AI-BASED RECRUITMENT AND TALENT ANALYTICS

Artificial Intelligence (AI) has fundamentally reshaped recruitment and talent analytics by enabling organizations to transition from manual, intuition-based hiring to data-driven, efficient, and predictive talent management. This paper provides a comprehensive review of
AI-based recruitment systems, focusing on their role in enhancing candidate sourcing,screening, and employee retention. Using a PRISMA-based systematic methodology, relevant literature from 2015–2025 was analyzed to identify trends, applications, and research gaps.
The findings reveal that AI technologies—such as machine learning, natural language processing (NLP), and talent analytics—enable automated resume parsing, intelligent
candidate matching, and real-time attrition prediction. These systems facilitate AI-driven video interviews, automated job matching, and workforce planning, thereby improving operational efficiency and hiring quality. However, challenges such as algorithmic bias, data
privacy, "black-box" transparency, and the potential loss of the "human touch" remain critical concerns. The study identifies key themes including automation, predictive hiring, bias mitigation, and employee lifecycle management. Despite advancements, gaps exist in empirical cross-industry validation and ethical governance frameworks. The paper proposes future research directions focusing on explainable AI (XAI) in hiring and hybrid human-AI
decision-making models.