Integration Patterns in Unified AI and Cloud Platforms: A Systematic Review of Process Automation Technologies

This article comprehensively analyzes unified AI and cloud platforms, examining their role in transforming process automation and decision systems across industries. The article investigates the architectural frameworks and integration patterns that enable the convergence of AI tools, machine learning operations, and workflow orchestration within cloud-native environments. The article explores key innovations, including federated AI implementations, real-time data processing architectures, and multi-cloud integration patterns. It provides insights into their practical applications across finance, healthcare, retail, and manufacturing sectors. The article identifies critical success factors in platform implementation, including integrating MLOps frameworks, automated decision engines, and compliance tools for AI governance. Through case study analysis and architectural evaluation, we demonstrate how unified platforms address traditional challenges in AI deployment while enabling scalable, cost-efficient solutions. The findings reveal emerging patterns in platform architecture that facilitate seamless integration of edge computing, real-time analytics, and distributed AI systems, contributing to the broader understanding of enterprise AI implementation strategies. This article provides valuable insights for researchers and practitioners in cloud engineering, artificial intelligence, and systems integration while highlighting future directions for platform evolution and standardization.