Artificial Intelligence in Talent Acquisition: An Empirical Assessment of Recruitment Outcomes and Responsible Use
Abstract: Artificial intelligence (AI) is increasingly used to source applicants, display screening packages, match candidates, communicate with applicants, and analyse workforce data. Processing speed alone does not indicate whether it is AI-powered. Recruitment outcomes also include shortlist relevance, applicant delight, recruiter productivity, and high-quality decisions. This study examines the relationship between AI adoption and five perceived skill-acquisition outcomes, drawing on questionnaire responses from 70 individuals. The analysis uses descriptive statistics, Cronbach's alpha, Pearson correlation, and separate simple linear regression. AI adoption undoubtedly improves recruitment performance (β = 0.546, R2 = 0.282), recruitment effectiveness (β = 0.514, R2 = 0.295), perceived candidate experience (β = 0.569, R2 = 0.363), recruiter productivity (β = 0.498, R2 = 0.329) and overall performance of talent acquisition (β = 0.341, R2 = 0.120). All five regression relationships are statistically significant at the 5% level. Candidate experience has the highest R2 among the five factors, while specific skill acquisition performance has the lowest. Additionally, respondents who report greater AI adoption tend to report more favourable recruiting outcomes, but the study does not establish causality or confirm changes in overall objective hiring performance. The article discusses these findings in relation to fairness, transparency, confidentiality, and human oversight. It recommends that firms assess the use of multiple end-result markers in AI-enabled hiring and conduct meaningful human assessment of the resulting options.
Keywords: Artificial Intelligence; Talent Acquisition; Recruitment Efficiency; Candidate Experience; Recruiter Productivity; Algorithmic Fairness; Human Oversight
How to Cite?: Mohan Manjunath, Dr Patcha Bhujanga Rao, "Artificial Intelligence in Talent Acquisition: An Empirical Assessment of Recruitment Outcomes and Responsible Use", Volume 15 Issue 10, October 2026, International Journal of Science and Research (IJSR), Pages: 1268-1272, https://www.ijsr.net/getabstract.php?paperid=SR261019220926, DOI: https://dx.doi.org/10.21275/SR261019220926