Imagine a smart GPS for the job market that doesn't just show where workers are, but where they need to be. It looks at four continents to find the best match between available talent and open jobs. It helps companies decide whether to train current staff, automate the task, or hire from specific international corridors.
Companies struggle to find qualified staff and cannot decide whether to train existing employees, increase salaries, or hire from abroad. There is a lack of data-driven tools to match specific skill gaps with global talent pools.
An AI-Assisted Skill Navigator (TRL5), which is a Knowledge-Based Expert System for matching skills and analyzing labor market gaps.
If you are a recruitment agency dealing with severe talent shortages in the EU — this project developed an AI-Assisted Skill Navigator that identifies emerging skill needs and maps them to human capital in countries like India, Morocco, and Ukraine.
If you are a training center dealing with outdated course catalogs — this project developed a Knowledge-Based Expert System that identifies exactly which re-skilling is needed for inactive women and migrants to fill current gaps.
If you are a plant operator dealing with a lack of specialized technicians — this project developed a tool to analyze whether to raise wages, automate the role, or recruit via established migration skill corridors.
Based on available project data, the AI-Assisted Skill Navigator is designed as an open access system available to the public.
The project analyzes skill flows across 4 continents and 10 countries, suggesting a design intended for global scale.
Based on available project data, the system is intended to be open access, though specific licensing for commercial derivatives is not detailed.
The project builds a Knowledge-Based Expert System that goes beyond standard policy dashboards, but specific API or integration details are not provided.
The project period runs from 2024-01-01 to 2026-12-31.
The consortium is heavily weighted toward academic and research institutions (11 out of 14 partners), providing strong theoretical and econometric backing. However, with 2 industry partners and 3 SMEs, there is a modest 14% industry ratio, indicating the project is primarily research-driven with limited direct commercial validation at this stage.
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