It is so great to kick off CES 2026 with you and to see what can happen when AI begins to animate the physical world. In a moment, you're about to experience what this looks like at a global scale, from a company uniquely positioned to embed intelligence into our real-world systems. A company that has nearly two centuries of experience shaping the infrastructure that powers modern life, transportation, factories, energy grids, and it's now driving the next wave of innovation through comprehensive digital twins, intelligent automation, and yes, AI. That company is Siemens. This morning, Siemens President and CEO Roland Busch will share his vision for how AI will transform the everyday for everyone. Roland is a physicist, an engineer, a strategist, and one of the world's most influential technology leaders.
He understands that the success of AI won't be defined by algorithms alone, but also by infrastructure, by integration, by innovation, and his perspective aligns closely with something that Kinsey and I shared in our keynote this morning. AI is the catalyst for a once-in-a-century transformation. All across the show floor this week, you'll see AI on display, innovation everywhere, and we believe deeply that companies that can scale AI into the physical world will define the next era of human progress, and that's where Siemens stands apart. They're not just imagining the intelligent age, they're building it. Under Roland's leadership, Siemens is enabling factories to operate with fewer errors and greater output. They're bringing resilience to global supply chains, sustainability to buildings, they're driving breakthroughs everywhere from drug discovery to grid stability.
And they're developing the technologies and partnerships needed to ensure that AI can actually scale reliably, responsibly, globally. And today, you will get a first look at technologies and partnerships that will shape global industry for decades to come. You'll hear stories of real-world impact. And it's a remarkable keynote from a remarkable company led by a remarkable person. Ladies and gentlemen, please join me in welcoming to the CES 2026 stage the President and CEO of Siemens, Roland Busch. Welcome to the CES stage, Jochen Eickholt.
Well, I want you to think back to the time before electricity. The world moved at the pace of people. Horses bridged our distances. Steam powered our machines. I have black screens now. I think somebody checked for the technology. Okay, I keep on going. Well, that's the idea. And ideas moved as fast as a letter or as a human voice. Then electricity arrived. Our general-purpose technology became the foundation of modern life. It's refining how we redesign and manufacture products, from phones in your hands to the cars you drive. It revolutionizes how we build and operate infrastructure, including buildings like this one. And it allows us to make entire systems, grids, cities, economies more adaptive and more resilient. A century ago, Siemens helped build the world in the light of electricity.
Now, now we get to do it again. But this time, it's not about energy. It is about intelligence, artificial intelligence, and artificial intelligence will be as transformative for this century as electricity was for that one before. We are powering the industrial AI revolution. Thanks to this beatboxer's mosaic. Well, the industrial AI revolution, it has already begun, and it's picking up steam faster than steam ever did. In fact, steam took 60 years to transform society. Electricity, 30. Computers, 15. For AI, we're looking at seven years or less before intelligence is embedded in the system we rely on every day, and that changes something fundamentally. Because when AI enters the physical system, it stops being a feature, it becomes a force. A force with direct real-world impact. A force that transforms how we design and build, how factories produce, how infrastructure operates, how the world powers itself.
Now, how do we do it? How can we make this happen? How can we do all of this at speed and scale? And at the same time, in a way that it's reliable and safe. And hallucination is not acceptable when AI is deployed in the industrial world. Today, we are making it easy for you to scale industrial AI in the real-world systems to create real-world impact. We are bringing together AI-powered technologies, industrial domain know-how, and the right partners. And the whole thing is connected by data, valuable industrial data. And all in one place in our Siemens Xcelerator Marketplace. So let's have a look at these elements one by one, and let's start first with the right technologies. It is about software, hardware, compute, highly performant compute with GPUs, and of course, it is about data. Here comes the good news.
Many companies have already some important elements in place, but this does not make them AI-ready yet. Digital twins can simulate, and software can solve problems, but they do not recommend what to do next in the real world. Compute is GPU-based. With AI, it needs GPUs to deploy its full power and speed. And too often, data is fragmented, trapped in silos, and unused. That's why it is so important to have an end-to-end AI industrial stack to make impact in the real world. And at Siemens, we are able to build this because we have been working with AI for industry for more than 50 years. And today, Siemens has more than 1,500 AI experts, and all our engineers live in both worlds, in the digital and in the real world. And all our colleagues, over 250,000 of them, bring deep domain know-how in our 30 industrial verticals.
And that's because for decades, Siemens has been helping customers in the United States and all over the world to automate manufacturing, to run railway networks, to design and operate grids, or to operate buildings more efficiently. In fact, today, in one out of three manufacturing machines worldwide, it runs a Siemens controller. With our software, you can build the most comprehensive digital twin of products and, of course, of a whole manufacturing site. And now we are adding AI. Decades of experience in software, hardware, and AI. And this experience means we have the domain know-how to identify which data matters and how to cluster it, which AI applications make sense, and which decisions should be left to humans. And finally, our partners.
For industrial AI to make use of all of this industrial design, manufacturing, and operating data, you need a huge amount of compute, gigantic centralized AI factories that are run by our partners like AWS or Microsoft, but also at the edge, very close to your machines, to your infrastructure. With this massive GPU process, powered compute, digital twins no longer explore just thousands of options, but hundreds of thousands of them. Complex industrial foundation models can get trained on unprecedented amounts of industrial data. An additional twin can control a whole plant in real time. When these things come together, the right technologies, industry domain know-how, and the right partners, that's when we stop just reporting issues and start anticipating them. That's when humans stop reacting to errors because machines have started to act and adjust autonomously.
That's when companies can turn ideas into real-world impact with speed, quality, and efficiency. It's the industrial AI revolution. Now, I mentioned partners, the right partners, the best partners, including a very special friend of mine, and we hit it off immediately. He has a visionary engineering mindset. He thinks through problems from first principles. He anticipates the next development step of AI technology, and he has both bold vision and the ability to make things happen. Please welcome Jensen Huang.
Hold this for me. Roland.
Hi. Good to have you.
Happy New Year.
Happy New Year, everybody.
Happy New Year.
Please.
200 years old. You've seen a few of these industrial revolutions, actually.
We did. Not quite 200 years, but we're heading for it.
175.
Exactly. Exactly. And we came a long way with our partnership too, right? I mean, we kicked it off a couple of years ago. I remember that very well. We have the same vision, Industrial AI operating system that we want to build, huge opportunities for our customers. So what's with you? Where are we on this journey now?
Yeah, it's an incredible thing. So we did a press conference at Siemens headquarters.
In Munich.
Yeah, how many years ago was that?
It was 2022.
It was only.
2022.
Okay. Yeah, it was three years ago. And we spoke about this moment, in fact. And Siemens is unquestionably at the foundation of every industry that we participate in, that the world builds on. And we had a vision that Siemens, and this was your vision, to turn Siemens into a software-defined company. In a lot of ways, described it in a way very similar to a computer company. And it would, of course, have the underlying infrastructure of computing, but mostly on top, your infrastructure software, artificial intelligence, and it's going to revolutionize the way things are designed, simulated, planned, and operated. And that was the vision you described. And we spoke about three years ago, and here we are. Here we are at the beginning of that journey, and all the pieces are now finally coming together.
It seems to, and talking about what we achieved, I brought an example. You know that, HD Hyundai. I mean, they built ships, huge ships, and shipyards, by the way, and they use our technology.
That's right.
They're sitting on NVIDIA.
This is a digital twin of an entire ship. Every nut and bolt is in there. It's incredible.
I mean.
This is the full CAD of the ship.
Visited.
The perfect precise digital twin.
You see also the people working on the ships who simulate everything. By the way, these ships are. They look alike. So the body is the same, but each one of them is individual. So you really have to be very, very clear how you design it. You optimize in the digital world, and then you build it, not before that. And that's where our whole stack comes together, complementary technology for the photorealistic representation. We use Omniverse.
This is really quite a perfect example of the type of work that we do together and this realization of this digital twin idea that you would design every aspect of engineering. It's not just the CAD, but the computing, the electronics, all of it would be integrated and built into a digital twin. It would, of course, run all of the software in the digital twin. In the future, I hope this ship, the digital twin of a ship, will actually put it in the ocean, a virtual simulation of the ocean, and see it completely operate.
Exactly.
Yeah.
And you have only one option to get it onto water. Eventually, you have a problem, by the way. So, well, we talked about bringing our partnership to the next level. And the whole idea is obviously to take these examples and scale them even faster. And of course, to make it AI-enabled. I'm talking about Industrial AI, which really hits the real world. And we picked five areas of collaboration where we want to intensify. And that is good news for our customers too, I guess. Let me start with the first one and take them out one by one. The first one is AI-native chip design. So if it comes to chip design, I don't need to tell you something. Obviously, with each next generation, you're getting closer to the physical limits. So tell me what the challenges are and what can we do?
Let's use our latest generation that we just announced yesterday as an example.
Congratulations.
Vera Rubin, in order to build the latest generation GPU, people think that our GPU is a chip. It is a chip. It consists of a chip. But ultimately, in order for these GPUs to scale up, to process the level of AI that's necessary at the frontier, these GPUs are essentially one giant rack. And this entire rack is, in the case of Vera Rubin, 240 kilowatts. It consists of altogether 220 trillion transistors. We had to design six different unique chips: CPU, GPU, networking, multiple types of networking switches. One is for scale-up, one is for scale-out, and a Smart NIC and a DPU for storage, all for AI memory, basically. All of this stuff came together is 220 trillion transistors, is two tons. And 150,000 engineering years came together to build this one system that we announced yesterday.
In order to do this perfectly at a rhythm that our company runs at, the level of chip design, system design, system integration, thermals, electricals, all of that, in a lot of ways, our GPU is a little bit like that HD Hyundai ship. What we are hoping for, and the reason why we're partnering so closely together, is so that we could build that Vera Rubin in the future as a digital twin, the entire system as a digital twin, not just the chips, but the systems, the chips, everything together, cooling, thermals, and run the thermal simulation directly through it as it is a digital twin. That's our vision. We're accelerating the first part of our project together, our partnership, is to accelerate Siemens EDA, Siemens Simcenter, accelerate everything we can with GPUs.
This way, we could scale up the simulations that we do and achieve the virtual digital twin that we hope for.
So what we're going to do is we will use CUDA software to rewrite our EDA software so it can take advantage of GPUs.
That's right.
So, working with GPUs and designing GPUs.
Imagine EDA software being 100 times faster.
Exactly.
Or being able to scale up a million times more, right? That's our hope and dreams.
But we don't stop there. Still, we talk about AI-native chip design. What we want to do now, we want to train our technology, our models on a massive amount of data so that it's not only about validation of design, but really making a proposal of new designs in EDA. This is breakthrough. It's never done before. That's what we are heading for.
The goal of an engineer is not to write Verilog. That's not the goal of the engineer. The goal of an engineer, of course, is to solve problems, imagine great solutions. And it would be incredible one day. There would be agentic Siemens EDA designers that are sitting with our designers.
Exactly.
They're exploring ideas together, trying iterations, and exploring boundaries.
Number two, we talk about the same thing here, AI-native simulation. So when it comes to simulation, obviously, this is a lot of number crunching. And it very often still runs on CPUs. This is the first step we want to bring this technology, the heavy number crunching elements of our simulation to GPUs that, again, accelerates X times, 100 times, 1,000 times. And if you can, I mean, there's an example. This is a car simulation, by the way. It goes back to when we started off our partnership, it was from BMW, where we simulate the airflow, the aerodynamics. And it's a lot of consumption and it takes a lot of time. So if you speed it up 1,000 times, you can do much, much more iterations. Again, we do not stop there. We want to have simulation not only to validate, but to create.
That's right.
Again, you need a lot of data. You need our technologies. And that's where NVIDIA has come in.
This is our technology running in real time. Our technology together running, isn't it? Simulations. Physics is beautiful. I mean, that's just the bottom line. Numbers are beautiful.
We do that.
Only an engineer would say that. Numbers are just so beautiful.
We do that for trains. Again, we're using PhysX and NeMo from NVIDIA and the full Siemens simulation stack with ours.
Siemens has the world's leading suite for industrial simulations. Simcenter and all of the suites of solvers in Simcenter, we're going to accelerate completely using CUDA and CUDA-X. We are also going to work together to create AI physics, to teach AI the laws of physics so that it could emulate physics, not to simulate physics from first principles, but emulate the outcome of physics, essentially guessing where the physical properties are going to go at the next second or the next nanosecond, the next microsecond, the next second. By doing so, we could speed up physics simulations, physics experimentations, if you will, 10,000 times, 100,000 times, and essentially create a real-life digital twin of a wind tunnel. We essentially put a real design into it, right? Real digital twin design into a digital twin of a wind tunnel and see it work.
The opportunity to do all of these experimentations in real time is really exciting.
Which brings me, talking about physics, the real world, physical AI, which brings me to the third area where we step up, which is AI-driven adaptive manufacturing, and this always reminds me when we talk about our technology stack, what we want to bring to the plants, which reminds me what you always say. I mean, actually, a plant is almost like a robot, isn't it? It's a big robot.
Exactly.
Or a car.
You know, if you look at a car today, it's sensors connected to a computer. That computer is running software, software-defined, and today the software that we run on top of it is largely AI. Well, that car is going to be made inside a factory that has sensors running computers, Siemens software stack and operating systems, and all the AIs that you'll have running on top of that. In a lot of ways, that factory, the car is an inside-out AI. The factory is an outside-in AI, and it's going to look at the cars that it's going to build, and it's going to build it robotically, so the entire factory is going to be one giant robot. It's going to orchestrate a whole bunch of robots that themselves are robotic, and they're going to make products like cars that are software-defined and robots.
So it's going to be robots orchestrating robots, building robots in the future. And so this level of integration of technology has never been achieved before.
Exactly.
And so it's really the reason why we have to build the foundation of future manufacturing from the ground up. And nobody could do it better than Siemens because you're already the operating system of manufacturing plants all over the world. And so from that baseline, we're going to, number one, make it software-defined, and then two, make it AI-driven. And all of that is so great to see you drive that vision and for Siemens to really make that possible for the world.
We're going to use it ourselves.
Exactly.
Exactly. We're going to start in Germany in 2026, a first fully AI-driven adaptive manufacturing site. We bring an AI brain on top of our software-defined automation and our operation software, which basically consumes that what a digital twin produces. And here comes the beauty. We do that in real time.
That's right.
In real time, so you can really control manufacturing.
Our partnership, we work together and run Siemens factories.
Meanwhile, w e have our partner, Foxconn, working with us in the United States to run the factories for building. Now we're talking about manufacturing, all powered by GPUs, all powered by CUDA, all powered by Siemens, and all powered by AI.
To see an example of how Foxconn is using the technology already. Again, the AI layer on top, the AI brain with connects to the real-world data and real-time data makes the.
It looks so real, but you know this is a digital twin.
It's a digital twin.
This looks so incredibly real. And that's really the whole purpose of the digital twin. It has to, in every physically important way and operationally important way, it has to be a perfect representation. And so the ideal is that the digital twin and the real version of it, there is no ability for the computer to know the difference. It's to the extent that our AIs can't tell the difference between whether they're inside a digital twin or inside the physical world. That's our goal.
Exactly.
Yeah.
There are other factories too. They produce intelligence. We call it AI factories, so that's the next area of collaboration, and I know that when you talk about Vera Rubin, you need obviously new AI factories. They have to be sized differently, and you're building one on your own as we speak, so we might want to share about the challenges, what needs to be different.
If you take a look at the reason why I don't call it a data center. A data center is a place where people store data.
Data.
These plants, in the case of a one-gigawatt data center or one-gigawatt AI factory, it's $50 billion. The investment level is unheard of any type of factory the world's ever built. The amount of technology inside is incredible, and so if you're building something that costs $50 billion, you want to make sure there is absolutely zero schedule delay. You're not going to be able to tolerate design changes. You're going to have to make sure that we create the digital twin, we plan it upfront, we simulate everything upfront. We're pushing the power, we're putting electricity, we're going to push cooling all the way to the limit, and so if we don't simulate this entire digital factory in a digital twin, we have no chance of success.
Then, of course, once you operate it, managing all of these computers, these AI supercomputers, the networking, the storage, and running it all at the precision we need to run it at, the control of it is incredible. So you're controlling, of course, the management of the systems, you're controlling the power, you're controlling the cooling. This entire system is essentially one giant factory and is running all the time. When you put that much capital at work, you better make sure that the uptime is perfect and always, and that you're going to make sure that your scheduled delay is basically zero. Without us working together and building these together in a digital twin, the chance of success is extremely low.
Exactly. We can build them faster.
This is no different. Roland, this is no different than NVIDIA today. The idea that we would design chips without software is completely illogical. The idea that you would design these electronic systems without design software and verification software and simulations, come on, forget it, and so in the future, the idea that we would build plants, manufacturing plants, whether it's a car plant or a chip plant or an AI factory plant, the idea that we would build any of them without first doing it in a digital twin is completely inconceivable, and the idea that we would be able to operate these incredibly complex systems without artificial intelligence, completely inconceivable. And so I think that we know that future is here, and we know that the stakes are incredibly high, and that's the reason why our partnership with Siemens is so important to me.
We build these blueprint designs for our factories together. We have automation technology, which is much faster. It's industrial automation technology because the demand pattern of AI factories is so fluctuating. We know how to connect them to grids because we know how to operate grids.
You've been doing it for 175 years.
Exactly. We do that. We know how to do that.
After 175 years, you're going to get good at something.
What we also do for more than 175 years is using our own technology.
That's right.
Which is the last area we want. I mean, actually, the people said, "Don't use the saying, 'We drink our own champagne.'" So now we modified it. We drink our own beer or whatever.
That's right. German company.
But we actually reuse each other's technologies. We look deeper into it. What can we do more and better? And it starts, for example, with the plant in Germany I was talking about. We talk about EDA software simulation and bringing that to the next level to speed up each other's development.
It's incredible. We're going to work together to accelerate all of your EDA software, which we can then use to design our chips faster so that we could accelerate your EDA software faster. We're going to accelerate all of your Simcenter software so that we could use it to design and simulate our AI factories faster so that we could create AIs that make Simcenter faster. And if you look at this, we're going to use Siemens so that we could automate our factories to build even more amazing supercomputers so that we could create AIs to make your AI factories even more productive.
Doesn't that sound like the perfect partnership of two companies coming together?
Yeah, and that's why whenever Roland reaches out and he needs a bug fix, I fix it right away. Because I fix his bug, which directly comes back to my company and my engineers go faster.
And I tell you, this works in both ways. That's good when you have a call. So Jensen, thank you very much for the partnership.
Thank you.
Best is ahead of us and looking forward to what we do.
We are at the beginning of a new industrial revolution, and what perfect company for all of that capability to come together than the company at the center of every industrial revolution, and so it's a great privilege to partner with Siemens, and it's incredible to be friends with you and to see your vision of Siemens come together for our journey of reinventing industry to achieve this milestone is incredible, so thank you.
Siemens and NVIDIA building data operation system. Well, when I last spoke at CES, I shared a vision of the Industrial Metaverse, a vision for a virtual world that helps us make the real world better. Today, we are making this vision a reality. We are excited to launch our Digital Twin Composer. With the Digital Twin Composer, you can create a virtual 3D model of any product, any plant, any process, bring it to life in a futuristic scene that you built and in real time. The Digital Twin Composer is so powerful because you can connect the digital twin to the real world into real-time data, from engineering data to weather data, even time series data coming from your machines. And when something worked well or something went wrong, you can go back in time and you find out why.
Or you can jump to the future to simulate designs before you build in the real world. And if you want to unlock the full benefits of the Digital Twin Composer, you connect it to our operation software and our hardware stack. That way, you can make real-world changes from the virtual environment, optimize machine speed, temperature, pressure, or any parameter that determines the quality and the yield of your plant. This is how we combine the real and the digital worlds. This Digital Twin Composer will be available on our Siemens Xcelerator Marketplace in just a few moments. PepsiCo. PepsiCo is among the first companies to use it. And this brings me to our next guest, who is leading the way when it comes to the digitalization of PepsiCo, making the company smarter, more efficient, more sustainable with industrial AI. Please welcome Athina Kanioura.
Happy New Year. Happy New Year to everyone. It's a great year. New Year, great year.
It is. It is. So tell us about the challenge PepsiCo is navigating through? And these challenges are very much alike to what the whole food and beverage industry has.
Yeah, of course. And as Ronald said, I'm very privileged to be on stage with Roland. PepsiCo has been pioneering some of this innovation with you and NVIDIA. But from farm to shelf, PepsiCo has been operating in a very extraordinary and very complex environment because we have operations everywhere. We serve billions of consumers. We have billions of touchpoints per day. So consumers expect the products to be everywhere, every time, in real time. And we want to be able to fulfill the supply. So imagine a world where you have demand spikes, where you have unusual events, when you have tornadoes. You have to be able to fulfill this demand in a very digital way where the physical footprint cannot be a showstopper.
Right. So a ton of the supply chain and delivering. Let's take a look at your existing warehouses.
Yeah. So I mean, many of you know, especially the ones that come from the manufacturing world, we have great warehouses that are fully modernized, but we also have warehouses that are very old. So in this specific case, these warehouses are half a century old. And that means in the current state, it really cannot serve and deliver the demand that might change depending on the events. So what is the answer? And the answer cannot be, "I will just build more capacity."
The answer must be, "I need to find a way to maximize the capacity that I have to drive more operational efficiency and therefore increase the throughput." So what we wanted to do is adopt a digital-first design approach. That means leveraging digital twins and adopting AI at scale in all the parts of the operations where we were able to co-design and simulate and optimize the layout before any physical build begins.
To do that, you decided to use our Digital Twin Composer. Share your experience.
Yes. First of all, thank you. Thank you for the partnership. Thank you for the investment. Thank you for taking our feedback into product innovation. Today, we just had our press release. We are very excited to help shape this technology in partnership with NVIDIA. And of course, Siemens, this is the first of its kind for the CPG industry. It's how we build AI and digital twins into our operations across the board globally. When we say operations, that is manufacturing, warehousing, distribution centers, mixing centers, and every part of logistics. So the Digital Twin Composer allows us to do everything in the virtual world, being able to simulate everything in a way where we don't have to spend $1 on the physical plant before we know the design is the final design.
So therefore, we are able to pull now massive amounts of data that we are able to simulate in a unified immersive environment. That means we can create a beautiful photorealistic way, which you saw before in the discussion. And with the AI-powered simulation now, we can explore hundreds or thousands of potential layouts to find the most efficient options. Therefore, with a digital twin, this type of task that could have normally taken months now takes days.
The question would be then, what does that do to your customers?
Yeah. Well, obviously for us, making sure we serve our customers in the most efficient way where we provide the inventory and we have the intelligent network where both the plants and the warehouses and the network is able to anticipate the demand and fine-tune operations real-time is a must. So therefore, we have had significant impact even in the first three months. So one good example is in the Gatorade plant in the U.S. We use the Digital Twin Composer. We were able to drive two very meaningful results. One is to increase efficiency 20% just in three months.
And obviously, if we were to look and we have estimated how these capabilities will benefit the company, we estimate a CapEx reduction of 10%-15% across all the operations. So for us, this is just the beginning. We are very hopeful about what this technology can do when it comes to serving our customers, serving our consumers, and I want really to thank you for the great partnership.
Thank you for the partnership. And this is just the start. We want to see more, and we will see more.
Absolutely.
Athina, thank you very much.
Thank you so much, Roland.
Thank you. You have seen the great impact the Digital Twin Composer is having on PepsiCo's facilities. Using this technology, even in the design phase of new plants matters, especially in the United States, because it accelerates the ramp-up of new manufacturing sites and allows our customers to make them more productive and energy-efficient. But this is not just for individuals. KION uses our tech nology to transform warehouses and tire supply chains too. Let's have a look
[Presentation]
You can learn more about the Digital Twin Composer at our booth. Also on the CES show floor when you will meet the team from PAVE360. PAVE360 Automotive is our digital twin for autonomous driving system, and it's ready to use right off the shelf. It replicates real vehicle hardware in the digital world. Developers can simulate real-world driving conditions. They can test the autonomous system before a car even exists. This accelerates development. PAVE360 Automotive also replicates how the software in the car behaves.
And better yet, it integrates the latest automotive IP from our partner Arm for advanced driver-assistance systems, infotainment, and AI-driven cockpit features. It's a solution that will scale autonomous driving faster with the power of industrial AI. Now imagine what happens when we apply industrial AI to something more personal, even more complex: your body, your health, life sciences. Bringing new medicines to the market costs a lot of time and money. Over the last 50 years, these costs have risen dramatically. It often takes more than 10 years, and costs are up to $2 billion. Patients and pharmaceutical companies obviously want to have faster innovations. That means tackling every step in the chain from early research to manufacturing. And we bring AI to all these steps. Let's take the example of a new cancer drug. In research labs, scientists create billions of data points.
Today, this data is scattered across industries, instruments, and files around the world. Through our platform, Luma, scientists use AI to bring this data together and structure it so you can ask questions in natural language. Next step, scientists identify the most promising molecular structure of the cancer drug. With AI-powered simulations, they can simulate the behavior of the molecule 2.5 million times more efficiently than ever before, and this includes how molecules move and flex, how they interact with each other, or how stable they are over time. But then you need to go from making a small batch in the lab to producing at scale without the slightest deviation in the recipe and with exactly the same result. Even for world-class production experts, this means a lot of trial and error, a lot of experiments in a bioreactor.
