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AI
The European Union is deploying AI across strategic sectors
This article was published initially on OECD’s AI Wonk by Antoine-Alexandre André, Guillermo Hernández and Lucia Russo.
Across major economies, trustworthy artificial intelligence is rapidly moving from high-level policy to deployment in core industries such as health, manufacturing and mobility. The European Union is positioning itself for this shift by focusing not only on innovation capacity but also on trustworthy and coordinated implementation across Member States. Gaining a deeper understanding of where AI is already being applied and gathering evidence on adoption determinants is essential to assess Europe’s competitiveness and policy readiness in that context.
The European Union is pursuing its ambition to become a global leader in trustworthy AI, moving from high-level policy to on-the-ground implementation. The OECD worked closely with the European AI Office to monitor efforts to develop trustworthy AI and promote its development across the economy, with a two-volume publication series analysing how this transition is taking place in practice. The first volume focuses primarily on national strategies, initiatives and governance mechanisms for AI in EU Member States. The second, Progress in Implementing the European Union Coordinated Plan on Artificial Intelligence (Volume 2), shifts the lens to sector-specific impact.
The report supports efforts by the European Commission and EU Member States to promote the development, deployment, and use of AI technologies across priority sectors. It draws on extensive multi-stakeholder engagement, including semi-structured interviews with industry experts and insights from dedicated stakeholder workshops. It focuses on concrete use cases, addressing specific needs in agriculture, healthcare, manufacturing and mobility, selected high-impact sectors where AI can contribute to digitalisation, sustainability and economic resilience. These sectors emerge most prominently across national AI strategies (Figure 1) as priority sectors for AI applications.
See Figure 1. Key priority sectors in national AI strategies/policies of EU Member States.
Globally, agricultural producers are increasingly turning to AI-enabled precision tools to address labour shortages, environmental pressures and resource constraints. Within Europe, similar dynamics are shaping experimentation with AI-supported farming systems aligned with environmental targets under the European Green Deal.
As AI-driven solutions help optimise resources, reduce chemical inputs and maintain yields, the EU’s agricultural sector is exploring AI deployment to address structural workforce shortages and sustainability requirements. AI-powered agricultural robots and crop and soil monitoring systems are playing a growing role in improving resource efficiency. Robs4Crops, for instance, illustrates how computer vision and sensor-based systems can enable autonomous mechanical weeding and spraying in vineyards, crop fields and apple orchards. AI4SoilHealth, in turn, is developing an open-access, AI-driven digital infrastructure to help assess and monitor soil health metrics across Europe.
However, many initiatives remain at pilot or experimental stages. Limited digital infrastructure in rural areas, fragmented and inaccessible datasets (due to the resources required to collect high-quality, diverse data across crops, soil and livestock and limited interoperability of existing public datasets), financial barriers and uncertainty around return on investment continue to constrain large-scale adoption.
Health systems worldwide are using AI to improve diagnostic accuracy and manage increasing service demand. In Europe, demographic ageing and workforce shortages are strengthening the case for deploying AI across both clinical and operational settings.
AI can help address rising costs and workforce shortages in healthcare while improving patient outcomes through faster and more accurate diagnostics.
One of the most impactful use cases is AI-enhanced medical imaging for the early detection of conditions such as cancer, supported by initiatives including the European Cancer Imaging Initiative. Similar approaches are already being deployed in the United States and Japan, where AI-assisted radiology is helping reduce diagnostic backlogs, highlighting the strategic importance of scaling comparable capabilities across Europe.
Beyond clinical care, the report explores how AI is improving hospital operations. Predictive and optimisation AI systems can help forecast patient inflows and manage bed occupancy, helping healthcare providers reduce staff pressure and waiting times. Perplex, an EU-funded initiative, illustrates how AI can help automate and optimise scheduling and resource management in the outpatient department of a Madrid hospital.
Despite this potential, barriers such as fragmented health data environments and trust challenges remain significant constraints.
