The Global Race for AI Talent: A Multi-Level Analysis of Workforce Transformation from OECD Trends to Local Realities

This thesis investigates the impact of artificial intelligence (AI) on labor markets through a multi-level approach, combining global trends with national data and local insights. The objective is to examine how AI adoption influences job roles, skill requirements, and organizational practices across different contexts. At the global level, data from the OECD.AI Observatory is analyzed to assess AI skills migration, cross-country skill penetration by industry, hiring trends, and gender participation. The national perspective focuses on a dataset of 862 AI/ML job postings in the United States, revealing patterns in job titles, required skills, experience levels, and hiring sectors. The local level presents findings from interviews with five companies in Avezzano, Italy, offering qualitative insights into AI-related transformations within small and medium-sized enterprises. The results highlight how AI is reshaping workforce dynamics, accelerating the demand for technical and adaptive skills, and creating new organizational challenges. The research contributes to the literature on innovation and labor by demonstrating the uneven pace of AI integration across sectors and geographies, and by emphasizing the strategic importance of workforce adaptability in managing technological change.