AI-Powered Customer Journey Optimization: Personalizing User Experience in BPM Systems
DOI:
https://doi.org/10.5281/zenodo.15741918Keywords:
Artificial Intelligence (AI), Business Process Management (BPM), Customer Journey Optimization, Personalization, Machine Learning, Predictive Analytics, Sentiment Analysis, Conversational Agents, Federated Learning, Process Automation, Intelligent Systems, Customer Experience (CX), Real-Time Decisioning, Explainable AI (XAI)Abstract
With customer expectations changing at a very high pace in the current digital environment, businesses are rethinking how to provide richly personalized user experiences throughout the customer journey. Business Process Management (BPM) systems, which have been conventionally built for operational efficiency, are now being revolutionized by Artificial Intelligence (AI) to facilitate customerfocused innovation. In this study, we consider how new AI technologies such as machine learning, conversational interfaces, sentiment analysis, and federated learning augment BPM systems to achieve dynamic, context-dependent, and adaptive interactions. Based on recent research and business case studies, we consider how intelligent automation enables real-time decision making, predictive engagement, journey optimization, and process personalization. These technologies have the potential to exponentially revolutionize customer satisfaction, retention, and organizational agility. Simultaneously, we address fundamental ethical concerns, including algorithmic bias, data privacy, and system explainability, which are critical to gaining trust in AI-infused systems. The study concludes with a future research agenda that targets responsible AI adoption, large-scale personalization, and the continued evolution of intelligent BPM platforms.
