The rapid advancement of Artificial Intelligence (AI) technologies is reshaping traditional business processes, and recruitment is no exception. As organizations strive to streamline hiring and improve talent acquisition outcomes, AI-driven recruitment tools have gained significant momentum. From résumé parsing and predictive analytics to chatbots conducting initial candidate screening, the application of AI in recruitment has ushered in unprecedented levels of speed, accuracy, and data handling capabilities. This research paper critically examines the dual-edge nature of AI in recruitment —exploring whether it genuinely enhances hiring efficiency or threatens to supplant the nuanced judgment and empathy inherent to human recruiters. The study begins by mapping the current landscape of AI tools in recruitment, highlighting leading applications such as applicant tracking systems (ATS), machine learning algorithms for skills matching, and AI-based video analysis software for behavioral assessments. Through case studies across multiple industries —including technology, finance, and healthcare —the research investigates how AI has transformed operational aspects like time-to-hire, cost-per-hire, and quality-of-hire metrics. These quantitative improvements are then juxtaposed with qualitative insights gathered from HR professionals and candidates, exploring their perceptions of fairness, transparency, and trust in AI systems. A core focus of the research lies in interrogating the limitations and ethical challenges of AI recruitment. The paper identifies key risks, such as algorithmic bias, lack of contextual understanding, over-reliance on historical data, and the reduction of candidate evaluation to quantifiable metrics.