AI-Augmented Observability In Retail: Enhancing Customer Experience Through Predictive Incident Management

Abstract

The fast scaling of digital retail ecosystems can only enable incident management to be smarter as it needs to enable seamless customer experiences. The conceptual framework proposed in this paper samples artificial intelligence in observability systems with an aim to increase either anomaly detection, predictive maintenance or automated remediation. Provided that machine learning is used, the framework clears the noise, narrows the response time, and detects issues with high impact in both customer and system telemetry. The actual results and comparative studies show a sharp rise in accuracy in detection, lead-time of incident, and customer satisfaction. Issues like interpretability of the model, trust and system integration are dealt with. This study provides a prospective guide of intelligent and customer aware observability in large scale retail operations.