In the first post in this series on the hidden risks of AI in the energy enterprise, I outlined the problems and risks of AI hallucinations. Today, I’d like to move beyond ungrounded content to present the hidden risks of AI adoption amid energy’s unstructured data dilemma.
Gartner predicts that 60 percent of AI projects will be abandoned by the end of 2026 due to data quality, completeness, and availability challenges.. In a nutshell, it’s not organizational acceptance that is holding back AI scale-up. Energy professionals are eager for the productivity-enhancing benefits of AI, but it appears their data is not. Specifically, integrating corporate data and knowledge repositories with AI models to reduce ungrounded content through retrieval augmented generation.
This means broadening the focus beyond the types of data that fit neatly into a database to the much larger volume of unstructured data and documents that every energy company runs on. Retrieval augmented generation, or RAG, is how you access this critical information.
With RAG in mind, consider the problem of data availability first. While building the digital pipelines that drive AI success remains a struggle for many energy companies, new capabilities are simplifying integration. Recently, ChatGPT users gained a powerful feature called Connectors that allows corporate knowledge to be instantly integrated with the model through OneDrive, Box, Google Drive, and Dropbox. This places a laser focus on documents and unstructured data as primary sources for RAG. Yet even with these powerful integration features, most organizations are not ready to connect their data because file structures are flat, sprawling, and plagued with multiple versions of the truth.
Availability aside, energy data simply is not ready for AI. Lack of structure is one side of the coin. The other is stranded knowledge inside unstructured data and documents. And these are the problems Pepper Energy Partners exists to solve.
Pepper is empowering energy’s data and AI transformation with our 100% focus on industry workflows and a deep expertise in data integration and document management. Not only can we get your data and documents RAG- and AI-ready, Pepper can also assist in building digital pipelines and helping you apply AI to the workflows where it can have the biggest impact.
In the third and final post in this series, I’ll discuss why solving ungrounded content and making data RAG-ready can present a final hidden risk to energy companies that jeopardize compliance and data security.
Contact Pepper today to put your AI journey on the right path, avoiding the pitfalls and hidden risks along the way.