In our digital twin of a bioreactor, you can run these experiments in the digital world first. You simulate until you have the highest quality and the highest output, and only then you start the production. Siemens technology accelerates all steps from discovery to manufacturing. Lifesaving therapies can make it to the market as much as 50% faster, and at every stage, AI speeds things up. This requires powerful AI infrastructure too. The GPUs and the AI factories I talked about with Jensen, but also the cloud infrastructure we get from partners like Microsoft. On that, let's hear from my friend, Satya Nadella.
Thanks so much, Roland. It's fantastic to join all of you at CES. In the industrial sector, we have an incredible opportunity to bring together digital intelligence with physical operations to reinvent things that are designed, built, and run in this era of AI, and we're already seeing the industrial AI drive better outcomes in both productivity as well as in safety. That's why our partnership with Siemens is so important to us at Microsoft. By combining your deep domain expertise with our trusted cloud and AI capabilities, we are helping our customers tackle some of today's most pressing challenges, from sustainable energy and transportation to smart cities and precision medicine. I'm especially excited about the co-engineering work our teams are driving to build custom models and agents that get trained with real-world industrial data, and of course, we're just getting started.
I could not be more optimistic about what we do together and achieve together. Thank you all so very much.
Thank you, Satya.
So custom models, agents, and of course, Copilots. Let's talk more about it with Jay Parikh. Hi.
Hi.
Good seeing you. So Jay, you are leading Microsoft's core AI team. You put all AI experts together, which tells me again, this is a foundational technology which scales across the whole company. So where are you in the step of really making this AI journey, creating impact at your customers?
Yeah. Well, first of all, thank you for the partnership. Excited about what we're working on today and also going forward. So what we're seeing today is really three waves with more waves coming in terms of AI. The first wave was really folks using AI as a chatbot, right, to ask it simple questions, maybe do code completions. The second wave is much more focused on delegating a specific task to AI, maybe giving it a task to do and come up with a market strategy document or proposal to do these longer-running, kind of more sophisticated tasks. Now, what we're seeing emerging in this third wave is this set of agents working with and orchestrating a set of different agents to be able to do asynchronous work, but much more complicated work, right, longer-running tasks.
It is this third wave that we're focused on building because you need multiple models. You need to be able to train your own models, right? You need to be able to bring in your enterprise data. You need to be able to orchestrate these agents, but you also need to do it with security, with observability, with compliance, with all of that scale enterprise-ready abstractions, right? And it is this area in this third wave that I'm most excited for us to be working together because this third wave is how we bring this technology to specific use cases and industries. But Roland, I'm really curious, this third wave, how does this show up as you lead industrials with AI? How is this manifesting with your customers?
And I mean, there are many examples. I brought one. This is a very exciting one. We talk about Rolls-Royce-made turbines for airplanes. And I mean, obviously, you want to have these turbines to work 100% reliable and, of course, fuel efficient. And within these turbines, you have a lot of design parts coming. These are designed with Siemens software and technology from Microsoft. And there's a particular one which is a hydraulic pump, and that makes this machine turning. So this pump has now a digital twin of the pump itself, but it also has a digital twin of the machines machining it. And we simulate the whole process in programming it. And we use your technologies together with ours and simulate it with amazing outcomes. These pumps are much stiffer and lighter. But we use Microsoft as well here and AI technologies for that.
Yeah, and excited about the other things that we're working on with these joint AI Copilots, right? We're able to help reduce the programming time for CAM by 80%, drove up productivity in the factory by 30%. And then also excited to work with your team to bring the GitHub platform to all of the developers to drive that innovation, that value for your customers faster. Again, all built together on Microsoft's cloud and AI.
Exactly. And there are many more examples. The demand is huge. And the benefits of not only shortening the development time, but also the production time is amazing. We talked about technology, but we cannot stop there. And we know that you're very passionate about also the other part, the cultural, the way how you transform your team, the people.
Absolutely. It's hard.
It's hard. I know that, so let's talk about it.
Yeah. So you know I spent probably last year talking to about 200 or 300 customers, and it was fascinating because somewhere between probably 70% and 80% of my conversations with customers started or ended or was dominated by this topic of change, about culture, about people, about organizations, and it was such an interesting set of conversations, and my kind of three current takeaways from those conversations were one is we're all learning together, and we're sharing openly about what's happening in organizations, small startups to companies that are much, much, much bigger, multinational, so this culture of change is something that we have to share openly and work together on. The second thing that I noticed and comes up in conversations is actually many of the employees, many of the workers are using this technology in their personal lives, right? They're using it.
They're actually really sophisticated with it for family, for personal growth, et cetera. But there are institutional headwinds to adopting it, using it inside the company, whether it be compliance, whether it be risk, whether it be security, whether it be finance, whether it be just misaligned incentives, right, and then so it is our job to try to work and try to unlock this value, this creativity, this collaboration, and then the third thing that I always encourage folks to do is really the potential intelligence of these models, the capabilities of the platforms that we're building here at Microsoft are so much more capable than what we realize today, so my biggest encouragement to myself, to my teams, to our partners is to really raise our level of ambition of what we can do with AI to drive the world forward and to deliver more impact.
Nadella, I can say from my perspective, we see a lot of these changes also in working with you, working with our customers, but also with you. You come from a different environment, but we bring that together to our customers, and they do appreciate it, Naella. Thank you very much for doing that. This is just the start.
Yes. Thank you. Thank you for the partnership.
Jay, thank you. So more powerful Industrial Copilots and now new Copilots. Today at CES, we are launching nine additional AI-powered Siemens Industrial Copilots. They bring intelligence along the entire industrial value chain, transforming how we design, engineer, and operate. And well, to help everyone access this kind of technology more easily, we are now putting it in the hands of our shop floor colleagues, or to be more precise, on their faces. Together with Meta, we're making Ray-Ban Meta glasses for industrial AI. Actually, we do not use the tinted ones, as you can imagine, maybe if the sun is shining, but they're also clear ones. And they tell our colleagues to get real-time audio guidance through the glasses, which button to press, which parameters to change.
Let's take a look at one of our factories when these glasses will turn colleagues into connected experts so they can have more impact in the real world hands-free.
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The first tests show that colleagues are more confident in their work, and they're also more productive. That's impact in the real world by scaling industrial AI. But we're heading for another huge bottleneck: power, electric power. AI factories and data centers require gigawatts of electric power. Now, what if? What if we had an energy source that was clean, safe, affordable, and practically limitless? That's the promise of fusion power, which brings me to my next guest. Commonwealth Fusion Systems is working on the world's first commercially viable machine to make fusion real. Please welcome Bob Mumgaard.
Hello.
Hi, Bob. So for starters, what is fusion?
Yeah, it's a good question. So fusion is the power of the universe. It's the power of the stars. And what we want to do is build machines here on Earth that create that power, that harness it. And that would mean that we would go from an energy system that is about natural resource consumption to an energy system that's about technology. Can you build these machines? How fast can you build them? How well do they run? And we work on a type of machine called a tokamak, basically a bottle that holds a star inside of it.
So these are massive machines, and there are for decades researchers, engineers trying to make it. So tell us, why should it work now?
Right, so we're at CES, where many of the technologies we see were ideas even just five years ago, but the technical stack that has been developed from simulation tools, from the physical understanding, has enabled a lot of these technologies, and fusion's no different. We have very large simulations where we understand the underlying physics of the process. We can layer that into manufacturing these machines that are complex, but that perform these very delicate procedures, and we can build all that up into a plant that makes energy. That whole stack is enabling this entire industry.
So let's go a little bit deeper into these challenges because, I mean, putting a complex machine together is obviously very complicated, but you also have to produce it at the same time. So you have to design and manufacture, and that's what you do also with the software from Siemens.
That's right. In fusion, we're developing an entire new industry. You can't just go and buy a fusion plant or a fusion part. We actually have to take it from the first principles of how do these magnets work, how do you manufacture a factory that can take new designs really quickly, that can iterate really quickly, that can compare test results to simulation tools and see where there's a mismatch, what we don't understand. And this produces a huge amount of data. It produces a huge amount of insight. And how do you harness that? And that's where we use Siemens technology from the design of the actual parts to how the factory runs to how the plant itself is controlling very large amounts of current and cooling to hold this star inside of it.
Right. Actually, I was walking one of your plants where you built SPARC, which is your first demonstrator where you really create more energy you put in, which is make a viability check of your technology. And it's amazing what you do. And imagine that the next one is even two times bigger. It's hard to believe. So tell me a little bit more about how Siemens technology played in that, designing it, and of course, in supporting the manufacturing as well.
Right. So we're building a plant in Massachusetts that is a machine that will make more power out from fusion than it takes to heat it up. And to do that, Siemens was there from the beginning of how do we actually do the design work. They're there in the factory with the PLCs that automate the factory. And this is a factory that has engineers standing right next to the equipment that's running, that's being reconfigured all the time. And then in the actual SPARC facility, the brains that are actually running all the different processes that has to all come together to make the right conditions of a star inside of it, those machines are Siemens machines, and they're running software that has been checked out in large computer simulations accelerated by NVIDIA, computation, and DeepMind.
It's a very big stack all the way from idea to a plant.
So now here comes the key question. When will we see real-world impact? When will that commercially be viable?
It's a great question. It's a question I always get, and the key part is that you can start to see things today, so we have the plant in Massachusetts, a factory running, but the SPARC facility is being put together, and in fact, at CES, we have pieces of it that we've brought. There's the Siemens booth. Go check them out, but we also announced yesterday that we've put the first big magnet into place for SPARC, so you can see it coming together. That's setting up for the next machine, which is going to be in Virginia. That's what we call ARC, and that's a commercial machine that will make about 400 megawatts of fusion power, electricity. That can power a data center, and in fact, Google has agreed to buy the power from ARC. That is getting started here soon.
And so we can see out here on this technical stack of going from science all the way to energy, we can see a new age of abundance.
So Bob, thank you very much. There are a lot of customers counting on you to make that work, and we are here to support. Thanks for the partnership.
Thank you.
Well, but even if we have abundant clean energy, there's another huge bottleneck: grid availability and grid stability. Today, many electricity grids are on the verge of collapse. Intermittent energy sources like solar, wind are increasing the stress on our grids. We need to predict loads better and stabilize the grid, and that in real time. Our industrial AI-enabled technologies can do this, even for huge grids and autonomously. We can simulate the impact of adding 10,000 electric vehicles to the neighborhood. Buildings all over the city can act together and coordinate energy consumption so they could turn down their air conditioning for just half a minute to help stabilize the grid. And already today, we are using AI to maximize existing grid capacity by 20% without new infrastructure. This is industrial AI creating impact in the real world. So what does this all mean for you?
No matter whether you design, engineer, or operate, or all of the three, no matter what you make: food, cars, medicine, energy, no matter what type of infrastructure you manage, the water supply, a transportation system, or a grid, we have the technology, the domain know-how, and the partners to help you do what you do, but faster, more efficiently, and more sustainably by scaling industrial AI in the real world. Scaling industrial AI in the real world. Electricity. Today, those of us in this room barely think about it. Our lights turn on, our coffee machines run, our computers charge. Electricity is a fact of life. Soon, industrial AI will become a fact of life. We will produce only what's needed, exactly what's needed, where it's needed. Equipment failures and outages will be rare, unpredicted ones even rarer. Medicine tailored to your unique biology will just be standard healthcare.
Autonomous vehicles will move with 100% reliability and zero emissions. Knowledge will transfer through generations as naturally as current through a wire. Energy will flow cleanly, abundantly to infrastructure that repairs and optimizes itself and serves a world so defined by AI that we hardly notice it anymore. When a breakthrough innovation becomes a real-world essential and a general-purpose technology becomes the invisible fabric of our lives, that's when we'll know that we have scaled AI in the real world and realized its potentials. Together with our customers, with our partners, with you, transformed the everyday for everyone. Thank you.
Good morning from Las Vegas, and welcome to those joining us from the Keynote. If that wasn't a strong start to the day, I don't know what.
You have now arrived at the only show that's part late night, part breaking news, and entirely about how smart technologies quietly, probably loudly, are reshaping literally everything we touch, this cup of coffee included. I'm your host. My name is Magnus Edholm, and when I'm not hosting events like these, it doesn't happen that often. I drive Siemens Digital Enterprise and communications for automation in the U.S. market, and as for my IKEA accent, yes, I am Swedish. At Siemens, we like to say that we surround ourselves with technology that transforms the everyday for everyone, and that's exactly what we will be talking about for the next two days. Believe me when I say that this isn't just a tagline. It's the North Star for what we will explore together during this broadcast from CES 2026 in Las Vegas.
We will talk about how software, automation, and AI are combining the real and the digital worlds to make industry more adaptive, predictive, and yes, even more intelligent. And if you're here at the expo, you can find me reporting from Siemens Industrial AI headquarters. And we are in the North Hall, number 8010, where Siemens and AWS will bring you two days of nonstop live content and conversations. Yes, it is live, so I feel absolutely no pressure, none, nothing, nada. And while this broadcast is part of Industrial AI headquarters, we are not all of it. If we can move the camera, we also have the Explorer Tour, Siemens Digital Industries mobile experience. There you go, focused on the possibilities and the impact of smart manufacturing.
If you visit, which I think you should, you will be guided by those docents through a 12-minute immersive experience led by a wonderful team. So break a leg, guys, and keep on rocking the show for the next four days. It's going to be tough work, but it's going to be worth it. So thank you very much for that. And you know what? While there is so much to see here in Las Vegas, be sure to also swing by these key highlights. We have Siemens Intelligence Experience in the North Hall, same hall where I am in, in the booth number 8725, where you will see and experience hands-on design, simulation, automation, AI, and the most comprehensive digital twin woven into some truly compelling customer stories. And that booth is literally just 80 meters that direction.
Then we have PAVE360's autonomous vehicle lab in the West Hall, booth number 4352, where we hook a vehicle digital twin to a real car, and we run it in a virtual scenario that feels suspiciously real, I guess. And if you have been playing Need for Speed, you don't want to miss this one. And then we have Siemens and AWS together at the AWS booth in the West Hall, number 4099, 4099, where you can see how our collaboration is creating smarter and more sustainable technology. And last, but for sure not least, we also have Siemens and NVIDIA together at the NVIDIA booth in the Fontainebleau Suites, where you, among many, many other things, can discover our new solution, the Digital Twin Composer. I'm not looking at my studio manager to see if I got those numbers right, and she's giving me the thumbs up.
That's a victory. So nice and good. And for those of you folks tuning in worldwide, don't worry about staying up with me until 2:00 A.M. Vegas style. I mean, I would be very happy if you did. Don't keep me alone here in the studio. But you should know that all those sessions will be available on demand, so you can catch up whenever it fits into your schedule. All right. Today, on those chairs, I will be joined by an unbelievable lineup of tech experts. First, at 10:30 A.M. Pacific Time, we have Siemens' Linda Krumholz and Stuart Carlaw. He's an analyst from ABI Research. Linda and Stuart, myself, will discuss how AI and the rise of agentic AI is transforming the business as we know it, helping companies automate tasks and even predict trends.
And while costs and privacy concerns remain, I would say it's a safe bet that early adopters gain a clear edge over their competitors. And following that session, we will discover how robotics and physical AI in the real world at Siemens, Horst Kayser, and AWS's Sri Elaprolu will unpack the innovations that are driving smarter, more adaptive industrial systems using software-defined automation, among other technologies, because software-defined automation is actually needed in order to build software-defined products, which was also part of the keynote earlier this morning.
Then we have Kal Mos. That's Siemens' new head of research and technology. He will come and share how research translates into practical applications. And also later this morning, I will be joined by the fantastic Athina Kanioura. She is PepsiCo's newly appointed CEO of Latin America Foods, which she does in addition to her role as Chief Strategy and Transformation Officer.
With Athina, we will explore how one of the world's biggest food and beverage companies is adapting to rapid change. We're going to dive into the big questions shaping global operations and reveal why PepsiCo is relying on AI and the digital twin through a bold partnership with Siemens and NVIDIA. And that, folks, is all happening before lunch. And after a short lunch break, I assume it's going to be fairly short. I will be joined by Bob Jones, Ozgur Tohumcu, and executives from Hero MotoCorp. And Hero MotoCorp is India's largest motorcycle manufacturer. And we're going to talk through how the organization is using bleeding-edge technology as opposed to cutting-edge technology to enhance operational efficiency, accelerate time to market, and make strides in electric vehicles.
While we will be talking with industry titans, we are also going to feature some startups that continue to scale. We have the charismatic CEO of the company, Haddy, Jay Rogers. He's going to join us alongside an executive Imagineer at Disney. Imagineer, that's a cool title. This Imagineer works at Disney, and they will share together how Haddy's AI-powered 3D printing robots are enabling transformation at Disney. Then we have Siemens Software Executive Vice President, Mr. Joe Bohman, who will come by and share insights into the power of digital thread agents and how these agents streamline design and simulation, potentially reshaping industry as we know it. After Joe, we have Jason Hiner. He's the Editor-in-Chief of The Deep View, and he will join me to talk about technology trends to embrace as we move forward.
Following that, we have a discussion of the power of our ecosystems through Accenture and highlight how advanced engineering processes are driving the next era of manufacturing. We'll also hear from a group of technology influencers who are offering CES-specific insights. This will be of special value for those of you not attending in person. I can say the booth, the floor here at CES is actually filling up at a very high speed. You know what? At that point in today's agenda, I may need a break and actually a drink or a break and a drink, but we'll see what that comes down to. At that point, we're going to wrap up day one. Then there is tomorrow. I'd say we're not slowing down on day two either. Wednesday is equally packed with incredible guests and content.
We're going to start the morning with CES's own producers, the CTA. And we're going to have President Kinsey Fabrizio, who will share her perspective on the state of technology, what's real, what's hype, and what's coming next. And then, folks, we're going to move over to the Commonwealth Fusion Systems. And they will discuss how they are accelerating the possibility of fusion. Short words, they are essentially building stars on Earth to generate energy, electricity. And you will get an eye-opening understanding of the power and possibilities of the science behind commercial fusion. This is also a session you don't want to miss out on. And after Commonwealth Fusion Systems, we will explore how agentic AI is moving from concept to reality and driving real commercial impact with AWS.
And when we talk about commercial impact, Ruth Gratzke from Siemens Smart Infrastructure and Joran Tinnemeyer from AWS will take us into the logistics side of AI, exploring the smart infrastructure required to enable AI at scale, including the future of data centers. I'm looking forward to that one. And guess what? We have our very own Shark Tank. We'll be hosting a pitch challenge where startups will present their concepts for the chance to earn recognition and, even more importantly, licenses and support from Siemens and AWS to realize their vision. And one of the sessions I am most excited about features Siemens Chief Technology Officer Peter Koerte and AWS's Vice President of Corporate Partnerships, Mr. Martyn Mallick. They will unpack the expanded Siemens and AWS partnership, where the two organizations are working together to move industry forward.
And yes, for those of you keeping score, I think we're up to 15 sessions, 15 sessions, which is really incredible. I knew I wanted to do this. I mean, it's about technology, innovation, and people, three things that I really, really love. And I also know the conversations will be incredible. And I tell you, Wednesday afternoon is no exception for that. I'm going to have my friend and colleague Edwin Severijn, also from Siemens, coming across. And he's going to talk about how AI is transforming software development when using Mendix, which is our low-code solution. And then we have something interesting for you. We have Siemens Interim U.S. CEO Anne Fairchild and Dora Smith, who will join to talk about the workforce of the future. As of today, we at Siemens have reached 1.1 million people in our ecosystem with learning offerings.
Dora and Anne will talk about how to build on that momentum and what that potentially means to you, and we're going to close the day with NVIDIA, and if you were in the keynote audience, you saw Jensen Huang on stage, and you know why this conversation is one you do not want to miss out on. Hopefully, that was the hardest job I had the whole week, because from here on out, all I need to do, well, all I need to do is ask questions to all those experts that are going to sit in my chairs here in my studio, and if you missed any announcements, check out the full lineup on the Siemens website, and be sure to keep tuning into the Siemens YouTube or LinkedIn channels to see what's coming up next. All right.
With that said, I hope to see you all back here in just a few minutes at 10:30 A.M. Pacific Time. That will be in about 10 minutes from now. And that's going to be our first segment, and we're going to talk about the next wave of innovation in AI. Stay tuned and hope to se e you soon.
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That's okay. So if I can, yeah.
That's far better because now I can see. You can, yeah. I don't know why you weren't able to see Linda. Is that okay to set up? Because Stuart kind of blocked out. [Break] Hello and welcome. I am Magnus Edholm, and thank you for joining us in our little studio today. Today, I have two special guests joining me. We got Linda Krumholz of Siemens and Stuart Carlaw from ABI Research, and we're going to talk about some really exciting AI-related stuff from an analyst's point of view. We'll talk about numbers. We're going to talk about trends, and I believe it's going to be a very eye-opening session indeed, and I hope also that you will get some insights that you might find useful in your daily business. That said, why don't we start with introductions? Linda, why don't you introduce yourself to the lovely audience following us?
Yeah, so my name is Linda Krumholz. I'm with the Siemens family since 20 years now. I'm responsible for the Siemens Xcelerator ecosystem and marketplace team, and very much looking forward to today's discussion because Siemens, this is where we combine the real and the digital worlds and where we are pioneering the next wave of AI.
Fantastic. Thank you very much for being here, Linda. And Stuart, how about you?
Hi, I'm Chief Research Officer at ABI Research. I've been with the company for 20 years. My role really is to connect those companies within the user space, so those are deploying technologies with those that are actually creating the technology and make sure that those needs and requirements are best fit.
Okay, okay. Very good, very good. So supporting them, so how do you kind of study up on how do you get your insights into that?
That's a good question. I think there's a bifurcated way that we look at the world.
We do a lot of end-user research, so looking at what our customers' customers really want. Then we look at kind of the technology development side of things. Are those technology developments meeting the sentiment of the buyers, what they need, how they're being deployed, and are the capabilities within that environment matching the technology requirements as well?
All right. From an ABI Research vantage point, what are the most noticeable trends that are defining the AI landscape as of today?
I think that's a really interesting question, and I think for me, the first thing is that we have multiple stages that are advanced very quickly at the moment. Two years ago, we were very much in that descriptive AI stage where we're looking at predictive analysis. We've then moved to last year was really much about generative AI.
Now this year, we're talking about agentic AI, and then probably the second phase of that is going to be agentic systems. But again, already, we're beginning to move to a discussion around physical AI. So we're actually advancing at pace from a vendor perspective, a rapid tick from, say, very deterministic AI all the way through to very complex systems. The one thing I think that's been under-talked about recently is the fact that from a vendor perspective, they are still much earlier in those phases. Sorry, from a user perspective, they're still very much earlier in those phases. They're still focused around, can I get my data fabric in place? Can I get my architectures there where I can start leveraging complex systems?
So there's a little bit of a dichotomy occurring at the moment where we've got very advanced technological innovation happening from a vendor perspective, and the user market is struggling to catch up with that.
Gotcha. That's very interesting because looking at us Siemens, I mean, we are kind of both, aren't we? So Linda, from your point of view, I mean, you have the traditional time-to-market accelerating speed, become more flexible. What is the main driver for the accelerated adoption of AI across all types of industries?
Well, I believe that we have positive trends and negative influences. Why this is the case? I think when we talk about more negative things, it's of course this volatile supply chain at the moment, it's the geopolitical tensions we are seeing. It's about skill shortage. So these are more the negative aspects, but there are so many positives right now.
One of them, for example, is the data and the connectivity. I think we are seeing increasing data amounts within all the companies. Everybody has now data available. Everybody uses sensors, uses IoT, and so on to get to those data. This is positive. We are seeing even the most traditional companies doing this and learning out of the data. This is a positive thing, adoption. The second piece is I believe that we are seeing first use cases which show ROI. Yeah, we are seeing positive use cases. We are seeing good results on this one. The people are starting to trust AI here as well. This is the positive side and why we are seeing an accelerated adoption across the industries.
Right, right, right. When you talk now about industrial AI, and this is completely off track, we don't have this question in here, but I'm going to take a chance anyhow. Perhaps there's people out there wondering, why is she talking about industrial AI, and how does that differ from the AI I'm using when I'm writing in speech, or when I'm not writing in speech, I'm getting in speech written for my friend's 50th birthday? That's one way of using AI, I guess. But industrial AI, what's the thing with th at?
Yeah, I think we're seeing three areas of AI. The first one was machine learning, which was very easy and where you have one problem, one solution, which is, for example, the inspection, quality inspection, where you have then shown some images and so on. The AI, pretty simple.
The next era was the generative AI with the general purpose, so you see now the nice texts, the nice speeches which are there for us, the synthetic images and so on, so this is the second piece, but the third one, this is the most promising one, and what we are happy to pioneer here is the industrial-grade AI, so meaning we need to have the deep connection to the real world. We need to bring now the AI capabilities to the industries of these worlds. With that, we need to have robust, explainable, and purposeful AI available for industries, isn't it?
Yeah, yeah, but so that is a lot of things you have to do, so where do you get started?
Oh, yeah, I think you can start already with the earlier things, so using the copilots, using copilots in the manufacturing plants.
