CC BY 4.0Scheuffele, MarieMarieScheuffeleMartini, MelanieMelanieMartiniJohn, MarcusMarcusJohnBrecht, LeoLeoBrecht2025-07-162025-07-162025https://doi.org/10.24406/publica-4888https://publica.fraunhofer.de/handle/publica/48962010.24406/publica-4888The quantitative analysis of job postings data has already proven to be an insightful methodology for answering anticipatory research questions in various scientific disciplines, such as human resources management, leadership, or economics. But despite its forward-looking potential, the data source has not yet been heavily exploited for data-driven foresight purposes, opening up the research gap of exploring job postings as an additional foresight data source and its analysis as an innovative new tool for technology foresight. This study utilizes online job postings data to answer future-oriented research questions in the context of artificial intelligence technology. Applying text mining techniques to the job description texts of global job advertisements from 2023 and 2024, we identify the most in-demand AI disciplines, their recruiting dynamics over time and in different countries, as well as industry-specific AI skill trends.enJob postings analysisdata-driven foresightartificial intelligencetechnology skillstext miningrecruiting dynamicsregional analysisindustry analysisconference paper