Ep 55: When AI Meets Infrastructure with Duke Energy SVP & CIO Richard Donaldson

Evan Reiser: Hi there, and welcome to Enterprise AI Innovators, a show where top technology executives share how AI is transforming the enterprise. In each episode, guests uncover the real-world applications of AI, from improving products and optimizing operations to redefining the customer experience. 

I'm Evan Reiser, the founder and CEO of Abnormal AI.

‍Saam Motamedi: And I'm Saam Motamedi, a general partner at Greylock Partners.

‍Evan: Today on the show, we’re bringing you a conversation with Richard Donaldson, Chief Information Officer at Duke Energy. ​​Duke is a Fortune 150 energy provider, delivering power through a vast network of nuclear, renewable, and traditional generation sources. They serve over 8 and a half million customers across the American Southeast.

There are three interesting things that stood out to me in our conversation: 

First, Duke rolled out a conversational AI tool that is now transforming regulatory workflows, pulling answers from over 800 data sources. What used to take them days now happens in seconds.

Second, Richard also shared insights about the results of applied AI at Duke - One gen AI project helped automate nuclear re-licensing documents; saving 200,000 hours. That’s enterprise-grade leverage.

And lastly, before any AI was rolled out, Richard and his team spent a year on governance, security, and platform readiness. No shortcuts: just real investment in infrastructure to make AI scale safely.

Richard, do you want to share a little bit about, kind of, your background and how you got to where you are today?

‍Richard Donaldson: So, educationally, I went to college for engineering, civil and environmental engineering, and ended up doing that for a couple of years before this whole, you know, everyone, regardless of their major was getting into IT, and so I followed Suit. I actually worked at a software startup for three months before they laid the whole office off. So that's how I came running to the safest place I could find, but to also continue to do IT, which is Duke Energy. And gosh, that was in 2001. And I've been here since. 

I've probably done just about everything there is to do in IT and enjoyed every minute of it. The nice thing about Duke Energy is if you're one of those people that has this fear of getting bored with what you do, which is very much my persona, we have so many different jobs at this company. And so I always felt like there was no horizontal boundary to what I could go pursue. And that's been the case, although I've been able to find everything I've been looking for within IT, thankfully so.

Evan: I don't think most people really appreciate the kind of the scale of operations of Duke Energy. You mind, kind of, just like, put it in context, right? Like, you know, if I'm right, I think you're like the largest energy provider in the United States or kind of close. Like, you know, like put that in perspective and help people understand, you know, the extent of the impact you guys have on the world.

Richard: Yeah, I mean, we're in several different states. I mean, we have, you know, I can't even quote you the miles of transmission and distribution, but here's one that's interesting. so, because everybody's talking about data centers these days and they're saying, well, a gigawatt data center. Okay. I mean, that's a big, that's a big load.

Evan: Yeah, and that's like big news. The idea of someone talking about gigawatt data center is like a new thing.

Richard: Well, at Duke, we’re running eleven nuclear power units. Each of them are somewhere in the range of a gigawatt. And so, you know, just think about that. That's just on the generation side and the magnitude of, and the size of Duke Energy. 

And of course, that's, you know, we obviously we have a huge renewables portfolio. We have gas. We still have some coal. I mean, we've been around for a long time. We have a lot of hydro. And so just from generation and then just to transmit and distribute all that energy to all the places it needs to be, it is, is...

Most of the folks at this company aren't like the three of us. They are, you know, putting on the protective equipment every day, getting in a truck and going and climbing a pole or working in a substation or working in a power plant, and it's a real source of pride for me, even though I'm not in that side of the business.

Evan: Yeah, real, I think granted for those that are aware but not involved high gratitude too, because it's all those people that kind of literally, you know, power our cities, our states, our hospitals, our factories or houses. So I feel lot of appreciation for the product team.

Richard: Well, especially, I mean, you think about we're in North Carolina the year we had last year with those just horrible, horrible storms. I mean, you know, we are critical responders in those cases and we showed up very well, especially in Western North Carolina to help those folks during that time.

Saam: One of the reasons why I've been really excited about having you on is just the scale of impact that Duke energy has on like all our lives. I think may not be immediately understood by our listeners, but after this episode will be, and then obviously as that intersects with the big technology wave we're in right now, which is, know, the AI wave. And so I think there's a lot of good things to talk about as it relates to how you're using AI at Duke today and maybe how that’s going to evolve over time. 

