AI-Powered Skill Classification: Mapping Technology Intensity in the German Labor Market

Sabrina Grenz, Terry Gregory () and Florian Lehmer
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Sabrina Grenz: Utrecht University
Terry Gregory: LISER
Florian Lehmer: IAB Nueremberg

No 18415, IZA Discussion Papers from IZA Network @ LISER

Abstract: The rapid evolution of technology is reshaping labor markets by altering skill demands and job profiles. This paper introduces a novel skill-based measure of occupational technology intensity -- the Occupational Technology Skill Share (OTSS) -- that distinguishes between manual, digital, and frontier technologies. Using natural language processing, generative AI, and supervised machine learning, we develop an AI-powered skill classification that enriches occupation-linked skill labels with standardized GenAI-generated descriptions and structured indicators of technological content, enabling transparent classification by technology intensity. We compute OTSS for all occupations in the German labor market. For the average worker in 2023, manual technologies account for the largest share of skill content (42\%), followed by digital (38\%) and frontier technologies (20\%). Frontier technologies remain concentrated in specialized occupations, while digital technologies are widespread. Linking these measures to administrative data from 2012–2023 shows a broad shift from manual and digital toward frontier skills across occupations, and reveals a U-shaped relationship between changes in frontier skill intensity and employment growth.

Keywords: artificial intelligence; digitalization; skills; employment growth (search for similar items in EconPapers)
JEL-codes: J21 J24 O33 (search for similar items in EconPapers)
Date: 2026-03
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