Energy Transition Talks

From pilots to performance: Accelerating AI value in energy and utilities

• CGI in Energy & Utilities • Season 4 • Episode 3

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 How can energy and utilities move from AI pilots to measurable performance? CGI’s Peter Warren and Frédéric Miskawi explore how AI-led software acceleration, smarter data use, and the right algorithms drive rapid ROI, resilience, and business value. Tune in to learn how to turn experimentation into sustainable AI success. 

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Peter Warren:

Hello everyone and welcome to another edition uh on our podcast series Energy Transition Talks. Uh today we have a very interesting one where we're going to be diving into how do you get AI to ROI? Uh we're we've a lot of people have been playing with AI, uh, but now it's uh time to move beyond that and actually get results that actually improve the bottom line, improve efficiency. Uh to that point, I have a great guest. Fred, uh, do you want to introduce yourself?

Frederic Miskawi:

Hi, everyone. Uh Fred Miskawi. I'm a part of our global AI enablement team at CGI, and I lead our AI innovation expert services, which uh luckily has me work across nine SBUs across geographies, and I've been involved one way or another with artificial intelligence since the 1990s, and I love what I do every day.

Peter Warren:

And SBUs is uh our business unit, so that includes uh countries like uh Canada, North uh, and uh the United States. Uh it includes all of Europe, uh, Australia, and so on. So we uh have quite a bit of a mixture in that nine different groups in the UK as well. Um so let's kick it off here and talk about you know AI ops, data maturity, where do things start? You know, a lot of things have been moving forward. Uh people are looking for results. How do they get something in the next 90 days that actually brings a benefit to them?

Frederic Miskawi:

I mean, the uh the fastest way that I've seen, and this is what we do on a day-in-day-out basis, both with our internal teams as well as with client teams, is AI led software acceleration. Number one value for these types of uh tools, this technology. We can deploy licenses fairly quickly, but even with those licenses, what we're learning is that it's just not enough. So we bring in a lot of expertise and layering above that. But you can get start getting value very quickly. It's gonna start in that 3-5% uh productivity improvement. And it's gonna start a little slow, but you can get and gain that expertise very quickly. You just need to have the right partner, the right guidance. Uh, a lot of online learning as well kind of can help. Uh, and the biggest thing is to work with employees who are leveraging these tools to learn how to use them and to uh cater to them and let them know that it's okay to experiment and learn. And through these these uh approaches, what we've been able to do is to get people accelerated fairly quickly. And you're gonna get value very quickly because you're accelerating the process of value delivery. And software is what's powering most of our businesses today.

Peter Warren:

I think there's a belief out there that I can't get started until my data is pristine or uh you know I'm gonna have this is gonna be a major overhaul. And there's certainly a bit of change management, as you just sort of alluded to. You know, how do people approach those things?

Frederic Miskawi:

That used to be true. In artificial intelligence, you've got different types of algorithms. You've got uh structured approaches, unstructured approaches. And with these algorithms, what we what we were getting used to over decades is that you needed very clean data sets. Uh key value pairs, you needed to be able to have large of the large sets of these data, clean data, to be able to find the patterns, to enshrine the patterns, to tweak the parameters to get where you want it to. Um, what we're finding out with these new algorithms is you're now getting into a realm where the dependency on highly uh high quality data sets is being reduced. And the more this technology evolves, the less that dependency exists. To the point where what we're hearing from our partners, uh hyperscalers in other edge labs that uh that live on the edge of this technology is we're getting into a new era where data quality will not matter anymore. So we're already seeing in the labs uh approaches that automatically clean the data, collect the data, get the data ready for what's needed. What we're seeing today in production is the ability to add layering above the solutions that we deploy. That layering enables us to apply certain heuristics to the data that comes in. So even if your data is not clean, we could say, by the way, if this document is older than X, or if this is version 5.3, and then you've got a new version, maybe let's take a look at that. Uh so there's very simple heuristics like that that you can apply in a um in these types of solutions. But because of this layering, and now with agentic approaches, you're getting even less dependency on high-quality data sets. Um, and what we're gonna see is over time, these solutions, these agents are collecting more and more data. They're collecting it with the level of quality that they need for the next generation of fine-tuning or training. Um, and you see this flywheel effect that's happening today. So, no, you don't need to have high quality data sets to get started. It certainly helps for certain types of algorithms, and it will always be there for very specific types of algorithms. But what we're seeing is a reduction in the dependency on those on those data sets.

Peter Warren:

Maybe let's talk about algorithms. You've mentioned it a bit. You've got a bit of a famous saying there. I think I I enjoyed it the last time we chatted. I'll let you I'll give set you up to start off with that. But um, you know, it you know, people are looking. Do I build, do I buy, do I partner, how do I do this, how do I put in governance, and uh uh maybe kick off the thought about the algorithms.

Frederic Miskawi:

Yeah, so I think uh about life in terms of patterns, in terms of data, in terms of algorithms. And for me, it's the um the best algorithm for the job. And we saw that very early on with this technology in the last two years where we were asking these models to calculate 5,333 times 55. And next thing you know, you've got thousands of multiplication multiplications happening behind the scenes to get you an answer, which may or may not be right. And then the labs quickly realized that, well, hold on, maybe we can just kick off a very simple little algorithm that's procedural so that we could get an answer in the way that we know and and love. So that's what we're seeing the best algorithm for the job. And our job in this business is to figure out what is that best algorithm for the need that we have, for the value that we need to deliver.