So helping our engineers and customers' engineers to drive their production more efficiently, yeah, with the help of AI and the copilots. But the second piece is also that we are having digital twins for products, for production, for the factories itself, so that we have really built up a virtual world where we test and try out AI in the virtual world before we then translate it into the real world. I think this is how we can start in a very early phase with the product, with the production, with the design phase, simulation, and then bring it across the entire factory.
Okay, one more before I go. I'm going to go back to you, Stuart, in a few minutes, but one more question though. What would you be, in a very short answer, please, what are the main benefits that early adopters gain from starting with AI now? Because AI is not coming. AI is here, right? So what's the main advantages early adopters get?
Yeah, it's of course about training, isn't it? So we are training now the people very early on. We generate, we train our people, but we also train the AI with all the data which is available. So with that, I think there is so much change management happening in the companies, our own company, but also customers' companies. So it's really important that we start early with this one, that we train our people, that we help them to trust AI in order to do their job.
Good. Thank you very much, Linda. Okay, I have an analogy I've been working on. I'm pretty proud of it. Stuart, listen to me now. If you start working all in or go all in industrial AI now without having a foundation set up, it's kind of like having a turbocharged engine, which you put in a car chassis that hasn't got any brakes, no steering wheel, and surely no GPS system. What do companies need to do in order to ensure that they have the foundation to go industrial AI?
That's a really good question, and I think this is the golden trap that a lot of companies fall into. First of all, the data architecture within your organization needs to be there. You need to do a lot of work on normalizing, cleansing data, creating a data environment where you can start leveraging it. Then you have to map that knowledge to where it needs to be used.
And I think that's where it's really interesting when you see things like RapidMiner and the knowledge graphs. They're enabling some of those advanced data layers to be able to be accessed by AI applications. I think that's the first part of it. The second part of it is really about having a definable ROI that you can drive towards in terms of your projects. We've got a lot of projects in the AI domain that are stuck in that POC purgatory, sat there kind of going round in loops because there needed to be a story around AI. I think the ones that really succeed are focused on solving real-world issues and applying sound economics to it as well, working fast, reevaluating those projects, and co-creating with a wider team within the organization.
I think that's the other part of it is if you're going to assemble a team to look at AI within your organization, it can't just be OT, it can't just be IT executives. It has to be a broad base of expertise brought together in order to create a successful execution of a product that's going to give you definable, measurable ROI at the end in a way that you can then use that as a springboard into the next step and the next step after that. So building momentum through a solid foundation with the right people on the bus, the right team in place, and then a definable outcome.
Right, right, right. And I mean, obviously, I've been working many years. I've been around for 26 years. I've done my fair share of digital transformation in various companies. And I mean, a digital transformation without AI. I mean, you're going to jump in and bump into some hurdles. Now we can navigate those hurdles. Based on your experience, Stuart, are there like some obvious hurdles that most companies will run into as they start implementing industrial AI?
I think the first hurdle is the realization that you can't do this alone. There you go. I think a lot of our customers, especially in the end-user department, have the first iteration, we'll build this. The complexity is increasing tenfold year after year after year, and there comes a realization that we can't build the castle on ourselves. We need to have subcontractors that are going to build the walls or the drawbridge or whatever. So I think that then becomes an equation of like, who do I partner with? Who's the right choice here?
Who's going to bring me the best benefit here? And I think the other part of it is also funding. Let's not get beat around the bush. A lot of the funding for this and a lot of these AI applications are in the OT domain, which seems to be the lesser funded part of the operation when you look at kind of the digital envelope compared to the IT department.
Right, right, right.
So it's actually being very canny about where you involve different budget centers within those projects and bring that IT department in there as a strong partner that can leverage some of that IT budget in the OT domain as well.
All right, all right. That's very interesting, very interesting.
Let me add two.
Yes, please.
Let me add two.
Two.
So besides this funding issue and of course having the ecosystem, which is super essential for it, I think it's also still the trust, which is a problem, and I think failure is not an option in the industrial space, so this is why it needs to prove right, and it needs to be robust, and people need to trust in it, so this is still, I believe, a problem that we are seeing out there, and the second piece, which I wanted to really bring on now,
I'm excited,
the second piece is, yeah, the complexity, of course, the complexity of the legacy systems, because I'm very deeply engaged at the moment with our own IT systems in the company, I know what I'm talking about, so this is huge, and it's such a massive transformation also here to touch this complexity of legacy systems. I think this is also not to be underestimated.
Magnifique. Magnifique. I actually build on that. One other thing to add, but I think part of the problem we see with it is that the attitude is we're going to create a solution, then that goes into operation, then we're done with it, and I think that the lessons learned are that this is a continual process. If you're going to invest in an AI on your product, you are going to have to go many times through that iteration process to get it, make sure it's right and fit for purpose. I mean, examples like the spot welding solution that Audi use, built around vision-based systems, that's redesigned three times and optimized three times based on new toolkits, new language models, new availability. So I think when you talk about complexity, the complexity actually doesn't end. It continues.
When you're picking your partner and you're picking your route to application success, you really have to be thinking about like, it's not just now and getting this one project in place. This project is going to have a lifetime of maybe 10-15 years. So how do I support that long term?
Yeah, I mean, that's what we've been working on for so many years. And also what we have at Siemens, we call it the Digital Enterprise. And I think it kind of comes down to what Linda and Stuart have been talking about how we are able to combine the real and digital world. We have a continuous flow of data that could be a horizontal flow of data, but it could also be a vertical flow of data connecting top floor with shop floor.
We're generating huge amounts of data floating around between all the stakeholders involved in the development of the product and creating the most efficient production environment. So what's next with AI? Can we actually say what's next? I mean, we're moving at a very high speed. How do you look ahead? What's coming next?
Yeah, I believe it's about autonomous agents, of course. So things that are not just having one single task to fulfill, but really having multiple step tasks to fulfill and then also execute. Yeah, for example, when we are talking about autonomous manufacturing instead of predictive manufacturing, then we are talking about some systems which are not just identifying the problem, but then really executing directly. So for example, in your manufacturing, if I have a downtime there, that the system, the agentic AI can tell on how to improve this one and how to reschedule the production system, for example. I think this should be the next step.
But you said autonomous system. How far away are we from that? I mean, we look at a traditional manufacturing environment. We're automating things. We might have virtual PLCs at the moment. We do virtual commissioning, which is not really a new thing, but now we can do it even better with AI. So how far away are we with that? And perhaps from a Siemens perspective, I mean, we are a pretty big manufacturer of stuff as we are in Erlangen, in Hamburg, in all the facilities around the globe. What is your feeling of that?
Yeah, I think when we see Hamburg in Germany, for example, this is a plant where we have electronic parts, and this is where we are applying already self-optimized production. So yeah, this is where our plant is already in the case of having agentic AI being used in order to apply being goal-oriented. I think this is the case. So saying, hey, I have such ESG targets in front of me. ESG? The sustainability targets. I have sustainability targets in front of me. So how can I optimize my production system in order to achieve this? This is what we are doing in Hamburg, for example, as well. So we are there in some parts. I think what we see in the future is more of this.
And for example, the human being is more than a strategic commander in the future, while the Agentic AI will do a lot of tasks by themselves autonomously. Oh my God, that's good. Sounds good.
Huh?
I probably had a couple of things to that as well. I don't necessarily disagree. I think the first one is a lot of the focus we have historically has been around traditional cloud architectures with everything going up into the cloud and being executed there. I think what we're going to begin to see, and we are beginning to see, is more emphasis around edge-based environments for infer ence.
Like on the machine and the lines?
Maybe on the premises, on the machine. I think when you start looking at things like the incorporation of small language models in a lot of the platforms now to enable those execution environments to do much closer to the point of manufacture. I think this is kind of to your point as well, Linda, I believe. When you look at the vast majority of industrial operations, when you look at AI adoption, it's at one end or the other end of the operation. It's either supply chain goods in or is it quality assurance outwards? I think what we're going to be seeing, and I think this is the biggest move, is that encroachment of AI into the real-world production phase. I also think this also comes to your point earlier about trust. The biggest thing that manufacturers are scared of is downtime and poor quality performance.
And I think that's the biggest hurdle we've got to get over as an industry is help them trust that the AI is not going to introduce unplanned downtime. And I think that's why we'll probably have human on the loop rather than in the loop still for a foreseeable future.
So what does it take to become an industrial AI leader?
This is to you, I think.
To me?
Yeah.
Just leave it.
You sure? There's no one other.
We're a bit too biased, I guess, Linda and I both.
Yes. So as a company or as a supplier or as a company?
You're sure, sir. You choose.
Okay, great. Thanks very much for that.
You're welcome.
Well, I think there's two things. There's an element of scale here as well. And I think if you're looking at industrial in particular, you need to have intimate knowledge of the environment you actually operate in these AI systems in. You have to be able to deal with 147 different machine languages being kind of incorporated within one AI model. You also have to have the ability to be able to support this in the long term. And I think that narrows the equation down significantly into who's going to be your key partners. And I think there's some really interesting aspects here that are occurring. I think there's a really healthy understanding that you're not going to be able to do everything yourself. So even with Siemens, you've got a relationship with NVIDIA, with AWS that are driving an ecosystem play here that can have accretive benefits for everybody. And I think that's healthy.
For a vendor side, for a customer side, what do they need to be doing? What's going to make them successful? I think it really comes down to understanding they can't own it all, they can't build it all. They've got to look very clearly at their partners, and they've got to base their whole raison d'être around how they're attacking AI in these environments about delivering real-world benefit to their own operati ons.
Right, right, right. So I completely agree with you. And I don't think that any company, any country, or anyone can do this on their own. And I mean, also here we have partnerships. We got AWS, we got NVIDIA, and that is crucial. Okay, that was not my mic. So now we're not going to hear anything from Stuar t now. Your mic is gone. Anyway, so what we have here at Siemens is obviously a Siemens Xcelerator, which you can actually find out more about on the web and talk about. So Linda, I know you are extremely skilled on the Siemens Xcelerator. How does Siemens Xcelerator match to what Stuart said? He talked about ecosystem, partnering, portfolio. Can you give us a pitch on that topic?
I think it was great that Stuart started with it because otherwise it would be just a promotion. Yeah, Siemens Xcelerator is exactly applying everything you just said. So it's about having a portfolio which is easy to understand, which is plug and play, easy to scale. It's about an ecosystem because yes, we understand that we need to have other companies helping us along the journey, which means not just the big corporates you were mentioning.
It's also about the startups, and we are having a lot of startups at our booth, which are really super helpful and which have so great solutions out there, so this is important to us. In order to make it democratizing a bit the technology, we have a marketplace, so everybody can look at the marketplace, seeing what is there from Siemens, but also from the ecosystem, so what kind of solutions do we have for them at hand, so this is, yeah, we are well prepared for the next thing.
Maybe I can add to it, and I think it talks to your point again, Linda, is the fact that I think the company that takes their customers with them. Their customers are faced with this situation now where they realize they do not have the skill set to develop their own systems.
They realize now that the organizational stress that's being delivered to them by the vendor community, by the acceleration of the technology path, is something they're never going to catch up with. So the company that can actually take them through that journey as painlessly as possible is the one that's going to succeed. And I also think that one of the assets that you don't talk about much as a company is Mendix and that low-code, no-code environment. I think that has a really exceptional part to play in democratizing access to AI and actually bringing it onto the shop floor where you can have OT-centric operators delving in complex IT environments without having to go through a long process of learning those skills.
Exactly. And those of you out there don't know, in Mendix it's what we call a low-code solution, and it's able to collect data from various data lakes. And then you can build environments giving the various stakeholders information in the format that they need. At the end of the day, most of us are working on the same type of data, but for my specific task being, say, a plant engineer, it might be different from Linda being a product designer. But at the end of the day, we're working with similar data, and that's why Mendix comes into play and making sure that you get the data served in the way that you need it. I'm looking at the time. We're still good, but perhaps to round things off. I know we can talk a lot about this, but any final words?
And I'm going to start with you, Linda. What would be, from your point of view, with your experience going into industrial AI, what are the key takeaways?
So it's the first time, at least for myself, that I really understand that IT-OT convergence is super important. Yeah? Information technologies, so all the business data we are having, plus the operational data, we need to have everything at hand in order to train our AI, our industrial-grade AI, to have really the connection to the real world, yeah? To have tangible, solution-oriented, outcome-oriented solutions with AI. So I think this is, for me, the most important thing. I think we are looking at the autonomy of AI in the future without forgetting that human being is still important, plays a big role still, but some of the repetitive tasks are being now compensated by AI in the future.
Stuart?
I mean, I think when you boil it down, it comes down to a simple set of instructions that everyone should follow. Number one, ground your project in ROI. Number two is get the right people on the bus when you're doing a project. Number three is build for velocity, so be prepared to fail fast, and you're going to fail.
but you got to recover faster.
Recover faster, exactly. Be prepared that it's a long, iterative process, and I think the other part of it is pick your partners carefully. You don't want to be running down technological bunny holes or being tied into models. You need to be in an environment where it's flexible, where you can start growing with that organization and your solution can grow as your competency grows and your familiarity with the benefits of AI can be to your business.
So they're, I think, the key foundational rules that I know they sound simple, but you'd be surprised how many companies don't follow those and end up in that bog of despair.
All right, and U.S. ABI Research are able also to support that type of activities in the industry, or how would you do that?
Yeah, 100%. A lot of our work is really around identifying correct technology choice, architecture, and also partner choice. I mean, that's the biggest question we get at the moment is like, who should I work with? Who's going to deliver for me within the environment, and who's going to be a long-term partner that's going to be able to deliver and augment my team?
I think that one thing we haven't talked about here is there's an element of trust between the customer and the vendor as well, especially when you're talking about mission-critical data. There's still an element now of distrust about where's my data going, where is it being housed, how is it going to be used, is it going to be accessed by my customer s? So I think that element of trust and can I pick a partner that I can trust to be a good custodian of my data, can be a good broker of my information and allow me to do what I need to do to be innovative, but not expose me to risk as well.
Excellent. Fantastic. If you are looking at going all in industrial AI, which obviously you should do because there are clear benefits, as we heard from Stuart and Linda, doing so, you need to go start a project. You're going to have a defined ROI, return on investment, like a project plan laid out. You should start working on the foundation model. How is your data sorted out? Where do you have your data? And then also make sure you're connecting with the correct partner because this is something you might be able to do it on your own, but I assure you, you'd be better off working with a decent set of partners. You got to select the right partners. Linda and Stuart, it was a true pleasure having you here in this little studio of mine.
And thank you very much for your time, and it was really greatly appreciated. So thank you for that.
[Presentation]
Welcome, everyone, to our session, How Physical AI is Transforming Industry. In this session, we are going to explore where digital intelligence transforms the physical world through manufacturing, automation, robotics, and more. My name is Maria Rutte, and I'm a Global Partner Management Lead for Siemens, focusing on industrial software, edge-to-cloud integration, and industrial AI. Let me introduce our speakers. Sri Elaprolu is the Director of Generative AI Innovation Center at AWS. Sri is a technology leader of more than 25 years for artificial intelligence and machine learning. Sri leads a global team of engineers and scientists who apply industrial AI to solve complex challenges for enterprises and the public sector. Welcome, Sri.
Thank you.
Joining Sri is Dr. Horst Kayser. Horst is the visionary leader who drives the digital transformation in manufacturing by integrating informational technologies with operational technologies, and his focus is on artificial intelligence combined with the physical machines to drive sustainable, resilient industrial operations. Welcome, Horst, to th e session.
Thanks for having us.
Together, we will reveal what physical AI initiatives you are doing and how it's developing today. We are all witnessing the big growth of physical AI. According to the latest report of SNS Insider, the market is predicted to be $5 billion right now and growing up to $50 billion by 2033. It's 33% over a year growth. It's enormous. But let's start with the basics. What is physical AI and why is it actually happening right now, Sri?
Sure, I can start. So we've seen over the last two, three years the growth and impact of generative AI, more broadly artificial intelligence. And what we're seeing with physical AI is now, rather than just limiting it to the digital world, we're actually able to take AI into the real world or the physical world. So when we think about physical AI, it's the ability for us to take the artificial intelligence capability, fuse that with the real world, the physical objects, and get functionality going in that way. So it's in a very exciting space.
I would probably double-click into four areas of physical AI that's making this possible at this point in time. The first is the technology improvements themselves. We're seeing technologies such as sensors and fusion of those sensors between vision, audio, and movement all happening, and it's progressing at a rapid pace. So that's one factor that's driving this innovation to move at a rapid scale. Second is the amount of data that you're able to gather and generate, in fact, in order to tune these models that actually power these physical objects. So that's the second area. The third area is the models themselves. So something that's different between generative AI, where we dealt with large language models or LLMs, was where we were dealing mostly with language. With physical AI, we need to actually deal with objects, so real perception, depth, all of those sorts of object areas.
And so for that, vision language action models, or VLA, is an area that's seeing rapid growth and improvement in the ability for those models to perform. And then the last thing is just the market readiness. We're seeing the market being much more ready and willing and accepting. And you see companies such as Amazon deployed more than a million robots in our fulfillment centers, driving innovation and reducing cost and reducing speed of delivery, helping customers achieve outcomes that they're looking for. So it's the right time with the right technology enablers that are driving this to happen. And we see that growth happening over the next decade or so.
Horst, what is your perspective?
Yes, absolutely. I couldn't agree more, Sri. I also think that physical AI is now the landing, if you will, of the exploding capabilities of artificial intelligence now on the real-world floor.
May it be the street with autonomous cars, or may it be the factory floor leading to more and more autonomous manufacturing? So we are, in fact, seeing real-life impact and real-life actions as output of the AI models, rather than only digital analytics or digital results. And so physical AI is here now because it offers these huge opportunities to push the boundaries of automating human labor and enable us to accelerate that process to replace human labor, and not only in the repeatable fashion, the standardized worker kind of activities, but also ever more in reasoning and decision-making and really going towards autonomous action. And that has, of course, a tremendous opportunity in productivity to drive flexibility and resilience of plants. Automation has come a long way. I mean, if you remember, it actually started two centuries ago with mechanical automation.
And then the mechanics were driven by steam, later by electricity, fundamental technology. And now, since we went to electricity, electronics, digital control, now we're going towards AI-based control. And AI, thus, really proves to be a new general-purpose technology that will infiltrate all physical activities, I'm fully sure.
Yeah, technology is converging. Manufacturing is also moving from rigid to intelligent systems. But let's dive a little bit deeper to understand what are the technologies really driving physical AI? What is enabling it, Sri?
Yeah, so automation is not new. I mean, we've been doing automation since the 1950s in factories and in other settings. What's really changing over the last few years is three particular areas. And for physical AI to function well, those three areas are critical. The first is the ability to sense.
So when you have, for example, a machine that's able to sense, and when I say sense, it needs to have cameras for vision. It needs to have depth perception sensing. It needs to have audio sensing. It needs to have the ability to move and navigate. That sensing capability has improved dramatically over the last few years. And we're seeing that improvement continue to double down as we move forward. So that's the first enabler. The second area is once you sense, the object has to take an action. So that's where you actually do something. And that something, again, if you think of a robot, being able to Arm and provide an A rm to a robot so that it can actually lift things, it can drop things, it can touch, feel, that ability to actually do actions is also rapidly improving over the last few years.
And then the final piece, which is central, and I would call it the brain, is critical to operate all of this. So again, the difference between what we have seen in the past with automation and robotics than what we're seeing now with physical AI is the AI element, which is letting physical objects determine the course of action that they're going to take and not necessarily always pre-program exactly the way you want them to do, but rather have enough flexibility and reasoning ability. And that's where the brain comes in. That's where the models, the foundation models, come in.
And so that's an area where there's tremendous progress that has happened, both from a training perspective, where you're able to train these models in a much more comprehensive way so that these models are able to guide machines to navigate things that they have not seen before, that they have not been trained on before. So that's the first thing, is the ability for us to train these models in a much more comprehensive manner. The second is actual inferencing or actually doing things in the real world. And that's where real-time inferencing matters, whether you're training a model in the cloud, deploying it to the edge. And that combination of distributed brain power is critical. And that's really an exciting area that we're seeing innovation on. And then the third area is the models themselves.
The models are getting really comprehensive, where, again, you're bringing new techniques such as transfer learning, reinforcement learning, where even though you may have trained the object to do or the model to do one thing in a particular area, when the object goes into a different setting, it's able to adapt. And that's where transfer learning and reinforcement learning and other techniques such as that come in. So again, by combining these innovations happening in different areas, we're actually seeing that resurgence and surge in physical AI taking shape.
Thank you. Thank you, Sri. So it sounds like magic. Physical AI gives eyes to machines to see, brains to think, and then hands to act. But the companies still have challenges to implement this and go from the pilot to production. So Horst, what are these challenges that companies experience?
Yeah, I think it's basically in three areas.
It's the general complexity and diversity of manufacturing, then it's the difference between brownfield and greenfield, and it's, in general, the necessary transformation from OT, operational technologies, to IT. Talking about complexity first, manufacturing obviously happens in the real world with real physics, and there are so many different things. You can be grinding. You can be smelting. You can be metal bending. You can be assembling. You can be welding. And there's a countless number of different manufacturing technologies that always have to be combined to make a very specific product a reality. So to train the models on all these activities, you have to, as Sri described, you have to use these wide ranges of models that we are now being able to construct. And that's really where the impact is now happening with the tremendous amounts of data and compute we can handle nowadays.
We can do these more generalized models to learn manufacturing activities in all their varieties. So training and learning is the key driver, obviously, behind all of this. And for that, you need access to data. And access to data has a technical issue because if you have very specialized processes that you only have in limited scale, then you don't also have the high scale of data that you need to train your models on it. On the other hand, it's also a little bit of a political business aspect. Everybody wants to own his data and knows that data are the new gold, so to speak. What you need to do is build ecosystems, build trust, work together with people, and have joint data pooling activities, if you will, to enable models to get wider access to learning and training them.
So the other thing is that in manufacturing, of course, we have hundreds of billions of $ or EUR invested in existing manufacturing infrastructures. We can't throw them all away and start new with technically, mechanically more flexible approaches. But then, on the other hand, besides these brownfield, you have new greenfield activities. And so you have to also, as a player being active in this field, you need to offer your customers possibilities to optimize the brownfield step by step in a more incremental way. And of course, you need to really be able to install state-of-the-art AI-driven flexible manufacturing in the arena where it's possible when you start from scratch. And that goes then further to the overall OT/IT transformation, as I tend to call them.
And we, I think, as a company that has been working with the OT community, that sometimes are mechanically oriented technicians that are working with our machines. And we need to be able to work with them and make them learn and understand what the computer scientists are talking about when they are talking about these AI capabilities. But then, on the other hand, we have to work also with the AI experts to have the patience to take the customers with them on this journey to really leverage all these capabilities that you have when you apply AI tools to an existing OT environment.
Yeah, you mentioned Siemens is already for a long time in this space, yeah, and bridging this gap. So what are these initiatives that Siemens is already doing in the space of physical AI, and what are the new perspectives that are you looking to in the future?
Yes. I mean, Siemens absolutely, around for 175 years, has been, if you will, grown with the basic technology of electricity, and then over the years with electronics, digital control. And now, of course, we are making this bridge into AI-based digital solutions, and so as a general focus, we are developing our digital control base that's closer linked to our hardware controllers, still automation models. We want to bridge that and bring them into a more software-defined automation architecture that's then also more open and perceptible to AI tools to drive engineering process, to make it faster and more efficient, and also to optimize manufacturing while it runs.
To get there, we do that, of course, also step by step. For example, we have developed the first fully virtual PLC, where the PLC is no longer contained in its typical PLC-like box, but is able to run in a cloud-based environment in our customer Audi, for example. It's a private cloud on their premises, but it's clearly a cloud-based virtual PLC installation. We have gone towards engineering tools that are much more IT-like. So going beyond the traditional function block and other engineering tools that traditionally was used for automation purposes, now to a classical programming language type of approach that, again, is open for AI-based Copilots, where the co-pilot can then help you to really generate the code much faster and really generate 30%-40% productivity boosts in the engineering of your automation solutions. So these Copilots are an important next step.
We have also developed an Industrial Edge system that allows you to integrate control and have other processes like AI-based algorithms to optimize your production run on the same platform and also to collect data from the manufacturing process more systematically. That is a typical bridge, if you will, reaching from the classical OT sphere into classical IT-based landscapes now that you can then approach with typical IT tool and approaches, and which is still integrated into our landscape of totally integrated automation. Besides these concrete products, we have certainly ongoing a number of important initiatives to continuously develop our AI capabilities and also show our customers what's possible. For example, we have an autonomous factory lab where we can always try out the latest developments.
We have, as we have announced quite recently, a data alliance with a number of oftentimes small and medium-sized enterprises where we try to pool concrete operational data to, as I explained earlier, to train the models on a broader base of manufacturing processes. Of course, we are continuously widening the scope of our strategic partnerships. We have certainly one with AWS, for example, what we have also an important partnership with NVIDIA, with Microsoft, with companies like Sony or PhysicsX on different aspects of the wide area of possible AI-driven tools to drive productivity in engineering and automation.
