Progress in Implementing the European Union Coordinated Plan on Artificial Intelligence (Volume 2)

Artificial intelligence (AI) is becoming a strategic enabler across priority sectors in the European Union, offering tools to boost efficiency, resilience, and sustainability. In agriculture, health, manufacturing and mobility, AI supports more informed decision-making, automates complex processes, and unlocks new capabilities ranging from disease diagnostics and predictive maintenance to automated transport and precision farming. By integrating data from diverse sources and applying advanced analytics, AI can help address major challenges ranging from workforce shortages and ageing infrastructure to environmental pressures and supply chain vulnerabilities.

AI adoption remains uneven and focuses primarily on relatively narrow functions. As of 2024, adoption rates were of 8% and 11% in transport and manufacturing respectively, compared with an average 13% for the EU’s economy as a whole. Although comparable figures are not available for healthcare or agriculture, based on anecdotal evidence, uptake seems to be limited for these sectors as well. Current deployments are often limited to narrow functions, such as text mining or basic automation, or are at the pilot stage - with only a minority of organisations integrating AI at scale and into core operational processes. Larger, better-resourced actors typically lead adoption, while smaller enterprises, which tend to lag behind in digitalisation overall, struggle to keep pace due to gaps in infrastructure, skills, and investment capacity.

Sector-specific use cases highlight AI’s capacity to transform operations and service delivery in all four sectors. In agriculture, AI-powered precision farming, robotics, predictive analytics and advanced monitoring can improve yields, optimise input use and enhance climate resilience. In healthcare, AI supports advanced diagnostics, predictive hospital management, and automation of administrative tasks. In manufacturing, predictive maintenance, quality assurance, and supply chain optimisation are among the most impactful applications identified. In mobility, automated driving, AI-enabled public transport management, and intelligent freight logistics offer pathways to safer, more efficient and sustainable transport systems

A range of emerging applications point towards the next wave of AI-driven innovation. These applications include multimodal transport integration, explainable AI for human–machine collaboration in manufacturing and healthcare, drug discovery, AI-supported circular economy practices and advanced edge-computing solutions for on-site agricultural monitoring. Although many of these applications are still at an early stage, they illustrate AI’s potential for developing new products, services and business models.

A persistent shortage of AI-skilled professionals is slowing progress. Be it to deploy AI in clinical settings, integrate predictive models into factory workflows, manage AI-enabled fleets, or use advanced analytics in farming, organisations require staff with both technical expertise and sector-specific knowledge. However, the limited availability of AI-related talent and skills limits the ability to develop, customise and integrate AI tools effectively in all sectors considered. While training initiatives and multi-stakeholder partnerships are emerging, many smaller organisations struggle to access and retain specialised talent, further widening the gap with leading adopters.

Shortcomings regarding data availability, quality and interoperability constitute major barriers to scaling AI. Across all four sectors, the lack of large, high-quality, representative datasets hampers AI model development and deployment. Challenges include fragmented data infrastructure, inconsistent standards, and potential legal challenges around data sharing. Sector-specific initiatives such as the European Health Data Space, the Common European Agricultural Data Space, and open road asset data platforms (e.g. shared registries of road signs, traffic signals, or digital maps used to train automated vehicles) could improve the situation if implemented effectively and coupled with governance mechanisms that build trust and promote broad stakeholder engagement and equitable access.

High upfront costs and connectivity gaps create further adoption challenges. Significant upfront investments in equipment, connectivity, integration, and compliance deter many organisations, especially smaller players. Legacy information technology systems and business processes, and insufficient broadband connectivity - particularly in rural and peri-urban areas - limit the feasibility of deploying advanced AI tools. In this context, SME-focused support mechanisms, including advisory services (on both technical and organisational matters), simplified funding applications, and shared AI resources, are critical to widening adoption. Fostering open and interoperable AI ecosystems can also facilitate adoption by SMEs while favouring market competition and cross-sector knowledge transfer and integration.

Navigating complex and potentially overlapping regulatory frameworks, such as the EU AI Act, General Data Protection Regulation, Medical Device Regulation, as well as other sector-specific rules, can also be challenging when it comes to adopting AI. Improving availability and accessibility of safe testing and experimentation environments that can help de-risk adoption, and providing adequate sector-tailored guidance can help to remove some of these obstacles.

Targeted policies, investment, and collaboration will be essential to unlock AI’s full potential in key sectors of the EU’s economy. This involves stepping up efforts to develop infrastructure, skills, and R&D excellence, together with measures to improve data governance, interoperability, and competitive dynamics. Moreover, public-private-academic partnerships, open innovation platforms, and cross-border collaborations can accelerate AI development and adoption, particularly when grounded in sector-specific needs. Building ownership and trust through transparency and co-design with end-users, and demonstrating tangible benefits, will be key to ensuring that AI strengthens Europe’s economic competitiveness, sustainability, and societal well-being.