Adaptive Decisioning in Pega: Leveraging AI and Machine Learning for Real-Time Customer Engagement

As enterprises compete in increasingly dynamic digital ecosystems, real-time decisioning has become critical to sustaining meaningful customer engagement. Pega’s adaptive decisioning engine, powered by artificial intelligence (AI) and machine learning (ML), enables organizations to personalize experiences at scale. This study investigates the theoretical underpinnings and practical applications of adaptive decisioning in Pega, with a focus on its use in industries such as insurance and telecommunications. Through the lens of decision theory and dynamic learning models, the paper analyzes how adaptive decisioning systems influence key business metrics like churn, upselling, and satisfaction. The framework highlights the feedback loop between behavioral data, predictive modeling, and contextual relevance, positioning Pega’s approach as a model for future customer-centric engagement.