Purpose The application of artificial intelligence (AI) in recruitment and selection (R&S) has gained ground by improving efficiency and reducing bias in decision-making. However, there are gaps in the literature about how AI technology models work in the task of automated curricula vitae (CV) classification. This study aims to analyze the application of AI, using artificial neural networks (ANNs), in human resources R&S processes as a decision-making support system to improve efficiency (productivity) and organizational effectiveness. Design/methodology/approach Based on a sample of 600 CVs, a machine learning AI model was built, specifically a multilayer perceptron feedforward ANN for classifying curricula. Findings The results show that the model is effective and brings strategic advantages to the organization. This approach contributes to theoretical studies that reinforce the prominent role of AI as a support tool in strategic human resource management and contribute to research into advanced human–machine integration. Practical implications This offer practical value by introducing a novel, empirically validated approach to recruitment and selection. By leveraging AI technologies, organizations can enhance the efficiency and effectiveness of their hiring processes – particularly in screening large volumes of CVs without compromising quality. This methodology supports recruiters and HR managers in making more informed decisions, streamlines candidate evaluation and contributes to modernizing traditional recruitment practices, especially in roles such as sales consultancy. Originality/value The research also advances knowledge by offering an AI model adept at confirming the ability to replicate human accuracy in CV classification.
Jatobá et al. (Mon,) studied this question.
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