When AI Enters the Hiring Funnel: Variation in Employer Use of AI-Enabled Hiring Practices Within and Across Organizations

This study provides evidence on how employers integrate artificial intelligence into hiring across three phases: human capital sourcing, information collection and assessment, and algorithmic decision support. Extending prior research that relies on binary measures of AI adoption across managerial practices, this study focuses specifically on AI use in hiring and develops a more fine-grained framework that captures variation across hiring stages, organizational characteristics, and vacancy types. Drawing on an establishment-level dataset that combines primary and secondary data from New Jersey employers, the authors find that AI use remains modest across these phases, with relatively higher use in human capital sourcing, followed by algorithmic decision support, and the lowest use in information collection and assessment. Moreover, across establishments, AI use for managerial vacancies is positively associated with organizational exposure to AI and affiliation with a larger organization; for non-managerial vacancies, AI use is higher when growth-driven demand is stronger. Additionally, we present suggestive evidence that establishments prioritizing efficiency in hiring use AI more in both vacancy types. Within establishments, AI use is consistently higher for non-managerial than for managerial vacancies across phases of the hiring funnel.