AN EMPIRICAL INVESTIGATION OF ARTIFICIAL INTELLIGENCE ADAPTATION ON TALENT ACQUISITION: A FOCUS ON THE IT SECTOR IN SRI LANKA’S WESTERN PROVINCE

Abstract

This study examines the influence of AI adaptation on talent acquisition processes in the IT sector of Sri Lanka, with a focus on organizations like Dialog and Virtusa. While AI is swiftly redefining talent acquisition landscapes worldwide, this research addresses contextual and empirical gaps in understanding the consequences of AI-driven tools on efficiency, candidate engagement, and bias reduction. Informed by the theoretical lenses of the Technology Acceptance Model and Resource-Based View, the study adopts a quantitative research design, utilizing a structured survey to gather insights from HR professionals in the IT sector. The methodology involves a survey-based approach, analyzing AI’s role in optimizing recruitment processes. Data were collected from HR professionals, focusing on AI-driven automation, predictive analytics, and decision-making enhancements in hiring. The study examines how AI streamlines candidate sourcing, shortlisting, and selection while mitigating biases and improving engagement. The analysis highlights that AI significantly improves recruitment efficiency by automating repetitive tasks, enhancing decision accuracy, and enabling personalized candidate interactions. However, challenges such as algorithmic bias, lack of technical expertise among HR professionals, and ethical concerns regarding fairness in AI-driven recruitment were identified. The study emphasizes the need for transparent AI implementation and regulatory oversight to ensure fairness and inclusivity in hiring processes. The conclusion affirms that AI offers transformative potential in talent acquisition but requires a balanced approach that integrates human oversight with automation. Ethical considerations, such as algorithmic bias and data privacy, must be addressed for responsible AI adoption. Organizations should invest in AI literacy, ethical guidelines, and continuous monitoring to maximize AI’s benefits. Future research should explore AI’s impact on workforce diversity, long-term recruitment trends, and cross-industry applications.