Modern enterprises operate on data, yet their ability to generate intelligence is constrained by siloed systems, duplicated pipelines, and costly integration processes. This book presents a practical architectural framework for integrating ERP systems such as SAP, Oracle, and Microsoft Dynamics using semantic models, hybrid storage, and selective AI-assisted techniques where automation adds genuine value. Rather than applying AI indiscriminately, the architecture leverages machine learning and large language models specifically for resolving semantic ambiguity, schema alignment, and knowledge discovery, while relying on deterministic, efficient mechanisms for core data movement and execution. Traditional ETL-heavy pipelines are replaced with knowledge graph-driven integration, adaptive ingestion, and low-carbon execution strategies, resulting in an enterprise information system that delivers connected, discoverable data with high efficiency. Through architectural designs, performance metrics, and real-world examples, the book translates research-driven concepts into actionable design principles for architects, engineers, and technology leaders seeking scalable, sustainable, and future-ready integration systems.
