AI in Utilities: Moving from Pilot to Production

**Marc Miller** (0:12)
Welcome to the ScottMadden Energy Exchange, conversations with leaders shaping the future of the energy industry. I'm your host, Marc Miller, partner and energy practice leader at ScottMadden. In this podcast, we explore the most important issues facing utilities and energy companies, from infrastructure and regulation to operations and strategy. We focus not just on what's changing, but on what it takes to execute in a complex and evolving environment.
Today's episode is titled AI in Utilities, Moving from Pilot to Production. Utilities are under increasing pressure from multiple directions. Load is growing again, driven by data centers, industrialization, and electrification. The system itself is becoming more complex, with distributed resources and new technologies. And at the same time, utilities are managing workforce constraints and an explosion of data, from AMI systems and other sensors deployed in their infrastructure. Against that backdrop, there's a growing conversation about how artificial intelligence or AI, can help utilities operate more efficiently and make better decisions. But while many utilities have experimented with AI, far fewer have successfully scaled it. For many, AI still feels more like experimentation than execution. So the question becomes, how do you move from pilot projects to real, sustained value from AI?
To explore that, I'm joined today by Jon Kerner, a partner and AI expert at ScottMadden, who works closely with organizations on successfully applying AI and advanced analytics.
Jon, let's start with the picture as we like to do. AI has been talked about for years, but it feels like the conversation has begun to shift recently. Why is AI becoming more relevant for utilities now than it was even a couple of years ago?

**Jon Kerner** (2:16)
A few things came together at once, and that's really the story.
For years, AI was a slide in a strategy deck, interesting but not urgent. What changed is that new industry pressures and data raw materials showed up at the same time. On the pressure side, as you mentioned, load is growing again after two decades of being basically flat. Data centers, manufacturing coming back on shore, electric vehicles, and building electrification, all of it landing on the same grid. And a lot of that grid was built for a different era. The same time the system is getting harder to run, solar, storage, and EVs have turned what used to be a one-way system into something that flows both directions and changes by the minute.
On the raw materials side, the AMI rule outs of the last decade quietly created an enormous amount of data. A utility that installed smart meters now has considerable data on every customer, every 15 minutes. Most of that data has been sitting in a data warehouse doing very little. AI is what turns it into something you can actually act on. And then there's the workforce, a big share of the people who understand how the system really behaves are retiring. And you're not replacing that experience one for one. So you've got more complexity, more data, and fewer of the people who understand how this works.
That combination is why this has moved from someday to now.

**Marc Miller** (3:48)
So there's a lot going on for sure. If you had to pick one factor, what is the biggest driver that's making AI relevant right now?

**Jon Kerner** (4:00)
If I had to pick one, it's the workforce, but not the way people usually mean it. It's not just that crews and engineers are retiring.
It's that the knowledge is walking out the door faster than the data and the tools are filling in behind it. You've got more information than ever, and fewer people who know what it means. AI is the most practical way to close that gap, to take the judgment that used to live in a few experienced heads, and make it available to everyone running the system. Load growth and complexity make that gap matter more, but the gap itself is the driver.

**Marc Miller** (4:36)
When people hear AI, it can mean a lot of different things. It's become a term thrown around for a broad category. We also tend to use the term more broadly at ScottMadden. When we discuss AI with utilities, what are we usually talking about in practical terms?

**Jon Kerner** (4:57)
Honestly, most of what gets called AI today is not new to utilities. Let's strip away the label, and there are really three things. The first is analytics and machine learning, which is just finding patterns in data and making predictions from them. If you can predict which transformers are most likely to fail this summer, that's machine learning.
Utilities have been doing versions of this for years. The second is automation, software following rules to do work a person used to do by hand. That's not really intelligence, but it usually gets lumped in. The third, and this is generally the new one, is generative AI, the technology behind tools like ChatGPT.