Improvement of Quality Management Methods of Mechanical-Building Enterprises through the Use of Artifi cial Intelligence Tools in PLM Systems at Various Stages of the Product Life Cycle

This article examines modern approaches to integrating artifi cial intelligence into product lifecycle management systems. Using case studies from the automotive industry, it demonstrates how machine learning technologies, graph databases, and digital twins can signifi cantly improve the effi ciency of key processes—from design to operation. The article examines not only the expected benefi ts but also the typical challenges of integrating artifi cial intelligence, supporting its arguments with concrete practical data: increased inspection accuracy, reduced design time, and reduced operating costs, which collectively ensures a high project return on investment.