← Me, Myself, and AI
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- [00:03]
Podcast Host (Intro/Outro)
Consumer AI makes headlines daily, but industrial AI increasingly enhances and enables nearly everything we do. Learn how one multinational company approaches data management and deployments at scale on today's episode.
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Peter Corte
I'm Peter Corte from Siemens, and you're listening to Me, Myself and AI.
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Sam Ransbotham
Welcome to Me, Myself and AI, a podcast from MIT Sloan Management Review. It's exploring the future of artificial intelligence. I'm Sam Ransbotham, professor of analytics at Boston College. I've been researching Data analytics and AI at MIT SMR since 2014 with research articles, annual industry reports, case studies, and now 13 seasons of podcast episodes. In each episode, corporate leaders, cutting edge researchers, and AI policymakers join us to break down what separates AI hype from AI success. Today we're talking with Peter Corte, Chief Technology Officer at Siemens. Siemens is a German multinational technology company focused on industrial automation, smart infrastructure, and mobility systems, all increasingly important topics. We'll discuss industrial AI, what it means for the workforce, and what the implications are for data sharing across industry. Peter, welcome.
- [01:19]
Peter Corte
Well, thank you, Sam, for having me.
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Sam Ransbotham
Great. Let's start at a high level. Some of our listeners may not be familiar with Siemens. Can you give us a brief overview?
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Peter Corte
Yeah, sure. So Siemens is out there since 180 years almost. And what we say is we transform the everyday of everyone. And what that means is, if you think about the chair right now that you're sitting on, the clothes that you're wearing, the water that you're drinking, the electricity that you're using, the transportation systems such as trains that they're using every day, all of that was enabled by Siemens. When it came down to the way we design these things, we produce them, how we actually make sure electricity is safe and distributed, how transportation is run smoothly and safely, all of that is coming through Siemens. Except as a consumer, usually you don't see us, but in the industrial world, Siemens is a very, very big brand name. And we are recognized for the high quality, but also for the great solutions we bring and the simplicity to our customers.
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Sam Ransbotham
Yeah, I think that's a great example, because so much of the world we rely on, we just don't pay attention to. We don't notice it unless it isn't working for some reason. You talked about industrial AI. What exactly is the difference between industrial AI and consumer AI that most people would be familiar with?
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Peter Corte
Yeah, the big difference is today, of course, consumer AI is making the headlines, while we think industrial AI is quietly but profoundly changing the physical infrastructure, the physical world that we know of. So think about, for example, the building that we are sitting in right now. And that building has, of course, some climate control. About 30 to 40% of all the electricity that we're using today goes into buildings. What we are saying is, what if we actually can take all the sensors that we have in these buildings, then develop an AI that automatically learns every minute or 15 minutes and that case, and then automatically adjusts all the temperature settings, all the lighting settings and everything in order to cut cost and energy. And that's exactly what we're doing. We just launched an application that saves 30% of your energy bill and therefore reduces greenhouse gases by 30%. Just by doing that. It runs autonomously in the background. And this is what we do for grids, we do this for factories, we do this for machines, we do this for, of course, buildings, and we do this for trains. So everything in the real world, we are making it more efficient simply by what we say, connecting the real world and the digital world where we try to optimize and make things better.
- [03:59]
Sam Ransbotham
Yeah, that makes a lot of sense. I mean, I'm sitting here on a university campus, it's spring break and I guess we are probably heating this place about the same as we would be if it was full of people. I don't even want to ask. I don't want to know here.
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Peter Corte
That's it. That's it.
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Sam Ransbotham
Well, I think we're all familiar with consumer applications and I think the failures of AI in consumer applications get a lot of attention. You know, with the hallucinations and these sorts of things. Somehow that seems very different if you're connecting this to the physical world. It's not just a funny anecdote that goes across the Internet when AI screws up. It could have some real world consequences when you make that connection. How is Siemens thinking about that?
