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Modernization of Cloud Digital Enterprise Platforms with Generative Artificial Intelligence and Data Integration

Abstract

The rapid evolution of digital enterprise ecosystems demands continuous architectural agility, seamless data integration, and real-time cognitive capabilities. However, legacy cloud digital enterprise platforms are consistently bottlenecked by structural data fragmentation, rigid schema requirements, and high maintenance overhead associated with deterministic integration pipelines. This paper presents an optimized architectural paradigm for the modernization of cloud digital enterprise platforms using generative artificial intelligence (AI) and semantic data integration. By embedding foundation models directly into the enterprise data plane, the proposed framework transitions traditional data orchestration from static Extraction, Transformation, and Loading (ETL) routines to dynamic, context-aware semantic pipelines. We formalize a structural transformation layer that leverages generative AI for automated schema alignment, intelligent metadata generation, and real-time synthesis of unstructured and structured corporate repositories. To ensure enterprise-grade stability and security, this integration pattern combines retrieval-augmented generation (RAG) with strict role-based access control (RBAC) and data lineage tracing at the ingestion boundary. Empirical evaluations across large-scale multi-cloud digital platforms demonstrate substantial reductions in pipeline development lifecycles, an increased capacity for parsing unstructured files, and enhanced query accuracy. Ultimately, this research provides enterprise architects with a definitive, scalable roadmap for unlocking corporate data assets while maintaining zero-trust governance boundaries.

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