AI’s role in business
“AI is today where the Internet was about 30 years ago,” says Peter Weckesser, Chief Digital Officer of Schneider Electric, in this episode of the “AI at Scale” podcast. Just as the Internet revolutionized every aspect of business and personal life, AI has the potential to do the same, and it has already become a catalyst for transformation and innovation for many companies.
In this conversation, Peter outlines strategies for leveraging AI to make a significant impact. He gives concrete examples of AI applications developed at Schneider Electric that resulted in meaningful improvement in customer care service response time, or greatly enhanced software engineering productivity.
As President of DIGITALEUROPE, leading trade association representing digitally transforming industries in Europe, Peter shares his views on building AI competitiveness through smart, balanced regulation. He highlights AI’s current influence and future potential, providing practical advice for businesses navigating the digital landscape.
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Transcript
Gosia Gorska: Welcome back to AI at Scale podcast. This is Gosia Gorska and today I have the pleasure of hosting Peter Weckesser, who is the Chief Digital Officer of Schneider Electric and a member of our Executive Committee since June 2020. Prior to working at Schneider, Peter served as the Digital Transformation Officer of Airbus Defense and Space Division since 2017. Before joining Airbus, Peter had extensive experience as a senior executive at Siemens, most recently as the Chief Operating Officer of the Siemens Product Life Cycle Management, leading the IoT and digital enterprise business and activities. He also held other executive-level positions with Siemens such as the CEO of Industry Services and CEO of Value Services. Peter holds a degree in Physics as well as a PhD in Computer Science from the University of KIT in Germany. Welcome, Peter.
Peter Weckesser: Thanks, Gosia. Thanks for having me.
AI dominates boardroom discussions across industries
Gosia: Yeah, I was really looking forward to meeting you. The first question I have is that I know you regularly meet with many CEOs, and I was really curious to know what’s the atmosphere in the boardrooms today about AI? Is it still top of the agenda? Is there a feeling of a gold rush, or are we at the phase already where companies follow their AI use case To-Do List?
Peter: So, yeah, thanks for that very interesting starting question. I can clearly say in all the conversations that I am having with customers, vendors, and partners, AI is top of the agenda. That has been the case for at least the last two years. Very honestly, it really started with the emergence of generative AI in large language models. But actually, the conversation is broader than generative AI only. What we see today is that all software vendors are really creating an AI-enabled portfolio, and we see this all over the place. Of course, all companies are asking themselves how they can utilize AI in the best possible way. We see this very broadly across all industries that we are serving at Schneider. Of course, we are deploying AI at scale at Schneider ourselves. For us at Schneider, it’s both generative AI, which is about creating new content from mostly unstructured data, and what we call analytical AI or what was called machine learning in the past, which is about creating insights into mostly structured data. We have use cases where we can combine both. Yes, AI is very much a boardroom discussion at partners, suppliers, and at Schneider as well.
AI’s expanding role in business operations
Gosia: That’s great to hear. It’s good to know that we are leading in some areas in terms of AI implementation. If we look into the future, what do you predict will be the most significant developments in AI over the next decade?
Peter: Maybe let me start answering that question with a bit of a bold statement. My personal belief is that AI is today where the Internet was about 30 years ago. If you think of the Internet for a moment, it has pretty much changed every company, every business process, and it has changed all of our personal lives, right? We couldn’t easily order pizza anymore without the Internet, nor could we find the way to a meeting that we drive to. The Internet is everywhere and it has significantly changed how companies operate. It has significantly changed our personal and private lives. I believe AI has the same potential, and what we are seeing right now is only the starting point. We can already see use cases where AI significantly impacts the way we operate. Maybe the single biggest use case today is in content generation in software engineering, which is not only adopted by Schneider but pretty much across any company that generates software because the new generative AI capabilities are really capable of supporting software engineers in their daily work and significantly driving productivity.
The numbers that I’m hearing range between 10 and maybe 40% of productivity gain for software engineering. We are clearly seeing that productivity gain in Schneider and we have decided to roll out the respective tools to support our approximately 10,000 software engineers in the company. Our goal is, of course, faster time to market. We want to release new products more quickly. This is why this is a very important tool and platform to become better and serve our customer needs more quickly. Now we have many other examples where we cannot simply use off-the-shelf tools, but where we need to train foundational technologies to really address bespoke cases that we have in Schneider. So, in the future, I see this will be a mix of more off-the-shelf technology, but also where companies have to tailor the AI solutions to really create more differentiated value for them.
AI Agents: accessible tools for non-programmers
Gosia: I see. And something that has come out recently about agentic AI, how do you see this topic unfolding?