- [04:40]
Peter Corte
Yeah, you're absolutely right. And semps. Thank you for saying that. So when we compare consumer AI to industrial AI, there's three things at the very least that are profoundly different. And the first one you already alluded to is the level of precision and accuracy of those models. So obviously you don't need any hallucination when you make recommendations for an engineer to design the next part for, let's say your smartphone or. You certainly don't need an AI mistake when you think about how to optimize an electricity grid, because that's critical infrastructure. So what we need to ensure is the highest level of quality of those models, which, as you can imagine, that's where we get to into 99, 99.9 and so on. The percentage of accuracy of the models. And a lot of work goes into that to make sure that these are reliable, safe and trustworthy. That's the first part. The second part is actually, how do you train these models? Because I think you and all of us, we are very familiar with what we call large language models. Now in industry, we not necessarily talk about large language models. We usually talk specific data when it comes to going back to the building example temperature settings. So we have a lot of time series data, we have construction data, we have engineering data, we have simulation data. This is very different. So these are geometries, pictures, vectors, what have you. And so we have to make available these models in a very, very different way. And the third difference is how do we get that data? Because when we build these models for the physical world, we cannot just go on the Internet and just download a bunch of data from sensors for your buildings or CAD data or whatever. This is very often even very proprietary data. So customers are only willing to share that data if we are able to express an incremental benefit of when they use our model, then they in return share the data with us. So of, of course, in your case, better energy savings in the building, but also for designers, faster time to market because we can get them designed faster and so on. So that way of how you actually get to the data is very different. So the language you're speaking, the accuracy that we need, how we get the data, this is in the industrial world, quite different than to, of course, what we use in the consumer AI every day.
- [07:09]
Sam Ransbotham
That's pretty fascinating. My naive reaction when you first started talking was that, oh, what you're describing is much more structured data. So I was pretty excited when you were talking about, oh, man. A lot of this data is temperature data or structured data, but the idiosyncratic nature and how it applies only to your building or only to your machine and only to your setting seems very difficult. Tell us a little bit about how you're getting people to give that data to train machines and how that transfer works between organizations.
- [07:40]
Peter Corte
Yeah, it's a very good question, and you're absolutely right. Because if you think about it, if I say it's a great day or the day is great, the LLM does understand the meaning that actually it's a great day in engineering terms, it's very different. So therefore we need to adjust and cater for that. The way this works in the industrial settings is you go of course, after the industries, step by step and say, okay, what is the semantics in there? I've alluded to buildings, and in buildings there are certain standards and there's certain data formats and what we call ontologies. So it's the semantics. And there we try to get that understanding about what is it, that data that is there. It is more structured, as you say. But as you can imagine, right now you're sitting in a room with Fahrenheit, I'm sitting in a room with Celsius. And so therefore, if you then say, well, even this is a temperature setting. Actually it is, but it's quite different If I'm talking 20 and you're talking 20. Right. So for me it's warm and for you it's actually really freezing cold. And so that's something to adjust for. So it's not a slam dunk. But understanding these use cases by industry, by industry is really key. And so in buildings, it's all about energy consumption. But as I said, in engineering, very often it's time to market, it's in production, it's usually quality and throughput. And so understanding the data and the key variables that drive that is important. Which brings us to a keyword that I want to mention and that is called domain know how. Because you can argue. Well, any data scientist can do that. It's true. However, you really need to understand the domain that you're operating in and the key parameters. And I'll give you just one very simple example. But I find it fascinating. I'm not sure when you used the last time a train, but maybe the next time you use a train, and I ask you, what is the most critical component of a train? And probably would say, well, probably the brakes. And that's true, is safety critical. But it turns out it's the doors. And why is that? Because if you think of the job to be done of a train is to move people from A to B, that means it stops, it gets people on and off, and so you go from station to station to station. So the whole day? Indeed, yes. The doors of the train go open and shut and thereby they break down. So the most critical part in that regard for operations is the door. And this is the main knowledge you need to understand that part. Once you understand that, then it's fascinating because then what you can do is you can say, well, give me the voltage reading of that motor that drives the door. Look at, of course, the profile of how that motor actually operates. And in the meanwhile, today our models can predict any door failure 10 days prior of its failures. So therefore, you can get into the depot and you can fix it, which means higher uptime, higher reliability and all of it and better passenger comfort. So these are the examples where you have to combine the domain know how together with the technical know how meaning AI. And that's how you create customer value.