Peter Warren:

So building upon the concept of the best algorithm for the job, I mean, uh, one of the big concerns in our industry is asset maintenance. Um, it's heavily impacted by the weather storms, uh, both uh and fires recently. There's a lot of things going forward. How do you see AI sort of coming in and helping the operations improve?

Frederic Miskawi:

Yeah, and that gets connected to that concept of the enterprise neuromesh or digital triplet where you get that near time, near real-time view. We're seeing an evolution in that space. You're getting new solutions, weather maps, weather data that is being fed into some of the systems that we're working on. Uh, partners like Microsoft, for example, are introducing that kind of capability. And with a new layering on top of that and new maturity in how to absorb that data, you can start working in that next generation of predictive algorithms, leveraging the data to be able to navigate the data and understand where which areas of the network might be read under certain weather conditions. That level of visibility, transparency comes together with that growth of algorithms in a multi-agent type of ecosystem. And we're seeing that evolve. It's not a revolution necessarily, it's an evolution. But that evolution is moving on an exponential curve. So as you're evolving, improving your digital solutions, going through digital transformations, using the technology to accelerate the migration of legacy systems, your long tail of digital technical debt, um, you're building this new capability that enables you to absorb these new data sets, absorb this new insight, these new patterns. And now you've got companies like us that come in and build this layering to give you that transparency, visibility, and understanding. And from there you could start feeding that into your planning cycles. And your planning cycles start accelerating a little bit. And now you're you're you're empowered with a new generation of solutions and and pattern recognition engines that enable you to fine-tune what's happening across the enterprise and making sure that for limited assets, you're deploying them in the in the best places possible. We're seeing that evolve. It's an evolution, not a not a revolution. Uh, but I think it's an important evolution of the technology. So when we hear about AI bubbles, for example, I I laugh because I see the value every day, I understand it, uh, I see it evolve very quickly. And it's um really at the end of the day, it's our human ability to absorb it and to put it in practice. And that's what we're seeing. And and a lot of that data, a lot of this this empowering of the planning process, for example, uh, you're not gonna necessarily see that in revenue. You're not gonna see that necessarily in margins, at least not yet. But it's there, it's happening. It's accelerating your near real-time understanding of what's happening in the enterprise. And that that evolution, even though it's moving at an exponential rate, I think is incredibly important. And you've got to understand it. You've got to understand the the the digital push, the wind that's that's causing these things to evolve, so that you can start planning for the next generation of solutions, of digital transformations, of of legacy realignment. Um and and these are the patterns that we see every day.

Peter Warren:

Yeah, it's interesting. It's not a big bang, it's a slow uh evolution, as you said, or a steady evolution, if not slow. Thank you very much. We'll talk about the next point. So don't use a large language model just to do calculations, in other words, uh use something that's uh dedicated for it. Um this really before we maybe wrap up this uh part, and we'll catch up in part two where we'll talk about large language models versus small language models and the impact of hardware, uh both large and small. How do you see about the KPIs and budgets? Where are people going, you know, in the first part of this coming year, uh the end of this year even? Uh where do you think things are going and you know what should people be putting in place as a leader?

Frederic Miskawi:

So the the KPIs, we tend to work with clients in a very fine-grained, fine-tuned way. So depending on what particular business outcomes they're looking to get, we're gonna fine-tune the nature of the type of KPIs that are being used. So if we talk about um software acceleration, for example, um, a lot of what we're being requested is to look at developer productivity, the uh quality of what comes out, the trust factor that comes with what gets produced, and understanding how to leverage this new technology in a way that can cut your time in half or by two-thirds. And that requires a certain set of data points that you got to collect. So we work with hyperscalers, we work, we built our own uh data collection engines and dashboards to be able to get a feel for uh what are the trends. Um, personally, when I look at that, I don't look at individual productivity levels. I think it's a you're you're not getting the value for the money when you do that. What we tend to look at are maybe at the from a granularity level, we look at the team level. We look at the value that's delivered by the team, value delivered over time, the quality that comes with that. So we have a set of KPIs that come with it. So depending on the particular business goal and the nature of the solution that you're deploying, there will be a different set of KPIs. When you're looking at uh chatbots or knowledge engines, where you've got uh the need to unlock the power and the knowledge of the enterprise, of the industry within and give it to the hands of your employees. When you take that path, you've got to look at certain things like um the nature of the interaction, how often these requests are coming in, uh, the nature of the results that you're getting from surveys. There are a lot of different data points that you bring in to make sure that you're getting the answer you're looking for.

Peter Warren:

Oh, that's excellent. Well, thank you, Fred, and thank you everybody else for listening. Uh, we'll pick this up in part two and have a great day. Bye bye.

Frederic Miskawi:

Thank you.

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Peter Warren

Host
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Peter Warren

Host
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Frédéric Miskawi

Guest

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