The integration of Artificial Intelligence (AI) into recruitment and talent acquisition processes has transformed the dynamics of modern human resource management. Traditional methods of hiring, often constrained by subjectivity, time limitations, and high operational costs, are increasingly being replaced or supplemented by intelligent algorithms that enhance efficiency, accuracy, and fairness. AI-driven tools such as applicant tracking systems, natural language processing, and predictive analytics are reshaping the ways organizations source, screen, and select candidates. These technologies enable large-scale data processing, reducing bias in decision-making and allowing recruiters to focus on strategic and human-centric aspects of talent management. However, the adoption of AI also raises critical challenges, including concerns about algorithmic transparency, data privacy, ethical implications, and the potential risk of dehumanizing recruitment. This paper explores the dual impact of AI by examining both its opportunities—such as improved candidate experience, diversity promotion, and predictive workforce planning—and its limitations, which include over-reliance on technology and unequal access across industries. By analysing current trends, case studies, and theoretical frameworks, the study provides a balanced understanding of how AI is redefining recruitment practices while highlighting the need for responsible integration that aligns technological efficiency with human values. The findings suggest that AI, when applied ethically and strategically, has the potential to not only optimize talent acquisition but also contribute to long-term organizational competitiveness in a rapidly evolving global labour market.

Purpose: The purpose of this study is to analyse the impact of Artificial Intelligence (AI) on recruitment and talent acquisition processes, with a focus on how emerging technologies are reshaping efficiency, transparency, and organizational competitiveness.

Design/Methodology/Approach: The paper adopts a qualitative and analytical approach by reviewing recent literature, organizational case studies, and theoretical frameworks in the domain of Human Resource Management. Special emphasis is placed on AI-enabled tools such as applicant tracking systems, natural language processing, and predictive analytics to evaluate their effectiveness in streamlining hiring practices.

Findings: The study reveals that AI enhances recruitment efficiency by automating repetitive tasks, reducing bias in candidate screening, and improving overall candidate experience. It also facilitates data-driven decision-making and workforce planning. However, the findings also highlight potential limitations, including ethical concerns related to algorithmic bias, data privacy, transparency, and the risk of over-reliance on technology at the expense of human judgment.

Practical Implications: The research suggests that organizations must adopt a balanced approach, integrating AI with human-centric strategies to ensure fairness, inclusivity, and ethical responsibility in recruitment practices.