- [10:44]
Sam Ransbotham
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- [11:34]
Peter Corte
Industry by industry.
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Sam Ransbotham
I like that because I can get my mind around that example. Some of the things that I was reading about Siemens were complicated to understand, but that makes a lot of sense. I think everyone has some sort of application where they would like to know ahead of time that something is going to break and before it breaks, because when it does, it's a mess. Siemens doesn't necessarily own trains though. So how do you get that data about those voltage into your systems versus your customer who has purchased and bought that train? They have to have some sort of way to send that data. They've got to share information with you somehow, weirdly enough, they would benefit from someone else's train data for a train they don't own. How do you manage that infrastructure?
- [12:19]
Peter Corte
Yeah, it's a great question. And that's why I said it's very different the way you collect data in the industrial world and let's stay on the train example. Truth be told, there's customers that just simply don't. They just say give me the train and I'm fine. And then I build my own model. And so we have operators like that that do so usually, however, they are not the ones that are most successful, usually the ones that are saying you can send me a train, but the train. If you look at the total cost of ownership across the entire life cycle of a train, which is let's say 30 years, the train in terms of CapEx, so the investment, about 10% of the TCO, 90% is operations. So what if I go to you as the oem, you know best your system and I share the data with you and you helped me to optimize, so you helped me to optimize with regards to reliability. That's a draw example. You helped me on the efficiency. This really goes down to, of course, the way you operate the train. So we have, believe it or not, we have AI that helps you to think about how to accelerate and decelerate or break that train in order to save energy. So energy is one of the biggest operating costs that you have on that train. This is where we take then that data. It's connected. So all of these devices are then connected and then of course, reliably and encrypted. And then we have the data and then we make use out of this data and we build our own models in that regard. And we do this customer by customer. And very often we do have a data sharing agreement so we can use that data. We don't own the data. It's important. It's still our customers data. But we can use it and train our models for that purposes. And then as you said, we can combine it with other data so everybody gets better in that regard. And that's exactly what's happening, not just in, let's say in trains, but you see this in many machines. But it turns out it's not enough data to build your own models because you need to have much more, much, much more data across different settings. And this is where Siemens comes into play. Because usually we don't build machines and we don't build all the trains. Usually we build components that go into. So. So we work with car manufacturers, we work with aerospace manufacturers, we work with life science companies, we work with food and beverage companies and so on in order to help enable them. And so they come to Siemens and naturally say, you know, how can you help our specific industry to become better?
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Sam Ransbotham
I hadn't quite thought about it that way. That if one person has insufficient data to train a model by themselves and another person has insufficient data to train a model, but together they do, then the idea of connecting those people together creates value that neither of them could. And we had a guest from Land O' Lakes on a prior episode. They're sharing information with farmers. So farmers build things, they have a lot of data about their crops, but how they share that data, I feel like there's a lot of that going on where we are recognizing that idiosyncratic data is more valuable when combined with other data. At the same time, I'm not sort of naive. People don't want to share stuff. How do you encourage people to do this?
- [15:29]
Peter Corte
There's a simple, not an easy, but a simple answer to this. And that is value. So if I'm not able to translate that and say, you know what, share the data with me and then thereby you can improve your reliability of the train to stay there, or I improve the efficiency of your building, then they will not share the data. That's as simple as that. But if you do, then that's great. And then they say, that's fine. And sometimes it's built into your solution. So it's built into the contract where they just say, well, we don't care, it's fine, you can just use it. Others are saying, hey, I want to have also a negotiated discount, which is also possible. But the simple answer is you only share your data if you get some value in return. So that's a little bit like the model. And just in industry it's slightly different in terms of the kind of value we're creating, but still there's some value in return.
- [16:19]
Sam Ransbotham
You're describing largely partnerships, but sort of between customers or with customers. But you've also done some recent connections with industry. And like, for example, your partnership with Nvidia, can you describe what you're thinking there and what an. I think the goal there is an industrial operating system. How does that work? What's the plan there? What's the thinking?
