AI in European Industries: Forward-Looking Analysis of the EU R&I Ecosystem (Part 3)

This is Part 3 of EuroComply's series on the European Commission's futures study on artificial intelligence and the EU research and innovation ecosystem. Part 1 introduced the study's methodology and baseline findings. Part 2 examined the talent and infrastructure dimensions. Part 3 turns to sector-by-sector AI investment trends, the competitive gap with the United States and China, and how the EU AI Act's compliance architecture creates cost pressures that directly affect SME competitiveness in European industrial AI.

Sector-by-Sector AI Investment Trends in European Industry

European industrial AI investment is heavily concentrated in four sectors: automotive and mobility, pharmaceutical and life sciences, manufacturing automation, and financial services. Collectively these sectors account for approximately 70 percent of private sector AI investment in the EU, according to the Commission's futures study.

The automotive sector leads in absolute terms, driven by the transition to software-defined vehicles and the embedded AI required for driver assistance and autonomous functions. German and French OEMs have substantially expanded their AI research budgets, though a significant share of that spending flows to US and Asian AI chip suppliers and cloud infrastructure providers rather than to European AI model developers. This creates a structural import dependency in the most AI-intensive sector of European manufacturing.

Pharmaceutical and life sciences AI investment is growing fastest in percentage terms, propelled by drug discovery applications, clinical trial optimisation, and personalised medicine platforms. Here European R&I institutions retain competitive depth through their clinical data infrastructure and regulatory relationship with the European Medicines Agency.

Manufacturing automation AI — industrial vision systems, predictive maintenance, and robotics — is an area where European deep-tech SMEs have genuine strength. Companies in Germany, Sweden, and the Netherlands have built internationally competitive AI-enabled automation products. However, the go-to-market for these companies increasingly runs through US or Chinese platform ecosystems, creating distribution dependencies that undermine the sovereignty dimension of European industrial AI.

The Competitive Gap with the United States and China

The competitive gap with the US and China in foundation model development is well documented and has widened since 2023. European organisations do not operate any frontier large language model at scale comparable to GPT-4 class systems or China's leading models. The futures study attributes this primarily to three structural factors: the absence of a European hyperscaler with compute resources at the relevant scale, fragmentation of training data across linguistic and institutional boundaries, and a venture capital ecosystem that historically exits at Series B rather than funding the decade-long capital commitments that frontier AI requires.

In applied AI — adapting existing foundation models to industrial use cases — the gap is narrower and, in some domains, reversed. European industrial AI for safety-critical applications benefits from the regulatory credibility that comes with CE marking and conformity assessment under established product safety directives. This credibility premium is not captured in investment league tables but represents a real competitive advantage in regulated markets globally.

The futures study projects that without structural intervention, Europe's share of global AI value creation will decline from an already modest 8 percent to approximately 5 percent by 2030 in the high-investment scenarios pursued by the US and China.

How AI Act Articles 9 and 62 Create Compliance Costs Affecting SME Competitiveness

Article 9 of the EU AI Act requires providers of high-risk AI systems to establish a risk management system that is proportionate to the risk level, takes account of reasonably foreseeable misuse, and is updated throughout the system's lifecycle. For an SME producing an AI-enabled predictive maintenance system for industrial machinery — a common product in European deep-tech — this means documented risk identification at design, testing, and deployment stages, updated whenever the system is materially modified.

The recurring compliance burden is the more significant concern. Each software update that modifies the system's performance must be assessed under Article 9 to determine whether it constitutes a substantial modification triggering a new conformity assessment. For SMEs releasing continuous updates — which is standard practice in software-defined industrial AI — this creates a compliance overhead that scales with development velocity rather than with the company's size or resources.

Article 62 introduces post-market monitoring obligations: providers must implement a system for actively collecting and reviewing performance data, report serious incidents to market surveillance authorities under Article 73, and maintain records for ten years after market placement. These obligations extend into the operational life of the product, creating long-tail compliance costs that are difficult to provision for in early-stage company financial planning.

The futures study estimates compliance costs for a typical industrial AI SME at between 3 and 8 percent of annual revenue in the first year of full AI Act applicability, declining to 1 to 3 percent in steady state once documentation infrastructure is established. These figures are not trivial for companies operating at pre-profitability scale.

Data Act Article 5 and AI Training Data

The EU Data Act, which entered into force in September 2023, provides in Article 5 that connected product manufacturers must ensure that users can access data generated by their products. For industrial AI applications trained on machine sensor data, this creates both an opportunity and an obligation. Manufacturers deploying AI-enabled equipment must design data access interfaces that permit users to retrieve and port their operational data — data that may itself be valuable training material for the next generation of the manufacturer's AI system.

The interaction between Data Act Article 5 and AI Act Article 10 data governance requirements creates a tension: Article 10 requires AI providers to implement controls on training data quality and provenance, but Article 5 of the Data Act ensures that the same data flows more freely across the industrial ecosystem. Compliance programmes need to address both dimensions simultaneously, mapping data flows from connected equipment through AI training pipelines with attention to both data quality controls and user access rights.

Horizon Europe Funding and Strategic Recommendations

Horizon Europe's Cluster 4 on Digital, Industry and Space allocates approximately €15 billion to AI and advanced manufacturing over the 2021–2027 period. The futures study identifies key funding priorities for the remaining calls: AI for sustainable manufacturing, human-robot collaboration systems, and trustworthy AI infrastructure for cross-border industrial data spaces.

Strategic recommendations from the futures study for EU industrial AI competitiveness include: accelerating the deployment of European AI testing and experimentation facilities (AI TEFs) under Article 57 of the AI Act to reduce the cost of pre-market validation for SMEs; establishing sectoral AI compliance centres co-funded by member states and industry associations; and creating a European industrial AI data commons that enables collective training data curation without requiring individual SMEs to build proprietary data infrastructure.

Frequently Asked Questions

Which EU industrial sectors face the highest AI Act compliance costs? The highest compliance costs fall on sectors where AI systems most readily qualify as high-risk under Annex III of the AI Act: employment decision support, creditworthiness assessment, critical infrastructure management, and medical device AI. Industrial automation AI in non-safety-critical applications generally falls below the high-risk threshold, reducing mandatory compliance obligations significantly.

What does Article 62 post-market monitoring require in practice? Article 62 requires providers to establish a post-market monitoring plan covering performance metrics, incident collection procedures, and periodic review timelines. The plan must be proportionate to the risk level and the number of users. For B2B industrial AI, this typically means contractual provisions requiring customers to report incidents, telemetry data collection where permitted, and annual performance reviews documented in a technical file update.

How does Data Act Article 5 interact with AI Act training data obligations? Article 5 of the Data Act grants users the right to access and port data generated by connected products. This data, when used to train AI systems, must comply with Article 10 AI Act data governance requirements including accuracy, representativeness, and freedom from errors that could cause discriminatory outcomes. Compliance teams should establish a data lineage protocol that tracks the provenance of training data back to the original user consent or contractual basis under the Data Act.

Sources

• Regulation (EU) 2024/1689 (EU AI Act), Articles 9, 10, 57, 62, 73, Annex III
• Regulation (EU) 2023/2854 (EU Data Act), Article 5
• European Commission, Futures of Artificial Intelligence: Implications for Europe's R&I Ecosystem, Study Report, 2024
• European Commission, Horizon Europe Work Programme 2023–2024, Cluster 4: Digital, Industry and Space
• OECD, AI Investment in OECD Countries, 2024
• McKinsey Global Institute, The State of AI in 2024, October 2024