Peter: Very interesting topic and very clearly the world is moving towards agentic AI. What does that mean? This means that it will become relatively easy for everybody to create an AI agent, which is an AI-based application that delivers a certain outcome, a certain value. It always needs data to either create some new content or generate insights into that data. More and more companies are providing either AI agents or even tool suites that make it relatively easy for people who do not have programming skills to create an AI agent. Now, I have a view that this is something that needs to be governed because not everybody in every company has the skills to create new software applications that include AI. When I mention skills, I don’t mean technical skills because the barrier is lowered so that almost everybody can do that. But putting a software application into operation certainly requires the necessary quality assurance. It requires a certain governance—are we really doing the right thing? So this is clearly something where I want to put the right governance around so that we deal with this in a very professional and ethical way at Schneider.
AI’s impact on business: preparing for the future
Gosia: I see, that’s a really interesting future ahead of us. When you speak about it, it seems so easy. But I believe there are some challenges next to, of course, the opportunities associated with the digitalization of the industry. How can executives prepare for that future to avoid maybe some of the challenges or just face them?
Peter: I believe the first thing is to acknowledge that there is a relatively good chance that AI will have a significant impact on almost any business. Then any company needs to ask the question, what is that impact and how can this be steered in the right direction to have the best benefit from that? There are multiple dimensions of questions to be addressed. What are the use cases where AI can play a major role? Where will AI potentially change the way we do business? Where can AI drive significant productivity? What is the change management that is required in any company? And also, what are the technical skills that you have to build or acquire to master that? My personal experience is that the technical problems are not the hardest problems to solve. These are usually the relatively easier ones to solve. That’s also our experience at Schneider that we are actually in very good shape already to deal with the technical challenges. We have created an AI organization with around 300 people. We have built up the technical skills, but we have done way more than building the technical skills. This is really where the rubber hits the road because our ambition at Schneider is not to play with AI because it’s cool technology, but our ambition is to drive significant business impact through AI for the company and really use it at scale. To do this, we have decided to drive a use case and business case-driven approach across the organization. Everybody in Schneider, that means every function—finance, manufacturing, HR, or every product organization that owns a product becomes a partner of our AI Hub. The way we work is that these spokes really own the business case, and the AI Hub is responsible for the technology platforms and the delivery of the business case. The experience we also have is that you need to put quite a bit of focus on the necessary change management, particularly if you want to deploy AI to optimize internal processes. It’s very important that we put the right focus on driving the necessary process change, which includes first informing people, educating people, upskilling people, so that some of the work we do will be done in a different way in the future.
AI-driven productivity gains in software and customer service
Gosia: Yeah, so we have to all be on the same page in terms of AI benefits and limitations. What’s really interesting in what you said is really this process, the journey in the whole AI implementation that requires some thought, the initial thought about capturing the value that it can bring to the company. This means we are not thinking about how many new shiny projects we have in terms of AI, but really what’s the value that we can bring to the company. This drives me to the following question about where do you think we are at Schneider right now with the implementation of AI? Was there a particular use case where you thought, “OK, I’m satisfied because we have reached a certain level of AI maturity in the company?”
Peter: Yeah, thanks. Very good question. I want to give you not only one example but two examples which are a bit different. The one example which has most likely the biggest impact on our productivity was our decision to introduce an AI-based technology that allows our software engineering community to get significantly more productive in software engineering—the way we write code, how we test code, how we deploy code. Here we see certainly double-digit productivity levels across our software engineering community. These are off-the-shelf tools. They are relatively easy to use. You don’t need to have any specific AI know-how to deploy these technologies. These technologies can be used relatively quickly by everybody. Of course, you need to adopt your processes and fully embrace this. Our ambition at Schneider is to become much more efficient and faster in how we deliver new products to the market. Many of our products have a significant software content, so software engineering is a key value contribution to the value proposition of our products. This is one example.
I want to give you a second example, which actually required a bit of tailoring and bespoke engineering to make that happen. That is a use case where we are supporting our customer care organization, helping to answer questions from our customers more efficiently. With thousands of customers, we have a lot of customer requests when customers come back to us with questions on pricing, availability, technical questions on the product. These questions usually go to a customer care agent who gets back to the customer with an answer. This can be via many different channels—phone, email, chat technology. We are using all channels for that. With our very broad product portfolio, these customer care agents need to be trained and have a knowledge base that they can easily search. We have now created a chatbot based on generative AI, which allows our customer care agents to find the answer to specific questions much more quickly and efficiently. We have been able to reduce the time spent on answering a customer question by up to 50%.
Gosia: Wow, that’s interesting.
Peter: This is a huge efficiency saving and a huge customer value because we are able to get back to our customers much more quickly with the right answer they are looking for. So it’s serving both dimensions—customer satisfaction as well as productivity improvement for ourselves.
Measuring AI success: focus on impact and value
Gosia: Now that’s a great example. Also, how do you recommend measuring the success of AI initiatives in the organization? Because we shouldn’t probably be just happy that we have 100 projects. Should we be looking into the value that we can grasp in the company?
