Navigating the AI revolution in talent acquisition: A qualitative, hypothesis-driven study of adoption drivers, regulatory barriers and governance imperatives across global organizations

Artificial intelligence (AI) is transforming talent acquisition (TA) practices across global organizations, yet empirical evidence on the organizational, regulatory, and governance dimensions of this transformation remains fragmented. Against a backdrop of global RPO market restructuring (Everest Group, 2024) used here as contextual market intelligence, this study presents findings from a qualitative, hypothesis-driven interview investigation involving 50 in-depth stakeholder interviews spanning 14 industries across North America, Europe, and Asia. Data were captured through video recordings and transcripts; AI-assisted transcript analysis was employed to surface themes across the dataset. A hypothesis-driven protocol grounded in the Technology Acceptance Model (TAM) and Institutional Theory systematically validated and invalidated 15 propositions. Key findings reveal that AI integration in candidate screening reduces time-to-fill (validated across 39 interviews); regulatory concerns, particularly the EU AI Act (Regulation 2024/1689) and New York City's Automated Employment Decision Tool (AEDT) regulation constitute the primary adoption barrier in regulated industries (29 interviews); executive governance structures are a decisive implementation success factor; and skills-based hiring is gaining traction as an AI-enabled paradigm shift. Three hypotheses were invalidated, including premium pricing for speed and inclusion features and the belief that unbundled tools drive higher adoption. These findings contribute a theoretically grounded, cross-industry empirical perspective to the literature on responsible AI deployment in talent acquisition.