Multi-Agent Decision Intelligence for Unified Customer Lifecycle Optimization in Enterprise Environments

Abstract

Customer lifecycle decisions in enterprises are often fragmented across marketing, finance, customer experience, and operations, resulting in inconsistent actions and suboptimal outcomes. This paper introduces Multi-Agent Decision Intelligence (MADI), a framework that models enterprise functions as autonomous yet coordinated agents aligned through shared objectives. Using centralized training with decentralized execution, agents negotiate actions that balance lifetime value growth, churn reduction, customer experience, and cost-to-serve efficiency under operational and regulatory constraints. Experimental results on industrial and semi-synthetic datasets demonstrate consistent improvements over siloed optimization, centralized reinforcement learning, and heuristic baselines. A pilot deployment within a Saudi enterprise further confirms practical feasibility, improved crossfunctional alignment, and reduced decision latency. MADI provides an enterprise-ready blueprint for coordinated AI-driven decisionmaking in complex organizational environments.