Deliverable D2.2 presents recommendations for developing AI-supported tools for Active Labour Market Policies and skills-matching systems.
Based on WP2 analysis, national Communities of Practice and the CoP Survey, it translates stakeholder needs and policy challenges into practical guidance for future EU-ALMPO tools. The deliverable shows how AI can support policy design, matching, monitoring and evaluation, while keeping human judgement central.
Why it matters
This deliverable helps bridge the gap between labour market policy needs and AI-supported tool development.
It shows that AI can strengthen Active Labour Market Policies only when it is transparent, evidence-based, human-centred and adapted to real institutional contexts. The report provides practical guidance for developing tools that support better policy decisions, improve skills matching, and help Public Employment Services and other actors respond more effectively to labour market needs.
Key information
Objectives
- To translate WP2 analytical findings into operational recommendations for AI-supported ALMP and skills-matching tools.
- To identify priority AI functionalities relevant for ALMP design, implementation, monitoring and evaluation.
- To define governance, ethical, data and institutional conditions required for trustworthy AI-supported labour market systems.
- To support the further development of EU-ALMPO tools by linking stakeholder needs with system requirements.
- To ensure that AI-supported solutions remain evidence-based, human-centred, transparent and context-sensitive.
Target audience
- EU, national and regional policymakers.
- Public Employment Services and labour market institutions.
- ALMP managing authorities.
- Training providers and skills ecosystem actors.
- Social partners and labour market representatives.
- Labour market researchers, analysts and evaluators.
- Developers of AI-supported policy and matching tools.
End users
- Policymakers designing and adapting ALMPs.
- PES practitioners and counsellors involved in service delivery and matching.
- Labour market analysts using data for monitoring, forecasting and evaluation.
- Institutions responsible for skills-matching systems and labour market intelligence.
- EU-ALMPO technical partners developing AI-supported tools.
- Stakeholders involved in inclusive labour market policy and support for vulnerable groups.
Keywords →
Policy
ALMPs | Policy design | PES| ALMP lifecycle
Analysis
Skills matching | Labour market intelligence | Evidence-based policy| Monitoring and evaluation
AI
AI-supported policymaking | Data interoperability |AI governance | Human-in-the-loop AI
This deliverable presents the EU-ALMPO recommendations for the development of AI-supported tools designed to improve Active Labour Market Policies (ALMPs) and skills-matching systems. It builds directly on the analytical work carried out in Work Package 2, especially the comparative analysis and Context–Mechanism–Outcome (CMO) configurations developed in Deliverable 2.1.
The report is based on national Communities of Practice sessions conducted in Greece, Germany, Italy, Poland and Spain, complemented by a CoP Survey. These activities gathered structured input from Public Employment Services, ministries, regional and local authorities, training providers, social partners, researchers, labour market experts, data specialists and other stakeholders involved in ALMP design, implementation, monitoring and evaluation.
The deliverable identifies the main institutional, operational, data-related and governance conditions that influence the feasibility and usefulness of AI-supported labour market tools. It highlights that AI should not replace human judgement or institutional decision-making, but should function as a supportive “co-pilot” strengthening evidence use, matching, monitoring, forecasting, policy learning and adaptive ALMP design.
The report proposes a lifecycle-based framework for AI-supported ALMP development, covering policy initiation, programme design, targeting and profiling, implementation, monitoring, evaluation and feedback. It also formulates recommendations concerning data interoperability, labour market intelligence, transparency, explainability, human oversight, bias mitigation and institutional readiness.
The findings provide structured input for the further development of EU-ALMPO AI-supported tools, including the ALMP Design Wizard, labour market intelligence functionalities, recommendation systems and AI-supported matching tools. The deliverable therefore acts as a bridge between WP2 empirical and stakeholder-based analysis and subsequent technical work in the project.