The process of recruitment is one of the most critical tasks in modern human resources management, but companies still apply intuitive and sometimes ineffective hiring approaches. With the increase in the amount of information about employees, the possibility of recruiting staff using the methods of data analytics emerged. In this paper, the impact of the use of data analytics on recruitment efficiency in medium-sized and large firms that work in the service sector and IT industry is explored. A mixed method approach was applied, where quantitative data from 152 organizations in India and the GCC area were obtained using a structured questionnaire, complemented by semi-structured interviews with 24 HR directors. Multiple regression, correlation matrix, and structural equation modelling (SEM) techniques were applied to study the relationship between DA, HEI, TTH, CPH, and CQS. It is found that DA positively impacts HEI (beta = 0.482, p < 0.001), decreasing time-to-hire by 18.6% and cost-per-hire by 22.3%, compared to companies that have limited experience in applying analytics. The influence of artificial intelligence (AI) ) exerted a further positive influence on the hiring process (β = 0.317, p < 0.01), whereas organizational size (SIZE) moderated the impact of analytics implementation on recruiting efficacy. The total model accounted for 61.4% of variability in HEI (R² = 0.614, F = 29.87, p < 0.001). These results highlight the importance of establishing data-oriented recruiting systems and call for a strategic allocation of resources to predictive hiring tools, especially in organizations facing large-scale talent recruitment difficulties.