Then, of course, we have our Siemens Xcelerator, which is an open digital business platform with a digital and AI-based portfolio, an ecosystem of many and increasing number of partners, and actually also a marketplace where our tools and also tools by third parties can be acquired and downloaded for rapid application in the manufacturing environment. We are pushing really on many fronts forward. Our CEO, Roland Busch, has announced just two months ago that in the next years, we're going to invest over $1 billion investment into the development of future AI capabilities.
This is a broad range of topics, initiatives, and real products here with software-defined automation that you provide to the customers and driving this physical AI closer and closer. At AWS, what are the initiatives we are driving? What are the goals?
Yeah. So as Dr. Horst has said, Siemens is doing a number of things in the physical AI space to drive innovation and take the value out of that ecosystem. What we've done at AWS recently is we announced a fellowship focused on physical AI. And the whole idea is that there's a lot of innovation yet to happen, right? While the technology is exciting, there are real-world problems that we want to solve. And to do that, we've launched a fellowship in close partnership with NVIDIA and MassRobotics. And the idea behind the fellowship is we will bring startups that have really compelling ideas to solve real-world problems into the fellowship. We enable them by providing them access to AWS Cloud services, whether it's the compute, storage, AI services, and so on. We bring our expertise from a science perspective to help enable those startups to move faster.
We also bring capabilities from NVIDIA, their software and hardware stack, as well as their expertise, and then open up the network from MassRobotics, both from an investment side, but also from a partnership side. All of this to jumpstart these startups to go do real-world problems, solve those problems using physical AI. And we've gone through the first cohort, which we announced in October of last year. And that has now resulted in a number of startups having built real problem-solving solutions, for example, in the autonomous construction space, in the healthcare space where robotics are taking certain actions, in sustainable farming. So there are a number of areas that these startups have built real-world solutions that are scaling to solve problems for enterprises across the globe. We've also announced the second cohort. Applications are actually open right now until the end of this month.
But the idea here is that innovation has to happen across the board. While companies such as Siemens and AWS will invest and continue to innovate, we want to bring the ecosystem together by partnering with companies like NVIDIA, bringing experts from MassRobotics, and then getting the next wave of innovators to build and build solutions for real-world problem-solving. So we're excited. We think that we're at the very early stages of Physical AI and where we can go with this. But the next few years are going to be really exciting in this s pace.
Thank you. It's amazing to see how we move from the concepts to real, real initiatives, real projects, and products. What AWS and Siemens can do to democratize their Physical AI to the companies of all sizes? What are the key things you would like to share?
Yeah. I think, as I mentioned before, I think what I think is really important is that we're both promoting open access to data and build trust with our customers to share data and that they will benefit from that, so the wider we can train the models, although, of course, nowadays there is the possibility through simulation also to work with synthetic data, but in the end, if you have real data and real customers on board and really big global enterprises as well as small and medium enterprises, the machine builders, which are such an important part of this manufacturing process, and we have very long-standing customer relationships with, so I think that's really crucial.
And so when we both can also promote concepts like the Siemens Xcelerator as an open digital business platform with mostly open APIs of the tools that are being offered to seamlessly integrate and easily deploy to applications, that will help to lower the barriers to gradual implementation. On the other hand, between our two companies, we have very concrete initiatives like our Siemens low-code engineering and programming environment to run on the Amazon Bedrock platform, where we can show how more OT-oriented solutions can benefit from IT-like infrastructures. And the same is true for the AWS IoT SiteWise Edge and the Siemens Industrial Edge that can work in an integrated fashion and thus provide a bridge for operational technologies for OT into the cloud and also have a larger or drive the larger integration between OT and IT.
And I think if we do this together, again, we are forwarding the trust with our customers to try this out and to see the benefits when they're making this jump forward.
Anything to add, Sri?
No, I fully agree with what Dr. Horst has said. One thing, maybe I'll add, is the combination of Siemens' expertise and the data and the centuries of expertise when it comes to manufacturing processes and combine that with the IT experience and expertise that AWS brings with the cloud technology plus AI and artificial intelligence science capabilities. That fusion is really, really impactful because now we can solve problems at a grand scale. And out of that research, out of that work that we do jointly, there's going to be a number of innovations that will come out that will help the entire ecosystem.
So while we're working together, the benefits will be felt not just by the two companies, but rather the entire ecosystem. So to me, it's a match made in heaven.
Yeah. We are on the great path to democratize physical AI. And we saw that generative AI had a great adoption in 2023. So what watershed moment do you foresee for physical AI? What are you excited the most going forward, Sri?
Yeah. I don't think there is one single moment, unlike generative AI, when two years ago we had that moment. I think with physical AI, because this is one of those areas where it won't happen on a single day. It's going to be gradual, but accelerating rapidly.
And so as the sensors become more capable, as the action activities become more safe and secure and reliable, as the models become more comprehensive, we're going to see rapid expansion of where and how robotics are going to get deployed with AI brains. And that expansion is going to happen across every industry, across every geography that you can imagine. So I don't think there is a single moment per se. If there is one that has already happened, which is AI in itself and the fusion of robotics. So I think that this is going to be a rapid, but rather expansion that's going to continue to happen. I don't know if you agree.
Yeah. No, you're fully right, Sri. I fully agree. I think somehow the inflection point has already been reached. Yeah.
We already see an accelerated movement and more and more people not asking what and why, but how and how quickly, and so I think we are already getting into that point, and I think one real issue is when we talk about learning about these models and now the foundational models will be built. Siemens itself will really invest heavily under the leadership of Cedrik Neike now in the development of an industrial foundational model, and these models will no longer forget. Whatever they learn, they will add to what they have learned before, and so that in and of itself lends itself to an explosive growth curve, and that is something that we will be observing. We have to keep in mind, though, one thing. We have to be careful with the trust and the concrete real-life experience that our customers make with our first applications.
Because in physical AI, 85% right is not enough. And it has to be 99.99% right, if not even nine-nines after the comma in some processes. And we have to know when we apply new tools to such processes whether we can fulfill the promise. Because trust, as we all know, is lost very quickly, but only earned the hard way. And I think we have to go the hard way step by step to demonstrate the capabilities that we already have and that are expanding rapidly. But be perhaps sometimes, especially in the physical AI field, careful of not overpromising.
That could backfire tremendously. But if we do that, I think we are on a joint journey with our customers and big important partners like yourself. And I think it's exciting times. We will push things forward. We will see more autonomous factories with higher levels of productivities, higher levels of flexibility, sustainability, resilience that in the end will serve us all for the better. So I think that's exc iting to do.
Thank you very much, Sri and Horst, for this insightful conversation about physical AI. Today, we explored how physical AI is transforming all industries, giving machines eyes to see, brain to think, and hands to act, while showcasing how innovations from Siemens and AWS are democratizing physical AI for companies of all sizes. Thank you.
Thank you.
Thank you. Thank you.
[Presentation]
Welcome. I am Magnus Edholm. And when I'm not sitting in the studio, I drive the digital enterprise for Siemens and also communications and automation in the US. Today, I am joined by Kal Mos, who is Siemens' newly appointed Head of Research and Development. Wel come, Kal. How are you doing today?
I'm doing great. Thank you very much.
Great having you. Before we start talking about innovation and the stuff that you're working on on a daily basis, why don't you give us a short introduction of you and your background?
Absolutely. I, as you said, am in charge of Siemens' research and pre-development. Pre is important. I have been in my role for about three months now. Basically, my role is to bring the research from the labs into a product-ready state so we can actually implement that in the industrial settings that we have at Siemens. Before that, I used to work for Google. I was at Google building the software platform for deploying AI on the edge. My role was to bring AI, the edge AI products that Google is developing, and bring them into the car, which is one of the edge products that we have. Before that, I worked at Amazon. At Amazon, I led a global organization that built local search based on AI products.
That was a cloud-based product that is deployed and still deployed for millions of Amazon customers on all different platforms that Amazon has. Before that, I spent several years in the automotive industry working for companies like Mercedes-Benz, like Renault, Nissan, and basically focusing on AI products for user experience and for autonomous driving. So through my whole career, I've been trying to bring research ideas into the real physical world to be deployed in critical missions like autonomous driving or industrial settings and things like that.
Oh, that is impressive, Kell. That's impressive. So would you kind of agree with me that what you have been doing and what you are doing could be like, if I go back to the Stone Age, I mean, it's safe to say that the Stone Age didn't end because they ran out of stones. I know because I grew up on a farm. The Stone Age ended because someone came up with a better way of using stones turning into bronze. Would you say that that is kind of what you are doing and what you have been doing over the last couple of years?
That's a very great analogy. My job, actually, using this analogy, is to look at all those people who are trying to find new usage of the stone and bring the best ones into the industrial settings. So how we can implement all these great ideas of how to use stones into our factories, into the grid automation, into the critical industries that Siemens has. So this is what I'm doi ng. .
OK And so, when you do that type of development, I mean, it's probably a bit of a challenge to make sure that the new ideas that you have are perhaps not just incremental changes, but there are changes that have an impact. And also, and this, I think, is quite challenging, that there's also future-proof ideas. How do you go about doing that?
Yeah, yeah, absolutely. So the first thing that we look at, actually, is customer focus. So we focus on the customer challenges. And that is what inspires us to look for the right research. What are their main pain points? And it's usually in industrial settings. It's things like reliability, like, for example, the latency, things that impact the usage of different machines when you bring them in the real physical world. So we start with that. We start with the customer pain point.
And so that's our guidance. And then the other way we look at it also is how we can collaborate with the ecosystem. We have brilliant engineers inside Siemens, brilliant engineers, brilliant scientists. But at the end of the day, the technology is moving very, very fast. And we need to tap into the ecosystem around us. So we look at three areas in the ecosystem. We look at how we can collaborate with startups. We look at how we collaborate with universities. And we look at how we collaborate with our industrial and technology partners. So when you look at, for example, the startup collaboration, which is a very important part of our work, and it's already like we showcase that in our booth, we have a program called Siemens for Startup. And that program helps us to work with a startup in three different ways.
The first thing is that we collaborate with them to empower them. We empower them with our tools, so we give them the tools that we have that they need to test their ideas in the real world, and by using these tools, they can actually start creating some proof of concepts. And at that point, we sit down together with them, hands in hand, to work with them in creating these proof of concepts and prototypes and pilots, and so that's the second part, which is the collaboration. And then from there, when that idea is successful, we take it and we connect them to the right areas within Siemens where they can actually make this a product and release it in the world, so that's kind of the collaboration scheme that we have with startups.
And then we have the other part, which is how we collaborate with universities. And we actually collaborate with many, many worldwide universities, well-known, top-grade universities. At any point of time, we have like 35 different joint projects with universities. The one I want to maybe talk about a little bit is the one we have with Berkeley, so we work with,
but also, you know, before we go away from that, I mean, surely there are a lot of people out there in startups, in the business. How do they go about to get in touch with you? How can they become a part of that startup business? Is it the Siemens Accelerator web page, or how would they do it?
It is the Siemens Accelerator, yes. That's where they start. And they can start from the web page. But they can also reach out to our Siemens Accelerator program managers who can actually take them and connect them to the right area of the business. So that's the right way. And actually, for any startup who's out there today, they can look at our booths and connect with us at the booth. And they can be part of that program.
All right. Very, very good. Very, very good. And then you come back to what you said about you're working with, I mean, that we have been working with the universities over the last, I don't know how many, many years. And it's something that's obviously essential to our business. How do you do that? Do you go out to the universities and talk to them? Or how do you make sure that you are supporting them with software, you say, the know-how? Is that something you do personally also?
Yeah. Well, all of us, or most of us actually, in my area of work, we have a community that must have been like all of us came from universities. We still have relationships with our professors and advisors. We are still connected to the ecosystem of the university. Either we know the professor or either we look at the research that the professors are publishing. And then we collaborate with them in developing new ideas and bringing their ideas into reality. The example I wanted to talk about today is with Berkeley and basically with the robotics labs at Berkeley. We work closely, very closely with them in fine-tuning VLA models. VLA is Vision, Language, Action models. And these are basically the brains that run the robotics and embodied AI today.
So the idea there is that your language model is just for chatbots. So that's what it is. So you can communicate through language. But when you put AI in the physical world, you need now vision. You need a perception. You need to kind of understand the environment around you. But you also need action so you can implement things. So you need all the three components. And to fine-tune that on industrial data, that's a very heavy and very important work we have with Berkeley.
Would you say also, I mean, I am an engineer. I've been working with manufacturing, planning, and simulation for many years. And there was a lot of a big part was also offline programming on robots. So what you're saying now is that you're taking the, say, traditional automation, offline programming on robots, and you're taking it to the next era. You're developing. Is that what you're doing so that you have cobots? It's going to be easier to program. You're going to have flexibility to work with a huge variety of different robot brands. Or what is the reason?
Yeah, yeah. So robots today, and they are deployed everywhere in factories and every industrial settings, they have fixed function and fixed task. You today, you have to program them. And you have to commission them with very accurate instructions and scripts to tell them, OK, well, you pick up an object from that coordinates and put it in that coordinates. Or you approach a certain setup in this way. And it has to be 100% accurate because you need a very high level of success at the factory settings. So we take that from that kind of fixed environment.
And we moved into a flexible environment where some variations in the function will be possible. And also, some mix of different actions will be possible. So that's taking from a fixed, taking the robotic setup from a fixed way of doing things into a flexible way of doing things. And in order to do that, you need to kind of add intelligence. So you don't have to redefine everything from the beginning. You need to give the robot the possibility to actually figure out things on the fly. And in order to do that, you have to train the robot. So you basically create a model, an AI model. And you fine-tune that model on the environment so actually the robot can understand the environment and interact with it. So that's the move from fixed to flexibility.
And for that, so you're talking about a foundation model where industrial data is being gathered. And then you use the robot to train that. Is that also where, I mean, we always hear a lot about AI. It's generative AI. It is AI agents. It is physical AI. And physical AI, when you hear it, sounds extremely powerful. Is that what you're talking about? And can you perhaps also explain what physical AI actually means? I would be very happy to hear your explanation of that.
Yeah, yeah. So physical AI is exactly what it means. It's like when you bring AI from the digital world into the physical world. So you don't run AI in your own computer. You don't run the AI model in your own computer accessing some digital data. You actually bring that and put it in a device. T hat device can be a robot, can be a car, but a device that can interact with the real world, so in order to do that, the first thing is that you need to provide that intelligence on the edge, so that device has to have its own intelligence, its own hardware to run that, and its own model to run that AI.
and so that's the first step of this, but also, that device, which is a car or a robot, has to have a lot of sensors to understand the real world: vision sensors, tactile sensors, maybe proximity sensors, like lots of sensors in order to understand the physical world around it and interact with it, and then in real time, that device, which is a car or a robot, can try now to understand the world and then decide what to do next based on that understanding. So that's physical AI. That's what we call physical A I.
Interesting, very interesting. So I mean, that's also what we are doing at Siemens. We talk a lot about combining the real and the digital world. And we do that by creating digital twins and having data flow between all the people involved. It could be a horizontal flow. It could be a vertical flow of data. And at the end of the day, it comes down to collect data, analyze the data, and then use the data to continuously update everything that we are doing. Would you have like a practical example of what Siemens are doing with this? Are we using that technology?
I mean, we are a big producer of our own stuff. We've done that for so many, many years, as you know. And I mean, also a bit of a commercial break here. You can also visit us in our factories in Germany. We have in Hamburg, Erlangen, and Bad Neustadt, where you can see this actually taking place. So what would you say if someone's interested or if they want to find out more about what we are doing? Is there a concrete example that you might have?
There are. There are lots of examples. And as you said, they can visit us in Germany. But here on the show floor here at Siemens booths, you can actually see one real example of that, which is an example of how you can, we call it teleoperation of the robots. So the idea is that in order to train and fine-tune the robots, we use what is called teleoperation. It's a standard technique that's been used in the industry for a while.
You can do that through a virtual glass, or you can use this. The idea there is that you actually control the robots to do certain actions. By doing that multiple times, the robot will learn how to deal with that action. The model will be trained on that. What we have on the booth now is that we have a glove. That glove, you can add in your hand. That glove will enable you to control the hands of a robot. The new innovation there is that you can feel what the robot is doing.
Haptic and tactile feedback?
It has a haptic and tactile feedback. When you actually try to hold on an object in your hand as a human, you can feel the force.
You can decide how much you can pressure that object in order to hold it correctly. And that helps us very much in understanding this and training the robots to handle multiple different types of objects: solid objects, soft objects, objects that are regular form, and using this data into our simulation pipeline, Siemens simulation pipeline. So you can actually simulate. You can validate. You can fine-tune this. And you can take it all the way into understanding and creating that model in a way that it's ready for production.
Fantastic.
All in a virtual world before you actually take it to the factory.
Excellent, excellent. I'm looking at the time. I'm kind of low on it. But essentially, it comes to start working very tightly with robots. Don't be afraid of them. You've got to work with them. Look into the development of things. And if you're interested in finding out more about what we're doing with robotics, you can always visit us on Siemens.com. And you will find a lot about a lot more. You will have the glove. You will have the research what Cal and his team are doing. Extremely impressive. For five seconds, what did the future look like? Two sentences.
Well, the future is using virtual training and tuning into solving the real industrial problem. And we're doing this. And we're helping all companies, big and small.
Fantastic. Cal Moss, totally nice to meet you. It was a pleasure having you here. I hope to talk to you more. And we should have at least another hour because I have a lot of other questions. But with that, then we are signing off for this time. Thank you very much.
Excellent. Thank you very much. Appreciate it.
[Presentation]
Welcome. Today, we are exploring one of the most ambitious transformations in the consumer goods industry. We got PepsiCo, who we're going to talk to today, and PepsiCo is in a very bold partnership with Siemens and NVIDIA. They're doing that in order to reimagine manufacturing, warehousing, and logistics.
Houston, the most comprehensive digital twin, and AI, which is also, of course, the groundwork for the industrial metaverse, including also software-defined automation. But that's a different gig. And properly utilized, this is obviously driving productivity, increasing efficiency, and driving flexibility, which is something that is of utmost value for a company in the food and beverage industry. Joining us today, I'm very happy to have you here, is Athina Kanioura, PepsiCo's Chief Transformation Officer. And as of, I think, yesterday week, you are, I've got to read through it here, the new CEO of Latin America Foods at PepsiCo.
I know. So good to see you. I'm so excited to be here with the audience and with you.
Yeah, yeah, yeah. No, we're very happy to have you here.
So very excited. Especially after an amazing keynote with Roland and Jensen.
It is perfect timing. Totally, totally. So why don't we start with the big picture? We've got a lot of viewers out there, so perhaps you're going to set the stage on what we're going to be talking about. You have been going through a very big transformation. We talk about digital transformation. To you, it's also digital transformation, perhaps even more, though, because you're including AI. Being a strategy manager or boss, as you are, what is the ultimate business goal that you try to achieve or want to achieve with this transformation?
Yeah. Well, I'll start with the obvious, which is amazing financial benefits for our shareholders and our company. I mean, ultimately, with this transformation, we want to drive top-line growth for the organization. We want to improve bottom-line performance through productivity, efficiency, and effectiveness. We want to make the experience of our employees much better, making their jobs very meaningful. If you were to look at three key priorities, one is financial performance. Now, the second is kind of create a company which is future-fit. We've always said this cannot be a transformation which is on the side. Everyone loves AI. Everyone loves digital. But we want to be in a world where PepsiCo leads the thinking of the transformation.
What do you mean by future-fit? Future-fit.
So imagine a world where consumers suddenly want, "I want this lovely can of Pepsi, but I want this to have my little customized emojis, and I want this to have a blend of three flavors, and I need this to come in a can which is much smaller, and I want this to be at my home like in half an ho ur."
I get you. I get you.
So just imagine the complexity of the supply chain and the fulfillment to be able to deliver exactly that personalized demand to the consumer.
I can totally see that. And I mean, that's also something with the food and beverage industry is critical. Would you talk also say that you said, "I want my Pepsi can to have this and that"? What about traceability and also that stuff? That's also something that you...
Absolutely. Absolutely. Because if you were to look at every pack, you have QR codes. Like in every can, in every pack, in every product of PepsiCo, in this QR code, you can track and trace ingredients. You can track and trace kind of the raw materials. You can track and trace ESG targets. You can track and trace how many days of inventory we have had. So it's traceability, not just from sustainability KPIs, but it's traceability in terms of all the product origination from the source to the shelf.
So essentially, what you're saying is you're having one ear to the market, probably a marketing collecting information that you use. You feed the product developers with it, the recipe transformation folks, and then you want to be able to produce and be as flexible as possible. I guess that is quite a challenge.
It is.
And how do you make it work?
Yeah. So we have a program which we call Design to Value. And the Design to Value is, firstly, you need to sense what the consumers want. And sensing the consumers seems very simple, but it's very challenging because you and I don't want the same thing. You and I don't have the same experiences. You and I don't want the products the same way. So how do you create those consumer experiences, but in a way that the product that you deliver is affordable, number one, is of the right quality, number two, meets your expectation in terms of your sustainability and other key KPIs, number three, and as I said, is being delivered to you at the right time.
So what happens with this type of challenge is like all the demand signals that you are getting as a consumer, you have to ingest. You have to create this immersive experience the same way we saw earlier in the digital twin of the factory. You need to create the digital twin of the product within the factory so then you can customize the supply chain planning process to be able to deliver that in a price that is affordable.
Fantastic. Fantastic. So recipe transformation, bill of process, and all that is something you got to manage.
Absolutely.
So now, PepsiCo and Siemens and Nvidia have been in a project for, I think, not that...
And by the way, what I forgot to say, for the purposes of that and PLM, we work with Siemens.
Today, we didn't discuss about other areas, but for product lifecycle management, Siemens is our partner.
I think you can close the interview now. I think I got everything I wanted. But we're going to move on, though. Can you tell us a bit about the product we've been working on for not that long, though, right?
No. I mean, we have been talking about it for 18 months. So when Siemens came to us with this idea, you know we are developing DTC, which is the Digital Twin Composer. And we know that you guys are very ambitious about creating the future-fit company. I said, "Okay, let's do it." I mean, NVIDIA was already a partner when it comes to the Omniverse. And we said, "We want to influence the product development roadmap.
We want to be the first ones that you guys test the capability so then when you take it to market, you know how it works in the context of a company as complex as PepsiCo." So that is why the marketplace will be operational pretty much now. But already, as part of the implementation, we have had results for more than three months. So what we've done, even when the product was, what we call it, in the beta version, we've seen tremendous potential, both in terms of how you were able to optimize the efficiency of the space and the layout of the existing facilities, so existing warehouses, which for the audience that has heard in the Keynote, I mean, some of our facilities are legacy facilities. They are...
Brownfield, we call them brownfields.
Ye ah, exactly, brownfields, right? So in those cases, getting 20% more efficient is a massive improvement.
Absolutely.
Massive. So we are very happy with the outputs of the first run, and now it's about scaling.
All right, all right, right. I mean, also scaling, what you said also, and I'm coming back here. I'm not a food and beverage specialist, but I know how to enjoy a good beverage, for sure. You said quality is key.
Quality is massive for us.
Yeah. And if you have a recipe of this beverage of yours, the Pepsi, you want to scale it up. You still want to keep quality going. And that's also, is that something you try to get out of also the approach that you've ta ken?
Absolutely. I mean, you cannot compromise quality. I mean, there are a couple of principles that we have in PepsiCo that you cannot compromise. One is quality. The second is compliance. The third is health and safety. So if you were to take those hard constraints in the whole manufacturing, logistics, and operations process, then you have to develop that product taking those constraints into account, but without them being hard constraints. Still, you need to be able to go as real-time as possible.
And that is why the technology gives you this massive opportunity, because if you are able to simulate all those parameters in a way that allows you to look at the blind spots, to look at the parameters that matter versus the parameters that don't matter as part of the optimization or the simulation, to assess potentially external disruptions that might happen on the way while you design the implementation roadmap, then when you go to the actual implementation, then pretty much you are 90% there.
Whereas typically what has happened in the past, people just go and try and test and do the trial and test in the production line, which then, of course, that costs money, effort. Sometimes it compromises if you're not careful. Other big ha rd KPIs that we don't want to compromise.
So essentially building a digital twin of the facility. Absolutely. Simulate the flow of material, making sure that the conveyors and the AGVs and what have you are working in sync, optimizing the storage. I mean, I've seen some beautiful things that you have been doing. And also what is really, really inspiring is Back to the Future. Do you know that movie?
Of course, I know that movie. Back to the Future 1, 2, 3, which one?
Well, 1, 2, 3, all that. There you have this ability to go backwards and also in the future.
And that is, in a way, this is a very cool analogy. I just came up with it. It's kind of what you guys are doing, though. You're able to do now with the Teamcenter in the background and also see where are we now, where were we six months ago, and where are we going. Isn't that something also that the engineers and the plant managers are going to have ?
Oh, they love it. They love it. They love it. They love it. When we actually went to the engineering team and they said, "Okay, this is what we want to do. Are you guys on board?" They say, "We are all on board." I mean, it was a matter of where would we start?
We wanted to start with one of our most challenging facilities, because if you are able to drive benefits and unlock KPIs, financial KPIs in a warehouse that is as old as the Gatorade plant and the warehouses that I mentioned in the U.S., then just imagine the potential everywhere else in the world. No, the engineering team is over the moon. They can't wait to scale. I got so many texts from everywhere, including my Latin America team. We want to do everything now.
Yeah. I mean, yeah. How do you...
