The integration of Artificial Intelligence (AI) in talent acquisition has revolutionized traditional recruitment processes, presenting both opportunities and challenges for organizations aiming to build diverse and inclusive workforces. This research paper aims to critically examine the impact of AI on recruitment practices and its implications for workforce diversity. The study will employ a mixed-methods approach, combining quantitative analysis of recruitment data with qualitative assessments of organizational strategies. Key areas of investigation include the efficiency and effectiveness of AI algorithms in candidate selection, the potential for bias and discrimination, and the overall influence on the composition of the workforce. The research will delve into case studies of organizations that have implemented AI-driven talent acquisition systems to identify best practices and challenges faced. Special attention will be given to understanding how AI tools may inadvertently perpetuate biases and hinder diversity initiatives, as well as exploring mitigation strategies employed by progressive organizations. Additionally, the paper will assess the perceptions of both job seekers and hiring professionals regarding the use of AI in recruitment, aiming to uncover attitudes, concerns, and ethical considerations associated with these technological advancements. Ultimately, the findings of this research will contribute valuable insights to HR practitioners, policymakers, and scholars, offering evidence-based recommendations for optimizing AI in talent acquisition while ensuring fairness, equity, and diversity in the composition of the modern workforce. As organizations increasingly turn to AI to streamline recruitment processes, it is imperative to understand and address the broader implications for workforce diversity and inclusivity in the evolving landscape of talent management.
Artificial Intelligence (AI), Talent Acquisition, Recruitment Processes, Algorithmic Hiring, HR Technology