Branches of artificial intelligence in the onboarding process in Chemical industries

Abstract

This research project aimed to determine the impact of artificial intelligence on the optimization and development of the onboarding process within companies in the chemical sector. The study adopted a positivist and descriptive methodological approach, characterized by a non-experimental cross-sectional field design. The study population consisted of a total of 18 employees, carefully selected from two leading companies in the chemical sector. Data collection was based on indirect observation through surveys, using a structured questionnaire as the main instrument. Data analysis was conducted through rigorous tabulation and descriptive statistics, which provided a detailed and nuanced understanding of the perceptions and opinions expressed by participants. The findings revealed that the implementation and use of machine learning algorithms and natural language processing in the onboarding processes of companies in the chemical sector is nonexistent. This absence represents a significant gap in the modernization of personnel onboarding processes. Consequently, there is an urgent need to actively promote the functionality and benefits inherent in these advanced technologies. It is essential to foster their strategic incorporation into the onboarding process, in order to enhance their efficiency, personalization, and, ultimately, the satisfaction of both the company and new employees.