quality standards: integrate operational AI literacy into hospital accreditation frameworks to ensure sustained demand for skills development and safe adoption.
Support trust building: invest in public communication campaigns and success stories that highlight responsible AI use to build societal confidence in AI in healthcare and promote responsible adoption.
Promote co-development and knowledge-sharing: encourage early engagement of stakeholders, including healthcare professionals and patients, in the design and deployment of AI technologies.
AI in manufacturing
The manufacturing sector plays a key role in the economy of the European Union. The sector employs more than 30 million people and represents approximately 16% of total value added. As the European Union strives to modernise its industrial base and strengthen competitiveness, AI to help modernise manufacturing operations, enhance productivity and reinforce resilience across value chains.
AI is increasingly recognised as a catalyst for transforming the manufacturing sector, offering powerful tools to optimise production processes, boost efficiency and enhance resilience across value chains. AI – through its ability to detect anomalies, assist decision making and learn from operational data – holds significant potential in key manufacturing domains such as predictive maintenance, quality assurance and supply chain optimisation. These applications can help firms reduce downtime, improve resource efficiency, strengthen risk management and respond more flexibly to disruptions.
While earlier forms of automation and data-driven systems have long been used in industry, recent advances in machine learning, computer vision and generative AI significantly expand the scope of potential applications. In addition to improving existing processes, AI can increasingly support more advanced functions such as simulation, design optimisation, adaptive control systems and human–machine collaboration.
Predictive maintenance
Predictive maintenance (PdM) represents one of the most mature and highest-impact AI use-cases in manufacturing. AI-enabled PdM systems enable the continuous monitoring of equipment condition and performance, supporting early anomaly detection, root cause diagnosis and accurate predictions of remaining useful life. These capabilities allow enterprises to shift from reactive or scheduled maintenance to dynamic, data-driven strategies, helping to reduce downtime and extend machinery lifespan. Digital twins and explainable AI are increasingly being used to enhance system performance and build operator trust.
Quality assurance and control
In quality assurance and control, AI can change how manufacturers monitor and improve product quality. Automated visual inspection systems are leveraged to detect surface defects, misalignments or missing components with speed and consistency exceeding that of human inspectors. Beyond defect detection, AI can be used for classification, localisation and process parameter optimisation, helping to reduce waste and improve yield. Literature also identifies emerging use-cases in AI-supported documentation, real-time reporting and the application of explainable AI (XAI) to improve human–machine collaboration on factory floors.
Supply chain optimisation
AI in supply chain optimisation supports greater agility, efficiency and sustainability across the value chain. From forecasting and procurement to transport and distribution, AI tools enable more responsive and predictive planning. Key applications include demand forecasting using time-series and unstructured data, intelligent inventory management, supplier risk evaluation and real-time routing. These capabilities can help firms respond more effectively to market volatility and geopolitical disruptions. In parallel, emerging use-cases highlight the role of AI in enabling circular economy practices through emissions-based routing, optimised resource use, improved traceability and more efficient reuse and recycling of materials.
The modest uptake of AI in manufacturing
While the strategic potential of AI is widely recognised, AI adoption in the EU manufacturing sector remains modest and highly fragmented. Between 2021 and 2024, the share of manufacturing enterprises using at least one AI technology rose from 7% to 11%. While this upward trend is encouraging, manufacturing continues to trail behind other sectors, such as information and communication, where nearly half of enterprises report AI use. Even within manufacturing, the pace of uptake varies widely. Industries such as pharmaceuticals and electronics have emerged as frontrunners, with over one-quarter of enterprises reporting AI use. In contrast, adoption remains limited in more traditional industries such as food processing, textiles and basic metals, despite their economic weight and employment share.
Firm size and access to talent play a decisive role in shaping adoption patterns. Most enterprises rely on off-the-shelf or third-party AI solutions, with only a small share engaging in in-house AI development. Internal development typically requires advanced digital capabilities, which remain concentrated in larger and younger enterprises. Smaller enterprises, which show considerably lower AI adoption rates compared to their larger counterparts, face difficulties in customising external tools, particularly when these require the integration of sensitive operational data. Overall, according to LinkedIn data, the availability of AI-skilled professionals remains limited in most EU Member States, with less than 2% of the manufacturing workforce possessing relevant expertise.
Lack of AI integration into core production
The types of AI applications deployed suggest that the technology is not yet fully integrated into core production processes. The most widely adopted AI functions in manufacturing relate to language-based or administrative tasks, including text mining, natural language generation and speech recognition. In contrast, uptake remains significantly lower for applications more closely tied to manufacturing operations, such as image recognition, robotic process automation and optimisation based on ML. These patterns indicate that while awareness and experimentation are growing, the transformative potential of AI in areas such as quality control, PdM and process automation is far from being realised at scale.
