AI in energy: Cross-sector strategies reshaping real-world operations

 In this episode of Energy Transition Talks, CGI’s Peter Warren speaks with AI experts Diane Gutiw and Lukas Krappmann about how artificial intelligence is transforming the Energy & Utilities sector. They explore key insights from Hannover Messe, how AI is closing workforce gaps, optimizing hydrogen systems, and driving efficiency through cross-industry innovation. Discover how AI is enabling faster decisions, predictive maintenance, and real-time operations in a sector under increasing pressure to adapt and evolve. 

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Chapters

• 0:00 Welcome Back and Introductions
• 1:03 Industry Challenges and AI Solutions
• 3:32 Cross-Industry AI Applications
• 7:56 Digital Twins Workshop Insights
• 10:40 Future Vision and Key AI Trends
• 13:20 Conclusion and Final Thoughts

Transcript

Peter Warren: 0:00

Hello everyone and welcome back to part two of our discussion on AI in energy and utilities how this is impacting the energy market. It's part of our ongoing energy transition conversation. In part one, diane and Lucas talked about what a digital twin is, digital triplets and how it's being applied. Today, we're going to pick up a few more things on innovation and the impact of it, but why don't we start with a reintroduction of yourselves, diane? Do you want to go first?

Diane Gutiw: 0:27

Great, hi, peter. Thanks for having me back. My name is Diane Gucu. I'm a vice president at CGI and I lead our global AI research center. Over to you, lukas.

Lukas Krappman: 0:36

Yeah, thanks, diane, for the introduction. Yeah, so my name is Lukas Kruppmann. I'm from Germany and therefore responsible for all of our manufacturing, or more the Haltrim business there and during the past. We're up with a couple of companies and trying to actually generate more data from the value of the product.

Peter Warren: 0:54

Thanks very much and just bringing in some information from our ongoing surveys that we do with our customers and other folks that are not our customers in the industry. We call it our voice of the client survey, so our voice of the customer survey, I guess is correct. We pointed out this year that and this is not fully published yet but there's a lot of people interested in AI. Obviously they're interested in automation. They're trying to do more with less. They're trying to manage the fact that they're not being able to get all the people they want. They've got cutting budgets, they've got the fact that they have people retiring with knowledge. All of that is sort of happening all at the same time. One of our customers said this everything's happening everywhere all at once, and I think that's a quote from a movie. Perhaps you know, diane, what's your thought on the most exciting trends for energy and transitions? How do you see this AI actually being able to fill those problems I just described?

Diane Gutiw: 1:51

You know, I think we saw a lot in Hanover and I know we all just were at the Hanover Messe event and there is so much going on. It's hard to nail down one thing Automation and the move to agentic AI to extend what we were already doing in intelligent automation into agentic AI is brilliant. Also, the efficiencies that organizations are gaining through use of some of these general purpose tools.

Peter Warren: 2:17

And Lucas. I mean we also saw the confluence between the logical layer, which we just sort of described, but also the physical layer of which we just sort of described, but also the physical layer of devices like your clients are using, but also the overlap into physical security, just a ton of things, the use of imagery, lidar, radar, overlapping and bringing really a bigger holistic viewpoint to people. But what's your viewpoint on sort of what was the most exciting thing that we picked up at Hanover and where you see your customers going?

Lukas Krappman: 2:46

Yeah. So, frank, what's interesting from, or what was interesting from my perspective, was that Now everything's basically available in terms of technical knowledge and the tools, macroeconomical challenges, meaning how everything comes together and how the whole ecosystem, for example, of manufacturers, energy companies, logistic companies and also health and life science, can really work together to solve the problems which are burning um on our plate right now. Yeah, and this is, I think, especially due to the geopolitical and other economic influences, really the thing which is most interesting for most of our clients out there.

Peter Warren: 3:42

Yeah, you bring up a couple of good points. There is that the supply chains or the ecosystems are all under attack. People are having to re-vector. What some people would take two years to do now has to happen in like two days. We've also heard from a voice of the clients. People are looking for new, better software tools to be more agile in both their regulatory and tariff type of management, given what's going on. So there's a lot of things that people are trying to deal with. Diane, how do you see AI playing a role in all of that? It seems very complex.

Diane Gutiw: 4:14

Yeah, it's a really good point, but when we come to innovations, we're at a good place in having tools that are allowing us to do things that were really complex, expensive, complicated to do in the past.

Diane Gutiw: 4:25

You know, in addition to the economic climate globally, which energy is absolutely at the forefront of, we also have a rapidly aging population globally, and when Lucas is talking about parallels across different industries, that's a challenge all industries are trying to solve.

Diane Gutiw: 4:44

How can we do more, do more personalized, specialized type work with less people being able to do it? And, as you mentioned earlier as well, a lot of the people that have the deep knowledge into systems are retiring. So, when we look at AI, some of these things that are the challenge maintaining, retaining knowledge, upskilling people quicker, being able to take over some of the menial tasks so people are working at the top of their skills rather than spending days of their week looking for information, collating information and trying to do analysis. There's tools that are not replacing people, but are replacing tasks, and replacing tasks that could be done much quicker, perhaps more efficiently, so that people are able to do what they need to do to be creative. I think across all industries, we're starting to see this challenge and finding patterns and opportunities both to reduce risk and improve efficiencies, and improve those opportunities.

Peter Warren: 5:43

Yeah, if I can ask an open-ended question to both of you and you can let me know who would like to answer first, the cross-industry thing. We've hit it a couple times and we're seeing at least from our point of view as CGI, because we look across industry that the problems we're trying to solve in one industry actually parallel or have already been solved in another industry. And looking to the benefit of those things, whichever one of you would like to go first, maybe you'd make a comment on how we're actually learning from other industries and pulling things back in.

