Cloud-Native Microservices for Scalable AI-Driven Business Process Automation

Abstract

Business Process Automation (BPA) is evolving from traditional rule-based systems toward intelligent, adaptive, and context-aware automation driven by artificial intelligence. While enterprises increasingly adopt AI to enhance operational efficiency and decision accuracy, monolithic automation architectures limit scalability, degrade performance under variable workload conditions, and fail to support rapid iteration or governance requirements. This paper presents a cloud-native microservices framework for scalable AI-driven business process automation that integrates event-driven communication, modular AI services, container orchestration, and continuous learning mechanisms. The proposed architecture leverages microservice decomposition, intelligent workflow orchestration, cognitive RPA, and real-time analytics to orchestrate dynamic, data-driven business workflows. A methodology encompassing process mining, data pipeline design, model lifecycle governance, and Kubernetes-based deployment ensures modularity, fault-isolation, auto-scaling, and observability. A finance-domain case study demonstrates how intelligent microservices, NLP-based document automation, probabilistic anomaly detection, and human-in-the-loop feedback systems streamline invoice processing, improve accuracy, and reduce processing latency while preserving compliance and auditability. This research establishes a unified conceptual blueprint for enterprises seeking elastic, trustworthy, and governable AI-augmented automation ecosystems, enabling continuous business improvement and resilience in complex operational environments.