Peter: Yeah, you’re asking the question and giving the answer already, at least partially. For us, it’s not the number of projects and it’s certainly not playing with technology, but it’s about measuring the impact. There are some hard KPIs which are impact in terms of productivity, efficiencies, cost savings that we can generate. We are also very consciously looking at how AI can drive more top line, so more customer success with our customers. How do we make our portfolio more attractive? These are two dimensions where we created a principle. Before we embark on any AI project or use case, we want to be very clear about the metrics, how we measure success. Usually, these fall into either productivity and efficiency or increased top line. Some use cases drive both. These are the hard measures. I also believe because we have the ambition to be the most digital company in our industry, we need to measure the satisfaction of our people utilizing these new AI capabilities. I want to make sure we have the most motivated people, people that really embrace this at scale. This is part of our KPI system—we want to deliver the best experience for our people. It’s important to measure satisfaction.
Digital Europe: strengthening AI competitiveness
Gosia: Thank you, Peter, for sharing the journey of Schneider Electric in implementing AI. Now in the second part of our conversation, I would like to deep dive a little bit into your role as the President of Digital Europe because we are meeting in Munich, which is a great opportunity to meet you face to face. My question would be, what’s your plan to foster intercontinental cooperation and increase the competitiveness of Europe in the AI space?
Peter: Thanks for that question. For about six months, I’ve had the honor to serve as the President of Digital Europe. Digital Europe is an organization that comprises 130 corporate members plus about 70 trade organizations from all the member states of the European Union. Digital Europe has become a major voice when it comes to the digital transformation of Europe and the competitiveness of Europe. One of the key ambitions of Digital Europe is to help bring Europe back to a level where we are competitive with other geographies like the US, China, and India. Numbers show that Europe has fallen behind in the last 10 years. We need to step it up in Europe, particularly with recent political changes. It will be even more important that Europe starts to stand on its own feet and gets control of its own future. In the context of Digital Europe, it’s very important that we look at Europe as one marketplace, step up investment into technology, and deploy technology. The Draghi report nicely points out the necessary investments that need to be done in Europe. And of course, it’s called Digital Europe, right?
So because we believe that digital is a huge driver of any development economically in the foreseeable future and we need to also master and own these digital technologies in Europe. This means not only that we create some of these technologies, we also need to deploy them at scale.
Creating necessary AI regulations for the future
Gosia: I see. And one of the elements that comes up when we discuss the usage of AI in Europe is, of course, the AI Act. How do you address public concerns about AI and its potential impact on society? And how important is regulation in this?
Peter: Yeah. So I think all experts agree that AI needs a certain level of regulation, and this is not unusual. There’s a good number of industries that have regulations and need regulations. Think pharmaceutical, aerospace. These are clearly industries that depend on really having smart regulation in place. Now, I personally believe that AI also needs regulation, but we need to make sure that we create the right and smart regulation to prevent unethical use of AI, but also enable the use of AI to make European industry more competitive in the future. There is certainly an area that we need to discuss more in Europe. Our ambition is not to create as much regulation as possible, but the ambition for Europe in the future should be to create as much regulation as really necessary. Let’s make sure that we create smart regulation that creates the right boundary conditions for the right use of technology, but also enables European industry to be competitive on a global scale.
Towards smarter AI regulation in Europe
Gosia: Yeah, that’s definitely necessary for the future of Europe. If we dive into this topic, what regulatory challenges do you think AI developers and users face and how can they be addressed?
Peter: Europe has an AI regulation and this AI regulation is a risk-based approach, right? For any AI application that you need to put into operation, you need to go through a certain level of risk assessment. I think this is an OK approach, but we need to handle it practically so it doesn’t overburden the usage of AI for applications. Just want to throw out an example: when it comes to optimizing water usage in washing machines or the navigation of vacuum cleaner robots in homes, we need to make sure that this regulation doesn’t overburden European industry and users in Europe so we can easily deploy these new technologies. Right now, I see us at a pivotal point where we need to make sure that we do not create more regulations, but our ambition should be less regulation and make it smarter. Here we have some way to go in Europe.
Embrace AI across your organization
Gosia: OK, I see. Good. I really thank you for the conversation. It was very insightful. We have time for one more question. For our audience, what would be your recommendation in terms of getting started with AI projects, investing in AI, and navigating through the changing landscape of AI solutions?
Peter: Maybe a few very simple pieces of advice that I can share. First is probably a very conscious decision: is AI relevant for your business? I believe almost everybody will come to the conclusion that yes, it is. If you come to that conclusion, I recommend that you fully embrace this as an organization, not only in one department, but you need to embrace this AI transformation with your complete organization. Everybody in your organization will have a role in this AI transformation or at least be a user of AI in the future. Clearly, the recommendation is to drive it from a use case-driven approach. Which use cases have the most impact on your business? Then put the right teams on delivering these use cases. Do not try to create technologies that you can source from the market. Everything that you can buy from the market, you should be using from the market. Fully embrace it, measure the impact, and pivot if some of your use cases do not deliver the impact you expected.
Gosia: Thank you so much, Peter, for being with us and for sharing all the insights. It was a pleasure to talk with you.
Peter: Thanks, Gosia. I really enjoyed the interview and maybe looking forward to doing another one in the future.
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