Composable Personalization Architecture: A Scalable Framework for Modular AI-Powered Experience Engineering

Abstract

Personalized experiences have become a more important strategic objective in many industries, but organizations still deal with architectural and other barriers to scaling, experimenting, and maintaining cross-channel loyalty. This article proposes a Composable Personalization Architecture (CPA), built to enable the value of personalization in a modular, layered approach. CPA decouples central personalization functions such as signal collection, decision-making logic, and content delivery into interoperable services that are later connected at shared telemetry and orchestration layers. This separation of concerns allows organizations to integrate new models more easily, ultimately allowing for controlled experimentation and adaptation to changing business needs. The architecture has been tested in enterprise environments, and organizations found improved agility, system robustness, and engagement. By delivering personalization as a composable capacity instead of a monolithic feature, CPA more effectively addresses the sustainable and adaptable design of a customer's experience at scale.