AI Driven Talent Management: Optimizing Candidate Selection and Resume Evaluation

Abstract : AI is an enabler of the digital transformation era, and it is no less of an enabler of the hiring practices transformation. To improve the accuracy and efficiency of talent acquisition, this chapter explains how intelligent recruiting solutions driven by AI can enable intelligent sourcing and resume screening. The main purpose of this study is to provide the acceptance and feasibility analysis on how such AI algorithms or predictive analytics can be utilized for screening, hiring qualified candidates and reduce operation cost & human bias. The study uses a systematic literature review based on study of companies using AI-based hiring technologies like chatbots, automated applicant tracking systems (ATS), and resume screeners with integrated natural language processing (NLP). By analyzing research findings from the article based on semi-structured interviews with HR professionals and data synthesis of the recent empirical studies (2018-2025), the study evaluates the practical integration, difficulties, and ethical concerns of AI recruitment technology. The findings indicate that AI is much faster than humans in sourcing candidates, enhancing decision consistency, and expanding the shortlisting candidate pool by analysing huge volumes of data.  Nevertheless, another issue raised in the report is that of data privacy, transparency of the algorithm, and excessive reliance on machine predictions that could worsen the degree of unconscious bias. This study summarises that human control should be encouraged to enhance fairness and situational judgments, even in the light of the improved strategic preparation of human resources and recruiting efficiency. The research gives a theoretical outline, which highlights the interaction between ethical governance, recruiting flexibility, and AI features.