“Competence in general IT skills alone does not drive innovation. To truly unlock AI’s potential, we need to go beyond just digital skills and cultivate advanced, interdisciplinary capabilities that reflect the real-world needs and opportunities of each sector.” (Dr Magí Lluch-Ariet, Data Manager at EIT Health)
A recent groundbreaking report co-authored by EIT Health and EIT Digital examines the distribution and significance of AI skills within start-ups across Europe. Using real-world data from over 23,000 professionals in 3,600 start-ups specialising in AI generated through SkillSync, an AI-powered platform, and ESCO (European Skills, Competences, Qualifications and Occupations) taxonomy, the report identifies critical skills and training gaps and regional disparities that support the development of Europe’s AI talent pipeline.
The AI talent landscape
The report finds that European AI startups have robust expertise in core technical skills like Python programming, machine learning, SQL, and data management. These skills are broadly distributed across both roles and regions, and form the backbone of AI-driven innovation.
However, there is significant regional variation in the skill intensity in areas such as project management, communication, and domain-specific knowledge like healthcare and manufacturing. The report also suggests a possible link between the skills and innovation output as regions with higher innovation scores have stronger interdisciplinary and sector-specific capabilities.
Innovation and skills clustering
Using a novel clustering analysis of European regions by workforce skill profiles, the report reveals six regional clusters and two broader meta-clusters.
- Meta-cluster A (Western & Northern Europe): Including Central, Northern, and Southern Europe, and Ireland, these regions are rich in cognitive, managerial, and collaborative skills, contributing to their strong innovation ratings.
- Meta-cluster B (Expanded Eastern Europe): Comprising Eastern Europe, Austria, Hungary, and Greece, this group shows a higher concentration of technical and manual skills but underrepresents managerial and transversal capabilities.
Additionally, the report also introduces a robust, standardised metric, Skill Intensity (SI), to map, compare, and benchmark skill distributions, and enable continuous monitoring of workforce dynamics. This metric also demonstrates how certain skill concentrations, especially in natural sciences, statistics, and engineering, correlate positively with innovation, while generic IT skills do not.
Training gaps
An analysis of more than 76,000 professional training courses across Europe reveals that, despite a seemingly complementary training ecosystem, there is a distinct divide in focus with universities providing courses in foundational and transferable skills, such as communication and statistics, and non-university providers offering more technical, hands-on courses in areas like Python programming, machine learning, and cybersecurity.
However, the report also highlights that significant training gaps remain, especially around legal and regulatory skills, domain-specific operations, and audiovisual technologies, all of which are essential in helping scale AI.
Policy recommendations
To close the AI skill gap and drive innovation, the report outlines several key policy recommendations:
- Support cross-regional collaboration via training consortia and shared resources, especially within the defined regional clusters.
- Scale high-impact training in STEM and regulatory skills, focusing particularly in underperforming innovation regions.
- Encourage non-university providers to deliver training that responds quickly to industry demands and needs.
- Support universities to design and develop specialised courses on emerging AI areas.
Invest in platforms like SkillSync, to monitor evolving workforce trends and forecast future skill needs in real time.
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