Foundational AI models: A practical guide for Utilities

AI continues to revolutionize the business landscape, and for utilities, effectively implementing foundation models is crucial for success. These powerful neural networks, initially trained on vast datasets, offer immense potential when tailored to an electric utility's unique operational data and challenges. This customization can be achieved through various methods: parameter-efficient tuning for quick adaptation to specific jargon, fine-tuning for mission-critical applications requiring high precision, and reinforcement learning to align AI outputs with safety and customer service protocols through continuous human feedback. Utilities can adopt AI through practical paths, such as Retrieval-Augmented Generation (RAG), for secure, context-specific answers from internal documents. This approach enables building custom solutions with pre-built tools for specialized needs, as well as comprehensive enterprise integration for real-time anomaly detection and optimized crew dispatch. Strategic AI implementation involves identifying specific pain points, selecting the appropriate customization approach, prioritizing security and privacy (including vendor certifications such as SOC 2 Type II and ISO 27001), and initiating small, manageable pilot projects that demonstrate immediate value. The goal is to achieve the right balance for a utility's objectives, ultimately leading to new levels of operational efficiency

Mike Hejmej

CEO, Co-founder

July 11, 2025