In the information technology (IT) sector, artificial intelligence (AI) is crucial to human resource management (HRM). HR organizations may improve employee engagement through data-driven insights, optimize talent management strategies, and streamline recruiting procedures by utilizing AI technologies. AI-driven analytics also help HR professionals find areas for skill development, increase staff productivity, and make well-informed decisions. These benefits ultimately help HR professionals stay competitive in the quickly changing IT industry and promote innovation. Overall, AI integration in HRM enables IT companies to draw in top personnel, adjust to changing market demands, and foster an agile and continuous development culture. Thus, with an emphasis on organizational, environmental, and technological readiness, this study explores the factors that influence the adoption of AI for efficient HRM practices. The purpose of the data collection process was to investigate the correlations between these drivers and AI adoption in HRM. The sample size consisted of 220 employees from the Information Technology (IT) sector in Noida, India. The results show that the adoption of AI for HRM practices is highly influenced by Technological Readiness, which includes infrastructure and IT skills. Furthermore, Organizational Readiness, which encompasses employee skills, organizational culture, and leadership support-has been identified as a critical factor affecting the adoption of AI. In addition, environmental readiness, which includes industry standards and regulatory support-is a critical factor in whether integrating AI into HRM procedures is made easier or harder. This paper offers useful insights on the complex adoption of AI in HRM through regression analysis, with practical implications for IT businesses looking to efficiently utilize AI technologies. Organizations may improve their preparedness for AI adoption in HRM and promote innovation, efficiency, and competitiveness in the digital age by comprehending and addressing these antecedents.