Methods for developing expert systems in corporate information systems: enhancing enterprise economic stability

This study aims to provide an in-depth analysis of the impact of expert systems integrated into corporate information systems on the economic stability of enterprises. In the modern business environment, the role of expert systems is significantly increasing, as they serve as valuable tools for strategic decision-making, risk management, and improving operational efficiency. This article explores the use of modern methods such as artificial neural networks, fuzzy logic, swarm intelligence, and Bayesian networks, with each method’s effectiveness and limitations evaluated through comparative analysis. The research employed machine learning tools developed in modern programming languages like Python and R, with libraries such as scikit-learn and TensorFlow showing notable results. In addition, the efficiency of the systems was assessed using specific metrics based on expert opinions and real enterprise data. The authors emphasize that the implementation of expert systems is accessible not only to large enterprises but also to small and medium-sized businesses. This study can be seen as a contribution to regional and international scientific work exploring the synergy between CIS and ES. The findings show that expert systems have a significantly positive impact on companies' financial indicators, including a 20% increase in current liquidity, a 23% rise in net profit, a 12% reduction in expenses, and a 25% decrease in debt load. Furthermore, the article discusses the specific features and opportunities of implementing expert systems in the context of Kazakhstan, offering practical recommendations for accelerating the digital transformation process. The study also highlights the importance of building a data-driven management culture within the digital ecosystem.