AI that revolutionizes the company
AI is just a tool, but when strategically deployed, it can become the key to more efficient future of energy and industry. In this special episode of AI at Scale, recorded live at Innovation Summit Copenhagen, Schneider Electric’s Chief AI Officer, Philippe Rambach, shares how the company innovates with AI by implementing it across the entire portfolio. This deployment at scale is focused on solving real-world problems: optimizing energy consumption, extending asset lifecycles, and empowering engineers with intelligent tools that boost productivity and reduce operational workloads.
Philippe explains how AI is enabling demand-side energy optimization that is an essential but often overlooked pillar of the energy transition. Moreover, he explains how Schneider is blending domain expertise with agile AI squads to bring solutions from concept to market faster than ever. For leaders navigating digital transformation, this episode offers an experienced executive look at how AI can drive measurable impact, accelerate sustainability goals, and future-proof operations.
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Transcript
Violaine Cola: Welcome, and thank you for joining us. This is the AI at Scale podcast, and we’re recording live from Innovation Summit Copenhagen. I’m Violaine Cola, and I’m delighted to be joined today by Philippe Rambach, Chief AI Officer at Schneider Electric.
Philippe: Hello, Violaine.
AI in the company
Violaine: Hello, Philippe. Since we meet at the Innovation Summit, let’s first unpack how AI changed the way Schneider Electric innovates.
Philippe: We started this AI acceleration in Schneider a bit more than four years ago, probably around mid-2021. This was before everybody started to speak about LLM, OpenAI, ChatGPT, and all of that. When I look at what we are showing to our customers now in this innovation summit, AI is everywhere in our offer. You will find it in our maintenance operations. AI helps us in operation, showing how we can help our customers extend the lifetime of their assets, send technicians less often, and get more outcomes. I see AI in our homes offer, where we’re helping our customers use less energy, use their energy better, and avoid buying energy when it’s expensive in medium-sized buildings. In large-size buildings, AI again helps optimizing the way we heat, the way we cool, and the way we manage energy in the industrial world, of course, with assistance everywhere. Not only is AI able to help our customers optimize our products and offers, but it is now also, with the coming of language model agents, really helping our customers in their daily work, the way they do their jobs. In smart grids, we are providing co-pilots or supports to help them—we call them interactive agents—to help them operate and use their solutions in a better way. The same applies to our resource advisor solution. So, AI is now in all our offers, both on operational agents helping optimizing, making things more efficient, and interactive agents helping the human do their work in a much better, clever, and efficient way.
New opportunities for customers
Violaine: So AI is everywhere in Schneider Electric offers. Could you share with us an example where AI completely unlocked new opportunities for customers?
Philippe: So many. But I would probably start by saying it is unlocking, from my point of view, the energy transition. When you look at the need for humanity to still have access to energy—energy, as we often say, is a basic human right—but at the same time stop impacting the planet in a way that we will not be able to live anymore sustainably. We quite often look at the production side of the equation: let’s bring more solar panels, more wind generation, and that’s good, and we should do it. But we forget the importance of the demand side of the equation. Without AI, we will not be able to optimize the demand, and we will not be able to go through this energy transition. With AI, we can consume less energy by optimizing our processes. We can avoid using energy at peak demand. We can find ways to average the demand. At peak demand, the demand is very strong, and it’s harder to stabilize the network. So, everything that can help reduce the peak demand will go in the direction of helping energy transition. The first example I would give is that energy transition needs optimization on the demand side, and demand side optimization needs AI to happen. We see that at home level, building level, factory level, and data center levels. That is not only unlocking value but making sustainable life easier, faster, and possible.
Violaine: And would you say we’re smarter in a way with this?
Philippe: Yes and no. The other unlocking that I see today a lot in this innovation summit is the fact that AI helps us where as human beings we face some difficulties. To some extent, AI makes us smarter. But from my point of view, it mostly makes us much more efficient, and that’s very important. One of the big difficulties that our customers are facing every day is the lack of qualified engineers. They have a lot of projects and great ideas. One of the things that stops or blocks them in deploying those projects is, for example, the lack of qualified automation engineers. With AI, we are not replacing the automation engineers, but we are providing them with tools to write software in a more efficient way, check their software, write tests, and making them more efficient—to some extent smarter, but clearly more efficient. We are unlocking a lot of possibilities for our customers. Take the maintenance problem. We never have enough field service technicians or maintenance people as an industry. With AI, we are now able to make them more efficient. We can optimize where they go. We can make sure we send the right technician to the right place. We can make sure they don’t spend hours driving and spend more time supporting and helping their customers, more time doing their job. We do that by using AI to optimize how and when we send them, but also using AI to give them more access to knowledge, an easier access to how to repair, and make their daily life easier and our customers happier. So smarter, for sure, and more efficient.
Future investments in AI
Violaine: Now let’s talk a little bit about the future. Which technology are you investing in now to prepare the future?
