AI Driven Talent Matching Enhancing Recruitment Efficiency And Accuracy

Abstract

This study discovers the role of “Artificial Intelligence (AI)” in transforming recruitment by enhancing efficiency, improving talent–job alignment, promoting diversity, and addressing implementation challenges. The main goal was to learn how AI-powered solutions may make hiring processes faster, more accurate, and fairer and more open, as well as to find ways for organisations to get beyond their own problems. A “quantitative research design” was employed, and data was gathered from 100 specialists across various organisational settings. The study utilised a structured questionnaire consisting of 26 items, with reliability confirmed by a high “Cronbach’s Alpha (0.941)”, representing excellent “internal consistency”. The statistical analytics software “SPSS (Statistical Package for the Social Sciences)” has been used, applying Spearman’s rho correlation, model fitting tests, and the one-sample chi-square test to analyse relationships between AI usage, process efficiency, hiring accuracy, fairness perception, and implementation barriers. The outcomes show that AI technologies significantly improve the quality of applicants chosen, decrease time-to-hire, and increase the accuracy of talent matching. Additionally, respondents acknowledged AI's role in promoting “diversity and inclusion” in hiring practices and lessening unconscious bias. However, obstacles to complete adoption were found to include issues including organisational preparedness, system complexity, and a lack of experience with AI tools. The study concludes that, with the aid of effective organisational methods, AI-driven hiring systems can have a revolutionary impact. To ensure the seamless integration of AI technologies and optimise their potential to maximise recruiting efficiency and inclusivity, practical consequences include the need to invest in training, technological infrastructure, and change management activities.
Keywords: Artificial Intelligence (AI), Recruitment, Talent Matching, Hiring Efficiency, Candidate Quality, HR Technology Adoption, Training and Change Management, Technological Infrastructure, AI-Driven Hiring Systems