AI-Augmented in Enterprise Domain Modeling and its impact on Data Modernization projects (International Journal of Engineering & Extended Technologies Research (IJEETR), 2025)
Enterprise domain modelling now serves as a workload trigger in the easily massive data modernization, where AI offers organizations a method to create standard, multi-purpose business entity, business process, and business value flow representations faster and more accurately when they possess fragmented data ecosystems, mixed formats, and dependencies with the legacy system. To fill these shortcomings, the present paper will talk about the solution based on AI, which utilizes machine learning, natural language processing (NLP), and the use of graphs to get an edge in the domain modeling life cycle by way of automation and optimization. The suggested solution will involve the processing of structured and unstructured data using multi-source data ingestion pipelines, such as metadata repositories, lineage logs, data dictionaries, and APIs, as well as operational documents. It uses a system where the domain entity is referred to as the entity recognizer (NER), the recognizer relationship relationship extraction paradigm, and similarity classifier transformers that are utilized to cluster the conceptually similar products and business functions. The graph embedding and schema generation algorithms are then run to generate the initial domain models, which are further expanded as the human-in-the-loop feedback to ensure that the business validity is achieved. A 40-55% decrease in the amount of manual modeling labor, a 30 percent corrected accuracy of schema alignment, and an immense increase in cross-domain interoperability that speeds up system integration, data product definitions, and standardization of the commonality of performance measurement have been pointed out in three pilot programs of enterprise data modernizations. Moreover, the AI-enhanced process revealed hitherto not visible connections, e.g., duplicated entities, repetitive processes, and untapped customer value connections, to make more informed decisions about the architecture. All in all, the role played by AI enhancement in the quality, speed, and scalability of enterprise domain modelling is gigantic and has been an expedient of the data transformation processes that happen today.
Keywords: Enterprise Domain Modeling; AI-Augmented Modeling; Semantic Alignment; NLP; Data Modernization