Overcoming barriers to AI adoption in manufacturing
A range of interrelated technical barriers continue to hinder broader AI adoption in manufacturing. Data-related issues, including fragmented systems, lack of interoperability, and high costs of labelling and preparation, are a major obstacle to building effective AI models. Promoting interoperable standards and trusted data-sharing mechanisms, including through industry consortia and secure data spaces, could help address these constraints, particularly for SMEs that lack sufficient data volumes.
Infrastructure gaps are another constraint, especially for older sites lacking sensor networks or access to high-performance computing resources. Financial barriers also persist, as the deployment of AI solutions often entails substantial upfront investments in equipment, system integration and specialised talent. These costs can be mitigated through shared infrastructure, collaborative investments and easier access to tested, off-the-shelf solutions. Targeted upskilling and reskilling programmes, co-developed with industry actors, are needed to enable workers and managers to effectively use and trust AI systems.
In parallel, regulatory complexity – particularly around the interpretation and implementation of GDPR and the EU AI Act – can impact disproportionately smaller manufacturers lacking dedicated legal or regulatory expertise. Clearer, sector-specific guidance could help support adoption and encourage experimentation. Organisational and workforce-related challenges such as employee resistance and managerial scepticism further compound these constraints.
Finally, realising AI’s potential in manufacturing will require parallel investments in digital infrastructure and computing capacity, alongside stronger collaboration between industry, research institutions and technology providers. Initiatives such as dedicated industrial AI testbeds and shared compute facilities could support trustworthy deployment, accelerate diffusion and strengthen confidence in AI-enabled industrial solutions.
Key recommendations to enhance AI uptake in manufacturing in the European Union based on the findings from the analysis are presented in Box 1.3.
Data access and sharing
Foster data sharing within and across sectors: promote interoperable rules, standards and mechanisms to enable broader and more secure data exchange, particularly among SMEs. Increase awareness and incentivise enterprises to join these initiatives by demonstrating benefits while mitigating security concerns (e.g. by facilitating secure data storage) and identifying successful adoption examples. Encourage industry consortia to tackle data-sharing challenges and best practices and develop training and certification.
Infrastructure
Address the specific AI infrastructure needs of the EU manufacturing sector: as part of renewed efforts to help develop dynamic AI ecosystems by bringing together compute, data and talent (e.g. AI Factories), the European Union should help address the advanced simulation data, storage and processing needs of manufacturing enterprises. Support should be provided to establish dedicated AI compute and development centres with HPC capabilities focused on simulation data for manufacturing enterprises.
Strengthen domestic AI ecosystems: support research collaboration and strategic partnerships. Help bridge the gap between academic AI research and practical enterprise applications to accelerate AI deployment in manufacturing. Encourage partnerships that drive innovation and ensure AI solutions align with real-time needs. Simplify applications for public funding, especially for SMEs and start-ups, to improve the cost-benefit balance.
Regulatory frameworks
Develop practical guidelines and standards: provide clear, practical and sector-specific guidance on the EU AI Act and related regulation. This must address concerns about high-risk classification, conformity assessment, technical documentation and monitoring.
Reduce cumulative regulatory burden and fragmentation: simplify compliance across GDPR and AI regulations and foster industrial AI collaboration, while preserving competition. Advocate for the EU AI Board to help ensure consistent AI regulation enforcement and minimise regulatory fragmentation.
Skills and trust
Strengthen AI workforce development: allocate funding towards AI upskilling and reskilling programmes for the manufacturing workforce. These programmes should be co-created with industry associations, chambers of commerce and social partners to ensure relevance.
Build trust and awareness in AI adoption: promote communication campaigns and success stories to enhance societal confidence in AI use. Encourage mentorship, training and knowledge exchange to make the most of AI advancements and available traditional manufacturing expertise.
AI in mobility
The mobility sector plays a foundational role in the economy of the European Union, enabling the movement of people and goods; supporting industrial supply chains; and connecting cities, regions and markets. However, the sector is under growing pressure to adapt to a rapidly changing landscape. Rising congestion, ageing infrastructure, the need to decarbonise in line with climate targets, growing global competition and supply chain dependencies, along with increasing demands for safety and efficiency, all highlight the urgent need for transformation.
AI is emerging as a key enabler of smarter, safer and more sustainable mobility systems. As the transport sector faces growing demand for efficiency, resilience and decarbonisation, AI can offer powerful tools to enhance decision making, automate operations and optimise resource use across all modes. Applications range from vehicle automation to streamlining public transport and modernising freight logistics, transforming how people and goods move throughout Europe.