Philippe: That’s probably the tougher question for somebody in charge of AI, because technology changes every six months, and you have to accept the risk that you invest in something that will change in six months, or you do nothing. What guides me when I have to make technological choices is first and foremost never forget our mantra: we want to do AI at scale which impacts. For that, we start with the business case. Of course, you need to be aware of which technology to develop and support to be able to support your business cases. But you should never start from the perspective: “New technology, where can I use it?”. You should always start from what my customers need, what the market needs, what my employees need, what the energy transition needs, and which AI technology can help. The right AI technology is the one that will help you to be at scale. That’s the most important, and still the most difficult: at cost, obviously, and which is also the most sustainable. We pay a lot of attention when we choose technology and when we decide what to work on, to use technology which complies with what we call frugal AI or sustainable AI, making sure we are not using too much energy, making sure we choose the right model, the right solution for the right business need, at scale, at cost, with the smallest possible impact to the planet.
AI at scale
Violaine: Philip, I would like to come back to the people topics because you were talking about those engineers to whom we’re giving those AI tools. We know that lots of applications proposed by Schneider require blending AI expertise with domain knowledge. So for Schneider, did it require introducing new ways of working to use AI?
Philippe: Absolutely. AI marks probably the first time in history that we introduce a new technology so quickly in production at scale in large companies to solve real-world problems. Nobody had ever heard about LLMs two years ago, but they are now powering the solution that we give to our customers. The question is, how do you bring technology fast enough while still merging it with domain knowledge? Because AI alone doesn’t work. You need to understand the deep understanding that we have of the industrial automation, the energy world, and the electrical world. We decided to focus on delivering AI at scale that impacts—not demonstrators, not proof of concept. We bring in people with a strong AI knowledge (often two or three years out of university) together with people with domain knowledge. We start from the business case and the business value, and then we use agile methodology. We create an agile squad or an agile scrum team where we put together the AI specialist with the domain specialist. That team is not an innovation team. We are not asking them to prove a concept or make a demonstrator. We are asking them to start from the business need, the customer need, and bring the innovation and the new development into production until it is adopted, sold, validated, the customer is trained, the salesforces are trained, and we sell it. For us, the key change is, on one side, finding ways to merge AI specialists with domain specialists leveraging agile, and on the other side, stop thinking of AI as innovation but thinking of AI as an offer at scale that we manage like many other offers. We focus on what the value is and how we truly bring it to the market at scale.
Leading in the age of AI
Violaine: You said it, it’s a fast-changing, fast-paced technology. So what’s one action leaders should take today to empower their teams with AI?
Philippe: The first thing I would really recommend any leader is learn. Spend some time understanding. There are two hypes about AI. The first says AI is as good as human, it can do everything, solve problems that were never solved, and the future of humanity will be perfect thanks to AI. Then there is the negative hype, which is either: “I’ve seen metaverse. I have seen blockchain. Let’s wait a bit, it’s going to go away,” or even worse, that the doom of humanity, “Robocop is coming, we are all going to die,” and so on. The reality is in between the two. My first message to leaders is train and learn enough so that you understand what AI can do for your business and domain. Since it changes, you need to keep updated. My second advice is make sure your teams learn and understand what AI can bring and how it can be used. They don’t need to be experts in the in-depth algorithm, but they need to understand what it takes (good data quite often), what it can deliver, what accuracy you can get, and what it means for your business. My second advice is practice, do it. With many aspects of AI, you can try it. You can try ChatGPT. You can try to build your own agent in GPT or GPT-4 for web or Gemini, whatever. Just try, just do it, just feel it.
What AI tools does Philippe use?
Violaine: I want to ask you a bonus question. Philip, as Chief AI Officer, what AI tools do you use in your daily professional or personal life?
Philippe: In daily life, I use a lot of AI tools without even knowing it. Every time I open Waze or Google, it actually uses AI. If I look at AI focused tools, I would mention two things. I use Copilot for web because this is the solution where we are certain of the confidentiality of information. At home, I love Perplexity for searching the web, where it’s quite impressive, and from time to time GPT. The other thing I use—maybe because I’m a Chief AI Officer—is a lot of learning. I learn on DeepLearning.AI, Coursera.AI, and I spend quite a lot of time learning again, behind the hype, behind the smoke and mirrors, to understand how it is really working. I find that fascinating. So, I use very end-user tools, and a bit of Python and coding from time to time to understand how it works.
Violaine: Great. Thank you very much, Philippe.
Philippe: Thank you.
Violaine: Thank you, and thank you for joining.
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The first Schneider Electric podcast dedicated only to artificial intelligence is available on all streaming platforms. The AI at Scale podcast invites AI practitioners and AI experts to share their experiences, insights, and AI success stories. Through casual conversations, the show provides answers to questions such as: How do I implement AI successfully and sustainably? How do I make a real impact with AI? The AI at Scale podcast features real AI solutions and innovations and offers a sneak peek into the future.
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