Competitiveness in the manufacturing sector increasingly
Across major economies, trustworthy artificial intelligence is rapidly moving from high-level policy to deployment in core industries such as health, manufacturing and mobility. The European Union is positioning itself for this shift by focusing not only on innovation capacity but also on trustworthy and coordinated implementation across Member States. Gaining a deeper understanding of where AI is already being applied and gathering evidence on adoption determinants is essential to assess Europe’s competitiveness and policy readiness in that context.
The European Union is pursuing its ambition to become a global leader in trustworthy AI, moving from high-level policy to on-the-ground implementation. The OECD worked closely with the European AI Office to monitor efforts to develop trustworthy AI and promote its development across the economy, with a two-volume publication series analysing how this transition is taking place in practice. The first volume focuses primarily on national strategies, initiatives and governance mechanisms for AI in EU Member States. The second, Progress in Implementing the European Union Coordinated Plan on Artificial Intelligence (Volume 2), shifts the lens to sector-specific impact.
The report supports efforts by the European Commission and EU Member States to promote the development, deployment, and use of AI technologies across priority sectors. It draws on extensive multi-stakeholder engagement, including semi-structured interviews with industry experts and insights from dedicated stakeholder workshops. It focuses on concrete use cases, addressing specific needs in agriculture, healthcare, manufacturing and mobility, selected high-impact sectors where AI can contribute to digitalisation, sustainability and economic resilience. These sectors emerge most prominently across national AI strategies (Figure 1) as priority sectors for AI applications.
See Figure 1. Key priority sectors in national AI strategies/policies of EU Member States.
Globally, agricultural producers are increasingly turning to AI-enabled precision tools to address labour shortages, environmental pressures and resource constraints. Within Europe, similar dynamics are shaping experimentation with AI-supported farming systems aligned with environmental targets under the European Green Deal.
As AI-driven solutions help optimise resources, reduce chemical inputs and maintain yields, the EU’s agricultural sector is exploring AI deployment to address structural workforce shortages and sustainability requirements. AI-powered agricultural robots and crop and soil monitoring systems are playing a growing role in improving resource efficiency. Robs4Crops, for instance, illustrates how computer vision and sensor-based systems can enable autonomous mechanical weeding and spraying in vineyards, crop fields and apple orchards. AI4SoilHealth, in turn, is developing an open-access, AI-driven digital infrastructure to help assess and monitor soil health metrics across Europe.
However, many initiatives remain at pilot or experimental stages. Limited digital infrastructure in rural areas, fragmented and inaccessible datasets (due to the resources required to collect high-quality, diverse data across crops, soil and livestock and limited interoperability of existing public datasets), financial barriers and uncertainty around return on investment continue to constrain large-scale adoption.
Health systems worldwide are using AI to improve diagnostic accuracy and manage increasing service demand. In Europe, demographic ageing and workforce shortages are strengthening the case for deploying AI across both clinical and operational settings.
AI can help address rising costs and workforce shortages in healthcare while improving patient outcomes through faster and more accurate diagnostics.
One of the most impactful use cases is AI-enhanced medical imaging for the early detection of conditions such as cancer, supported by initiatives including the European Cancer Imaging Initiative. Similar approaches are already being deployed in the United States and Japan, where AI-assisted radiology is helping reduce diagnostic backlogs, highlighting the strategic importance of scaling comparable capabilities across Europe.
Beyond clinical care, the report explores how AI is improving hospital operations. Predictive and optimisation AI systems can help forecast patient inflows and manage bed occupancy, helping healthcare providers reduce staff pressure and waiting times. Perplex, an EU-funded initiative, illustrates how AI can help automate and optimise scheduling and resource management in the outpatient department of a Madrid hospital.
Despite this potential, barriers such as fragmented health data environments and trust challenges remain significant constraints.
Competitiveness in the manufacturing sector increasingly