Limited uptake of AI in transport sector
Despite its promise, AI uptake in the EU transport sector remains limited and lags more digitally mature industries. As of 2024, only 8.1% of transport and storage enterprises across the European Union reported using AI – below the overall average of 13.5% across all sectors. This places the sector not only behind highly digitised sectors such as information and communication (48.7%), but also below traditionally less tech-intensive sectors such as manufacturing (10.6%). AI adoption in transport is more in line with water and environmental management sector, where uptake also remains modest. Adoption is concentrated and largely limited to a few functions such as text mining, speech recognition and marketing. Critical operational applications, such as production processes or autonomous systems, remain rare, with fewer than 1% of enterprises reporting their use. Logistics is the only area where transport enterprises report higher uptake compared to other sectors.
AI adoption varies significantly across EU Member States. In Belgium, Denmark and Malta, over 20% of transport enterprises report using AI, compared to below-average rates in France, Italy and Poland. However, AI adoption has grown steadily across the EU27 since 2021. LinkedIn data also show a gradual rise in the number of AI-skilled professionals in transport, although overall AI talent levels in the sector remain low.
Modest investment in start-ups
Start-ups are advancing AI innovation in mobility, but investment remains modest. Across the European Union, start-ups are developing AI-enabled solutions for self-driving shuttles, predictive logistics and infrastructure monitoring. Yet, despite this activity, venture capital (VC) funding in mobility-related AI start-ups is low – just 1-3% of total EU AI VC funding investment in recent years. This share is comparable to that of the United Kingdom but significantly lower than in the United States, where mobility-related AI start-ups have attracted on average around 12% of total AI VC funding in recent years.
AI support for automated driving
In automated driving, AI can support core vehicle functions such as perception, decision making and motion control. Research highlights how AI may improve the processing of sensor inputs, interpretation of road environments and adaptation to changing conditions. Applications include deep learning and multi-sensor integration for object recognition, reinforcement learning for adaptive navigation, and predictive models to anticipate pedestrians, vehicle or other human behaviour. Further areas of exploration include co‑operative platooning, AI-supported emergency response and digital twin simulations for training and testing. AI is also being used for cybersecurity and anomaly detection tools in automated systems, although widespread deployment remains a longer-term goal.
Stakeholders – both developers and operators – emphasise the need for harmonised regulation, robust digital infrastructure and effective data-sharing frameworks to enable wider deployment of automated mobility. Data availability was also a key concern, especially regarding real-world driving data, municipal infrastructure datasets, and consistent vehicle-to-everything (V2X) standards. Experts called for increased investment in computing capacity to support safety-critical automated vehicle (AV) functions. Proposed solutions include EU-wide digital twin strategies, and open platforms for road asset data to improve AI model performance and enable collaboration across jurisdictions.
AI in public transport
In public transport, AI can help optimise operations, enable PdM and improve the passenger experience. Research highlights its potential in demand forecasting, dynamic planning and energy management. ML models can support real-time vehicle dispatch, anomaly detection, dynamic scheduling and electric fleet charging optimisation. AI is also being increasingly explored for surveillance, fare evasion monitoring and infrastructure maintenance. Emerging applications also include multimodal transport integration, where AI can facilitate real-time co-ordination between buses, trains, micromobility services and ride-hailing services.
Interviews with public transport operators suggest that most deployments are in pilot or early implementation stages. Many operators face challenges in integrating AI into legacy IT and operational systems, with limited availability of off-the-shelf tools tailored to their needs. Fragmented data infrastructure, siloed organisational structures and skills shortages within public authorities further limit uptake. Other barriers include cybersecurity concerns, high implementation costs and procurement challenges. Nonetheless, interest is growing, with operators testing fraud detection systems, smart energy scheduling and AI-powered communication systems. Several stakeholders also emphasised the importance of partnerships with universities and start-ups to co-develop context-specific AI applications and build internal capabilities.
AI for fleet management
In fleet management for freight transport, AI offers potential for improved routing, maintenance and cargo safety. Studies point to the ability of AI to improve logistics efficiency by integrating telematics, weather and cargo data. AI models can forecast demand, optimise routes and support PdM through engine and vehicle condition monitoring. At ports and logistics hubs, AI supports more efficient stowage, automated yard operations and energy use management. Monitoring tools can also help with cargo safety and emissions tracking, though many of these applications remain at experimental stages or used only by large operators.
AI adoption in fleet management remains uneven, with large players experimenting and SMEs often struggling to keep pace. While some operators are piloting sophisticated AI tools to automate the co‑ordination of vehicle and container movements within logistics terminals and to optimise scheduling, smaller firms report limited access to technical expertise, digital infrastructure and funding. Challenges related to data interoperability and the absence of common standards, particularly in multimodal freight, were also frequently cited. Stakeholders called for more targeted support to help SMEs navigate AI integration, including shared platforms, regulatory clarity on data sharing and stronger incentives to experiment with AI in cross-border logistics operations.
