Policy Recommendations
CLAIM Policy Recommendations translate project evidence into actionable public measures to strengthen SME competitiveness, align skills with labour market needs, and support responsible AI-enabled competence development across Europe.
Overview
Two recommendations address complementary structural challenges: (1) enabling responsible AI uptake in SME HR functions, and (2) institutionalising permanent cooperation between SMEs and VET providers to reduce skills mismatches and keep training pathways demand-driven.
Enhancing AI Adoption in Human Resources
The first CLAIM Policy Recommendation promotes responsible and strategic adoption of Artificial Intelligence in SME Human Resource functions. It addresses structural constraints SMEs face, including limited HR capacity, difficulty identifying skills gaps, and reduced ability to anticipate future competence needs.
AI-based tools can support HR professionals in skills diagnostics, recruitment, training needs analysis, and workforce planning, enabling data-driven, personalised decision-making. However, AI uptake in HR remains limited due to insufficient training, financial barriers, and lack of clear ethical frameworks for SMEs.
- Public action: certified training programmes for HR professionals, and co-funding mechanisms to support AI adoption in SMEs.
- Responsible use: ethical standards and simplified audit mechanisms suitable for SME contexts.
- Stronger collaboration: connect SMEs, VET providers, and technology developers to embed AI-based skills diagnostics into continuous training pathways.
- Impact: enhance competitiveness and resilience, reduce skills mismatches, and promote lifelong learning across Europe.
Creating a Permanent SME–VET Cooperation
The second CLAIM Policy Recommendation emphasizes establishing a stable, long-term cooperation framework between SMEs and Vocational Education and Training providers. It addresses a structural weakness whereby vocational programmes are often developed without systematic SME input, resulting in skills mismatches, outdated curricula, and limited responsiveness to labour market needs.
The recommendation builds on CLAIM evidence showing that AI-driven competence diagnostics can accurately identify current and future skills gaps. However, without structured collaboration mechanisms, these insights remain underutilised. The policy calls for institutionalised cooperation models that translate diagnostic data into jointly designed, flexible, demand-driven training pathways.
- Institutional instruments: formal SME–VET collaboration instruments supported by national/regional frameworks.
- Training agreements: enable VET providers to adapt curricula and learning outcomes to enterprise needs.
- Incentives: encourage SMEs to co-design training provision and sustain dialogue over time.
- Ethical data use: aggregated, non-personal skills data to support continuous curriculum updates and strategic planning.
- Impact: reduce skills mismatches, strengthen workforce adaptability, and keep vocational education aligned with economic and technological change.
