International Journal of Science, Engineering and TechnologyAuthors: Karthik Ramesh
Abstract: Artificial Intelligence (AI)-based Root Cause Analysis (RCA) has emerged as a transformative approach in industrial and IT operations, profoundly impacting the efficiency and speed of problem resolution. Traditionally, diagnosing and rectifying issues on complex systems required manual investigation, often leading to prolonged mean time to repair (MTTR) and increased downtime. AI-driven RCA leverages advanced data analytics, machine learning, and pattern recognition to automate and enhance the identification of underlying faults. This dramatically cuts down the time engineers spend on diagnosing failures, thereby reducing MTTR and improving operational continuity. By incorporating AI in RCA processes, organizations gain predictive insights that facilitate proactive maintenance, minimize unexpected breakdowns, and optimize resource allocation. The ability of AI to learn from historical incident data and adapt to evolving system behaviors significantly boosts diagnostic accuracy and reliability. This article explores the profound impact of AI-based root cause analysis on MTTR reduction, highlighting mechanisms like anomaly detection, automated troubleshooting, and real-time system health monitoring. It further examines industry case studies across manufacturing, telecommunications, and IT infrastructure, showing concrete evidence of operational gains. Challenges such as model interpretability, integration complexity, and data quality are addressed, alongside emerging solutions in AI explainability and hybrid diagnostic frameworks. Ultimately, the adoption of AI in RCA heralds a new era of accelerated problem resolution and system resilience, critical for maintaining competitive advantage in fast-paced technological landscapes.
DOI: http://doi.org/10.5281/zenodo.17709141
