AI Human Capital, Jobs and Skills

Abstract

This chapter reviews the literature investigating the way Artificial Intelligence (AI) and automation change the type and distribution of job tasks that workers need to perform, as well as skill demand and employment patterns. The evidence surveyed shows that the impact of AI on jobs and skills varies across demographic groups, occupations, and regions. While the most frequently required skills in AI jobs relate to the programming language Python and to machine learning (ML), a number of skills bundles emerge, highlighting the existence of skill complementarities at the heart of the AI transformation. Technical skills related to ML, data mining, cluster analysis, natural language processing, and robotics emerge as being central to the deployment of AI but go hand in hand with other cognitive and socio-emotional skills. The chapter further discusses how best to form and augment human capital, and to help individuals adapt to AI-induced changes in the labor market, especially through reskilling and upskilling. It highlights the importance of access to quality education for all, independent of age or gender; and the need to provide especially female individuals with opportunities to participate in science, technology, engineering, and mathematics (STEM)-related education and training. It further underlines the need for lifelong learning, in light of constantly evolving AI-related technologies and changing skill requirements; more frequent job-to-job transitions as job tenures shorten; and workers’ heterogeneous skills, competencies, and abilities. The chapter concludes by offering a summary of the contributions analyzed and their policy implications and highlights possible avenues for future work.