AMTP Proceedings 2026

Document Type

Conference Proceeding

Publication Date

Spring 2026

Abstract

Artificial intelligence is widely adopted in marketing, yet many organizations struggle to translate analytical outputs into timely action. This paper presents a practitioner-oriented blueprint for deploying root-cause analysis (RCA) agents within AI-augmented marketing analytics systems. We propose a three-layer reference architecture—semantic data layer, autonomous RCA agent, and action layer—that enables continuous anomaly detection, causal diagnosis, explanation generation, and recommendation delivery. Through simulated e-commerce use cases, we demonstrate how RCA agents identify performance disruptions such as email engagement declines and checkout failures, and translate diagnostic findings into actionable guidance. Beyond system design, we outline organizational workflows, trust-building validation loops, scalability pathways toward multi-agent ecosystems, and governance safeguards addressing privacy, fairness, and oversight. The framework shifts the focus from AI capability discussion to implementable deployment strategy, offering marketing leaders a structured pathway to operationalize AI-driven diagnostic agents within responsible, closed-loop analytics architectures.

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