- [16:39]
Peter Corte
Yeah, so with Nvidia, we have a very, very close relationship because for many reasons. One, of course, you lose a lot of GPUs in order to train some of our models. Second tools that we're providing today. Siemens is the leader in industrial software. So we have about 10 billion euros of digital sales. People forget about that. So we are among the top 20 of software companies in the world. So we have a lot of simulation software where you can simulate cars, trains, rockets in the digital world. And of course, all these simulations take an awful long time when you think about computational fluid dynamics and so on, which is very complex, but turns out you really can accelerate them. And so what we're doing together with Nvidia is to say, what if we actually, instead of waiting eight hours for a complex computational fluid dynamic simulation, let's say of the air drag on a car, instead of eight hours, we can reduce that to minutes. And that's exactly what we're looking at. So accelerating simulation, accelerating design. When it comes to chip design, for example, in design, which is really interesting, as we get to lower, lower nanometers, 2 nanometers and less, the complexity of verifying those chip designs is enormous. It exponentially really rises. So instead of having human engineers going through every circuit and really testing it to every gate array, actually you can start to have an AI go through this and do this over and over and over again. So the chip design verification is one, and then lastly there, then the design transfer to manufacturing is a key issue because every of these really holds you up in how fast you can get these chips out there. And so there again, we can already, as you are the designer, we can have the AI in the background verify whether what you have designed is correct and whether it can be manufactured. And these are kind of examples that we have announced also at CES earlier this year with Nvidia, we are really excited about because we think we can further accelerate, and this is always the key word, acceleration of design, acceleration of manufacturing, acceleration of operations. And that's why we are so excited about it.
- [18:52]
Sam Ransbotham
I get the appeal of switching eight months to eight minutes. I mean that.
- [18:56]
Peter Corte
Yes, it's amazing.
- [18:57]
Sam Ransbotham
Isn't it pretty?
- [18:58]
Peter Corte
Yes.
- [19:00]
Sam Ransbotham
It doesn't take much quantification. We can do that in Fahrenheit or in Celsius, either way that works. But the other thing it makes me think about is that you probably have a lot of processes designed around the idea that it was going to take eight months to do that. And when it takes eight minutes, it feels like, sure, it compresses it, but it also might change the types of things you do, the order that you do them in. It seems like it could just have this ripple of upheaval. How do you manage that? Or am I extrapolating too much? It feels like it could be a mess.
- [19:36]
Peter Corte
That is very true. That's why I tend to say always AI is about 20% is technology and 80% is actually transformation. And what that means is we talked a lot about data, that's one thing. But then it is really changing the processes of how you do things. And usually what the AI is now doing is it really changes workflows. So instead of thinking sequentially where I do one task, let's say I do the design. The next one is doing the verification that what next one is looking at how do I design to transfer? Transfer it to manufacturing is very sequential. Now, what if you could do this all in one step? Because the AI is doing it, so obviously you're disrupting a very well established workflow process. The first question that comes is, who is doing this? Is that the designer from the very end, from the beginning? Is it somebody completely else? So who's the Persona that you're actually talking to? Some very interesting questions. Second, how is that process then going to go and who's verifying that whatever the AI is doing is really correct? Then a third Question is, where do I do this? Where is the AI sitting? Is that a new application? Is that embedded into an existing application? Is it talking to all applications? All of these interesting questions arise and they are not usually all technical. Very often we find this is very much about the people that use it every day and involve them and then start to think, rethink what wasn't possible before and of course thereby addressing also some anxieties because many would then argue, well, the AI is going to take my job away. So then you have a lot of resistance and then all of a sudden a technology conversation becomes into a cultural, cultural change transformation conversation. And we find this time and again.
- [21:24]
Sam Ransbotham
Now the natural follow up is for me to ask about workflow and these type of issues. And they're all important and I don't want to discount those or whatever. But you're pretty fired up about smart glasses and workers wearing smart glasses. What's next for them? How do you see them in the industrial world?
