Explainable AI for Real-Time Enterprise Analytics and Cloud Decision Support

Abstract

Enterprises increasingly rely on machine learning models embedded in real-time streaming analytics pipelines to support operational decisions as events occur, rather than hours or days later in a batch report. This shift creates a tension the explainable-AI literature was not originally built to resolve: post-hoc explanation methods such as LIME and SHAP were developed and evaluated largely against static, already-trained models, while a real-time streaming pipeline continuously updates its model in response to concept drift, so an explanation generated even minutes earlier may no longer describe the model version currently making decisions. Decision-support interfaces, in turn, are frequently designed without reference to either the streaming system's update cadence or the explanation method's staleness characteristics, leaving the human decision-maker with a prediction, an explanation, or both, but no assurance that the two are mutually consistent with the model that produced them. This paper proposes TRACE (Transparent Real-time Analytics for Cloud-based Enterprise decision-support), an architecture in which a real-time streaming analytics engine, a per-prediction explainability module, and a decision-support interface are coordinated by a shared explainability-integration controller that keeps every surfaced explanation synchronized with the exact model version that produced the prediction it explains, including across concept-drift-triggered model updates. Following a design-science research approach, TRACE is illustrated, not empirically validated, through a comparative capability table, a short case scenario, and two illustrative figures, with every quantitative value explicitly labeled as an illustrative estimate rather than measured production data. The paper situates TRACE against the literature on post-hoc explainability methods, explanation-grounded human decision-making, real-time and streaming analytics infrastructure, and cloud-native infrastructure for enterprise decision support.