Artificial intelligence (AI) has found its way into talent acquisition, where it is used in automating sourcing, parsing resumes, matching candidates, using chatbots, analysing assessment scores, scheduling interviews and predictive decision making. While these technologies hold the potential of reducing recruitment expenses and enhancing quality of hire, there are various conditions necessary for their effectiveness: Data validity, process integration, Recruiter oversight, Ethical governance and Post hire measurement. This paper explores how AI is influencing two critical indicators for successful recruitment: recruitment cost efficiency and quality of hire. The paper then uses a conceptual literature synthesis to create an integrated framework that links AI capabilities to cost drivers, decision quality, candidate experience and long term employee outcomes. The analysis suggests that AI can help decrease administrative workload, time-to-fill, enhance pipeline conversion, and assist in skillsbased matching. But cost savings are likely to be exaggerated if implementation, integration, audit, vendor and compliance costs are not considered. Likewise, quality of hire does not occur automatically, it’s a process that involves having job-relevant features validated, regular human review, bias tracking, and feedback through post-hire performance and retention data. The paper presents a measurement model, risk-control matrix, and recommendations for implementation for organizations aiming to leverage AI in recruitment while ensuring fairness, transparency, and strategic alignment.