Welcome and thank you so much for joining in. I'm Nina Gara. I'm the moderator for today. There's a lot of talk about industrial AI and what's new in AI. Siemens and Amazon aren't just talking about it. They're making it real, working together to scale industrial AI across some of the world's most complex systems. And today we're joined by two of the leaders driving this impact. First, let me introduce you to Peter Koerte. He's a member of the managing board of Siemens and the company's chief technology and chief strategy officer. Welcome Peter.
And we have Marty Malik with us. He's Amazon's vice president for corporate partnerships and business development. Hi Marty, nice to have you there.
Thank you. Great to be here.
Great. Thank you. Let me thank you for joining. And I would say let's dive into it. And Peter, I would start with you actually. So everyone's excited about AI, but we're actually often talking about industrial AI. Can you tell me a little bit about why this is so important to Siemens?
Yeah, what we usually say is that consumer AI makes the headlines but industrial AI makes the impact. What we mean by that is if you think about how we use large language models in industry, we can significantly add value to customers by accelerating innovations, by delivering products much more cheaply, and by bringing more intelligence into the service we provide. We believe that if you take industrial AI into the physical world, and there's a lot of talk about physical AI, this is exactly what's happening. So this is where large language models are being deployed in an industrial setting and thereby making industry more efficient.
Thank you. And Marty, I know for Amazon, industrial AI is also a very important topic. Can you also elaborate a little bit more – where do you see the greatest impact there for Amazon?
No, absolutely. I mean a lot of how we think it starts with the customer. As we see the customers go through the journey of learning about AI, what does it mean for their business, and then starting to experiment with AI. I think the natural evolution is how does this apply to the physical world, and how do you start taking some of these concepts and bringing them where you have this immediate and very profound impact. Amazon has been investing in AI in so many different categories from the silicon to large language models like Bedrock and SageMaker, frontier agents, but as we look at how we think of industrial AI, partnering with Siemens was really critical. Bringing their real world expertise to the table combined with our technology allows us to help our customers invent and solve some of these real challenges.
Absolutely. And you were mentioning already the partnering. So the partnership between Siemens and Amazon is nothing new, right? It has been there already for quite a while. But can you elaborate maybe what changed also in regards to industrial AI?
Sure. I mean we have been partnering with Siemens for over a decade. So it's true we have a long relationship. I think that relationship started with us essentially being customers of each other, understanding each other's capabilities and solutions using them internally, and building trust across the organizations that we can count on them for the most critical capabilities and vice versa. It was just natural for us to start looking at how we can now start bringing these capabilities together to serve a larger audience of customers. So we go through a process of understanding what are the capabilities that Siemens has, what are the capabilities that Amazon has, how do those come together, and what can we do in a really seamless way to provide an end-to-end solution for our customers. Two years ago we announced Mendix. I think we've seen success from that. It's a kind of a good foundation in the AI space, and we just see so much more opportunity for us to grow together in this space, bringing the joint expertise that we have.
Absolutely. And Peter, we tend to call ourselves the customer zero. Can you elaborate a little bit on how has working with Siemens created value inside of Siemens?
Yeah. So you mean how has the relationship created value together with Amazon and Siemens? Let me give you two great examples. First, on the factory floor, we are using the industrial edge, and thanks to the hosting we have, we are now able to do this 80% faster than previously. So this is a great example of how a long relationship was the nucleus for accelerating and taking the latest technologies. Another one is using Amazon's large language model to increase our searches on our website significantly. We improved to three times, which is fantastic, so customers can find products much faster. So these are just two small examples, and there are many more to come as we work through this great partnership. And lastly, to answer your question specifically on customer zero: similar to Amazon, we are big believers in trying it out yourself first, being your biggest critic, because then you can improve before it hits the market and you have a reference. So culturally we are very well aligned on how we get new innovations together to the market.