This is interesting also. That's kind of also a cool segue over to you selecting the partners in this project. You are under time pressure, of course, because everybody wants it now or even yesterday. How did you go about choosing partners for this?
I have a very stringent process on the partners, because partners for us are not just people we do... We have a transactional relationship. They are for life. Because when you have a partner, you commit to a mid and long-term roadmap and strategic partnership and not just, "Oh, I'm going to have a contract with you for a year and then thank you very much." So the partner needs to have the specific qualifications. One, they need to have skin in the game. Of course. So as I'm obviously putting a lot of my faith and my money on the Siemens and NVIDIA technology, they need to put the developers, they need forward-deployed engineers, they need to open the hood of their product roadmap. They need to allow me to influence that product roadmap. And this is exactly what happened in this case with Siemens and NVIDIA.
They stuck with us. They listened to us, active listening. They sent their engineers. Their engineers work with us like hand to hand throughout the implementation. They've done the configuration. Now we have all the prototypes and the archetypes already ready. So this is what partnership is about, is caring about the success of the ecosystem.
And that's just fantastic to hear. And what you've been working on now, do you think that's going to be like a blueprint or a template for...
Absolutely. Absolutely. Absolutely. No doubt. No doubt. That's the plan. We want to scale in the U.S. We want to scale in Latin America. We want to scale in Europe. We want to start with those three regions, and then it's the rest of the world.
So you almost answered my first question was supposed to come now, so I skipped that, but I'm going to take a twist on it. So when you do this type of transformation in a company, you're talking brownfield operations, you are modernizing, taking the new technology. With your experience, because you are very experienced and very good at this type of work, what would be your approach to make sure that everybody's on board? How do you do that?
Yeah. And this is where I'm putting exactly my hat on the strategy on the change management. This is a big change management undertaking for an organization of our size. We're a $95 billion company, and we have operations in every market. So naturally, if you were to think especially how companies operated in the past, everyone believes they are unique. Everyone believes my requirements are very special because. You need to have a very robust change management process and with that, a learning and an upskilling process of the ecosystem so they can understand, number one, the value generated from the initiative, what it means for them and their part of the business, and not just the people who will manage the operations.
It's also the people that partner with these teams, like the sales organization. Third, how do you optimize the use of this technology? Very frequently, these technologies fail in a project like that when people use 70% of the potential, 50% of the potential. I want them to use 100% of the potential of the technology. A big effort that we have been undertaking in any major transformation, including this one as we are scaling, is having the leadership on boards, one, two, three levels down. Not just the top, not just the bottom, but also the in-between, because they have to be the ambassadors of the chains in every local market.
All right. Interesting. And also, if we look, you said you are everywhere, of course. Everywhere. And so are also we, pretty much. And I can say that one of the big challenges that I guess all companies are faced with today is the lack of people to find people working in this type of b usiness.
Absolutely.
What would you do to go about that? I mean, you're working with technology. You want everybody to use 100% of the capabilities. You're having AI coming on boa rd. You want people to get in there. But how would you make sure that people actually start working in manufacturing and development and in your industry and all industries?
Yeah. I mean, the manufacturing industry has been through waves, as you know very well. Some of the jobs actually are very attractive jobs for some of the markets. Some of the jobs is, especially if you ask a 25-year-old, go analog.
Yeah.
Right? I mean, they will say, "Why go analog?" I mean, it's like, "I'm using my mobile for everything, and now you tell me to go analog and go and do my quality and safety with a piece of paper?" No. Thank you very much. I'm going to the next one. So this is what they want. They want to be excited about the prospect of them using digital AI in their day-to-day job. When they see that, when you give them the opportunity to do that, there is no lack of talent in the markets. The flow of talent that we get for when people actually see that the job they will be doing, one, is promising. Second, it develops them. Third, it adds to their CV.
Yeah, yeah, yeah.
I mean, people will not work for PepsiCo for their whole life, right? But you prepare them for the next and the next and the next. So we want PepsiCo to be an academy, an academy for the industry. So with that, we have a massive training program, full upskilling, reskilling in any facet of technology, including, of course, AI, including on-the-job training as well. We give people opportunity to rotate market by market so they can learn also the intrinsic differences per market.
But more importantly, we tell them, "Okay, you have a massive opportunity to work on the latest technology." And that gives you the opportunity then to go from being a handler to a planner to a warehouse manager to a plant manager to a division manager to the chief supply chain officer. So you give them the whole career trajectory aligned with our technology vision. I mean, what else do you want? I mean, if I were a youngster, I would want to work for PepsiCo.
Or Siemens.
Or Siemens. Or b oth.
Or both. Both. Yeah. Cool. Cool. What keeps you up at night? What's your big concern?
My biggest concern, yeah, are we always thinking of what will disrupt our industry? Yeah. It's like, we are big. And when you are big, you want to be fast, but sometimes you cannot be as fast. So you can always have kind of the niche companies that are AI-first or digital-first that can eat at the edges or change the industry. So you have to disrupt yourself faster than anyone else.
So this is what keeps me at night. It's like, what is going to be the next disruption wave for the industry? What can I do to make sure that the company is protected in a way? And technology is the obvious one. All the consumer signals, as we discussed, all the supply chain disruptions. You see what is happening with geopolitics, having the foresight to be able to react in every changing landscape and environment. And giving the teams the ability to handle that in an independent way. So yeah, that's it. That's it. That is it.
That's it. Yeah.
So to kind of summarize, in order to go through with this vision of yours, it's been extremely successful, as we know. You have had a very open mindset. You have selected the correct partners, and you have made sure that the whole PepsiCo organization involved in this has been along through the whole journey and also keeping that dialogue going. I guess you always put up goals according to this project, and you've been talking through fail fast, but recover even faster, and all of those things. What would you say to any given company out there listening to this? Should they be open to industrial AI, and what should they think of, and how did they get up to speed? Because you guys are moving fast now.
No, we're definitely moving fast. I mean, if you are producing anything, you don't have to be a major manufacturer. You produce anything. If you don't subscribe for industrial AI, you will be left behind. I mean, the future dictates that you create an inclusive ecosystem that you know exactly what is happening in every part of your operation, linking that with all the consumer signals to our earlier point about personalization at scale that dictated a very different level of agility in the supply chain. Don't wait because you're going to be left behind. Invest in the right partners, invest in Siemens, invest in NVIDIA, and the future is very bright for the industry.
Collaboration is key, essentially.
Absolutely.
Athina, it has been a true pleasure to have you here. I wish you all the luck, also, in your new job in Latin America. I'm sure you're going to wing it like a champ, 100% of that. I t's been a true pleasure, and I hope to see you also, perhaps, in April this year.
April, Hanover.
Fantastic. So with that, everybody out there, this is Athina Kanioura, who is the CEO of Latin America Foods and the transformation...
Chief Strategy and Transformation Officer of PepsiCo.
There you go.
Thank you so much. Thank you, Magnus.
Thank you.
[Presentation]
Hi, and welcome. I'm Magnus Edholm, and I'm not sitting here. I'm driving the Digital Enterprise for Siemens. Today, I have a very exciting show for you. We have the world's largest two-wheel manufacturer visiting, Hero MotoCorp. We're going to talk with them and about them. And with me here, I have an amazing set of people, which we're going to go through. We have Kausalya from Hero MotoCorp. We have Ram, also Hero MotoCorp. We're going to come back to them in a minute. And we've got Ozgur from AWS, and we've got Bob Jones. And I'm going to start the other way around now. Obviously, you guys out there want to know who those folks are. Bob, tell us a bit about yourself. Give us an introduction.
Well, thank you, Magnus. Bob Jones, Chief Revenue Officer . That's kind of really a fancy term, which really means I'm responsible for all aspects of how we engage with the customer, from the pre-sales, the sales, but also the implementation, the support, and ultimately the customer success. What I would really tell you is there's really two key objectives to my job. Number one, make sure we're delivering the outcomes to our customers that they need to be successful in their business, like Hero MotoCorp.
The second part, which is equally as exciting, is also to stay very close to the industries we serve so that we can help our customers, again, like Hero MotoCorp, not just support what they need today, but anticipate what they're going to need tomorrow, to not just be a leader, but in the case of Hero again, to be able to disrupt their industries as they go forward. So very exciting role, and very glad to join everybody today.
Thank you very much, Bob. Ozgur, perhaps same from you.
Great to be here. So I'm Ozgur Tohumcu, General Manager for AWS, responsible for automotive and manufacturing. Maybe I'll just do a little bit like what Bob did. What does that mean? So I'm responsible for setting our industry strategy and executing it for automotive and manufacturing industries globally. So great to be here. Thank you.
Lovely. Then we do the same thing for you, Ram.
Awesome. Thank you for having us, Magnus. My name is Ram. I am the Chief Operating Officer for Hero MotoCorp. By the way, that's Hero MotoCorp, Bob. We are the world's largest two-wheeler manufacturer. Very proud of it. For the last 25 years, we've been number one in the world. We build and distribute and sell more than six million vehicles a year. That's motorcycles and scooters. We've got everything from a 100cc motorcycle all the way up to a 440cc beast that we manufacture and sell. These days, we've gotten into electric vehicles, which I'm sure Kausalya will tell us a little bit more about.
Yes, and that's actually what we're going to do. We're going to kind of divide this session up into two pieces. Kausalya, we're going to come to you in a couple of minutes. We haven't forgotten about you. I mean, you are the head of the mobility, electric vehicle mobility session. Anyway, Ram, back to you again.
Sure.
What's the connection between AWS and Siemens? Apart from the fact that we have three very good-looking guys right now on stage. I would say that. I'm the fourth, so thank you for that, buddy.
Didn't mean to leave you out, Magnus. But I must say that the three companies, the three organizations have embarked on a very exciting digital transformation journey. We've taken a very core piece of the beating heart, I would call it, of Hero MotoCorp, which is the product development journey. And we've worked together to now completely transform it. T his is the product lifecycle management software that empowers our entire development journey, our R&D within our organization. We've now migrated that to Teamcenter X, which is the latest offering from Siemens. We're doing that on the infrastructure from A WS.
Okay.
It's a phenomenal partnership. There are two large objectives that we want to achieve with it. One, we want to make sure that we take our deve lopment times and cut that by 50%. Wow. The second is we want to make sure that we platformize what we develop. A part that's developed once, we want to make sure that's used in as many vehicles out there as possible, which lowers the overall cost of designing it, manufacturing it, and putting it in vehicles for us. And we want to do these two objectives, by the way, while we leverage the power of AI. And we want to make sure we do that in every platform we use going forward.
Interesting. That is cutting it 50%. That is a pretty good target, to be honest. Bob, from your perspective, I mean, 50%, what makes this work we've been doing with Hero MotoCorp so important in adopting Teamcenter X on AWS?
Yeah, it's a great question, Magnus. I guess what I'd summarize is what this really is, is it's a strong validation to the market, the global market, the global discrete manufacturing market, or you could actually call it a signal. And why do I say that? Let's be honest, cloud and SaaS is not new technology. It's not like it's been around for several years.
And quite honestly, it's been adopted in other parts of industry. But when it comes to the PLM or the innovation process for our customers, especially in discrete manufacturing, there's been some reluctance to move to the cloud and take advantage of the capabilities of a SaaS deployment. And why is that? It's not because it's not ready. It's just sometimes perception lags reality, right? And the reality of it is the technology is ready, but customers are waiting to see other ones kind of lean in and prove it can work. And Hero MotoCorp, it's a strong validation that you can take a very large corporation that has many manufacturing facilities and move that into the cloud. And what I would really say is when I talk to customers, they don't question the benefits of the cloud or SaaS in terms of speed and collaboration.
But they ask me that question, is it secure? Is it reliable? Right? And then once they get past that, they say, well, can it truly scale? And am I going to get the performance that I got with on-premise? And again, I come back to what I said earlier. This deployment at Hero MotoCorp, it's a strong validation to other discrete manufacturers around the world that our technology, together with AWS, offers them secure, reliable, scalable, and performant capabilities so that they can focus more of their energy on their business as opposed to IT.
Fantastic. And also, that's a perfect seg ue to you, Ozgur. I mean, what do companies like big players like Hero have to gain from going all in native cloud?
So what we have seen, I mean, if you look at the PLM software as a whole, it's a mission-critical system for a company like Hero MotoCorp. I think it is at the heart of their operations, right? I think a couple of things come from AWS. We provide the most scalable, most available, most secure infrastructure out there. But particularly, I think how Siemens has architected Teamcenter X on AWS also provides huge advantages for customers. Like one thing that Siemens has done consistently well through our partnership is they almost always select native AWS services if it's available. So what does that do? It basically creates a much better and much more efficient operating environment. It lowers costs. When there's an upgrade, it's so much easier to do an upgrade.
The maintenance is so much easier because a lot of the unnecessary heavy lifting is actually sitting on the infrastructure. It's not sitting on the Siemens team. It's not sitting on the MotoCorp team. So I think that has been a big differentiator. And then the other thing we have seen is as that becomes available, I think ultimately it reduces cycle times, it reduces costs, and has a big impact on speed.
Excellent, excellent. So you kind of talked a bit about the IT environment and also that part. But Ram, what does this mean for the people actually doing the job, the engineers working with Teamcenter and that? How does that benefit them?
No, I'm excited to hear, by the way, the answers that I heard from both AWS and Siemens on this because I think it goes back to focus. I think you heard that from the earlier answers as well, which is we're going to focus on building motorcycles. That's where we want to put our effort in. That's where we want to put our energy in. We don't want to be procuring hardware. We don't want to be setting up networks. We don't want to be upgrading our software, and those are all things that are going to happen out of the box with the Teamcenter X platform.
Our ability to flexibly, elastically expand capacities when we need it, and when we don't, we sort of pull back. That just happens automatically in the back end with a platform like this. Our company is now getting larger and more geographically diverse. We have development centers in Munich, in Germany. We are opening up new development centers around the world.
Now we can do that in the blink of an eye. We suddenly have these set up and ready to go with the same level of performance that I would see back in India. We have security challenges that tend to come up all the time, and earlier, we had to take time off to go off and upgrade our infrastructure. We don't have to do that. It happens in the background. We count on our partners to be able to do that for us.
All of this will essentially mean more engineering time put towards the place where our revenue comes from, which is our vehicles, our motorcycles, our scooters. That's where we're going to spend a lot more time in, so very excited to have this platform live. Very excited to be one of the first partners to do it because if anything can give us a competitive advantage, if a platform like this can give us a little bit of an edge against my competition, we want to take it.
Excellent, excellent, excellent. That's a fantastic statement of what you said there, Ram. And Bob, what advice would you have for other organizations going in the SaaS business? It's not a new thing. I mean, now we have a huge one here with Hero. How does it work for companies smaller in the business?
Yeah, excellent question. I would go back to what Ram said as well as Ozgur is if you're considering what the benefits of SaaS and cloud technology is, yes, there's global collaboration. Yes, we can ramp up and deploy in different locations at a very rapid pace and sometimes days. But what does that really mean? It goes back to what Ram said. It allows you as the customer to focus on what makes you relevant or what helps you differentiate in your business. Every time you have to hire a resource to work on IT infrastructure or spend money on IT infrastructure, that's a dollar you can't spend on innovating products or innovating your manufacturing process. So number one is, I think you said that very well. Come back to what Ozgur said. I think this oftentimes gets overlooked. We're not experts in building two-wheel motors, right?
Neither is AWS. But we're very good at developing not just capability and software, but developing very robust capability. We're not an infrastructure provider. AWS is the best in the world. And if you think about the amount of money AWS spends on building out that infrastructure, that's way more than we could ever dream of. So really what Ram is saying is take advantage of your partners, leverage our strength, leverage AWS strength to allow yourself to focus on what makes you different.
And the key part there is not just technology, but it's, you said it very well, partners. So partnering with Siemens, partnering with AWS, you're partnering with companies that have been around for a while. We have the financial support. We have the stability. We have the domain knowledge and intellectual property to make sure that we can support you today. But we're going to be supporting you 10 years from today, 20 years from today. And that's what a partnership's really all about.
Yeah. So I think it's also very good. I mean, at the end of the day, it comes down to actually choosing the partners you want to work with. And that is, of course, extremely important. So Ozgur, what makes it so important for AWS and Siemens to team up? And what value can we bring? Bob touched upon it. We've been around for 178 years, or perhaps even more. I don't even know. But what would your input be on that?
So I think I'll answer it in two ways. The first part is the partnership goes back a number of years now. In 2022, Siemens decided to put the Xcelerator software portfolio on AWS. And that was a pivotal moment, I think, in their overall company strategy because it was part of Siemens' digital transformation moving into the software era. So when we look at it, it's not just the PLM software.
Ultimately, there are different elements of the Siemens Digital Industries Software that sits on AWS. And collectively, the impact for the industry is, I mean, obviously, it could be PLM in this case. It could be simulation software in another case. What we're focusing on collectively is how can we help the customers accelerate their digital transformation journeys? So they're actually focusing on the core business, on the innovation, and not these types of things. I think that's a big part. The other thing that I would like to say is, like for us, Siemens is a customer as well as a very important partner.
Like when I, in my role, when I look across the manufacturing industry, they're the most important partner in our go-to-market model. I mean, ultimately, they're the ones who are taking AWS to market. It's probably one way to put it. Because you have the expertise talking to the manufacturing customers out there. You have the presence. Like Bob was saying, I mean, the subject matter expertise sits there. Infrastructure sits on us. I think it's a perfect combination where we can deliver value to customers like we see here.
Yeah. And I mean, also what we need to remember is that we at Siemens, we are also a producer of goods. I mean, we're creating quite a lot of different products in the field of automation. So in one way, we're also drinking our own champagne. And I mean, we also want to make sure that everything that you guys are working with also works. And also we want to make sure that people who are involved in the design phase and the manufacturing phase are connected and making sure that everybody comes into play. That would be my transition to invite you, Kausalya, to the call. Why don't you give us a short introduction on who you are and what your role is in Hero MotoCorp?
Thanks, Magnus. Wonderful being here and it's amazing hearing some of the insights that were shared by the panel. I'm Kausalya, and I'm the Chief Business Officer for our Emerging Mobility Business Unit. One of the fundamental elements of being the largest two-wheeler manufacturers is also that we need to be in the forefront of innovation. A couple of years back, we decided to participate very meaningfully in the energy transition, and we launched a brand called VIDA. The idea is under VIDA, we are going to launch emerging mobility products which are driven by electric powertrains.
So very recently, we said we are two-wheelers, but we'll also be at the forefront of innovation. And we talked about it a little bit in an auto show last year where we also talked about products beyond two wheels and how the electric powertrain could now, in a very seamless manner, lend itself to consumers. So very privileged to be leading the business unit at Hero MotoCorp for the emerging mobility space
I think you're going to do a great job. But also in your job, you're going to be faced with a lot of challenges, I'm sure. I mean, you're going to have efficiency targets to meet. You need to reduce time to market. You want to make sure you're flexible because you talked about the platform before. All those things are coming into play. So I mean, you have quite a thing to move around there. In what way would access to, in this case, Teamcenter X, a SaaS solution add value to the work that you're doing?
So fundamentally, I think one of the reasons that Hero MotoCorp and VIDA are so successful is we're fairly obsessed with what the customer needs. And with today's day and age, we find product life cycles are actually shortening. Consumers are asking for more and way less time. So the idea was, how do we get products out in quick time? And they should be first-time right, the right quality, the right product features.
What we anticipate is now with this movement to cloud-based SaaS, we'll be able to accelerate our speed to market and give customers products which are tested not just with physical prototypes, but virtually as well, getting our chances to get a first-time right product with the highest levels of quality and performance out there in the market. I think that's what we are most bullish about because at the end of the day, as an organization, the customers who's driving value for us, and the idea is to get the kind of products that they fall in love with. That's our focus. That's what we want to spend time on. We're happy to have Siemens and AWS support us in this journey. You do the heavy lifting at the back end and let us take care of our customers at the front end.
How do you? Yeah, that's just amazing what you just said there. But how do you make sure that your products are the ones that your customers fall in love with? How do you listen to the market? That would be very interesting to find.
So I mean, it's a fairly evolved and quite established practice to be doing a lot of research. But one of the things that we've realized is that electric offers us, and today the world offers us connected products. So one is what the customer says and talks about when we meet them in these very structured research. But we love the fact that our vehicles talk back to us right now. So with connected, we have access to a lot of data that's being in the real-time usage space.
How do we take inputs from design teams, engineering teams, and customers and usage, bring that back into our product life cycle design and engineering to actually give customers the products that they want to use in a specific way? So from an electrification point of view, we're very privileged because we were born connected. And every product of ours is transmitting tons of data back to us and telling us how we could actually increase value to customers.
Amazing. So collecting data, analyzing it, and using it for a continuous optimization. I think that that is a great thing to do. I also happen to know that you guys are trying to expand globally. In what way would also SaaS be playing a role in that?
Yeah. So I think fundamentally, every product today has to be thinking global. So while we are an Indian manufacturer, we now have global ambitions. We are now present in 52 countries. So that's a very important part of who our future is. When we look at what we need to do, we have teams distributed across the globe. Ram talked a little bit about it in the panel. He mentioned that we have a design and R&D center at Munich, and we're expanding. We have suppliers across the world. So the idea is how do we create a unified digital thread that allows people to collaborate, suppliers to collaborate, partners, design teams to collaborate. So I think that's the fundamental element, but also then giving them the tools and resources to do rapid prototyping. We're also, as an organization, quite driven by the idea of being sustainable.
So one of the elements is we create a lot of physical prototypes before actual launch into the market because we need to test and validate. By creating this kind of infrastructure at the back end, it also kind of is in line with a very interesting goal of being carbon neutral. So we're able to kind of create digital twins, digital prototypes, and testing and validation teams, which allows us to minimize the number of times that we actually need to do physical testing. It doesn't eliminate it, but it supplements it and really, really complements our kind of goal on that front. So we're going to have the best of products created in the fastest possible time, first-time right, and be carbon neutral. I think it's like a win-win situation at all levels.
Bob, that sounds good, doesn't it?
That sounds great.
I mean, what can we add to that? I'm not even sure.
I mean, actually, that's the best commercial we could have. But I just want to comment on two things of a kind. Please do, please do. What you just heard is a great example when we talk about closed-loop manufacturing because you just talked about the fact going from design, having a digital twin, the design of manufacturing, but also having information about the performance of the vehicle while it's being used by your end customer and feeding that back. That's closed-loop. That's a perfect example. And then carbon neutral. A lot of people talk about sustainability. And I remember years ago, I used to argue, sustainability is never going to get legs and really move forward until people start looking at the business impact.
What you just talked about is the business impact of carbon neutral, which is how sustainability, I think, is going to thrive because sustainability is about doing more with less. You're not only doing more, you're having much better insights because you have a digital twin across that entire innovation process. So it's a great endorsement. So thank you.
Yeah. Ozgur, any thoughts on that too from your side?
I mean, I think hearing the globally distributed model, how you guys are scaling across the globe, I mean, this is exactly the way to go, building the infrastructure on something that's globally available, scalable, secure, digital twins. I think cloud-based development is something, I don't know if you guys are looking into it. You should definitely look into it. A lot of opportunities. But I'm super impressed about the trajectory that you have shown
Absolutely. And Ram, also COO, this is going to make you proud, right?
Of course. No, I think we are clearly stepping into a solution which is pointing us in the right direction. And for me, I think I mentioned this in my answer earlier. One of the key differentiating factors of this solution with the infrastructure from AWS is AI. I know everybody talks about it. We've started to see benefits come back to us with things like generative design, with topics like, are we able to find the same part which has already been designed, is sitting in my library somewhere? I'm able to grab that and bring that back to my designer and showcase it to them. Or am I able to now look across systems, not just this one silo, one particular model group that earlier had visibility only into what they were working on?
I'm getting visibility now to everything that existed for the 40 years that we've been in existence. All of that is extremely powerful, and AI is helping us make that happen. So we're very happy to have that enabler now part of our solution.
How did you start with the AI, the industrial AI approach? What would you say also for people out there listening?
How do you get in there? Because it's a big thing, right?
So as they say, there's a saying, right? I mean, how do you eat an elephant? One bite at a time. So we started off pretty much in a similar fashion where we said there's one use case. We said, we're going to make this particular use case for fasteners. We said, we're going to go in and make sure that I can find a fastener when I need it.
And then we started from there and started building out on it. But when we did that, the only, I would say, change we made was we said, we're not just going to do that one use case. We're going to train people to do it. We're going to give them the tools to do it so that it becomes more of a movement than just a single use case that we're implementing. And now it's got a life of its own. And with this platform, I do believe that the scale at which and the pace at which we're going to be able to do this is going to be fundamentally different. So very excited about that.
Excellent, excellent. Kausalya, anything on that also with AI? Probably also touched upon what you're doing, I guess.
Yeah, I think the canvas is just opening. When you read about what impact AI can have in lives and the kind of intrigue that we have internally in terms of how we could add value to consumers and customers using our product, we do believe that we want to make safe, dependable products. We want to help riders have an amazing experience on the product. So for us, we're kind of also looking at while AI can help us bring the best products in, it's also going to synthesize and get data in to make rider experience better.
So I think for us, the gamut is very open in terms of AI. But I think what we're most excited about is the fact that we know consumers want more in less time, and we have to get them right first time. So I think the fact that we can use AI to also help us as a co-pilot in many ways is also very exciting in this journey of emerging mobility for us.
