Utility Analytics Institute

Abstract: The article shows how integrating AI Agents with traditional Analytics Platforms may drive significant efficiency, expand discovery and deliver exceptional value to the Utility. 3,600 RPM. That’s the rotational speed required of a high-speed turbine coupled to a 2-pole generator to maintain 60HZ frequency (PJM Interconnection, 2018, p. 21). Lately, it seems innovations in the Utility and Analytics landscapes are spinning just as fast. Seven years ago, it was a different story.

Many energy providers have spent decades modernizing their tech stacks, building the data foundations that can now support emerging technologies such as agentic AI. A major turning point was the shift away from legacy, siloed IT systems toward centralized data lakes. Millions of disparate data points, from AMI interval reads to GIS mapping to outage management data, and billing records, can now live in unified cloud repositories.

Eight finalists. Three signature award categories. One community coming together to celebrate excellence in utility analytics. The analytics may happen behind the scenes. The people making it happen shouldn’t. The Utility Analytics Institute (UAI) is proud to announce the eight finalists for the 2026 UAI Excellence Awards, recognizing outstanding professionals, leaders, and teams whose work is helping move utility analytics forward.

Generative AI is quickly moving from experimentation toward real-world utility applications, but getting from a successful proof of concept to a sustainable enterprise capability remains a significant challenge.

AI agents can fail in production because the architecture cannot handle coordination across agents, maintain continuity between steps, or operate under regulatory scrutiny. For utilities deploying AI in operational workflows, getting the architecture right from the start is not a best practice. It’s the difference between a system that works and one that creates new risk.

For utilities, the challenge is no longer whether they have enough data. They do. Customer systems, meter systems, billing platforms, asset applications, ERP, HCM, supply chain, finance, weather, demographics, regulatory inputs: the signals are everywhere. The harder question is whether those signals can be brought together quickly enough, trusted enough, and acted on directly enough to change outcomes. That is where the old equation breaks down.

When it comes to analytics, it’s long been understood that how you develop your algorithms, what criteria and attributes you use and when you perform functions are all important to the accuracy and potential value of the results. With the advent of grid edge computing, we also consider a fourth key dimension: where. How. What. When…Where. Edge computing has gained traction across various industries, bringing computation and data storage closer to the source of data.

The rapid expansion of data center development, driven largely by artificial intelligence (AI), cloud services, and digital infrastructure, is introducing a new class of large, fast-moving energy users for local natural gas distribution companies (LDCs). While much of the public discussion has focused on the strain on electricity systems, the impacts of data centers on gas utilities appear to receive far less attention.

As artificial intelligence (AI) expands what is technically possible, utilities may be entering a new phase of digital transformation. The importance of usability may increase as data systems become more complex. Success may therefore depend not only on whether a system works, but also on whether people can understand it, trust it and adopt it. This is where user experience (UX) processes can become central.

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