Advantages and Problems of Using Artificial Intelligence in Cost Accounting and Their Optimization in Railway Transport Holding Companies

Cost accounting systems in large, asset-intensive organizations, such as railroad transport holding companies, are changing due to the quick development of artificial intelligence (AI) technologies. The benefits and difficulties of applying AI to cost accounting and optimization in this industry are examined in this article. It illustrates how the precision, timeliness, and transparency of cost data can be enhanced by AI-based tools like machine learning algorithms, predictive analytics, and intelligent automation. Additionally, these tools can support more efficient managerial decision-making and enable real-time monitoring of operating expenses. AI's potential to optimize cost structures for rolling stock maintenance, energy use, logistics, and infrastructure management is given special consideration. The article does, however, also point out problems that are preventing AI from being widely used in cost accounting procedures. High implementation costs, problems with data quality, integration, cybersecurity, methodological and regulatory limitations, and a lack of qualified staff are some of these. This paper also analyzes challenges presented by AI-based accounting system integrability with existing financial standards and internal control requirements in railway transportation organizations. The paper provides valuable practical recommendations on how to apply a structured approach to cost accounting to practically utilize the basis of artificial intelligence. These recommendations focus on improving data governance, ensuring infrastructure interoperability, developing human resources and establishing effective monitoring systems. These results advance our knowledge of how artificial intelligence can be strategically incorporated into cost accounting systems to boost railway transport holding companies' productivity and competitiveness.