Okay. So having the pilots guiding the engineers on their way, I mean, that's just the way you should be working, right, Bob?
Absolutely, absolutely. In fact, if I can. Please. A lot of people talk about AI, right? I mean, this whole show is AI. But it comes down to what you said, eating an elephant one bite at a time. There's three things you need for AI. You need data, right? You just mentioned you had decades of data. You need domain knowledge. And this is where partnerships comes in because domain knowledge and two wheelers, domain knowledge, infrastructure, domain knowledge in the innovation space. And lastly, you need to leverage the cloud. So this is, again, I come back to the partnership. Hero MotoCorp is in a perfect situation to really be able to not just take advantage, but accelerate the adoption of AI much faster than their competitors can because of this.
Excellent, excellent. Ozgur, any thoughts on that also?
I mean, maybe just to pick on what Ram said, I think the way you guys approach in terms of applying use cases is exactly the best practice we see across the several industries and different customers. You pick a use case, focus on business value. To scale that, then you need change management around it. It's all about organization and people, not much about technology. When you get the early believers, they actually start to do the work for you, and it becomes a movement, just like you said. So that's exactly the recipe we talk about and provide to our other customers as well.
Excellent, excellent, excellent. We are kind of running out of time. There's no panic just yet. So what do we just do around the closing? Like some final words from you, Bob, or a call to action or any insights that we might have missed? I don't think we missed out on anything. This session was extremely valuable. I learned so much about AWS. But what would you say is the thing to?
I would reinforce, I think, what you heard from all of us at one point, and that is taking advantage of the deployment in the cloud allows anybody, whether you're two men in a garage or a very large corporation like Hero MotoCorp operating in 52 countries, it allows you to put your resources, your limited resources, and your finances to focus on what makes you unique, what differentiates you in the business, while you take advantage of partners like AWS or Siemens to deliver the infrastructure and the capabilities you need. I think that's the number one takeaway I would get from this session, and you're a perfect example of what that means to a company the size of Hero MotoCorp. So thank you.
Yeah. Talking about size, Ozgur, is this also the cloud solutions are the only for the big ones?
No, not at all. I mean, that's the beauty of cloud solutions, right? Yeah. I mean, you could, just like we were talking about, if you want to scale all the way up or scale down, I think the elasticity of the cloud solutions makes a huge difference. And I'll just double down on what Bob was saying. I think the focus we have seen from you guys is exactly what we preach to all our customers. Focus on what you do best and focus on the partnerships that help you on this journey.
Excellent. Thank you very much, Ozgur. Ram?
We want to be miles ahead of the competition. So there's a lot we could do together that will allow us to offer things to our consumers which they've never seen before. So we're excited about the future possibilities of this partnership.
Excellent. And Kausalya?
I'm overall excited about the future of Hero MotoCorp and what VIDA will bring in. I'm so glad we have partners who could enable that journey because we are kind of obsessed with our consumer and what we could give them. We would be grateful for all partnerships that enable us to do better at the marketplace. Excited, like Ram said, for the future and what this partnership has in store.
It's fantastic. The enthusiasm and your openness to new technologies is just mind-blowing. I t has been a true pleasure having the four of you here talking with me at this event. For those of you out there looking at the camera, and that's probably the right one. This was Hero MotoCorp. This is the world's largest two-wheel manufacturer, and they have gone all in on SaaS solutions. If you want to find out more, please visit us on Siemens.com. Also, once again, thank you very much to all of you.
Thank you.
Thank you.
Thank you.
[Presentation]
Hello and welcome. My name is Magnus Edholm. And joining us today is Siemens' own Joe Bohman. And I'll tell you one thing. If there's anything you want to know about products, software solutions, Joe is the man to talk to. And I'll tell you, you're particularly good in the field of product development. So I'm going to ask you a pretty stupid question.
There are no stupid questions. Thank you very much.
So we'll try. So I'm going to ask you a non-stupid question. And that is, what is the current status of product development?
That's a very good question. Thank you for that question. And so the state of product development is the same as it's always been. People are interested in better, faster, cheaper. That's always what they've been interested in. What we've done at Siemens is we've invested in a strategy that's focused around a comprehensive digital twin and building our Xcelerator, our Xcelerator portfolio. Within that portfolio, we have the capability for design and manufacturing. We have a design center. We have a Simcenter. We have a Teamcenter. We have an Opcenter. So maybe you're getting a little bit of a pattern here of what we've put together. With these products, we're able to help our customers better, faster, cheaper.
Oh my God, that's really, really good. But you know what? I read a study the other day, and I have it written down here. I'm going to have to read to kind of get it all right. There was this global survey carried out with 1,300 companies by the company Splunk. H ere comes the thing. It showed that 55% of enterprise data is dark, as unknown. Only no more than 15% of the respondents are using AI to drive strategy, innovation, and customer experience. So is it just me, or is this a challenge, or perhaps is this an opportunity? What do you think about that?
I think it's both a challenge and an opportunity. I think that data like that's probably not a surprise to a lot of people that are listening to us because I think a lot of people experience this problem. It's a challenge because, of course, if you have data and you're not able to access it, you know that's a problem. It's a tremendous opportunity. A nd if I were to look at the state of the industry today, I think this ability to pull together those dark data, disparate data, that's really the big wave that's happening right now. A lot of people talk about AI, that AI wave is being powered by data. That's the big news.
Okay. So that is the solution. But how do you get there? How do you, well, first of all, why does so much dark data go dark in the first place? What's the reason for that?
It happens because of fragmented systems. When I talk to a CIO, a CIO will typically have maybe 1,000 different systems that they're looking after. And so if you think about that, that's an incredible challenge with so many different systems. And if you look in departments, in engineering departments, manufacturing departments, you know different groups have picked different systems.
That's a lot for people to keep track of and to keep that all together. And so the reason that that data is going dark in the first place is because you have in organizations, you have lots of different stakeholders. They bring in lots of different systems. And then you end up with this patchwork, and you end up with this dark data. And you end up with teams that can't work together because of these data silos, maybe information where they don't even know that they have this data. A lot of Sneakernet. Have you heard of Sneakernet?
I have not heard about Sneakernet. What's that?
Sneakernet is this idea of not sneaker. Sneakernet is taking data on a thumb drive and carrying it around. Right? And so you know this is. You say, wow, is that really happening in this day and age? And the answer is yes, it is. And so this is the environment that's out there, and it's a tremendous opportunity.
So how do you tackle that? I mean, when I'm listening to you, what it comes down to in my mind is that you're going to have a single source of truth, make all systems kind of work together, have data flowing from or between all the stakeholders, from the design, the planners, the automation engineers, and so on and so forth. That's how I see it. And how do you go about that?
Yeah. And so I think everything starts with having a really great data strategy. And so really understanding how to put all of your data together. And typically, when you think about a data strategy, you think about it in big buckets. You think about PLM data. You think about ERP data. You think about CRM dat a. And so really, it starts with, and particularly a lot of what we focus on, and I'll talk a little bit about how we focus across those three big buckets, but getting that PLM data under control. And that's where Teamcenter really comes in.
Teamcenter is the number one PLM product on the planet. It's used by almost everyone. And you can bring together, it's an open platform where you can bring in engineering data from literally hundreds of engineering schools, bring that, and all those data silos that I was talking about, single source of truth, as you mentioned, you can bring all that data together in Teamcenter. It's the most scalable system on the planet. Bring that all together and get that under control. And so that's one big set of work that a company can do.
Yeah. Obviously, you know everybody talks abo ut AI.
Are people talking about AI?
I thought they did.
I don't hear. I think you're right.
Yeah, yeah. So it's going to be a big part of the future of product development, correct? And how would you say that we as Siemens are making good use of AI in the way that we are developing solutions for the market?
Sure. Great question, and so we're thinking about AI in three different parts. The first thing that we're doing is the tools that I mentioned at the beginning, we're adding AI there so that if you're in design center and you're designing a product, we're bringing AI copilots to you right where you are and helping you with that design. So if you're thinking about designing any product, a car, an airplane, and so forth, we're bringing the AI there.
And we call that engineering AI. A lot of great stuff there with design space exploration. Instead of looking at one variation of the part, look at 1,000. Use AI to help you with that. The second big thing is this idea of AI fabric. And this is the idea of, you remember earlier I mentioned PLM, ERP, and CRM. People struggle to bring those together. And with AI fabric, we're able to bring those together and do workflows across all of those domains. And the last one, agents, digital thread agents. And we'll talk, I think, coming up a little bit about digital threads. But digital thread agents, so exciting, this idea of being able to use agents in AI to help taking routine tasks, just whipping right through those and making those happen.
So if I said that for the people out there watching this, when we heard about the digital twin, and now you say the digital thread, my way of explaining a digital thread in a very simple way is that it's kind of like a subway system.
Yes.
You know where you are. You know where you're going to go. And that's the same with those digital threads. Y ou can go for accelerated product development, for smarter manufacturing, and so on and so forth. There's several threads out there. Now, you're bringing in here the digital thread agents, right? What is that all about?
Yeah. Well, let's just start with a digital thread. Let's pick one. You mentioned a few. I'll pick smart manufacturing.
Good choice.
Thank you.
And so, if we take smart manufacturing, what a digital thread agent can actually do for you, if you think about smart manufacturing, where does it start?
It starts with an engineering change. And then you take that engineering change down into manufacturing, and you make a change down on the manufacturing line. What a digital thread will do for you is an agent will actually look at that change, look at the engineering change, analyze it, and actually execute the steps necessary to take that engineering change and drive that down into manufacturing.
That's a great example of a digital thread agent. So, me, growing up as an application engineer in the field of manufacturing, I have done numerous simulations of material flow, et cetera. So would you say that a digital thread would kind of go in and assist me?
A digital thread agent would kind of go in and assist me and say, "This is the way you need to do it if you optimize the storage area. You have a congestion in this aisle or something." Absolutely. It will come in, and it will help, and it will assist you. It will help show you what the constraints are that you have to work your way through. And then actually, it will drive the process for you and push it through.
So the customer value in this would be then that it would be faster. You'll be using your resources more efficiently. Are there any other ones that come to mind when you?
Better, faster, cheaper. And so I always go back to better, faster, cheaper. And so obviously, I think it's pretty clear how the digital thread agents help you go faster.
And so I think everybody's looking to streamline their task and be more efficient. Cheaper, there are things in terms of there's just simply less labor that you need to use because the agents are doing the work for you. And better because you can actually look at a lot more alternatives. And so instead of looking at maybe one design or five designs or for a line, thinking about it, one layout, you can look at multiple layouts. Better, faster, cheaper.
Better, faster. It almost sounds like a wrap. It kind of sounds like that. I think I heard it once or twice. Better, faster, cheaper. Better, faster, cheaper. Yeah, we can talk about that later. Excuse me. And in all of this, cloud plays an important role, I guess, for scalability, et cetera. Can you talk a little bit about the partnership between Siemens and AWS?
Super. Sure. I 'll talk. First of all, let me talk a little bit about cloud, and then let me talk about the partnership. So for the cloud, I often say my job is very simple. I mentioned a few products like NX, Simcenter, Teamcenter, and Opcenter. I always say our boss, my boss, our CEO, just gave me a very simple job. He asked me simply to put an X on all those products. So my job is very simple. And when you see that X, that means it's a full SaaS version of those products. And so we're offering the Xcelerator portfolio as a service with the X products. And so for our customers, this has a lot of value. They all have built-in data management. They all have modern collaboration features. They have this crazy cool AI that we're talking about and world-class operations.
And then how does AWS come into play here?
AWS is our premier partner for everything that we're doing with accelerators as a service and our Xcelerator products. So all of everything that we do is offered on the AWS platform. And we fully leverage all of those capabilities that are there. And AWS, let me just say, has been a tremendous partner in terms of helping us optimize this whole portfolio to work in a fantastic way.
Excellent. That's really, really good. We're kind of not running out of time just yet. So here's the thing. I've been talking with a lot of people. And sometimes, or every so often, a three-letter acronym comes up, CIA. I understand this has nothing to do with CIA. What does it actually stand for, and what does it mean for the people out there?
Super. Great question. And so, for CIA, comprehensive, intelligent, adaptive.
Let's go through those one by one. Yes. And so I started with our strategy of comprehensive digital twin. So what's the comprehensive? What does that mean? And so a lot of companies will talk about a digital twin, maybe if they can simulate a fluid or if they can model a particular line. When we talk about the comprehensive digital twin, what we're saying is that we can go into a company, and we can model end-to-end the whole thing. That's comprehensive. We've actually spent, and I don't know if the audience is aware of this, is we spent over EUR 25 billion putting together this portfolio. So that's another way to measure comprehensive. And I firmly believe that we have the most comprehensive digital twin on the planet. That's the C.
AI, intelligent, the things I talked about relative to AI, the engineering AI, the AI fabric, and of course, our digital thread agents. Then adaptive. So many customers, if you think about change, change is so constant, our ability to adapt their operations. If you think about changes that you have in your forecasting that says you have to change your factory, how quickly can you do that?
That's adaptive, AI. AI. Now we have the first one was cheaper, faster. Better, cheaper, faster. Better, cheaper, faster. Now we have. I've got to take my notes here.
Yeah. So that is comprehensive, intelligent, adaptive. Very, very cool. At the end of the day, we have not too much time left. To summarize, and you correct me if I am wrong, making sure that you have data under control. You've got to have it most likely in a single source, whether you're going to have ERP systems, CRM systems, and so on and so forth. Making sure data flows between the different stakeholders. Use that data. Make sure that the people who are involved in the process get access to that data at the right moment in time to build the best product and create the most effective production. For that, we're using the digital twin, which has a multitude of facets and the digital threads. And the next thing is the digital thread agents. If anyone out there wants to know more about this, what should they do? Should they give you a call, or what would you say?
No, I think Siemens is everywhere. And so please look up your Siemens rep, and they'll be happy to talk to you about it. There you go.
That's fantastic. Joe, it is always a pleasure having you.
Magnus, can I just say it's always a super pleasure to be able to talk together?
No, it's fantastic. And thank you very much for taking the time. I know you're a very busy man, and we appreciate it very much. And it's, as always, I said, a pleasure to have you. So that would be. I'm looking at that camera. And that is Joe Bowman, our Executive Vice President for Products, Software Products. And as I said in the beginning, if you want to know anything about software solutions that we have at Siemens and also the collaboration with AWS, Joe is the man. So thank you very much for having you.
Magnus, it's been my super pleasure. Thank you.
[Presentation]
Welcome. I am Magnus Edholm, and with me today, I have Jason Hiner, the editor-in-chief of The Deep View. You're basically writing a lot about AI. Before we start digging into that, though, Jason, give us the background. I mean, I'm reading here. You've been doing a lot of stuff.
Yeah. So I've covered technology for the past couple of decades, especially focused on enterprise tech. So especially focused on business decision-makers, professionals. I've worked for multiple publications. Up until recently, I worked for ZDNet. So I was the editor-in-chief of ZDNet for the past four years and really focusing on AI, especially the last three years at ZDNet. But I've been covering AI for over a decade. And then now I just recently started at The Deep View, where I focus on AI exclusively. So I went to The Deep View to cover AI every day because there's so much happening. It's such a paradigm shift, as Siemens talks about a lot as well.
And so now I get to cover it every day and also build a next-generation media company for the ways we can connect with audiences in the future because it looks a lot different than it looked even five years ago. The ways that we connect with audiences is so much more sophisticated and interesting. And so I'm working on building a media company focused on that.
Okay. Okay. Okay. So I know we spoke before that we went online that you began talking AI back in 2013. I got to tell you, I didn't even know there was a thing called AI back in then.
I know. Right. So in 2013, we talked about it in terms of AI, deep learning, automation, especially in the enterprise. And so we did a special feature, what we called it at ZDNet at the time, that brought people together, writers together from across the globe, from Asia, from Europe, from North America. And we wrote about the ways businesses were using automation and machine learning and these things to bring efficiencies, to drive efficiencies, and to really unlock innovation, unlock data, all of those kinds of things. Now, that was the seed or the germ of the AI that we're in now, which is so much more capable.
Right. Right. Right. So if you go back to 2013, we make a journey back in time. Yeah. And then cut to 13 years later. What has been like, give it the three biggest differences from back then. I mean, you had the mindset, obviously, technology, but what you said, those are the key things that you would like to talk about.
Yeah. So with generative AI, so much more has been unlocked over the last three years. The AIs are much more capable of operating independently is one way to think about it. And that's where sort of the number two comes in, which is AI agents. This outgrowth of generative AI, which is AI that can now create things, create scenarios, create content, create all kinds of things. Now there's AI agents where not only that, the AI can operate more independently. Well, that's a whole nother realm of possibilities.
And then the third is really this concept that Siemens knows well and is at the forefront of, which is industrial AI and enterprise AI, where we're still seeing consumers just starting to get a taste of generative AI and this new AI boom. But businesses are full-on speeding down the tracks. They want to take these new solutions, generative AI and AI agents, to solve business problems in ways that weren't possible. And I loved how Siemens CEO Roland put this in context this week, which was talking about that when we built software, even the software-defined enterprise, we had to define a lot of things for the software. And now the software is much more flexible. It can fill in a lot of gaps, and that unlocks a lot more value.
Oh, yeah. Totally. Totally. I mean, what you're talking about there is also what I kind of burn for. I mean, you have the software-defined automation, which is required to build software-defined products. Then you have, in our case, the very comprehensive digital twin, which has so many different facets. If you're a product designer or you're a simulation expert or a manufacturing planner, and then you have the digital twin, all those three coming together. So there's surely a lot of things happening in there. A lot of terms that perhaps not everybody's aware of.
But since you've been around for such a long time and you've been in this game, if someone wants to start working with industrial AI, which is somewhat different than the AI that you would use when you are looking for a recipe or using it for as a search engine, I mean, there's kind of a lot of big differences in there. What would you say is the best way to get up to speed as an industrial company based on your experience? I m ean, you've first seen a lot of companies
For sure. So this is one of the things that the generative AI movement boom has unlocked as well is that companies are now telling their stories a lot more. You can use generative AI to take a PowerPoint or a set of bullets and all that, and it can help you create content. So companies like Siemens, like AWS, like others are putting out a lot more white papers. They're putting out a lot more content on how do you use these things in specific industries. How do you take not just large language models, which power generative AI, but how do you do small language models?
Siemens has a number of very specific small language models for specific industries, for specific use cases. So if you're going to get started on this, I'd say look into some of those white papers and some of those case studies on what's it looking like in your industry, whether it's manufacturing or sciences or any of these things. There is so much out there. Even just in the last two years, I've seen so much more good content created around those case studies and really those scenarios and specific domain-specific language models.
Yeah. And also, now we are at the CES. I mean, it shouldn't be a secret to anyone. There's a lot of announcements going on. What do you see AI taking us in, say, the next 12 years? And when we get back here next year, perhaps we have a chance to sit down. What do you think is going to happen in the next year and perhaps also one year in that?
I believe, personally, we're moving so fast that we'd be curious to find out if you have an answer to that and how you managed to stay ahead of the pack. Well, I think that the biggest thing now, like I said, companies are starting to use these things, this new AI boom. Companies are starting to try these things. They're implementing proof-of-concept projects. But what they found, one of the unfiltered truths about AI is that it's very expensive and it's hard to get to ROI. So what I've seen is a lot of these companies really gearing down on, "Okay, now that we've learned how to use this, how can we make it less expensive?
How can we run these models in ways, maybe use a smaller model, use a more domain-specific model, use older hardware or use less powerful hardware in order to save money and even, in some cases, increase performance and really get to ROI?" So I think you're going to see in 2026 and 2027, especially now, a lot of these proof-of-concept projects are going to move to, "Okay, now we've learned how to do these, and maybe we trained it on one of the big general models. Now we're going to try to find the specific models that will let us do this in a more profitable way, a more efficient way, and in some cases, with better performance and better options, better knowledge and data sets too.
Yeah. And it's almost like one is forgetting that there are actually other technologies out there than just AI. And I swear to God, you probably have an eye on that as well, don't you? You're looking at it?
Yeah. Of course.
Is there anything that kind of stands out in particular from your point of view?
There are some that intersect AI, but are maybe related, but a little bit on the edges. And so I think one of them is, and I'm wearing a pair right now, smart glasses. So smart glasses, what does it look like when you have an LLM sort of at your beck and call right on your face, where it can help look at the world and evaluate, where it can bring the opportunity to ask questions of what you're seeing and multimodal or what yo u're hearing
So I think that's one of the big questions this year is, are consumers going to really be into this idea of, can we bring AI more into our everyday life? And we'll see. Does AI glasses become something that more people want to be using? The other is, and it's not a secret, and we've seen it for years and it's been promised for years, but really self-driving cars and autonomous is becoming, it's really the rubber meets the road, no pun intended, has started to meet road.
In the U.S., you see Waymo increasing to a lot more places, and others in Europe and Asia, you also see more of them being deployed. Tesla and Waymo are doing a lot more in the U.S. and have plans to roll it out to a lot more cities. I think that's where consumers are going to start to interact with AI in more everyday ways. They're not even going to think about it as AI. This is just a way to do something in a more efficient way and probably a less expensive way as well, and that's what will likely unlock some of that value, although some people also see it for safety and for other reasons too, so that's going to be really interesting. We've heard the promise for a long time, but I think it gets a lot more real this year.
You know what? I think that with driving is really, really interesting because I think that in a not-too-distant future, perhaps people are going to ask, "Why on earth did we let people drive themselves?"
Yeah. Right.
I mean, now you have everything under control and some of the cars that are out there, you mean you keep track of the people, the cars around you. So it is probably also bringing a lot more safety to the game. And I mean, that's one thing to have the smart product, et cetera. But there's also infrastructure where AI can be of use. Agentic AI, we talked about that. Which one of generative, agentic, and infrastructure AI do you think has the biggest growth potential?
Yeah. Agentic has to, I think, because we're still at the very beginning. It's very, very nascent. Once agentic starts to get use cases where you can say, "I can do this. I can give it my goals, my parameters, the outcomes that I want, and I can start applying it to that and let it do things on my behalf," that's going to be really addicting, I think, for a lot of companies. It was interesting. One of the things that Siemens, that Roland, CEO from Siemens talked about was that that's going to help a number of companies that they work with in manufacturing. They have a real challenge with staffing. They can't find enough people to staff all of the opportunities that they have.
They're going to use Agentic AI to help fill some of the gaps, to help some of the workers that they have work on higher-level things and be able to have some assistance that can do some of the stuff that's more automated, that they can automate and give them, yeah, better jobs, essentially. Let them do higher-level, more important, more valuable work that only a human can do.
Yeah. I mean, that's probably a global challenge. We know that those are the things that the companies are struggling with today. I think Agentic AI, probably when it comes to that point, will be something supportive to the industries. I think it's crucial that we actually have that stuff. If anyone's out there listening to what we're talking about, how would you or what would you say to them? What they should listen to? Is it a podcast? What should they read? How can they keep up to speed to be on? I mean, you are moving fast. I mean, we talked about it. You've been around for a couple of decades doing this. What would you be like, "This is what you need to do. Study up on that."
Yeah. It moves very fast, and it's very challenging to keep up. We live in an era of abundant information, and there's a lot of conflicting signals. There's a lot of information. There's a lot of confusion. There's a lot of information where you see one source say this and another source say that. So I'm going to, this is a little selfish, of course, but I'm going to say The Deep View. We have a newsletter that goes out every day.
We talk about the top three stories that you need to know in AI. This is really aimed especially at professionals and people working to put AI to work. But we do that every day. And our view is that there's an audience that's getting a lot of conflicting signals. They're getting a lot of information overload. And so we try to boil it down to, "Here's the stuff that you need to know for today, the most important stories, the stuff that you don't want to miss, you don't want to ignore." So we do that.
We also have a podcast called The Deep View Conversations, where we bring on experts in the field and talk to them about the things that they're working on, the insights that they have and that they're learning. And so we try to share that learning, disseminate that learning in the world with all of the smartest people who are learning a lot of really important and really great insights about AI. I'm going to make sure that when I get on my plane home, I'm going to download and I'm going to listen to that podcast.
I love it. Jason, it has been really good to have you here. It's also impressive that you've been working on this topic for such a long time. And I feel we could talk much more about this. And also, for those of you out there, make sure that you perhaps follow you on the social media. You can find out for the newsletter and listen to the podcast because at the end of the day, it's all about learning, and we got to be part of the gig.
AI is not something that's coming as we just learned. It's something that's here. It's industrial AI with all the different types of variants that you have. And it's going to be a huge support for the industry as we move forward.
True. Very good. I should have said subscribe thedeepview.com is where you can get a hold of
the deepview.com. Go there and subscribe.
Very good.
Jason, thank you very much.
Thank you. Appreciate it. Thanks for having me.
Thanks for having me here at the fair. And hope to see you around, perhaps in 13 years.
We'll see you before 13 years.
Yeah. Yeah. Might be retired by then. All right, folks. So this is Magnus now signing off. And I'm going to see if my studio manager is up to speed, and she is not.
