A COMPARATIVE STUDY OF THE USE OF ARTIFICIAL INTELLIGENCE IN INDUSTRIAL PROCESS MANAGEMENT

Abstract

Industry 4.0 represents a profound transformation in production processes, with Artificial Intelligence (AI) as one of its fundamental resources. However, much of the recent literature treats AI as a monolithic concept, ignoring the substantial differences between its sub-technologies: machine learning, deep learning, natural language processing, computer vision, and expert systems. The objective of this research was to conduct a comparative analysis of AI technologies, evaluating their applications, limitations, costs, and feasibility in the context of industrial process management. The methodology was based on a systematic literature review and recent studies complemented by an analysis of secondary data on technological adoption in industrial management. The results indicated that there is no universal AI solution and that each technology presents specific advantages and equally specific barriers. While expert systems offer low cost and rapid implementation for well-defined processes, machine learning requires massive volumes of data and computational infrastructure. It is concluded that the adoption of AI in industrial process management depends on realistic strategic planning, with special attention to professional training and long-term cost-benefit.