PERSONNEL-ORIENTED ENTERPRISE RISK MANAGEMENT MODEL BASED ON THE USE OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES

Abstract

The modern economic landscape, shaped by the impact of military operations and the urgent need for post-war recovery, requires a transformation of enterprise management systems. This article focuses on redefining traditional risk management by shifting from rigid models to flexible and adaptive frameworks. The purpose of the research is to develop a personnel-oriented risk management model based on artificial intelligence to improve decision-making speed and strategic resilience. The relevance of the study is driven by high uncertainty, where conventional methods fail to reflect the dynamic nature of crises and the importance of human factors in maintaining operations. The methodology is based on a multidimensional analysis of management processes, combining operational efficiency and adaptability. Using structural modeling and logical synthesis, the study explores the relationship between process decomposition and risk identification. It emphasizes a shift to proactive management, integrating qualitative assessment of personnel with quantitative data analysis. Furthermore, the methodological framework incorporates the development of intelligent decision support systems that utilize machine learning algorithms to process unstructured data from the external environment in real-time. This approach improves understanding of how administrative flexibility and automation can be aligned within a unified system to address internal and external risks. The results demonstrate a transition toward continuous monitoring and pre-emptive risk control. Effective mitigation depends on real-time analytical support for staff, reducing uncertainty. The study shows that detailed structuring of operations enables the use of intelligent forecasting tools that support, rather than replace, managerial decisions. The synergy between human expertise and algorithms ensures stability and early detection of potential crises. The practical value lies in offering a roadmap for modernizing risk management during economic recovery. The model helps reduce response time and improve forecasting accuracy. Its implementation supports resilience, operational continuity, and stronger corporate governance, providing enterprises with a competitive advantage.