Artificial Intelligence (AI) has moved from a peripheral experiment to a core infrastructure layer within Human Resource Management (HRM), and recruitment and talent acquisition (TA) are among its most visible applications. This paper examines how AI-enabled tools resume parsing, conversational chatbots, semantic candidate matching, automated interview scheduling, video-based assessment, and predictive analytics are reshaping hiring practices, using the Indian Information Technology (IT/ITES) and Textile & Apparel industries as contrasting case sectors. The IT sector, characterised by digitally native workflows and large applicant volumes, has emerged as an early and intensive adopter of AI in recruitment, while the textile sector, with its labour-intensive, geographically dispersed, and often informally structured operations, lags considerably despite growing pressure to modernise. Drawing on a structured review of industry reports, market-research data, and secondary case evidence, the study finds that AI adoption in recruitment delivers measurable gains in time-to-hire, cost-per-hire, and sourcing reach, but also introduces new risks around algorithmic bias, data privacy, and workforce trust. The paper develops a comparative framework of AI-adoption maturity across five recruitment sub-functions, identifies sector-specific barriers, and offers a set of practical recommendations for HR leaders seeking to adopt AI responsibly. The findings suggest that sectoral context digital readiness, workforce composition, and regulatory exposure rather than technology availability alone, determines the pace and depth of AI integration in recruitment.