[Presentation]
Welcome to our session, The Triangle of Innovation, here today with the startup Haddy, the technology foundation Siemens, and the visionary end user Disney. My name is Sebastian Wolf, and I'm very pleased to host the session today. As you know, we're in the middle of one of the biggest transformations that industry and probably the world has seen. It affects every one of us. It touches every aspect of our lives, and industry disruptors are at the heartbeat or are the heartbeat that is driving this transformation.
To provide home to the best industry disruptors, Siemens continues to build a strong and vital industrial ecosystem. But as you all know, we cannot do this by ourselves. So therefore, we need the commitment of our customers, of our partners, and the sellers. Today, we will talk about an extraordinary use case, an example of how a dream turned into a vision and finally into reality. I'm very happy to welcome our guests who started with their individual dreams, then joined forces, and leveraged the power of the ecosystem to succeed. Jay, let's start with you. So you are the founder and the CEO of Haddy. Just give me a little bit of an idea of what Haddy is all about. And then I heard an interesting story about your first meeting with Markus. Maybe you can share some insights there as well.
I think that for us, Haddy is a 3D printing world builder. We started in the furniture industry because we knew that they were simple things to make. But we print things that are sort of the size of a shoebox up to things that are as big as a train car. We print primarily in polymer composites of many different varieties. Now we're growing to print more and more things, from boats to lighting systems to decorative things. We're going to talk about our strongest customer engagement now today. That's with the Disney Corporation. So that's Haddy in a nutshell. Then let's see. The first time I met Markus, remind me when that was.
I think it was on Formnext a couple of years back, right?
I think that might have been, or maybe we met in a different era, in a different time portal, because I think Markus and I were destined to know each other across space and time, so I think that for us, we have been 3D printing for my team for a long time, and we started 3D printing cars in my previous company, and then at Formn ext, which has been a lot of th e world meeting associated with 3D printing machines and technologies, was, I think, the first time that we came together.
Absolutely, yes. I remember that one.
Yeah. It really sounds like a fun moment, at least from what I hear in this very short summary. Markus, so you're the manager for additive manufacturing business development at Siemens, quite a lengthy title, I would say. What do you remember from your meeting with Jay?
Yeah, as I said, it was on Formnext. Formnext is one of the world's leading 3D printing trade shows. I remember Jay coming to our booth and asking a few questions about 3D printing, and then got more specific about large format additive manufacturing, which is an area of my expertise, I would say. What followed that was that we had a very lengthy discussion, got into very deep technical topics. I got to know what Jay is about to do and was very exciting, obviously. What turned out is that Jay is collaborating with a company called CEAD.
They are also kind of slowly growing up startup based in the Netherlands. They're also a customer of mine. We had this connection there. Jay was collaborating with CEAD. Siemens is collaborating with CEAD. So there was already, yeah, different parties coming together here. So kind of like a small ecosystem forming, so to say.
Yeah. Okay, talking about the ecosystem, because this is what I really liked. And when I spoke to you yesterday about Jay, you got really excited about this collaboration, which I really liked. So ecosystem, Linda, I look at you. So you're the Senior Vice President for the Siemens Xcelerator Marketplace and the ecosystem. So this must really be music in your ears, I guess.
Yeah. And as you are talking about motions and music, yeah, actually, this is really music to my ears, hearing about the small ecosystem which is coming together. So at Siemens Xcelerator, we always said we want to bring companies together. Everybody should have an equal share of success in there, which is the partner, which is the customer, and which is us as Siemens. We are working with those companies in motions, in the build motion, in the service motion, and in the sell motion. When we talk about the build motion, this is actually where Haddy comes into play.
So Haddy helps us also in the additive manufacturing to shape our products even more because you are the expert here. So we need you to shape our software and our hardware in order to make it competitive in the additive manufacturing space. So this is helpful fo r us. For you, I think it's helpful that we help you increase reach and go global.
So this already sounds really exciting. And now I'm getting really excited because I'm really happy to introduce Nick. Nick has an interesting title too, and we discussed this a little earlier. Nick is the Executive Imagineer in the R&D technology and engineering department at Disney. Yes, of course, Disney. I'm especially excited because Disney has always been a company to my heart since I was a child because it always let me dream. What I really liked about Disney too, it was always a company that also made dreams come true. I visited the park in Paris, for instance, and I really loved it. It is something that really touches me, and I like having Nick here on this discussion with us today. Nick, tell me, what are the applications that you're currently working on, and what has H addy to do with
Yeah, absolutely. To be clear, Disney formed my dreams too. It's a very fortunate role to be a part of making the magic. We are working with Haddy on quite a few things today. Over the past year or so, we've actually started to experiment with using large format 3D printing in our parks in a few ways that have actually ended up in front of guests. One is props for a stage activation. We actually made a prop Lorcana staff from the Lorcana card game. We also actually made a prop for the Jungle Cruise. A boat, not the boat the guests ride in, but a boat that the boats actually go by. It's a beautiful prop. It actually replaced one that sank in the last year or so, and it had been there in about 40 years of services.
We were able to replace it very quickly and bring the show back to its original life in a really beautiful way. We don't want to stop there. There's a lot of ways in which we can work with Haddy to build durable, beautiful props that end up in front of our guests to achieve our number one goal of getting as much show and as many props in front of our guests as possible, to have all of that layered detail. This is another really important tool in our toolkit to do that as we build future lands and parks.
Okay. I like the introduction a lot. This gave me already some good context here. Let's get a little bit into the details about the collaboration, I would say. First question to you, Nick. Disney Imagineering has the highest standards in the world. This is what I was told. What was the specific reputation that you heard about Haddy that made you reach out to them?
Yeah. So it started with I knew at the time that I met Jay that Haddy was considered to be the foremost large format 3D printing company in the world. They were building unique large format objects. They were safe. They were durable. And they were being delivered on time and effectively. But to your point, nothing is enough. We want everything for our guests and more. So the real special sauce about Haddy is that they were flexible. They were willing to learn and work alongside us. As a part of research and development, our job in Imagineering is to actually push the limits of what we can do and make things more and more realistic, more and more true to the story world.
And Haddy is actually willing to work alongside us to test new things that have never been printed, new shapes that have never been printed, new durability that needs to be in our parks for 50 to 100 years at a time and in front of guests every day in a safe, beautiful way. And Haddy actually had the attitude and the know-how to get into it with us and to actually explore those applications. And that's what we really love about working with Haddy and with Jay specifically.
So what you just described sounds to me a little bit like hard problems, if I may say.
Jay, when I look at you again, when you were first challenged by Disney about these hard problems, how did your team's background, what you mentioned earlier in the car industry, help you to put on your engineering hat again and meet their safety and simulation requirements?
I do think you said at the beginning, Sebastian, that everything starts with a dream. The dream for me was that you could make amazing things happen in a digital framework where you could take a digital design. You didn't have a human in the loop. You didn't have paper instructions that were taken out. You really wanted to be able to take that all the way through. That was the dream. We had been doing it in a really difficult segment, my co-founder and I, in the vehicle world, but not everything was digitized.
So the dream for us was to find a place where we could build things that could be end-to-end digitized. So in a sense, we reached out to change the object so that we could achieve this dream. And we started making furniture. As Nick kind of said, when we met, we were making large format things for a really big retailer. And it had to look beautiful. They were very exacting. But it didn't necessarily have to be safe for people to sit on because most people weren't or jump on or other things like that. So we wanted to take it to the next level. And we were attracted by working with Disney because of their demands to want to do that.
I mean, our corporate value of innovation, which is one of our six corporate values, is really about taking hard problems and finding innovative solutions to do it. Even though we're using the same equipment almost every day to do it, we're always finding innovative ways of applying it. So that really attracted us to working with a customer like Disney. And I also would want to add that I think maybe you had made the statement, or maybe you had made the statement that we are an expert in something. I actually think that it is true of everyone in this chain that everyone is an expert enough to know to ask a question about what they want. And then all of us are willing and humble enough to listen to what the other one knows in order to be able to get there. And I love that.
And I love that about working with Siemens. I love that about working with Disney is that there is a common desire to get it right and a respect for like, "Oh, I don't know the answer to that. How am I going to figure it out?" And I think our team feels that way every day we show up to work.
That's exactly right. And to kind of tack on to that a little, one of the things that's been great about working with Haddy as Disney, is that Jay has a deep respect for our artists. And so we are able to work together to develop the core objects, say the infrastructure or the shape of the object.
But then when it's time to have that human artist touch that is most appreciated, Haddy's willing to step aside and let those artists step in and actually critique what has been done, make those edits, and then make those finishing touches themselves, so we still have that human flair that's augmented by the power of what Haddy's doing
All right, cool. Coming back to these hard problems, Markus, real quick, over to you. I guess with these hard problems, challenging questions come along, right, and so what made you start working with Haddy and working with Jay and answering these challenging questions, and how do we generally approach working with startups like Haddy?
So I think what I really like, I wouldn't call them problems. I would rather say there were challenges, and I think we at Siemens, as all of us here, like to tackle challenges. And I think we had some very good answers to those questions. Sometimes we still needed to figure things out. But I think from the conversation I had at Formnext with Jay, I really had the feeling that there are answers in our portfolio how we could address this issue or this challenge and the other one to make those things work.
And to the second part of your questions, how do we generally work with startups? I would say we have a program called Siemens for Startups, which works on several pillars. And Haddy basically is a perfect example for one of those pillars where we help the startups to get access to our portfolio equipment in a very low barrier and help them use that technology, get access to it. And yeah, then we can grow together with those startups.
And another thing t hat I think works very well with Haddy is to jointly spread the word, to have a kind of joint go-to-market marketing approach where we not only do fantastic things, but also talk about it and give the startups the visibility that the platform of Siemens and Siemens benefits then in a way that we say we have a perfect example of how to apply our portfolio in a very innovative way.
Portfolio is an interesting aspect I would like to cover with you for another moment, Markus. So you have called Siemens technology the nervous system of the Haddy factory. How does using Siemens solution allow Haddy to iterate and document parts so Disney can use them near their customers?
Yeah, that's a good question. So for everyone who hasn't seen such a factory yet, you have to imagine it's kind of a warehouse full of industrial robots. So you have robots. They are sitting on a rail. And those robots need to be controlled. So all those robots are wired up. They have a cabinet, which is kind of the brain. And that cabinet is fully driven by Siemens automation equipment. So Siemens' Sinamics motion controller controls the motion of the robot in a very precise way.
But not only that, you also have an entire digital thread, which allows the engineers at Haddy to design the objects, but then also to slice them and to define the toolpath, which the robots need to follow. So all that ends up in an end-to-end digital workflow, so to say. And that is what Haddy is leveraging here. I think that's maybe an aspect Jay can elaborate on as well. But the microfactory concept allows Haddy to be very close to the consumption market, to their customers, to bring the objects there, I would say.
Okay. So what I really like about this conversation already is I hear Jay is doing that. Nick is doing that. Markus is doing that. So there is a lot of individual power. But when it all comes together, I think we even allow a superpower to evolve, right? And this brings me back to this ecosystem topic. Linda, Siemens is a super massive corporation, right? And when I always hear about Siemens Xcelerator, the ecosystem that has evolved around that, I always question myself, how do we act as an enabler for small-sized companies like Haddy, at least at this point in time, to move fast, right? How can we help them scale systematically for customers like Disney
Yeah. How you say it, it feels a bit like we are working at Dinosauria. But yes, I think we also learn a bit in this corporate environment that we need to be open, like you said it, Jay. We need to accept that we cannot do everything on our own, that we need to collaborate with other companies in order to help our customers to digitally transform and scale and be successful. And this is what we've learned by heart since a couple of years now already. And we also know that we cannot just rely on companies like AWS, but that we also want to work with startups which are helping us to be, again, like a startup ourselves, that we are working more agile and more flexible with you.
So, the most important fact in being a partner of choice for us and being within this ecosystem is that you are bringing something to the table that is super valuable for the customer. It is about the goal which we want to achieve together. So we don't want to throw solutions over the fence to the customers, which doesn't add any value. So this is the first critical moment that we have people in the company, like Markus, who believe strongly in the solutions of Haddy, that we want to work with you. So and then we have this, like you said, Siemens for Startups program, where we are lowering the barriers, having a discount concept on how you get to our products that you can scale globally with that. I think super important for Haddy, as we said.
And then it's important that we are working along a digital thread together, so that we build a story together and see on what kind of components we bring to the table and what kind of components does the startup bring to the table, that we all together go to the companies of these words and how we help them. And I think this is the key to success, that we work with startups, like with all other companies. We've always worked since decades together, that we take you seriously on an eye level and want to really work with you and want to create together success.
And this is how we do it, also with other companies out there, also small and mid-sized companies and larger scale companies, along a digital thread, common use cases in an industry that we all play together and position towards the customer. S o this is actually how we do it. Pretty simple.
That's the way how we do it. Sounds like a strategic pillar for Siemens, right?
It absolutely is.
How can other startups benefit from that? So how are you building this ecosystem so everyone can participate and leverage from it?
Yeah, actually, like we said, so for startups, it's the easiest thing to apply for the Siemens for Startups program. So this is the first entry point. And then we have people working with you hand in hand to figure out on what is your value proposition. On the other hand, we are working, we're bringing you to the right people within the company. Because sometimes we got the feedback from partners that, yeah, I'm working with Siemens since many, many years, but it doesn't scale.
So yeah, of course, if you had someone working in a small town in Germany with you all along the last years, yeah, maybe it's tough then to scale with us. Yeah. And this is why we structured this very much, having one entry point for those startups so that we are coordinating this common journey. So this is what we are doing here. And once we are saying, yeah, this is enough in this program, we establish a strong foundation now in this collaboration, then we go in different motions. Like I said in the beginning, we have to build motions where we are working out on what common portfolio do we have and what common value proposition do we have.
And then we go over into the go-to-market motions, like the service and the sell motion, that we are having common marketing concepts, common go-to-market concepts, having our salespeople also selling portfolio elements of our other companies. So this is how we could do it for almost every startup, which brings value to the customer.
Great. I'm always impressed, honestly, by what an ecosystem can do to those people who contribute and leverage from it. So this really excites me. Jay, the next one is for you, because I assume right there is plenty of other challenges, and maybe one of those being the financials, right? So is there anything that you can share how you dealt with it when you set up your startup? And how is the ecosystem basically helping you to kind of perform what you want to achieve?
I think it's a great question. I mean, we play in this game of business. And it really is a game. It's like it's a chess game. You have all of these different inputs. And the output that you're measured by is usually employee retention and profit and stakeholder engagement and growth and other things like that. And these are a fairly common set of metrics. But the inputs are really complex. And so you'll excuse for a second, one of the inputs is my earpiece. But the inputs are really complex because the biggest complexity is customer change. And customers constantly are asking for new things. In fact, the various shows like CES are where they learn new things, and then they ask for them.
And so when we think about it, the person that's not in this discussion is all the park goers, all the guests that go and take in the Disney magic. And so we are all here, kind of like upstream, midstream, close stream, and then downstream is like what the child at Disney is experiencing wonder as you did and you did originally when you came in. And so I think that for us, really getting into that and understanding how to pay for it so that it can keep going is there. And in the end, the person who's paying for it is that child's parents, usually. And so they're paying Disney, and then Disney's paying us, and we're paying you. And that's often how the transaction goes. And when you think about that, that's the revenue dollar.
But to get started, what I think many people, especially in the world of software, there's a famous statement that Marc Andreessen said, which is, "Software is eating the world." Well, once it finished the dessert, then it had to eat the meal. And the meal is actually making hardware and doing it with software. And that's much more expensive because it actually has equipment that has a capital expenditure, and it has equipment that has service and maintenance and all these things. And so to pay for that is not done with revenue dollars right out of the box. It's also not done with equity dollars right out of the box because those are very either delayed or expensive forms of capital. So bridging that gap is about getting seller financing, and it's about getting loans and other things like that.
Haddy spent probably, and my co-founders, we probably spent a large part of our founding journey, maybe 10 years, figuring out how to do that. You mentioned a little bit that we were working together with a company called CEAD. One of the things in CEAD was, as a startup, just a little bit further along, was selling to universities. They were putting together hardware that was research and development and a little bit more into the commercial realm and making it more possible. We chose them because you could actually get a Blue Book value on the robots that they were using, meaning you could finance them. Because they were used in the automotive welding world. So, I mean, for some people who don't think about financing a company, these are boring topics.
But they're absolutely one of the more critical topics to launch a business. So we chose to focus as close as possible in the furniture world at the beginning on customers that would pay for something. And in that case, we sold to a retailer who paid for things that they then delivered. But we also chose machine partners who were capable of delivering those things in a way where we could finance them. And those have been some of the decisions that we've made along the way in order to be able to get deployed and get financed. Because it's not the world of software where you can do it in a venture capital dollar way or ship software before it' s ready.
Maybe, Jay, I can comment on this one because that's really a, I'm in the additive manufacturing world for quite a while now. What I experience a lot is additive manufacturing, depending a little bit on the technology modality, but typically those are parts that are not produced in seconds or in minutes. Those are parts that print overnight or print a couple of days or at least half a day or something.
So you have equipment that comes with a certain cost at one side. And then you have processes to produce a part, which takes some time. So in the end, the machine hourly costs are directly added to the cost of those parts. So it's quite crucial to make sure that the machine is delivering the output is a high-quality output that you can predict what the machine is producing, that it's producing little waste, but a lot of parts that are ready to use.
So therefore, I think having this digital thread that I mentioned earlier is also quite crucial to make sure that it's not a trial and error, but rather it's like you use your equipment in a financially sustainable way. I think that's absolutely crucial, and that's not only true for this large-format additive manufacturing. That's basically true for all the additive manufacturing industry, I would say.
I think in this, Nick, you should speak about your worries, if you will, for us on our side about what we want to do work with you, but what if we don't need to use you all the time? We talked about peaks and valleys and that kind of thing. Yeah. If you want to comment on that.
Exactly, right. We've thought a lot about this because, listen, building a theme park, you do a lot of development in a short period of time. Sometimes you take a break for a couple of years. Now, as a global company, we are continuously building parks all over the world, but not all in the same place, and so we've had to work very tightly with Jay to understand his supply chain and his demand chain so that we can actually slot in the work that we need to do with him, and in those valleys where there is less work, where we are delivering in front of the guests, we'll fill it out with some research and development work or with some of his other customers because there is that fixed capacity.
That takes a lot of work to actually pull that together in a way where we are being a good partner to Jay. But at the moment that we need to build an entire fabricated land in a couple of years flat, he's ready to help us when we need it.
Yeah, that flexible manufacturing footprint is so critical. And it gets back to the financing piece because when it's time to build, literally when Disney gives us an order today, we've worked on like 10 contracts or something already. And when we get an order, it's like, oh, it's go time.
And then when it's not, we need to know that, or better yet, when it does well enough, like we're putting in a boat at Jungle Cruise and now we want to do more things in a park that's distant from where we are. If there's enough of a demand signal from your customers and then from you, then we can build a new facility and we can start selling to other customers in the region, but then we have to finance it. And then we have to make sure that we have the robots available in time and give that demand signal to those providers so that it's possible to get it up in time.
And then let me bring a final moment, again, the ecosystem into the play. Please. Yeah Because it's maybe not just you who might have financing issues, then if you go global and need to do this for your customers like Disney, but we have Siemens Financial Services who can help out here and has also a broad ecosystem of finance players who can help here. Yeah, so I think this is, again, where the ecosy stem kicks in, which you sometimes might forget that we can also do finance stuff for you as well.
And you know the thing I love about Siemens Financial Services is you know the value of the asset. You know that the actual digital manufacturing and software supply chain is valuable to us and then therefore valuable to our customers and their customers beyond. So it makes it possible for you to value the loan, value the price of the financing, which is really essential.
Yeah.
So I guess we could talk for another hour or so. And it's always great to hear that there are so many possibilities in our ecosystem to help innovation and collaboration evolve.
I just want to take a quick glimpse into the future, if you allow me, Nick. So this collaboration has always been called the marriage of innovation. I'm quite interested now when we look at the next five years, where will this take us? Ye ah. Where will 3D printing ecosystems evolve to? And what is Disney doing out of this to create better customer experiences?
Yeah, absolutely. So Disney has openly talked about a very aggressive menu of things that we're building over the next five to ten years. And each thing is intended to be better than the last. And so we have this partnership with Haddy that we're trying to solidify as much as possible because of the aforementioned ability for Jay and team to think flexibly and to meet our standards so that we can improve every single time we deliver something over the next five years. So what do we want to do next? We want to get things a little bit closer to the guests.
We want them to be more durable. We want them to be more realistic. And we want to find more use cases. Because the more use cases where we can use Jay and team, the more stuff we get in front of the guests every single time. And so they're a critical piece for us to continue to try to improve that delivery ecosystem and that guest experience every step of the way.
So when you talk about durable, yeah, that rings a bell, right? Durability sounds a little bit like sustainability to me, right? So you want things out there. We want to be sustainable companies. That's right. So Jay, this goes to you and Nick real quick. We are a company where we have sustainability at our hearts, right? We really want to drive this as an important topic. So how does these assets, products that you use really translate into sustainability?
I'll be very fast because I know we're close to the end of our time. And so really for us, it's people, planet, and profit. And so we want to make sure that we're taking care of the planet, but we've got to create an ecosystem that is these young people coming into AI that they see a future for them to have a job.
And then lastly, that we can build it in a way which is sustainable, microfactory by microfactory, customer by customer, so that we're actually making a profit on something so we can be around for the next innovation.
Love that.
And we are equally focused on sustainability and have a lot of great efforts that we already do as a company. But it's really great to work with another company where the materials are recyclable and every new thing that we make is even more sustainable than the last.
I'm really happy to hear that. And I was really happy to have you as participants in this really nice conversation. I enjoyed it a lot. We're at the end of the time, unfortunately. Thank you very much, Linda, Markus, Nick, and Jay. Thank you. And thank you to the audience and have a great day .
Welcome. My name is Magnus Edholm. When I'm not sitting here, I drive the digital enterprise at Siemens, and I also manage the communication for automation in the U.S. market for Siemens. With me today, I have two very special guests. I have Brenda Discher, and I also have Götz Ehrhardt. Brenda is from Siemens and Götz is from Accenture. If you are not aware, Siemens and Accenture, we've been working together for more than 30 years, three zero years. We've had hundreds of successful projects running globally. It's a pleasure having you.
Brenda, a warm welcome. How are you?
I'm great. How are you, Magnus? It's so good to be here.
Thank you. It's been a bit a long day.
It's been a long day, but it's great to be together.
Every time we're together, I relish hearing what our customers do with our technology and our partners.
Yeah. And we have a long story, as you know, a long history with Accenture. And earlier this year, we also announced a partnership, didn't we?
Yeah, absolutely, Magnus. And Brenda was there also to celebrate and announce the Accenture Siemens Business Group, which is actually the first of its kind where we jointly help our customers to be more successful across engineering, manufacturing, and asset service, and reach new levels of productivity and success.
Yes, it was pretty incredible. We did a joint press conference at Hanover Fair earlier this year.
And not only did we announce some of the work that we're doing together with customers, there was a huge announcement about a 7,000-person expertise kind of investment from Accenture to really double down on making sure that the joint investments in our technology, your services, really enable these customers to do some amazing things, especially if you think about the advances of AI.
Ye ah, absolutely. And I think not only your technology from engineering, Teamcenter, and all the PLM-related software assets over to manufacturing, Opcenter, down to the PLC automation level, the paradigm for the future is everything will be software-defined and pervasive with Agentic and Physical AI. So I think it was the right time to announce it. And the last 12 months have also proven how successful we jointly can be.
Absolutely. It's been an incredible start to a journey.
We have been partners for quite a long time. But I really believe now with the dedicated resources and the joint customers we've really won recently, it's going to be pretty cool.
It's going to be great. Great having you both here. Also, Götz, we didn't introduce you, but you are the CEO of Industry X at Accenture. I think that's also important to mention. Industry X, could you perhaps spare a few words on that?
Yeah. I mean, it's our variant of industry, what used to be called Industry 4.0 and 4.0 or 5.0. The X stands for basically the version of versions, if you will, and the missing link somewhere using novel technologies, data, and AI to basically advance the cases of engineering and manufacturing.
We help our customers from consulting, so manufacturing, consulting, de-bottlenecking, everything around that. We help them to reorganize product development processes, embed AI into those. And we use technology, and fortunately for both of us, the Siemens technologies to the benefit of our customers.
Amazing. Fantastic. You know, we're going to dig into some real technical stuff in a few seconds. And we're going to talk a bit about AI. I mean, AI is everywhere. So Götz, I'm going to put you on the point here. Can you define agentic AI and physical AI, what those terms actually mean before we start moving to the other stuff?
Yeah. No, Magnus, AI basically has a lot of varieties, which can reach a range from machine learning, other forms, to the now very often mentioned agentic and physical AI. Very simply put, agentic AI is basically AI which does cognitive tasks. So it does. It reasons, it retrieves information, and basically does what we do as humans when we think. So that's the cognitive part. And as agents, it acts upon the knowledge and the reasoning. Physical AI is the set of AI which does interact and manipulate the world around us, the world of physical objects.
And so therefore, it's the cognitive part and the manipulation of physical environments. When people talk about physical AI, they think of robots, which are one part of it. But an AGV or other sort of autonomous devices are also other forms of robots, are also physical AI in that sense. And both are very closely related. You cannot have physical AI without some level of cognitive AI or agentic AI.