Absolutely, absolutely. And Peter, I would stay with you for a second. Now we're scaling well beyond our own operations to customers across industries. So where can we see the impact of industrial AI out in the real world? So this is beyond the partnership just in general – where do you see the biggest impact?
Well, the biggest impact is everywhere where we have the most data, because AI helps us to do this in an automated way where we have an explosion of data. Areas like pharma life sciences, where we are just about to start to gather all the data and then make sense of it by applying algorithms. So AI is going to be a great help because sometimes we don't understand the mechanism, but we can observe what's going in and what's going out, and AI is really good at articulating that. So that's one. Then the other one is the 30 industries that we serve today as Siemens, in particular anywhere where there are manufacturing challenges, be it automotive, machinery. This is where, because of IoT and Industry 4.0, we have all this access to data, and we have the ability to get more intelligence into their operations.
That's really true. And it's clear that industrial AI opens up all kinds of new value, and I think there's more to come, more to unlock. So Marty, what does it take to be ready for it? Where do companies need to focus first?
Right. Different companies will have different approaches, but at the baseline, it's really understanding what is the problem you're trying to solve, what is the customer challenge, and starting with that. It's called the problem statement. So understanding the objective you're working toward, and then building out who are the key stakeholders who will be accountable for running the system and driving the solution from beginning to execution and the outcomes. Within Amazon, we have a GenAI innovation center, and we've built a framework around this; we call it the five Vs. Essentially working through this framework to start with the vision, work through visualization, then verifying the solution, and working through that process is a big part of the implementation and achieving outcomes. We look at it very much from a solution perspective. So less about the technology – although technology is of course fundamental – but when we start talking about industrial AI, it's about the solution and the business outcomes we can drive together across our technologies and organizations for the benefit of the customers.
And can you elaborate a little bit more on the five Vs?
Sure. It starts out with what is the value you're looking to drive from this engagement, understanding that and quantifying it as well, then putting together the materials, capabilities, and solution set that will contribute to that. Validation is also key – are we seeing the outcomes? If we think of a mechanism, you have inputs and outputs, making sure it follows through as expected and adapting as necessary. So that's the verification stage. Finally, it's bringing that into execution and venture, and having the solution. We've seen within the innovation center using this process that 65% of the proof of concepts go into production, some in as little as 45 days. So it's been a very powerful and effective framework.
Absolutely. Thank you so much. And Peter, industrial AI is here and it's here to stay. What do you think is coming next for Siemens? So can you elaborate a little bit more on our strategy in the next one or two years?
Yeah, happy to. What we're seeing already now is that we have a lot of copilots coming, and we're really excited about these because copilots are lowering the entry threshold for engineers. When they use requirements tools, design tools, or lifecycle management tools, very often there is a training effect. By using copilots, we can guide new engineers and make them productive much faster. So we democratize the knowledge to get started. You're going to see this now; we have multiple copilots embedded along the entire value chain from requirements to lifecycle management to design to simulation and operations. You'll see it across the entire line, which is really cool because it accelerates the conversation. That's where we are today.
Now what we think is that the true intelligence in industry comes from industrial data, and industrial data is very different from most AI, because GenAI is trained on language, words. In industry, you have requirements where words are used, but then you go into technical details, drawings, calculations, data from manufacturing, etc. We realize we can't just take existing models to improve the entire design process from cradle to grave. So we are starting to build an industrial foundation model specifically trained on industrial data, which you can't just download from the internet. This requires building alliances, bringing it by industry for industry, developing it for them, understanding the use cases, getting the requirements data, design data, production data, and building a generalizable model that will significantly accelerate – let's say cut in half – the time to develop new things. We think that's really exciting for industry because that's exactly what everybody needs: faster time to market, more flexibility, and more tailored products for customers. AI gives us much more flexibility than we used to have.
Absolutely. And maybe for everyone who is not so much into the technical details: we're first talking about the copilot and then this industrial foundation model. Can you also elaborate what is the connection? Why is it so important? What is the difference?
