AI Tools for Utilities

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Effortless regulatory and internal reports

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Trained report generation

Draft reports generated with AI then enhanced with Copilot. Ask Copilot to add more context from specific asset fields, create charts automatically, and more. Trained on the largest collection of Utility regulatory filings.

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Copilot gives everyone the power to analyze

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Ask copilot for analysis

Get the insights and answers you need to make decisions, without all of the steps. Stop wasting time hunting for data, and instead... just ask.

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Copilot doesn't retire

Avoid losing context when team members leave. Copilot can analyze changes to scoring formulas over time, return old reports to ensure continuity, and more.

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Better decision making today and tomorrow

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Outage management system

Copilot uses AI to infer all available data to aid in the first few minutes of outages, helping narrow focus areas to speed up disaster recovery

Asset hardening tools

Leverage advanced topographic forecasting datasets to build asset hardening plans and protect against severe weather events

A map with Utility assets like transformers and distribution poles that shows AI helping identify severe weather areas to watch
A map with Utility assets such as transformers and poles that has a copilot AI agent suggesting growth placement of the grid

More confident grid growth

Use Senpilot to help plan grid growth. Multiple optimization functions can tap into Single Asset View.

AI for DER / DERMS

Managing bidirectional energy flows requires monitoring, bidding, forecasting, optimization, and quality controls done instantly. Build the capability with AI using copilot.

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July 11, 2025

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

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