- [21:41]
Peter Corte
Indeed, I'm very excited about their glasses. If you think about in particular US manufacturing. I just spoke to a major new EV electrical vehicle car manufacturer and they told me in their manufacturing their churn rate, so the attrition of their blue collar brokers is 35. What that means is you constantly have to retrain your employees and it's not just retraining them, but also the other question is how do you capture that knowledge? So what if you can take your glasses, you have that camera and let's say you are a specialist in operations and you are a maintenance engineer for a specific machine, that actually camera and that AI is overlooking your shoulders and literally and really is checking off what you, you're doing. Maybe you're narrating it. Even you record this, you do this over and over again, thereby you're democratizing that knowledge. Actually you can capture this. So for future people coming in. But even better than for the new worker working the night shift, 2am in the morning, a machine breaks down. Usually people are just tinkering around having no idea. But what if you had those glasses on now and they're saying those glasses are saying, you know, this is a CNC machine. Usually the failure code of E345 means actually it is a jam tool. Check LED and open this 1, 2, 3, 4, 5 and off you are. How amazing is that? And so I really think in terms of the keywords augmentation, so augmenting the workers, the blue collar workers, but also white collar workers on the shop floor and of course capturing that knowledge as they are exiting isn't that amazing? And I think it's going to make us all much more productive and much more enjoyable because you get faster time to result and thereby you get the factory running and so on and so on and you reduce a lot of anxiety and fear because very often people don't know what to do. And now all of a sudden they have a companion, they have a co pilot, colleague, whatever you want to call it, that helps them and that is there for them 247 as opposed to calling somebody who's probably somewhere home and sleep.
- [23:59]
Sam Ransbotham
Today on our branded interview segment, I'm talking again with Shayan Mohanty, chief data and AI officer at global technology consultancy ThoughtWorks. Shayan, thanks for joining us.
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Shayan Mohanty
Thank you so much for having me.
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Sam Ransbotham
I think governance is going to be a huge thing, even if it's going to catch us from behind. What do you think people are doing wrong here?
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Shayan Mohanty
The short answer is not treating governance as a first class citizen. Every single product or platform that I see that does some of these things, it's always like, hey, we're going to help you get up off the ground, like sprinting really quickly. And then by the way, governance, it's going to be bolted on after the fact. That's going to be easy, right? The hard part is getting your agents and all your tools to talk to one another. So I think it's really easy to get caught up in the 85% solution, which is really easy to just understand quickly get up off the ground. But the moment you try and put it into production, or the moment you start thinking about heterogeneous use cases with different security and compliance footprints, different requirements, all of a sudden you get into that world and things get way more complicated because you didn't make those decisions up front and think about them from a first class perspective. So I really do think thinking about governance as the operating system and not just as a bolt on after the fact is super important. And I would go a step further and say thinking about this as an operating system is important. Frequently people think I'm just going to grab Lang Graph and call it a day, right? And that's cool. But then what happens? We need to switch off of Lang Graph. You need to go from Lang Graph to something else, like your next tool of choice. Like now you're taking this tool of potentially large body of agentic work you've done and you're having to convert it. So the whole point of thinking about it as an operating system is now you think about protocols and standards. First you start thinking about how do I define my agents in a composable and portable way? How do I think of my runtime as replaceable? How do I think of my memory and my tool access as something that is registry driven? How do I think about all of these things as like components of a larger system that actually needs to work together as opposed to it being just stapled together?
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Sam Ransbotham
I feel like that's something that we in general mess up on. We reward features. Security is always the afterthought and this has a lot of flavors of the same thing. Do something now and we'll fix it. One of the early people I talked to was Hal Varian on this and he had a phrase that stuck with me, which is for now becomes forever. Whatever you're doing for now just eventually becomes the way it is because there's never time to fix it 100%.
- [26:43]
Shayan Mohanty
Think about Claude code as an example. Like, yes, there's a sophisticated, very well tuned model under the hood, but really it's in the orchestration of how you think about agents and sub agents and how they all work together. And it's all powered by roughly the same set of models that we had access to before. It's just, it's packaged in a different way and now all of a sudden the entire world is alight with it. So I actually think that to your point, if we don't think about these things now, you know, we kind of have these stubs out there. All of the invention that is happening is not centered around the model itself, but actually in a much greater surface area. There's just way more things around the model that are changing. And as a side effect is really hard to say, we're going to get back to it later because later is never going to come. Things are just going to keep changing. So now is the time for us to just align on a set of standards.
