Comparative Analysis of Recruitment Practices for Leadership Roles in Indian HRM: A Quantitative Assessment of Traditional and AI-Driven Strategies

Abstract

This study examines the comparison between traditional and AI-driven recruitment methods for leadership roles within Indian Human Resource Management (HRM). The purpose of the research is to evaluate the efficiency, cost-effectiveness, and impact of
these recruitment strategies, with a particular focus on leadership hiring. A quantitative approach is employed, utilizing surveys administered to HR professionals, to assess key recruitment metrics such as time-to-fill positions, quality of hire, employee retention, and recruitment costs.
The findings reveal that traditional recruitment methods, including job portals, recruiting agencies, and campus recruitment, continue to dominate in leadership hiring, with high familiarity and trust among HR professionals. However, while these methods are effective, they are also time-consuming and costly. On the other hand, AI-driven recruitment methods such as resume screening, video interviewing, and automated scheduling show potential in improving recruitment efficiency, particularly for frontline roles. However, AI’s ability to evaluate behavioral and leadership competencies is still limited, especially for complex leadership roles.
The study also assesses the financial aspects of both recruitment methods, showing that traditional recruitment incurs high direct and indirect costs due to manual processes and lengthy hiring cycles. In contrast, AI recruitment requires significant upfront investment but offers long-term cost savings by automating repetitive tasks and reducing reliance on large recruitment teams. However, organizations must overcome challenges related to AI adoption, including technological infrastructure, data privacy concerns, and a lack of AI expertise.