Artificial intelligence in human resources management in the agro-industrial complex: a neural network method of dynamic comparison of employer requirements and specialist competencies

Abstract

The digital transformation of the agro-industrial complex creates new requirements for human resources management, creating an urgent need for intelligent personnel selection tools. The study is aimed at developing a neural network model for automated comparison of the competencies of agricultural specialists with employers’ requirements. The methodological base includes analysis of Rosstat and the Ministry of Agriculture data for 2020–2024, a survey of 847 agricultural specialists, expert interviews with 38 heads of agricultural enterprises, and the use of machine learning methods. The results showed a 52% increase in demand for digital competencies over the period 2020–2024, the shortage of precision farming specialists is 34%. The developed neural network model demonstrates a matching accuracy of 87%, exceeding traditional methods by 31%. A correlation was found between the level of digitalization of enterprises and the transformation of personnel needs (r = 0.78). The system reduces the resume processing time from 45 to 2.8 minutes, provides forecasting of personnel needs with an accuracy of 82%.