Artificial intelligence in predictive IT incident management

• Autores: Amaya Jave, Luigui Jampierre, Querevalú Galán, Roger Alejandro, Mendoza de los Santos, Alberto Carlos
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Universidad La Salle
• Repositorio: Revistas - Universidad La Salle
• Lenguaje: español
• OAI Identifier: oai:ojs.revistas.ulasalle.edu.pe:article/177
• Enlace del recurso: https://revistas.ulasalle.edu.pe/innosoft/article/view/177; https://doi.org/10.48168/innosoft.s16.a177; https://purl.org/42411/s16/a177; https://n2t.net/ark:/42411/s16/a177
• Nivel de acceso: acceso abierto
• Materia: IT incidents; artificial intelligence; predictive management; incidentes de TI; inteligencia artificial; gestión predictiva

This systematic review synthesizes the literature on the application of Artificial Intelligence (AI) in predictive incident management in Information Technology (IT). The study focuses on evaluating the predictive capability of AI-based solutions and identifying areas for future research. Using the PRISMA methodology, comprehensive searches were conducted in academic databases using specific search equations. Fifteen articles were selected that addressed the topic from various perspectives, highlighting the use of advanced techniques such as machine learning, deep learning, and transformers to enhance accuracy in predicting IT incidents. Furthermore, it explored how AI for IT Operations (AIOps) facilitates the automation and proactive management of incidents, thereby optimizing operational efficiency and system availability. The findings underscore the effectiveness of these technologies in reducing incident resolution times and improving organizational resilience against emerging technological challenges. Overall, this review emphasizes the importance of continuous innovation and strategic integration of AI in IT service management to enhance operational efficiency and strengthen organizational adaptability.