Learning from Unstructured Documents: Extracting Value Using Machine Learning and Generative Augmented Intelligence

Learning from Unstructured Documents: Extracting Value Using Machine Learning and Generative Augmented Intelligence

AI/ML holds transformative potential for the energy industry, but its success depends on robust data preparation—particularly for unstructured data like reports, logs, and legacy documents. These datasets often lack metadata, have inconsistent formats, and require significant processing to unlock their value. By standardizing and enriching unstructured data, AI can uncover patterns and generate actionable insights that drive efficiency and sustainability.

Using a global multi-petabyte data store, we demonstrate how integrating structured and unstructured data enhances AI workflows. The FAIR principles—making data Findable, Accessible, Interoperable, and Reusable—are vital to project success. A “human-in-the-loop” approach ensures AI delivers reliable results while continuing to improve.

Applications such as natural language Q&A and fine-tuned generative AI models enable intuitive data discovery, offering measurable ROI. These innovations underscore the need for trustworthy datasets and industry-specific prompt engineering to achieve AI/ML success, ultimately aligning with the energy sector’s sustainability goals.