Intelligent Incident Management Leveraging Artificial Intelligence, Knowledge Engineering, and Mathematical Models in Enterprise Operations

Arturo Peralta (), José A. Olivas, Francisco P. Romero and Pedro Navarro-Illana
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Arturo Peralta: Escuela Superior de Ingeniería, Universidad Internacional de Valencia, Calle Pintor Sorolla, 21, 46002 Valencia, Spain
José A. Olivas: Departamento de Tecnología y Sistemas de Información, Universidad de Castilla-La Mancha, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain
Francisco P. Romero: Departamento de Tecnología y Sistemas de Información, Universidad de Castilla-La Mancha, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain
Pedro Navarro-Illana: School of Engineering, Tech Universidad Tecnológica, Av. Taco, 164, 38108 La Laguna, Spain

Mathematics, 2025, vol. 13, issue 7, 1-34

Abstract: This study explores the development and implementation of an intelligent incident management system leveraging artificial intelligence (AI), knowledge engineering, and mathematical modeling to optimize enterprise operations. Enterprise incident resolution can be conceptualized as a complex network of interdependent systems, where disruptions in one area propagate through interconnected decision nodes and resolution workflows. The system integrates advanced natural language processing (NLP) for incident classification, rule-based expert systems for actionable recommendations, and multi-objective optimization techniques for resource allocation. By modeling incident interactions as a dynamic network, we apply network-based AI techniques to optimize resource distribution and minimize systemic congestion. A three-month pilot study demonstrated significant improvements in efficiency, with a 33% reduction in response times and a 25.7% increase in resource utilization. Additionally, customer satisfaction improved by 18.4%, highlighting the system’s effectiveness in delivering timely and equitable solutions. These findings suggest that incident management in large-scale enterprise environments aligns with network science principles, where analyzing node centrality, connectivity, and flow dynamics enables more resilient and adaptive management strategies. This paper discusses the system’s architecture, performance, and potential for scalability, offering insights into the transformative role of AI within networked enterprise ecosystems.

Keywords: artificial intelligence; knowledge engineering; mathematical modeling; optimization techniques; natural language processing; enterprise IT networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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