But just to start, like, you've been through many of these technology waves, like, where do think we are, you know, in this current AI hype cycle?

Richard: You know, I'm hesitant to call it a hype cycle because I think we're finding value a lot sooner than former hype cycles. And I really do believe that this is the real McCoy. This isn't, I mean, no offense to people who love blockchain, but we've all had those hype cycles where it either didn't go anywhere or even with AI where you had the AI winters of, you know, the previous decades. But I think we're, we've got something. 

You're not, I mean, I see it, you see it, right? When you play with, you know, name your favorite, you know, generative AI engine, it's a game changer. So, I think we're in a good spot. I think that the value being sold and advertised and marketed, I think we're a ways from getting there because...

I mean, if you don't have your data and processes in the right place, you're just going to get kind of surficial value versus deep value, especially in a big enterprise like ours.

Saam: Yeah, absolutely. I agree with you. And, it feels like when we talked to different guests on the show, the people who have made those investments correctly are able to like actually harness the value of their data and then turn that into very successful AI investments and projects and others who are earlier kind of need to go back and get that underlying infrastructure correct.

Richard: Absolutely. I mean, in fact, just to go on the AI journey a little bit, 2024 was a year we spent a lot of time talking about AI, but in very unsexy ways. So we were talking about how we're making sure we're protecting ourselves from our data going every which way but loose. What kind of governance are we going to put in place to make decisions about AI? What kind of platforms and security measures are we going to have so that when we go to develop a solution in AI or even leverage a commercial model that we're not going to get ourselves upside down? And, you know, that was a lot of work.

I mean, platform work is hard and it takes a lot of time and, you know, to your accountant, it looks like you're spending money and not getting any return on that. You've got to pave the roads. And so, this is the year we're really excited to turn that page and say, we're kind of open for business and we think we can go pretty fast on the technical side.

But it's still, I mean, to my point earlier is we can go as fast until we hit data that's not of high quality or that's not organized or that's in 800 different places, specific use cases, by the way, that we just finished up. And that's what will ultimately become your hurdles and your slowdown of value.

Saam: Absolutely. So maybe building on like the last thing you just mentioned, you know, you mentioned the use case around identifying high quality and organized data, if I, if I understood it correctly. 

Like, what are I mean, zooming up, like, are there two or three use cases that you've implemented this year with generative AI that are interesting to talk about or would be interesting to our listeners?

Richard: I think so. I mean, interesting is in the eye of the beholder. I was just telling one of my business partners the other day, everyone's waiting for me to say, we did this AI thing and it sounds, it's right out of a science fiction book, but the most valuable stuff sometimes doesn't have that spice to it. And so I'm gonna talk about two. 

One is, we call it Duke Energy Explorer. And we work in a world where we are continuously working with the regulators on rate cases, on our energy delivery plans, what we're going to do. And there's a lot of kind of asynchronous communications with that. So we submit large, large documents and all of a sudden we get a response back with 50 million questions and 16 hours to return the answers. And I'm exaggerating grossly, but this was something where over time we have information on SharePoint sites, databases, cocktail napkins, you name it. And it was really hard, and in people's brains, was really hard to turn those around in a high quality fashion and in a repeatable fashion and in the amount of time that we had. And so we spent some time building a conversational AI tool to harvest the data that was in literally 800 different data sources and kind of build this, it's not a fabric. We didn't go bananas with the architecture though, that might be a next progression. And now you get the questions and you can ask and get a quasi response back that you can go, hey, that makes sense. And, I don't know about this one, but it's got a little link to where the actual file was. Let me go pull that file. And it really helps us move the needle with that, which is really important because we have, I mean, every year we're working on getting regulatory approval for something, right?

And it doesn't matter what the administration is. If it's a big clean energy push or if it's a big, you know, build big power plants push, you have to go through the regulatory agents in this state we operate in. So that's one I'm really proud of. 

The other one, and look at me, I'm picking all the stuff. To me, great AI use case is something that does something that I would find painfully boring to do. All right, so another one is, and if you've seen in the news, I mentioned those nuclear units, we've been doing a lot of re-licensing. mean, we're very proud of and very good at operating our nuclear fleet. And so we want to re-license those and run those for another 20 years, as long as we can operate them reliably and efficiently, because they're clean energy sources. And the cost to operate those is actually fairly competitive. And so we, as you can imagine, if getting rate cases done is a 6 out of 10 on the paperwork scale. Imagine working on getting a relicensed nuclear plant anywhere in the world, much less in the United States. That's a 13 out of 10. And so we've used generative AI to help us put those together, pull all the operational metrics and the data together. I mean, I heard something staggering, like a $40,000 project saved us something like, you know, 200,000 man hours. was something very, very, don't quote me on those, but great use case and another one we're really proud of.

