Transforming Talent Acquisition: A Critical Analysis of Artificial Intelligence Integration in Modern Recruitment Practices

Abstract

This study critically examines the integration of artificial intelligence (AI) technologies in talent acquisition processes through a systematic analysis of organizational implementations and stakeholder perspectives. Using the Technology Acceptance Model (TAM) and Socio-Technical Systems Theory as theoretical frameworks, we conducted a mixed-methods investigation involving 147 HR professionals across 45 organizations, supplemented by in-depth case studies of six companies with varying AI adoption levels. Our findings reveal that while AI technologies demonstrate significant potential for enhancing recruitment efficiency (reducing time-to-hire by 35-50%) and candidate experience, implementation faces substantial barriers, including algorithmic bias concerns, integration challenges, and resistance to change. The study has classified that there are three types of AI adoption patterns: cautious adopters (31 percent), strategic implementers (42 percent), and comprehensive integrators (27 percent). Critical thinking shows that proper consideration of ethical aspects, types of collaboration between humans and AI, and the organization level of readiness is essential to implement AI productively. The study is relevant to the literature on HR technologies because it presents empirical evidence of the multifaceted effects of AI on recruitment processes and provides a foundation for a reasonable approach to the implementation of AI in talent search.