- [27:33]
Sam Ransbotham
Really well, I've enjoyed talking to you for now and hopefully there's a for later too. Thanks for taking the time to talk.
- [27:40]
Shayan Mohanty
Thank you. Thanks so much for having me again, please Visit us@thoughtworks.com We've got a ton of information there and you can learn about what we're doing at AiWorks, our internal agentic platform.
- [28:02]
Sam Ransbotham
That makes a lot of sense. I want to draw a little contrast though. Earlier we were talking about data and you were talking about a need for deep expertise and deep domain knowledge. But it sounds like this is maybe a push against or you're not needing to know that the 345 error code means this, that or the other. Is it deeper? Is it more specialized? Those seem in conflict to me in some ways.
- [28:26]
Peter Corte
Well, obviously we need both. Actually, the example is pretty comparable if you think of it. So, yes, I can tell you the door is going to break down and this is now preventative maintenance. The other case was more as a reaction to. But in both cases it's maintenance. And so the preventive maintenance means there's still a worker has to go out there and replace the motor. Now, on the other hand, in our case here, it's the same thing. It just gives you the intelligence of what to do, and the doing itself still has to be done by somebody who's operating that machine. So I think it's pretty comparable. The interesting thing about this is because it still requires humans. Could we at some point automate that through the whole conversation about robotics and humanoids and everything? This is certainly then also a big push right now that we're seeing in the market. Whether this is going to come soon or not, we don't know. But for sure, we're missing at least 2 million people in the workforce in the United States already today to get them on the shop floor. So we need the only way how to stay productive is by automation. And so this is where Siemens helps also many companies to automate their processes in the factories.
- [29:38]
Sam Ransbotham
Maybe I'm reading too much into it, but I read something you'd written about humanoid robots and some skepticism about the actual human, and you were kind of hinting at that right there for one. I'm totally with you. The human shape is not anything magical. And there are a lot better shapes for industrial machinery in particular. Are things going to look humans or are they going to look like machines or different?
- [30:02]
Peter Corte
Well, that's the big debate, to be honest. It's too early to tell. I've seen both. As a matter of fact, today I just had two conversations of that sort. One was them going into the direction we need to have humanoids. The other ones actually say no, no, no. I think in the end it comes down to the ROI and the value, again, that we're creating. Let's take a very simple example. Let's say material handling is a big one. In a factory, you have to always make sure that there's ample supply of material. Let's say, in particular, if you're in a stamping plant and it's metal sheets, and so it's heavy, taking a humanoid is probably not a good idea. Although there's use cases. I'VE seen them and there's many reasons. One, the payload is very, very, very limited. Number two, humanoids are quite slow. If you look at them at least today the question is can you accelerate them? But today they are slow. And then lastly is up to 30% of the energy consumed in a humanoid is just to making sure that you're standing upright. And what if you actually had different form factors that would give you a higher payload, faster speed, less energy consumed and then it becomes an ROI conversation? It depends. It's very hard to generalize in this case though I almost would bet probably a different form factor to a humanoid is a better one. But there's others where you could argue a humanoid could do a better job, for example wiring harnesses, clipping them together where you need a lot of dexterity and versatility and all of it maybe, but that's exactly that's why it's fascinating field. I think anybody who claims who knows it, I think it's too premature. But it's a fascinating field actually.
- [31:40]
Sam Ransbotham
I like that because I think so many things are increasingly it depends that we don't have these one size fits all models that are going to work and that defeats our ability to make some sort of prognostications here. Thanks for taking the time to talk with us and sharing your insights about industrial AI, which is probably a different idea for some people, but also data sharing in the future of work and listeners. Thanks for joining us. So me, myself and AI thank you Sam.
- [32:05]
Peter Corte
It was great.
- [32:07]
Sam Ransbotham
Thanks again for listening today. Next time, Vineet Kozla, CTO at the Washington Post, joins us for a talk about AI innovation and publishing. Please join us then.
- [32:17]
Podcast Host (Intro/Outro)
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