Evan: It seems like with AI, right, I think there's some examples right in front of us, that are very obvious usage of AI. But when you think kind of over the next couple of years, like are there other maybe applications that are maybe less obvious, but you still feel are kind of great opportunities for Duke Energy?

Richard: I don't know that any come to mind and not to get out of the AI space, but I think the game changer is going to come when, because if you think about like how we use drones, it's very sensory based and collection of information and then having the AI engine do the fancy math to get insights and to get, you know, five nines of accuracy. But the drone's not doing anything yet. And so the simplest example I can think of is when we have a drone that flies a solar site, big solar sites, if you haven't seen a solar site, it's not like in your backyard, it's like seven football stadiums, and flying all over it to look for everything from thermographic imaging that says we have too much reflection or not enough reflection to a dead cell. Well, what happens when the AI, I'm sorry, when the drone can actually just kind of lower down and maybe sweep off a panel, something totally simple like that or, or change a part or do something like that. 

So I think that's what we need to be thinking about sooner rather than later is not just the, I'll say the confined AI, the stationary AI, but what happens when you actually give it action similar to a self-driving car, what is that gonna look like? That's when I think it's gonna get very, very interesting. And I don't think we're that far ahead away from that.

Saam: I wanted to go back to something you mentioned around the Explorer product that you built and that assistant for helping your team answer these sort of regulatory queries. I feel like with AI, one of the hard things is deciding what to go build. I like your framing of what's work we do that we don't like doing that we can go automate. It's a very simple, but I think powerful framing. But then once you've built these things is like all the process change of like, okay, how do you actually get people to buy in and like adopt these tools? I can tell you like, the companies we work with, there's wide variants, everyone has the same tools, but like, some teams really lean in and use it and like, reinvent the way they work. And some people don't and like, you see this bifurcation and performance. 

So I'm curious, just, I'm just picking on the Explorer product is just an example, but please choose any example you want. What have you found to be effective to get your teams and your employees to actually engage with these tools?

Richard: Yeah, it's a great question because, you know, you roll something like that out to the IT workforce and it's just, they're breaking the servers they’re hitting it so hard, but then you go into some of these business units and it's just the latest button they have on their workstation and they don't have time to..

I don't know if you guys have ever seen this cartoon, but it's the guy, he's pushing a wheelbarrow full of rocks, but the wheel is a square, it's not round. And there's a guy on the side of the road with a sign up saying, know, wheels, $15, come talk to me. He's like, I don't have time for that. got to move these rocks over here. And it's kind of that thing. one of the, because you're describing a very real problem. And when people start talking about the path to value, that's your barrier to value, right? Cause everything you say, well, I'm going to save an hour and a half per task across a workforce of X. Well, if you can't get penetration into that whole workforce, you're not going to save the value that you advertise in your business case. 

And the way we found is you've got to understand, so Saam, Evan, what is your pain point? Not what do I want you to use this tool for so that you will save, you'll become more efficient. It's where do you, what are your pain points? And let me help you make your job easier that day. And then that gets people, I mean, they used to call it the killer app, right? M obile check deposit on your banking app. That's what got people to start using your mobile banking app. So you got to find that. And it's not one person for everybody in your HR department. It's not one thing. It might be many things. And so that's the strategy we've used. You got to, I mean, you have to, it takes talking to people. 

And historically IT projects was, tell me what you want. I'll see you in 18 months and you can test it and see if you still like it. And we'll get it to where it's not that you like it, but you dislike it at a level that we can do a lot of training and change management and tell you how to click the 17 buttons to get to what you want and then get it into production. And I'm not trying to make this an agile commercial, but I think that whole side by side talking to folks and figuring out like, what do you need to help you do your job? And by the way, there's things we're gonna want you to do as part of your job because it's valuable to us holistically, but somewhere in the middle lies a real happy path.

Saam: So I want you to put on Richard your like dreamer hat for a moment and like, you know, look forward five years. It's now 2030. What do you think are some of the AI, the ways AI is transforming the way you work and the team works at Duke Energy?