Exactly, exactly, exactly. And also, one of the reasons that we have a partnership, Siemens and Accenture, is that I think we combine and we add value to each other. Siemens has got the software for engineering tasks. We've got world-leading automation solutions. We have some cool AI stuff coming ongoing. And then Accenture is coming on board with the. I wouldn't call it the fairy dust, but you have a lot of understanding the fairy dust to make it sparkle and making everything running. Would you agree with me on that explanation, Brenda?
I love that explanation. I love that explanation. I do think what's interesting is the more I look at some of the customers that we've talked to, and I have a couple of examples. Maybe I'll dig into one that's really interesting to me, Navantia. So Navantia is a Spanish government-owned marine and defense company. Been around for a long, long time. Joint customer of both Accenture and ours, and I have some amazing stats, which I want to get to in a minute, but what's really been interesting about them is that Navantia really developed collectively.
They started using some of our technology very early on. They're a user of Teamcenter, which is the backbone of their PLM system, but then we needed to expand their ability to use things along what we call digital threads, so another definition, we've introduced agentic AI and physical AI. I'll talk about a digital thread. A digital thread is really how we digitalize a workflow for a customer from design to maybe manufacturing, from manufacturing into operations, wherever. The customer actually optimizes something, removing silos in order to create a free-form flow of data to be able to convert themselves into a more digitalized enterprise.
It's your charter. It's your job, my friend. But Navantia actually evolved from using just Teamcenter through the help of our friends at Accenture. They actually are one of the companies that embraced the idea of a full-blown digital twin that becomes a digital twin of all their vessels that actually becomes the cornerstone of what they can use for simulation, for downstream, for anything that they need to do further on in their development cycle. And what I think is pretty powerful is a lot of customers start from a digital twin, but they don't necessarily also use the data.
And through agentic AI and some other things with Navantia, you guys have been able to do some amazing things with them. They've gotten 20% improvement in productivity, 20% less cost, better quality, better faster time to market. They've had results across every dimension. Thank you to the folks at Accenture. But really, it's been a joint, it's been the technology that we delivered with some of the capabilities that you've done in bringing in AI.
No, absolutely, Brenda. I think it's a great success story, not only from the value realized for the customer, but also if you look forward, I mean, they're a player in the defense sector. They build frigates and other sort of warships. But they also venture out into new areas. They build now offshore supply ships. They want to move from surface vessels into non-nuclear submarines. And if you think about a shipyard, a shipbuilder, this is long product life cycles. This is complex manufacturing in a shipyard. This is where you need to track and trace stuff through production. You need to have the workers in the shipyard very well equipped with all the necessary information to assemble, to weld, to do all the things.
Traceability, anything that's needed. Yes.
And moreover, there are engineering changes throughout the life cycle of more than a year from design into the release of the vessel into the harbor. So that is really where the Siemens stack and our work on processes have really made the difference for Navantia.
Excellent. I mean, what you're talking about is a lot of data flowing. I mean, now we're talking about a design process, in this case of a ship that Navantia is doing great. So you have a horizontal flow data, we can say. But you also have, and this has been proven extremely important for the industry, the so-called IT/OT convergence, data flowing top floor, shop floor.
From a customer point of view, having this amount of data floating around, you always have the security concerns. How is Accenture working on that?
So I mean, what we also did in that regard jointly with Siemens is helping our customers to secure their data. If you think about their engineering data as it relates to the product as well as to the product in use. Think about a ship. I mean, it's in the open seas. And therefore, in operations. And these are the crown jewels of the firm or the owner-operator of such a ship. So it needs to be highly secure, highly resilient. And therefore, what we do is help our clients to prevent intrusion, threats, industrial espionage with the help of technologies such as the Siemens Remote Industrial Operations Center and also our assets.
So we basically combine the best of what we have for our customers to have very secure operations and data.
Götz, before you leave that task, I mean, I've been almost studying up on the topic we're talking about today. I also came across the company Clorox. Is that something that you could be able to talk about? It's in the connection of security that you Accenture have been working on.
Clorox is a U.S. company, right? Yes. I'm not sure what the case refers to
No, it was about the data transfer and security, et cetera, that you guys have been involved in and making sure that probably that the stakeholders in the development process always have access to the right data at the right moment in time.
Oh, exactly. Exactly.
No, I mean, that's a given for us that data availability at the point of need, whether it's a recipe or an engineering bill of materials, the engineer working on.
Especially through the different life cycle states because of Teamcenter working together with your technology from as designed to as manufactured to as built. Not only there, but you can trace any part of that life cycle and any threat, any cybersecurity thing you guys can manage through that. So that's another part of the value chain. It's really well.
Yeah, yeah.
Yeah. And also back in the days when I was an engineer working in the field.
Long time ago.
Yeah, I hear the intro to Metallica Sad But True. But it is sad and it is true. I can hear it. Yeah. But then we also were working with a lot of data.
Perhaps also if you look at the field of manufacturing planning, we have something called virtual commissioning, which isn't a new thing. It's been on there for some time. We did hardware in the loop, et cetera. Now with AI, because we touched upon AI and you explained it in a very good way, Götz, how would you say, perhaps also you, Brenda, can chip in on this, that when we have AI in the field of commissioning, let's say a machine, a line, or a complete plant, wouldn't that type of thing speed up the process immensely, don't you think?
Absolutely. Absolutely.
Let me give a joint example and then Götz, you chime in with some additional information. Another customer that we've both been working with, KION. KION is actually based in Europe, in Germany. They are pretty much a supply chain company. They are transforming supply chains all over the world for any supply chain, automotive, food and bev, whatever. Again, another company that has really embraced the idea of digital twins and many of our technologies. But they're using AI for exactly what you're thinking about, which is agentic AI. They're actually using AI through the help with you guys.
You probably know better than I, but they're actually leveraging the AI, whether that's on the edge or it's on a device or it's somewhere in the supply chain area, to be able to understand the applicability, not only understand cognitive, I'm going to use some of the words Götz used earlier, but then they're also using the physical AI, some of the data coming off of whatever the assets are that are in your supply chain in order to collect that information, not only affect the productivity of the supply chain, but also use that data, we call it an executable digital twin sometimes, bring that data all the way back into product design or into manufacturing to be able to affect a change earlier on before you get to a supply chain issue.
I think there's been a lot of great work there we've done with KION with you guys.
Absolutely. I think KION is one of the, if not the prime case for not only the whole sort of product life cycle management, but also it has improved the value proposition KION can bring to their customers. It's faster cycle times in terms of sort of developing a large warehouse, including all of the material handling from AGVs, AMRs, forklifts to fulfillment stations. Think about all the movements in a warehouse. You don't want to reprogram the assets, the AI. You can't afford it. You can't afford it. Exactly. So the vision clearly is a lights-out warehouse, fully sort of almost autonomous in its operations from material which goes into the warehouse or pallets which go into depalletizing, packaging, outbound fulfillment. That's great.
Think about the customers of KION, which now can have a fully autonomous warehouse, and the value proposition is clearly immense.
When you guys talk like this, it's amazing and wonderful to listen to, but it's like a wave of information coming across. So people out there listening, what would, I mean, there's so much information. You got to do this and that and here and the storage and warehouse and production. How do you start a project? What is the kind of the low-hanging fruit? Where would you dig in if you're going industrial AI?
I mean, if you think about KION, so they get a request from one of their customers. The customer says, "Well, we intend to build or we have an old warehouse which is too small. Let's extend it. Let's build a new one." What would you recommend, and here are a couple of requirements.
So much square feet, that type of equipment, height. So I mean, the basic sort of stuff which engineers love and customers basically say because this is what they want the engineer to think about. But now this all with the use of AI gets much faster, better, cheaper. Absolutely. Because the AI can retrieve good warehouse designs, previous designs. It basically can test and predict the feasibility of certain designs. It can test and predict and optimize potential flows. That's all about what we talked about earlier. The virtual commissioning is the customer asks, "Can you build that warehouse?" and probably 24 hours later, here's the basic design and the costing of it and the operational data and metrics of such a warehouse. I think that's a great proposition if I would be your customer.
If done right, depending on where they start, this can be valuable to brownfield, can be valuable to greenfield. This works for both cases. Imagine a world. I know this sounds a little like it's too far out there, but it's not. AI is actually embedded into everything we already do. Imagine a world where the warehouse, the supply chain, the factory is adapting to what it knows it needs to do based on the requirements that have changed, based on what happened last night on the factory for the next day's job, based on the requirements of a new product or a new product line. Again, it is not that far into the distant future where that adaptability of the physical world is going to happen based on the data that's in the digital w orld.
Yeah I'm going to go off track a bit here now. So maybe you got to.
Of course you are.
Yeah, I am. I am. So we have technology. We talked a lot about that. But at the end of the day, the three of us are in people business. In what way would you say that this new technology can also attract people to come and work in manufacturing companies? Because it is difficult to find people. This is what I understand when I'm out talking. So Brenda, what would you say?
So first off, I think there's a lot of institutional knowledge in hardcore manufacturing and factories that is leaving the workforce, especially here in the United States. This is a really big issue here in the US. AI, which brings you back to some of the cognitive and the physical knowledge of all the information that this factory has ever done or this company ever has had, or maybe with some of these large language models, eventually they're trained, they will give me insights that happened at other companies' factories that are similar to mine.
So I think that that then allows anyone to be able to participate in this process. Now, you still need good problem-solving skills. You still need new business acumen. You need to understand what is my company producing, what's the value of my company. And that's where I think, again, to do these things, you need technology, you need people, you need process. And so I think between great process and understanding how that works through digital threads, great technology, and then with people , it all kind of comes together.
I think AI is an enabler to actually help us accelerate innovation if done correctly. What do you like to add?
I think that's spot on. And I would also always try to sort of separate it into more or less two camps. There are shop floor workers which are enabled by AI. So you can use AI to diagnose problems and solve them quickly without sort of extensive training and experience. That's a good thing. And then for the engineering side or the product development side, what we clearly can see and will become more important is that you need very skilled people to train those models. Take a practical example. A process which happens every day on the manufacturing side is maintenance. And you have a maintenance planner, typically very experienced people, done the job for 20-odd years.
They know how to basically best schedule different types of maintenance, which equipment goes there, which person can do the job, where it has the right skill profile. But think about it. All is in the knowledge base of the customer. It has been done before. So AI can clearly sort of accelerate the process of maintenance scheduling. What you still need is people who work on anomalies which no one has detected y et.
Correct. Correct
Or making sure that the AI recommendations or the agentic AI in terms of the new maintenance plan is solid, is valid, is workable. But so you need more experience in working with metadata rather than the task itself.
Yeah. And when I hear you talk now, Götz, what I see in front of me is industrial metaverse. We talked about the digital twin.
We spoke about AI, and we also touched upon software. Metaverse. Wow. There you go. Wow. Software-defined automation and all that stuff. So looking into the future, now we're moving fast. I mean, is it possible to look at the future? But if you have that environment, a single pane of glass where you have all the information, what would that bring to the industry? I personally think it's extremely exciting. What's happened over the last 26 years I've been in the business is just amazing. What are your thoughts on that? I mean, we have a few minutes to go. I will cut you off if we talk too much. But Brenda, do you want to ?
So I'll start, Götz dive in, and we'll go from there. So first off, I think the industrial metaverse was a little bit of a far-fetched concept just a few years ago. And now with a lot of the technology that's out there, whether it's the compute capability, whether it's software-defined automation, whether it is agentic AI, physical AI, all the digital tools and the software tools, software, hardware, it is truly now in reach. Truly now in reach. Now, I do think the industrial metaverse takes a village. So I do think it takes a lot of partners to work well together.
This is not even just like right now we're talking about Siemens and Accenture. I mean, this requires AWS. This requires NVIDIA. This requires. If I think about metaverse, we have a lot of partners that we really need to bring to the table because otherwise you have to decide what is the customer, again, of that entire ecosystem or digital thread of the process. The customer is trying to visualize or immerse themselves in a digital world.
What is it that they're trying to do? And let's give them the technology to do it, but let's also work better as an ecosystem ourselves to really do this. It takes a village. That's the way I would.
I would certainly agree. And my take on it is simply that the forces which allow the future, which is AI-powered, are cloud, which is the big enabler. It's large and small language models. So all the varieties of AI. Data. Data, which allow for autonomous systems to operate like we operate as humans. So that's all in reach. And we see the first instances of dark warehouses or lights-out warehouses. We see the first instances of level four autonomous vehicles. We see instances of fully software-defined automation from a change in the recipe to the manufacturing line in seconds, which took months to basically validate a change in the recipe.
So this is happening now. So the future is very, very, very near, very close.
And very bright, I would say.
Yes. Very bright. Yeah.
It's like here in the studio, isn't it?
It's very bright.
Yeah. So you can't hide from this light. So we have 30 seconds. So what it comes down to in this business to be successful is obviously to, first of all, choose the right partners because you can't do it. Well, you can probably do it on your own, but life is much more fun and more effective if you do it jointly with decent and good partners. And there's a lot of them out there to choose from. And also, when we have an industry environment, there's loads of data floating around. You want to make sure you have your data under control.
You know where you want to know where the data sits. You want to collect the data. You want to analyze the data and use the data to and a single source of the truth. A single source of truth. That's what it comes down to. You know what? That's going to be my closing words. It's a single source of truth, Brenda. So thank you for that. And also thank you, Götz. It's a true pleasure having you both here. We could probably talk a lot longer.
All night.
And we were talking fast.
No.
We were.
Thank you.
Thank you.
Thank you, Magnus.
Thank you, Brenda.
Thank you.
Thank you, Götz .
Thank you.
[Presentation]
Hello and welcome. My name is Magnus Edholm, and today we are with you from CES 2026, which takes place, as every year, in Las Vegas, and now I have an interesting set of gentlemen joining me. Two wearing westerns, one wearing a cap. That's kind of the selection process of coming on stage today. No, it's not, so I have Lucian, and I have Kevin, and I got Jake joining me, and you know what we're going to do now?
We're going to do around the coast. So I'll give you 30 seconds to pitch what you do, your focus area, who you are, et cetera, because we have a lot of viewers out here. And they are all very keen on understanding where they can find out more about what's happening in the industry, because that's essentially why the four of us actually are here in Las Vegas. Lucian.
Yes, I'm Lucian Fogoros. I'm an engineer by background, a journalist by mistake, if you will. And I'm focusing on industrial IoT and AI across manufacturing, energy, and infrastructure, and leveraging a community of about 300,000 people.
Yeah. And you travel the globe, being on big events.
Yes, it's a global community. We're 100% online, yet we do travel to in-person events and covering events.
Excellent. Thank you very much, Lucian. Kevin.
So I'm Kevin O'Donovan. I'm a Technology Evangelist. I spend my time looking at new technologies and making people aware of the art of the possible. And then I suppose bridging the gap between, look at the cool stuff you can do, but then how does that add value to manufacturing and the energy industry?
Fantastic. Thank you very much. Sir.
I'm Jake Hall. I'm the Manufacturing Millennial. I'm an advocate to bring the next generation into manufacturing and automation. We have a workforce problem. We're going to have 2.3 million unfulfilled jobs in manufacturing. And I want to showcase the technology and the change that the industry is having to get people excited about what we're doing in our indus try.
Cool. Cool. So you have three tech experts here. And saying that, and also having a Manufacturing Millennial here on board, surely you guys are counting your steps. How many steps have you been walking today?
I have 29,000 currently on my Garmin.
OK, you beat me with 27,000 step s, Kevin.
I was just in the North Hall here today, so I think about 15,000.
OK, Lucian.
I got about 22,000 steps, but 20,000 steps, yet my Oura Ring had to be recharged.
OK, OK. And during those steps, and now we have some 40,000, 50,000, 60,000 steps between the three of you.
Lucian, what has been the highlight that you've seen today at CES?
So a couple of things, right? From my personal perspective, it's great to see a lot of the apps for longevity-based. So like seeing an app that not only gets you your wearables and things from your ring, from your watch. So that's from my personal perspective. I like to see that in one app. And the Gyroscope? is one of them. But from the perspective of one of the excitement I see across the board is this transformation from digital transformation towards intelligent transformation. In other words, the AI across various industries that we're sure all be looking at.
So the AI, I mean, that's something that's definitely very present here. So is there anything there that stands out?
So it's definitely, wherever you look, it's there. So whether we are seeing on a daily basis, you walk into the booth here, you have the Explorer, definitely the truck here with the Explorer, the demos that you can see the examples in life sciences or other industries.
Yeah. So what Lucian is talking about is essentially our Explorer tour. You can't see it from here where you guys are sitting, but we have this big truck here. Here at the CES, it's a 12-minute guided tour through a life science use case where we are utilizing the digital threads and also AI to do some, well, finding where the issue actually occurs. I'm going to go to you now, Kevin. I know you're always filming and you're everywhere. What did you see today that actually stands out, even if you were only in the North Hall? It's a pretty cool place to be, though.
I was in the North Hall today. I was here since Saturday, right? We had CES unveiled on Sunday night. Yesterday we had press day and a lot of stuff going on. Two things stood out for me, I suppose, on the AI side, because it was Gary Shapiro at the opening was like, AI is everywhere. It is. But it was AI inference at the edge, the architectures for pushing it out to the edge. I'll come back to that in a second. And the second one was around just robotics, physical AI. And particularly, obviously, the humanoids are getting all the dirty AI candy.
But it's actually the robots, the things on wheels, and the robotic arms where the money is and where people are using today. But if I may, on the AI inference, obviously, NVIDIA have done an incredible job and are known for it. But AMD last night, what they're doing, Qualcomm, you have a bunch of startups here with their own inference chips. So when we look at the factory of the future, you're going to be pushing a lot of small language models. They called it vertical AI out to the edge.
That was Brian Comiskey from the CTA who had that word the other day. Now I know we have too many words, but whatever. A lot of buzzwords. A lot of buzzwords. But inference at the edge. And people kind of going, that's where I'm going to deploy the stuff. And then, as I say, robots. And the humanoid ones get all the kind of the AI candy. But at the same time, if you turn around and go, well, what's an autonomous vehicle? That's a robot. And then you go into the factories, like Siemens factories in Erlangen and wherever around the world, AGVs, AMRs, like Amazon. This stuff's happening.
Correct. Correct. Correct. Jake, Manufacturing Millennial. You've been at, what is that, 29,000 steps. Yes. You must have seen the world today, haven't you?
It was a lot. You know, the one thing that I really enjoy is I get to see a lot of trade shows, everything, every year. Last year, we went to 65 different shows across here, in the U.S., in the U.K., and for me, it's always I love it because I always get to learn something new. And so this is the first ever CES that I've attended, so it was one of those things for me where I was able to go in with a fresh set of eyes and say, what is this show all about? And the one thing that I really appreciate about CES, it's where we envision the future is going to be, where a lot of the technology that we're seeing on the show floor right now isn't fully deployed in a lot of use cases.
We saw, gosh, probably 100 different humanoid robots across the North Hall and the West Hall. How many of those are actually able to be deployed right now into a manufacturing floor? Probably not a lot. There's a few out there that probably could. But the one thing about it is how are these companies going out there solving problems that the manufacturers and the end users are asking for? That's really exciting. But the one thing that I really appreciate about it is when you look at the younger generations in the industries, the millennials, the Gen Zs, the upcoming Gen Ys, they view manufacturing as this dark, dirty dull, dangerous industry that their parents warned them not to go into because there's not a long-lasting career.
Now, all of a sudden, I can say, hey, you young kid who has a 3D printer at home, who can go on the computer, download a file, design their own solution through software, take a software that will slice that, put it in lines, make a physical product, and then work with all of a sudden, that kid who is 10, 11 years old is his own manufacturer. Then I can say, hey, you love building, you love designing, you love being innovative. Well, now look at this, all this new technology that's coming in. Where manufacturing is no longer this physical world, it's a digital world that new generations are growing up on.
All of a sudden, I can go out there, and I was at the NVIDIA keynote yesterday, and Gen Z is talking about what they're doing with Siemens and how they're moving the Omniverse forward. All of a sudden, it's not just what's on the physical floor; it's what's in the digital twin as well, and how we're driving productivity. All of a sudden, technology is not the old industry. It's the leading industry of innovation. Yeah, and that's why I've gotten from walking CES so far is there's a lot of technology that we're not going to see on the floor, but it's coming. And I think it's for what I love to do. I love to educate all the people who are watching this right now or the eople in manufacturing saying, hey, here's the technology that's coming. Get ready for it. Do you have the teams in place who are ready to adopt technology?
I think you're doing a great job of what you just said. I mean, that is a true evangelism there, if you ask me. If there would be one thing of all the things that you saw that you would say, this truly stands out, and you can't pick a humanoid?
Oh, man. I would say if I can't pick humanoid, I'm going to still stick with a robot. And I think for a long time, robots have been around for 40, 50, 60 years. They were first deployed in the automotive industry, and they're beginning to become more skilled and more flexible over time. What we're seeing AI do is AI is taking a once stagnant arm that could only do a specific task, and it's making that task so much more flexible.
Now, all of a sudden, it's no longer a robot doing the same thing over and over again. I'm working now in a flexible, agile environment that now automation can hit all these new industries and sectors, and it's not just manufacturing anymore. We're seeing stuff working in food and bev. We're seeing stuff in agriculture. We're seeing stuff in construction. Now, all of a sudden, AI is enabling technology that's been around 40 years to be something brand new.
Kevin, what would you say? Pick one thing that stands out.
I think what stood out for me today was fusion. Fusion. I do a lot and energy. I do. I spend a lot of time in the energy industry. I go to a lot of events. Not 65, though. We look at AI factories. We need more energy. W e look at bringing. Every country is bringing manufacturing home: more factories, more automated factories, more electricity, more EVs, more air conditioning. We need more energy. So fusion, Commonwealth Fusion Systems. They're using digital twins and simulation to bring fusion to a reality in the next two years.
Yeah. Incredible.
And like you'd say, well, it's energy and it's how, but it's all interwoven. And they're using the technologies from different industries to make fusion a reality. I thought fusion was a pipe dream. When I was growing up, fusion was in the future
Yeah. I mean, that's the thing. So we have Commonwealth Fusion Systems as an exhibitor on our booth. And the cool thing is they have a set of water bottles, like small bottles. I would say there's not even a liter of water or half a gallon.
That's the fuel. Yeah.
That amount of energy in that bottle of water would be enough to power the complete consumption of electricity that each one of us needs for an entire lifetime. There's definitely potential in here. Lucian, if you had to pick one of the big things you've seen today?
One of the things that I liked, I think I said Sunday night, it was this tool that allows you to almost like automate interviews. Imagine if you're a consultant, you interview 30 people, you set aside 30 half an hours. This one, the AI would do it for you. In our case, inputting that into a 300,000 community, that would be super helpful. For companies like a product manager, if you want to get feedback and summarize that feedback into one page, that was one of the things that was at least on my action to-do list to leverage immediately after this.
Fantastic. I'm looking at the time now, so I'm going to give you all the same question here, so my experience is when you walk around here at CES. I haven't done that so many times, but sometimes it happens, and every so often, you run across some technology that really say, oh, that is just so innovative. That is just so creative. Why didn't I think about that? That is a thing that comes to mind. What is the big thing that, I mean, we almost talked about it, but it's got to be something that, oh my god, that is just the coolest thing since sliced bread was invented.
Oh, man. You know, it was one of those things where I would say it was, why didn't I think of that before?
You see? That's exactly what I mean. Why didn't I come up with that?
So I was in the Hyundai Motor Group with collaboration with Boston Dynamics out there. And they just had some technology that was moving cars around for charging stations. And it would go underneath the car, pick up the car, and slide it around. I'm looking at this. It's like, it's so simple when you think about it. But the efficiencies, the technologies, how they're moving these cars around manufacturing facilities when they're being constructed, it's just a cool thing where it's not brand. I guess you say it's such a simple idea that now technology is making things capable to do that are just going to make people's lives easier.
Yeah. Cool. You know what? I'm looking at the time. So we don't have too much time left. But I mean, I want the people watching also to be able to follow you guys. So Lucian, where is the best way to find you? And where can they follow you? And how would you describe day one with one word?
Unfocused. Unfocused. One word. But yes, you can find us on X, LinkedIn page. We have IIo-World .com as well.
Lucian Fogoros at LinkedIn or something like that.
Correct. Lucian Fogoros. It's only one Lucian Fogoros on LinkedIn.
Yeah. I think there's only one. And there's probably also only one Kevin O'Donovan.
There are a lot of Kevin O'Donovans out there. Is that right? There is. It's a popular name. I sure am. Yeah. You can find me on LinkedIn or Instagram. One word? Inspiring.
Inspiring.
I suppose more than one word. The thing about CES, compared to all the energy events that I go to, you have every indus try here. So you don't know what you don't know when you see other things.
The last 10 seconds.
Yeah. Jake Hall on LinkedIn. You can just search up Jake Hall. Look for the blue cap. And then my website, TheManufacturingMillennial.com.
It's got quite a collection.
Exactly. 27 hats.
Gentlemen, it has been a true pleasure to round the day off with you three. S o much energy, so much experience, and so much to learn by following those guys on social media. And they will keep on posting throughout this week. So go in, follow them, like what they do, repost what they're posting. That's what I would do at least, because you can learn a lot from this. A lot of know-how here gathered by those three guys. So with that, thank you very much.
Thank you.
Thank you, boys.