Characterization of the artificial intelligence labor market in Chile: An approach based on unstructured data and the ESCO taxonomy

This study characterizes the labor demand related to artificial intelligence (AI) in Chile to identify the skills and requirements sought by employers. Using unstructured data from online job postings collected from major employment platforms, AI-related vacancies are identified and analyzed through a skills-based approach aligned with the European Skills, Competences, Qualifications, and Occupations (ESCO) taxonomy. The results show that demand is primarily oriented towards technical skills, while soft skills play a complementary and transversal role, with teamwork being the most recurrent. AI-related job demand is concentrated in high-skilled occupations and specialized professional profiles, while patterns observed in the data are consistent with skill-biased and routine-biased technological change hypotheses. The identified skill structure highlights the relevance of advanced digital competencies for emerging AI occupations and provides a basis for updating training programs. Overall, the findings offer empirical evidence on the early diffusion of AI-related jobs demand in Chile and contribute to the discussion on skills development and labor market adaptation in emerging economies.