Key recommendations to enhance AI uptake in mobility in the European Union based on the findings from the analysis are presented in Box 1.4.
Data availability and access
Foster secure, interoperable data sharing: promote EU-level data pooling and sharing mechanisms, including by leveraging the common European mobility data spaces. Address issues of data ownership, privacy and competition by incentivising secure, privacy-preserving data-sharing protocols to encourage participation across sectors.
Invest in data standardisation and integration: fund the development and promote the alignment of standards and common technical frameworks to support the seamless exchange of data across public transport, AVs and multimodal logistics.
Build collaborative data platforms: support development of shared data management and integration platforms, providing access to clean and standardised data and facilitating co-operation between public and private actors.
Infrastructure and connectivity
Upgrade infrastructure: provide targeted funding programmes and encourage private investments to retrofit roads, terminals and facilities to support safe, efficient, AI-enabled use-cases (e.g. AV-signage friendly signage, connectivity at logistics hubs).
Expand digital and physical AI-ready digital and physical infrastructure: increase investment in V2X systems, 5G networks, IoT-enabled infrastructure and AI-compatible equipment across urban areas and logistics corridors, and road networks to enable connected and automated mobility applications.
Bridge the urban-rural divide: offer dedicated funding, grants or vouchers to equip smaller cities, ports and terminals with the infrastructure needed for AI adoption.
AI solutions, software and interoperability
Promote open-source, modular and interoperable AI ecosystems: encourage development of open AI algorithms, model weights and interoperable software to reduce vendor lock-in, support SME adoption and stimulate innovation across the transport sector.
Enable experimentation through pilots and virtual testing: fund pilot programmes and support digital twin technologies to allow public and private actors to test AI solutions in controlled environments, evaluate their impact and reduce investment risk before large-scale deployment.
Foster development of sector-specific large language models (LLMs): support the development and training of domain-specific LLMs tailored to the mobility and transport sector, leveraging proprietary European datasets to build competitive and trustworthy AI solutions.
Regulatory frameworks and testing environments
Support implementation guidance and regulatory clarity: ensure that the AI Act and related regulations are accompanied by clear, practical guidance for transport-sector stakeholders, helping organisations understand compliance requirements, enforcement expectations and permissible experimentation pathways.
Establish regulatory sandboxes and testbeds: support the creation of cross-border regulatory testbeds and national sandboxes where cities, regions and logistics operators can experiment with AI solutions, waiving compliance burdens in controlled settings and facilitating innovation while safeguarding public interest.
Skills, talent and collaboration
Invest in AI workforce development and upskilling: support EU-wide initiatives and targeted programmes to train technical and non-technical staff in AI, data science and digital transformation, addressing skills gaps across both public and private actors in transport and mobility sectors.
Strengthen public-private-academic innovation partnerships: fund collaborative research and innovation hubs that bring together transport authorities, industry, start-ups and universities to co-develop AI applications addressing operational challenges in the mobility sector.
Promote peer learning and public trust: establish knowledge-sharing networks, exchanges of best practices and awareness campaigns to foster public trust in AI applications, address societal concerns (around privacy, safety and employment) and ensure inclusive adoption of AI-driven mobility solutions across Member States.
References
[1] EC (2021), Coordinated Plan on Artificial Intelligence 2021 Review, https://digital-strategy.ec.europa.eu/en/library/coordinated-plan-artificial-intelligence-2021-review (accessed on 15 December 2024).
[6] EC (2018), “Coordinated plan on artificial intelligence”, https://digital-strategy.ec.europa.eu/en/policies/plan-ai (accessed on 25 August 2024).
[3] ERTICO (2025), “ERTICO focus on event on data and AI for safe and resilient ITS”, European Road Transport Telematics Implementation Coordination Organisation, https://erticonetwork.com/event/ertico-focus-on-event-on-data-and-ai-for-safe-and-resilient-its/ (accessed on 5 May 2025).
[5] European Commission (2025), AI in Healthcare: EU Priorities and Ecosystem Synergies.
[4] European Commission (2025), “Expert workshop: AI in agriculture”, https://digital-strategy.ec.europa.eu/en/library/expert-workshop-ai-agriculture (accessed on 10 October 2025).
[2] European Commission (2025), Workshop - AI in Automotive: Barriers, challenges and opportunities, https://digital-strategy.ec.europa.eu/en/events/ai-automotive-applications-opportunities-and-barriers (accessed on 5 May 2025).