Mapping AI talent gaps in Europe: a tiered skills intelligence approach

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Description

Context & overview

AI has become a central driver of innovation in Europe, reshaping economies and job markets. Yet, labour market intelligence on AI often treats AI skills as a single block overlooking the fact that proficiency ranges from basic literacy to cutting-edge research. To fill this gap, the study Solving Europe’s AI Talent Equation, introduced a comparative framework to analyse AI demand and supply across European countries. The research divides AI roles into 3 tiers of expertise, mapping vacancies against available talent, identifying mismatches at both national and continental level.

This study is highly relevant to the AI-VET project because it demonstrates how data-driven skills intelligence can reveal mismatches between training supply and labour market demand.

 

Challenges addressed

The study sought to overcome three main shortcomings:

Traditional statistics rarely differentiate between beginner, mid-level and advanced AI roles.

Policymakers could not easily identify where shortages or surpluses existed across Europe.

Without clarity on skill levels, education systems risk to produce graduates who do not match labour market needs.

 

Solution implemented

Researchers proposed a tiered methodology to classify AI skills:

Tier 0 (AI literacy): individuals with basic awareness or the ability to use AI tools.

Tier 1 (mid-level): professionals applying AI and data techniques in technical contexts.

Tier 2 (advanced): experts developing models, algorithms or conducting AI research.

 

Two complementary datasets were used:

  • Lightcast for vacancy data (labour demand).
  • Revelio Labs for profiles and CVs (labour supply).

With the comparison of vacancies and profiles across tiers, the study calculated the share of demand and supply per country and the ratio of available talent to open positions.

 

Impact & results

Around half of AI vacancies and nearly two-third of AI talent fall into Tier 1, showing that Europe’s AI labour market is concentrated at the technical practitioner level.

Demand for Tier 0 and Tier 2 roles exceeds supply. About 29% of vacancies request Tier 0 skills, while only 22% of talent fits this profile. At the advanced end, 24% of vacancies target Tier 2 skills, but only 15% of talent is qualified.

Vacancies for Tier 0 and Tier 2 roles have fewer candidates per position, reflecting greater difficulty in filling these jobs compared with Tier 1.

Reference Link

https://www.interface-eu.org/publications/solving-europes-ai-talent-equation

Keywords

AI skills taxonomy, labour market mismatch, talent supply and demand, comparative framework, European workforce analysis

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