Integration of AI with Traditional Recruitment Methods

Abstract

The recruitment landscape has been significantly transformed by the advent of Artificial Intelligence (AI), which offers the potential to automate various aspects of the hiring process, such as resume screening and candidate assessments. However, while AI enhances efficiency and objectivity, it falls short in replicating the depth of human judgment, particularly in assessing qualitative factors like cultural fit and interpersonal skills. This research explores the integration of AI tools with traditional recruitment methods to develop a hybrid model that combines the strengths of both approaches. The proposed model leverages AI-driven assessments for initial candidate screening, followed by human evaluation to ensure a comprehensive and nuanced decision-making process. By employing advanced multi-criteria decision-making methods, specifically CRITIC and WASPAS, the model optimizes the integration of quantitative data with qualitative insights, resulting in a more balanced and effective recruitment process. This study addresses key questions about the strengths and limitations of AI in recruitment, the potential for AI and human judgment to complement each other, and the overall effectiveness of the hybrid model. The findings suggest that this integrated approach not only improves recruitment efficiency and objectivity but also ensures that critical qualitative aspects are not overlooked, thereby enhancing the overall quality of hiring decisions. The research concludes with a discussion of the future scope of the hybrid model, including its potential application across different industries and its implications for ethical AI governance in recruitment.