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Skill Mapping with AI: A Scalable Approach to Policy Analysis and Decision-Making

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  • Phoebe Koundouri
  • Conrad Landis
  • Georgios Feretzakis

Abstract

In an era of rapidly evolving labor market demands, aligning policy documents with the skills required for emerging and existing occupations has become increasingly critical. This paper presents a scalable, AI-driven framework for skill mapping that integrates advanced sentence-embedding models, FAISS for high-speed similarity searches, and the European Skills/Competences, Qualifications and Occupations (ESCO) classification. By automatically extracting and analyzing skill references in policy texts, the framework helps policymakers and analysts identify recurring competencies, detect emerging themes (e.g., sustainability or digital literacy), and pinpoint potential workforce gaps. Additionally, it introduces a systematic method for assessing occupation-level relevance-calculating the overlap between policy-cited skills and ESCO-defined occupations to guide targeted upskilling and reskilling efforts. Empirical results suggest that this AI-enabled approach can markedly enhance both the speed and accuracy of policy analysis compared to traditional manual reviews, ultimately supporting data-driven decision-making at scale.

Suggested Citation

  • Phoebe Koundouri & Conrad Landis & Georgios Feretzakis, 2025. "Skill Mapping with AI: A Scalable Approach to Policy Analysis and Decision-Making," DEOS Working Papers 2520, Athens University of Economics and Business.
    • Handle: RePEc:aue:wpaper:2520
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    File URL: http://wpa.deos.aueb.gr/docs/2025.Skill.Mapping.AI.pdf
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    References listed on IDEAS

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    1. Phoebe Koundouri & Nicolaos Theodossiou & Charalampos Stavridis & Stathis Devves & Angelos Plataniotis, 2022. "A methodology for linking the Energy-related Policies of the European Green Deal to the 17 SDGs using Machine Learning," DEOS Working Papers 2202, Athens University of Economics and Business.
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