Abstract
This study investigates how artificial intelligence (AI) streamlines core recruitment processes—resume screening, candidate sourcing, and early-stage engagement—to enhance organizational hiring efficiency and fairness. Leveraging machine learning and natural language processing, AI automates high-volume tasks, accelerates time-to-hire by up to 50%, and reduces manual bias, thereby supporting more objective, data-driven talent acquisition. Employing a mixed-methods design (quantitative surveys + qualitative interviews), the research maps a heterogeneous adoption landscape: larger firms report deeper integration and greater gains in sourcing speed and screening accuracy, while smaller organizations lag due to resource and expertise constraints. Despite measurable efficiency benefits, significant barriers persist—including employee resistance, technical integration challenges, and ethical concerns around algorithmic transparency and bias. Thematic analysis underscores the dual reality of AI-enabled streamlining: substantial productivity improvements coexist with human-centric anxieties, necessitating ethical governance, transparent communication, and change-management strategies to ensure responsible, inclusive implementation. The study concludes that strategic, ethically grounded AI integration is essential to fully realize streamlining potential while safeguarding fairness and trust in the evolving recruitment ecosystem.