It wasn’t long ago that artificial intelligence was more of an abstract yet exciting concept in development but not quite tangible.

Today, AI is no longer abstract. In fact, we’re seeing everyone from large corporations to small businesses deploy AI to troubleshoot problems, increase productivity, analyze data, write marketing or creative briefs and serve as customer service chatbots. In the electric energy industry, AI has moved from strategy and possibility into a viable presence embedded in field forms, invoice validation, fault detection systems and customer engagement platforms.

In the past two years alone, we’ve had several subject matter experts write articles for EEOnline about how organizations are or should be using AI in the electric utility space. In this Q3 issue, several articles discuss AI’s role in electric energy. This collection of articles shows how AI has clearly shifted. For this column, I am highlighting four articles that show the different ways AI has taken hold and is lighting up the electric energy sector.

In "AI Energy Trade-Off: Refining Grids for a Greener Tomorrow," April Miller, with ReHack, frames AI's growth in exactly those terms: a trade-off between opportunity and cost. Miller points out that AI is creating new opportunities across the energy sector, from grid optimization to demand forecasting, but the article really centers on the unprecedented demand for AI energy that this same technology is driving. Miller looks at what's behind that surge, from high-speed cabling to advanced cooling, and she covers the policy side too. Right now, the utility sector is still weighing AI and getting ready for it, not yet putting it fully to work.

The second article is "Managing AI Within Energy Fleets: Why the Hardest Problems Start After Deployment," by IOTech's Andrew Foster. Foster notes that the conversation has moved past whether AI belongs in grid operations and is now focused on where and how to deploy it, from predictive battery storage simulations to solar fleet maintenance and edge-based voltage optimization.

His argument is about what he calls the deployment-to-operations gap. A model trained on a snapshot of the world will drift as that world changes, and performance drops are usually small at first until they pile up into a real problem, such as operators who've started ignoring false fault alerts. Fixing this, he says, takes four things: keeping data normalized on an ongoing basis, orchestrating the model lifecycle across the whole fleet, watching performance continuously and drawing a hard line that keeps AI in an advisory role, with humans still making the calls. For Foster, this isn't really about the model; it's about whether the operations team is mature enough to handle one that changes underneath them, and that means data science and operations need to know exactly who owns what.

In "The Human Grid Asset: Scaling Grid Stability through Gamification," Alex Hill of Sendero Consulting examines AI's role in the behavioral layer of the virtual power plant, rather than the hardware side that Foster covers. Hill’s point is that the "human grid asset," the pooled demand flexibility of engaged customers, stays out of reach if utilities only have data to work with, because data alone doesn't drive action without a sustained feedback loop. According to Hill, AI’s role is to close that cognitive gap proactively by processing consumption data: predictive analytics let a utility send customers "challenges" ahead of a grid event, instead of scrambling to react once one occurs. Hill builds this around clarity, motivation and feedback: quests, layered incentives and real-time streaks and milestones, with AI as the piece that lets the utility anticipate load and shift behavior ahead of time. This gamification approach is a narrower application of AI than Foster's fleet-scale focus, but it's still a viable, operational use, not just a reporting tool.

The article featured in this issue’s Grid Transformation section, "How Utilities Track Large Capital Programs: Field Data Collection to Real-Time Balanced Scorecard at Scale," presents another clear indication of the shift from AI as an abstract concept to a solid role in grid modernization. Written by Hari Vasudevan of KYRO AI, along with CenterPoint Energy's Mythili Chaganti, Jerry Caldwell and Chris Harris, this article explains how CenterPoint is executing a $3 billion-plus grid resilience buildout across the Texas coast. CenterPoint Energy aims to cut storm-related outage minutes by nearly a billion for 2.8 million customers by 2029. Its Balanced Scorecard Command Center consolidates six previously separate data systems, such as scheduling, financials, invoicing and field reports, into a single real-time view, now tracking almost 600 projects with 99.56% invoice compliance.

Instead of rolling out a new tool for staff to learn, CenterPoint built AI into the workflows that already fed the KPI platform. It checks timesheets against rate cards and equipment allocations, catching billing discrepancies at invoicing rather than months later. Project managers and coordinators can ask questions in plain language to pull up project performance, and out in the field, an AI agent handles crew questions, provides context, and walks them through their forms and paperwork. On the analytics side, it is watching timesheets, invoices, and project data as they come in, flagging trends and anything that looks off. The Balanced Scorecard Platform is still the system of record through all of this; AI just speeds up the data going in and helps leadership make sense of what's coming out, with people still in charge of the decisions in between.

Each of these articles, along with the other articles in this issue that discuss AI, traces how the utility sector's relationship with AI has shifted from abstract to something tangible. Miller’s "AI Energy Trade-Off" treats AI largely as a concept to be weighed and planned around, driven by the demand it creates for energy infrastructure and by the reporting standards emerging alongside it. Foster’s article takes the conversation from pilot to fleet-scale operations, where AI must be actively maintained, monitored and governed once it's running on real equipment. "The Human Grid Asset" extends that shift into a new domain, using forecasting analytics to activate the behavioral layer of the virtual power plant. And CenterPoint's Balanced Scorecard system is the most concrete example of that shift: AI is already embedded in timesheets, field forms and invoice validation on a $3 billion active capital program, operating within a platform that remains firmly under human authority. Combined, these articles show a sector that has largely moved past the question of AI's place in utility operations, and is now concentrating on making it work reliably, day to day.

If you would like to contribute an article on an interesting project, please email me: Elisabeth@ElectricEnergyOnline.com

Elisabeth