A comprehensive review of 79 studies conducted by researchers at the Royal Docks School of Business and Law warns that overreliance on artificial intelligence in recruitment undermines employer competitiveness for top talent. Published recently in the Employee Relations journal, the findings highlight a critical tension in modern hiring practices known as the resourcing paradox. While automated systems significantly accelerate routine tasks such as resume screening, candidate matching, and interview scheduling, excessive dependence on these tools diminishes the human interaction essential for attracting high-caliber professionals. The study emphasizes that candidate trust hinges on organizational transparency and continued human oversight. Applicants respond positively to AI-assisted processes only when companies clearly communicate algorithmic usage and preserve human decision-making at critical stages. Co-author Professor Kirk Chang noted that future talent competition will favor organizations that integrate AI efficiency with human judgment, transparency, and empathy. Recruitment remains fundamentally an interpersonal process, and technology functions optimally as a decision-support mechanism rather than a replacement for recruiter expertise. Professor Toyin Adisa reinforced this perspective, observing that hiring interactions often serve as a prospective employee's initial organizational experience. Impersonal or opaque automated processes risk deterring qualified candidates before meaningful dialogue can occur. Consequently, AI deployment must prioritize process fairness and candidate engagement to remain competitive in tight labor markets. The researchers outline three primary recommendations for enterprises adopting hiring automation. First, organizations must maintain clear transparency regarding AI deployment throughout the recruitment lifecycle. Second, regular algorithmic audits are necessary to identify and mitigate potential bias. Third, human oversight must remain structurally integrated into final selection decisions to evaluate nuanced qualities such as cultural alignment, interpersonal potential, and empathetic leadership capacity. As enterprises across sectors increasingly embed machine learning into human resources operations, the study serves as a strategic advisory. The most effective recruitment frameworks will not maximize automation but instead optimize the collaboration between algorithmic precision and human expertise. Employers that treat AI as an augmentative tool rather than a procedural substitute will likely secure a sustained advantage in the ongoing competition for specialized talent.