AI-Driven Decision Intelligence in Enterprise Customer Service: A Framework Analysis of Pega's Next-Best-Action Platform

Abstract

This article examines Pega's AI Decisioning Framework as an enterprise solution for optimizing customer service through real-time, context-aware decision-making. The framework integrates business process management and customer relationship management capabilities via algorithmic decisioning, particularly benefiting regulated industries like financial services. Drawing from decision science, machine learning, and process automation theory, the system employs a multi-layered technological ecosystem including a centralized decision hub and hybrid decision engine. The article explores how financial institutions leverage predictive analytics for anticipating customer behaviors, the mechanics of next best action methodology, regulatory compliance mechanisms, and approaches for measuring business impact. Particular attention is given to ethical considerations in automated decisioning, including transparency requirements, bias detection, and privacy safeguards. The framework's architecture enables consistent decisioning across heterogeneous systems while maintaining regulatory adherence, though implementation success depends on balancing efficiency with ethical responsibility and developing robust measurement methodologies.