Diane Gutiw: 6:12

Sure, I'm happy to take that one because I think I probably spend my time in the most different industries lately. I think there is better collaboration now. Certainly you know ourselves at CGI. We spend a lot of time supporting the specialists across the different industries for that very purpose.

Diane Gutiw: 6:31

It's a best practice in data science as well is to not reinvent the wheel, to find efficiencies and reuse patterns. So a good example would be predictive maintenance. What we've done in drilling in the past long predating generative AI we've reused in a number of different industries to look at how to determine potential faults in different types of equipment when it's hard to nail it down. So clustering that data and then using a K-shape algorithm to nail it down. It doesn't matter if that's just mining or utilities or anything that's using heavy assets or any assets you know, even looking at modalities in healthcare.

Diane Gutiw: 7:09

That type of model and approach to solving problems can be shared. So the population retiring is the same thing. Where are their efficiencies and where in a workflow, in a value chain that we can enhance what we're doing with AI rather than replace, so that the people coming in are upskilled much quicker. People that are doing the work are able to focus on a meaningful task rather than menial tasks. That's a problem that we're all dealing with, and I think there definitely are efficiencies. Our vendor partners are good at sharing that across industries, and I think that's one of the real values that we have as our team at CGI.

Peter Warren: 7:46

So, Lucas, at the Hanover Messe you ran a workshop with customers on digital twins and digital triplets. What was sort of the key message that you took away from that? What were the clients looking for? What were the clients looking for? What were they interested in most?

Lukas Krappman: 7:59

Yeah, I mean like it was a pretty interesting workshop, to be honest. So we've raised the workshop around digital twins and triplets in the hygiene ecosystem, but the participants were actually from a couple of industries. So we had, for example, participants for the hydrogen industry, we had manufacturers, but we also had participants, for example, from agricultural background, and the most important thing all three basically have in common is that they kind of like have to make a lot of data available with certain speed and and then generate insights from it. So how do you connect, for example, data coming from a farmer's field, from crops, for example, to an infrastructure and add a business application on top so that you can do a proper farming or, for example, put out water for the seeds if there's rain in the weather forecast, stuff like that? This is, for example, for the agricultural perspective and then for the energy and utilities industry.

Lukas Krappman: 9:03

For example, I had a couple of customers with a more like a hydrogen background and their challenge basically is on how they can integrate the data from, for example, the hydrogen electrolyzers then into, for example, large energy grids, on how to optimize production depending on the weather, do a lot of predictive maintenance for example, when do we have to replace a particular stack. Is it better, for example, to nowadays turn on my hydrogen electrolyzer to generate hydrogen or the energy, or should I look for like an another source to do it? Yeah, so it's actually about connecting all the different data points out there in the different ecosystems and then making an informed decision based upon it. As you might know, there's a lot of data out in the industry and in the ecosystem. There is a lot of metadata out of it. That's even a more important thing. It's not only about the different signals you have out there, but also the metadata and how you process it, and I think this is going to be a real challenge in the next couple of years.

Peter Warren: 10:06

Lucas, I really appreciate what you said on that because it really highlights the data and it was interesting that a use case from what we're talking about hydrogen was so applicable to somebody in the agricultural industry. And what they were trying to do was an interesting dialogue with the researchers and other folks that were in the session. Just heading to wrap up here and give you guys a chance to sort of summarize what do you think is the vision on the global stage, what's the big thing moving forward? Where is this all going? And maybe we'll go back to you, lucas, first and give you a chance to give that summary and then we'll toss it to Diane.

Lukas Krappman: 10:40

Yeah, I'm like when you were walking around with all the different booths, right, so you saw that AI is actually part of the thing. Every booth. I didn't see like one booth where AI wasn't incorporated or incorporated into the business model, right. And this starts from, like intelligent co-pilots, from custom GPTs, for example, different use cases from energy suppliers out there. From my perspective, and what we talked about to different customers. It's really about how AI can contribute to, for example, the efficiency of each of our customers out there, how their day-to-day work actually can get more efficient and bring more benefit to them. And with that, I guess I'll head over to Diane.

Diane Gutiw: 11:23

Yeah, you know, I think the thing that's happened in this last year is AI now isn't just one thing, it's not just generative AI that people have on their phones. I find that innovations are splitting into three things, and those three things are really what we saw in Hanover, as Lucas said, as well as what's going to drive us forward. The first is tools for internal efficiencies. This is your operational, administrative access to information, access to data to be able to do things in your day-to-day workflow quicker. So a lot of what came out of the GPTs and software development and being able to integrate with everyday work tools is going to be a huge impact on every sector, but will be able to help a lot.

Diane Gutiw: 12:08

The second would be automation. We already see a lot of automation in this sector, lots of robotics and automation and process engineering being advancing technologies in this space. I think that by having solutions that are able to do more, having more autonomous automation, having automation that's very focused on more complex tasks which were hard to complete without really clear instructions, you're able to do more. And then the last one is data. We've talked about data a lot, but using AI to be able to mine that data, to find patterns and have more evidence-based answers to questions. You know, to me those three things are splitting out of this technology evolution that we've seen and those are, I think, what the three top areas that are going to have a real positive impact on the energy sector.

Peter Warren: 13:02

Well, I'd like to thank you both, and I will add one thought too is this was a conversation we had with other clients. Diane, when we were there, is using generative AI to actually error correct and check data and validate, so AI is actually helping us improve our data in real time. So that's a great thing. Well, thank you both, really appreciate this. So hopefully everybody enjoyed this session and we'll do more coming up in the coming weeks. Thank you, lucas.