AI Applications in Power Systems: The Role of Structured Data and the Acceleration of Legacy Data Structuring through Artificial Intelligence

AI applications remain limited by the fragmentation of critical engineering knowledge, which is often stored in unstructured legacy documents. This paper examines the role of structured, object-oriented engineering data as a prerequisite for scalable and reliable AI applications in the power sector. Grounded in methodologies such as Building Information Modeling (BIM) and Engineering Information Modeling (EIM), the study highlights the transition from document-centric to data-centric engineering practices. A review of the literature shows that, despite advances in standards and technologies, utilities continue to struggle with fragmented legacy data, which limits the scalable application of AI across power sector challenges. To address this gap, the paper presents an AI-based methodology for extracting structured metadata from legacy documents and integrating it into a centralized engineering database. A case study at a Brazilian Transmission System Operator demonstrates tangible benefits of the proposed approach, including a significant reduction in metadata registration time compared to manual processes, enabling large volumes of legacy documentation to be structured within realistic time and cost constraints. Additional gains include improved agility, information reliability, faster access to technical data, enhanced fault analysis consistency, and effective object-based querying of asset histories, supporting maintenance and substation modernization activities.

Finally, the paper discusses the next step toward interactive AI-assisted advisors capable of reasoning directly over structured engineering databases, including the evaluation of a newly released Engineering Advisor developed in this direction. The results reinforce a fundamental

AI role in power systems: advanced AI applications depend on high-quality structured data, while AI itself can be effectively used to transform legacy information into such structured data.

In this context, structured data environments emerge not only as a prerequisite for human access and data governance, but also as a strategic enabler for scalable and trustworthy AI applications, accelerating digital transformation and preserving engineering knowledge across the power system lifecycle.