Beyond Keyword Search: AI-Driven Log Intelligence for Proactive Support and Engineering Alerts

Abstract

Traditional reactive log analysis dependent on predefined keywords and manual searches—fails to address modern system complexity. This article demonstrates how AI-driven log intelligence leverages machine learning (ML) for anomaly detection, predictive failure modeling, and automated alert routing, enabling engineering teams to resolve issues before customer impact. Analysis of industry implementations reveals 40–75% reductions in mean time to detection (MTTD) and 30–60% decreases in downtime-related support tickets. Proactive interventions correlate with 15–25% improvements in customer satisfaction (CSAT) by preventing negative experiences. Technical architectures integrating stream processing, time-series databases, and AIOps platforms form the foundation of this paradigm shift.