Richard: Whenever I think in those terms, I like to think about where will the average person interact with AI first. I say that because we had this thing, AI can be a hammer looking for a nail, just like websites were, just like mobile apps were, just like your dashboards were. And so, we're never gonna be the first one to have completely agentic AI running our call center. Right? You just, we just, we won't move that fast for a variety of reasons, but I have to look at what our customers and our employees are experiencing interacting with entities other than Duke. And so I oftentimes think about, you know, Siri and Alexa or whatever the case is. I mean, those things are pitiful right now compared to, you know, go watch the movie Her with Joaquin Phoenix and see something that we're probably not far away from. I don't mean the part where I don't want to spoil it. There's a lot of fantastic things in there. Fantastic meaning unlikely, but the front end of that movie is very real. 

And I think what's going to happen is we're going to see people start to have some kind of onboard, and I love the word co-pilot, not to be a Microsoft Homer right now, but I love the word co-pilot because I think that's what everybody will have. They're Siri, they're Alexa, something that they are interacting with, talking with, is doing all this stuff for them all day long. And they're going to expect it in the office. And so, you know, we have to be prepared to deliver that. And when we do, now all of a sudden you think about, regardless of the task or the use case, when you're, every single employee is coming in with that kind of assistance on board with them, you can accomplish a ton of things. 

But if I'm dreaming about now what in the utility space do I think we'll be looking at, I do think you have to think about what the customer experience is going to be like in the future. I think that's going to be very easy, very seamless. And it's not that it needs to be overhauled. It's not terrible today. I think that the complexity of what we do as a company, balancing supply and demand of electrons and routing them through wires of various thicknesses and ages. And I mean, to me, you know, when you can truly harvest your OT data, I think there's going to be tremendous opportunity to really run the system, you know, a digital twin, another overused word, but really, really get into that digital twin space.

With AI, you don't even need it to be a twin. AI can just live run the system or at least make the recommendations to your operators. So that's where I think it's going to go. I just think we have to be very careful because the risk of making mistakes in those environments are unacceptable.

Evan: You just talked a little bit about kind of AI's role in the future of kind of energy. What about kind of the opposite? What about energy's role in the future of AI? Right? Because again, I'm far from an expert in your guys' business and field, but it does seem like some of these new technologies like AI has basically increased the value of energy, right? You know, that project you just talked about to save 200,000 hours. You know, that's powered by some GPUs using a bunch of energy, right? I think in the, even the last month or so, we've seen all these announcements from different companies saying they're building gigawatt data centers, there are plans for five gigawatt data centers. Again, don't know much about this, but I have to imagine if you could plot a chart that showed like demand for energy capacity, it's probably shot up recently, right? So how do you see that? How do you see AI effect in your industry? like, I mean, it's gotta be a, a very interesting time for you guys, right, with just how fast technology is evolving.

Richard: Yeah, it very much is. mean, that is the that's the center of all the conversations right now in this industry and which is, you know, I keep saying this is the best time in the world to be a CIO at a utility because you get this brand new toy, AI, but your company, your senior, your CEO cares about AI. as well because we have customers that want our product and how are we going to serve that reliably, affordably, safely, et cetera. And so I think the demand is real. 

Now you can debate is the curve like this or is it like this? And there's a lot of factors that go into that. But the reality is AI needs a lot of juice and it needs more juice than is out there and available today. And so we are, we as an industry are collectively looking at how do we meet that demand. 

And it's interesting because when you think about utilities, we're not typically, it's not like Target and Walmart trying to figure out how to sell more popsicles, right? It's we collectively, how do we think about this on the national level so that we can meet that demand? Now there is a little, it'll be kind of nice if you've got a few more data centers in North Carolina than maybe some of our, you know, another state that we don't operate in, right? Because that's good for economic development, that's good for the company, it's good for a number of reasons. But it's the very center of the conversation. 

And that's why you're hearing things like, you know, Microsoft struck a deal to restart Three Mile Island and you're seeing retired nuclear plants in conversations about what would it take to fire those up. Because again, you think a gigawatt and a gigawatt, a data center can take a whole, every single electron a nuclear reactor puts out, a data center claims to need at some point.

Evan: So for the end of our episodes, Richard, we like to do a bit of a lightning round where we ask you questions that are impossibly difficult to answer in the one tweet format, but we're going to try to push for that. So, we’re looking for kind of like the one tweet response, right? Kind of the, the, the quick, the quick hit version. 

So, um, Saam, do you want to maybe kick it off for us?

