International Journal of Science, Engineering and TechnologyAuthors: Laura Bennett, Dr. Richard Collins, Daniel Harris, Matthew Scott, Benjamin Clark, Jeji babu
Abstract: AI-Guided Support Engineering represents a transformative approach to modern enterprise support systems by integrating advanced artificial intelligence with human expertise to enhance escalation analysis and resolution processes. This study explores a human-in-the-loop framework where AI models assist in triaging, classifying, and prioritizing support escalations while experienced engineers provide contextual judgment, validation, and oversight. The proposed approach leverages machine learning algorithms, natural language processing, and historical incident data to identify patterns, recommend solutions, and reduce response times. At the same time, human intervention ensures accuracy, mitigates risks of automation bias, and handles complex or ambiguous scenarios that require domain expertise. The synergy between AI-driven automation and expert decision-making leads to improved operational efficiency, faster incident resolution, and higher customer satisfaction. Additionally, the framework emphasizes continuous learning, where feedback from human experts is used to refine AI models, creating a self-improving support ecosystem. This research highlights the importance of balancing automation with human intelligence to build resilient, scalable, and reliable support engineering systems in large-scale enterprise environments.
DOI: http://doi.org/10.5281/zenodo.20265370