Saam: Yes. So maybe to start Richard, how do think companies should measure the success of a CIO in this AI era?

Richard: I mean, all the usual metrics, because reliability, cost, all those are important, but it's, I would say, the ability to enable transformation. Not do the transformation, but enable it for your business partners.

Evan: So Richard, one thing, I know we're just getting to know each other, but I am quite impressed. Like for your role, it's very, like there's so many technologies involved, right? From like nuclear reactors, the drone fleets, to AI, to just normal enterprise software. How do you stay up to date on the latest, right? You got like, you know, for me and Saam, we have to be experts, like a pretty narrow slice of technology, right? You got all this stuff, right? Like, how do you stay up to date?

Richard: You know, I think it's just that it's interesting to me. So I don't know if you're asking where I get my information, but I just listen and ask a lot of questions. I mean, you ever run across one of those sports guys that can tell you every single stat, you know, seven, seven layers down on the depth chart for the, it's just like, it's, I'm an engineer at heart and I'm a science fiction geek. So everywhere I go, every meeting I go, I'm absorbing stuff that I would probably read about in my free time. So I think that's just where I've been really lucky is aptitude met interest.

Saam: What's a book you've read recently that's had a big impact on you and why?

Richard: I read a book called Fall by Neil, I forget the guy's last name, but it's called Fall. The premise is you have, basically it's like an upload story, right? Where you can upload your consciousness into this, you know, modeled three dimensional, versatile reality. And like, they literally had entire states in the United States where all that happened in that state was energy creation.

You got whole states that do nothing but run quantum computers. And I mean, depending on how you think about this, could really, mean, what's the, what's, what's going to really, I mean, when we start having people that are doing virtual reality or wearables or things like that, it's…Now all of sudden the data throughput is not just someone in my ear that's accessing the model and accessing the internet. It's someone looking and interpreting every millisecond of every day. I mean, that's huge, right? And we're not even close to that kind of usage. So all that's going to take power.

Evan: Yeah. so I this this book, you know, like, like five years ago or so, they're thinking about four or five years ago, like, again, it sounded like total science fiction, right? But it just every day these things become like less, less like fiction.

Richard: They're turning into history.

Evan: What's a upcoming technology that you're personally most excited about?

Richard: I mean, I think it's, know, intelligent robotics that we talked about earlier. You know, it's hard not to say quantum because that is, again, that's been in the science fiction books for so long and it's getting real. But I mean, the first five years of quantum is all going to be about protecting and making sure you got the right, you know, encryption algorithms and you meet the NIST standards. But I think when we get, you know, robots that are in the form factor of a human being, it's going to start to get really interesting.

Saam: We've talked a lot about science fiction this show, which is music to Evan and I's ears, but maybe keeping your science fiction hat on. Like, you know, if we think far out 10 years, 15 years, like what do think is going to be true about AI's impact just on the world that most of our listeners would not believe?

Richard: So I think you're gonna see some long, long standing problems come like, you know. Maybe not all of them, but a lot of cancers. I think the medical field, if you look at the medical field in 15 years, it's gonna be radically different than what you see today. I mean, I don't know about you guys, I don't, like going to the doctor is the biggest disappointment I can possibly think of. Like I'm going through it with my back. No one can tell me everything. Get an image, get this. You go to a doctor, you leave with one or two things or both. You leave with another phone number and a prescription.

And so I just think about AI as a data problem, AI problems or data problems and all the data that our body is generating and not being captured every single second of every single day. I mean, we maintain our bodies worse than we maintain our cars. We know more about our car than we do about our bodies. That's gonna change radically. I mean, obviously you gotta take into account the policies and regulations and what's the insurance company's roles, but that in my mind is the biggest thing we're going to see if you're looking at a 15 year horizon.

Evan: Well Richard, really appreciate you taking time to join us today. I thoroughly enjoyed the conversation and we'll have to do episode part two soon.

Saam: Thanks a lot, Richard.

Richard: Thanks for having me.

Evan: That was Richard Donaldson, Chief Information Officer at Duke Energy

Saam: Thanks for listening to Enterprise AI Innovators. I’m Saam Motamedi, a general partner at Greylock Partners.

Evan: And I’m Evan Reiser, the CEO and founder of Abnormal AI. Please be sure to subscribe, so you never miss an episode. You can find more great insights on enterprise AI transformation at enterprisesoftware.blog.

Saam: This show is produced by Josh Meer. See you next time!