AI isn\u2019t a new technology yet, today, AI is taking off like never before, finding countless new opportunities not just for consumers but in the much more demanding worlds of business and industry as well. Reaching that point is not simply coincidence but a confluence of factors culminating in the Industrial AI landscape of today and tomorrow
In this episode, host Conor Peick is joined by guests Matthias Loskyll and Samir Desai to answer the question of why now? As well as understanding what it took for AI to reach where it has today, the broad spectrum of technologies that fall under the AI banner, and what that all means for industry.<\/p>\n\n\n\n
Check out the full episode here<\/a> or keep reading for a transcript of that conversation.<\/p>\n\n\n\n Conor Peick:<\/p>\n\n\n\n So welcome into the Future Ready Podcast. My name’s Conor Peick. I’ll be moderating our discussion today and I’m joined by Matthias Loskyll and Samir Desai. They’re two excellent AI experts from Siemens here, so really excited to jump into a discussion today. So to get us started, I would love both your guys’ perspective.<\/p>\n\n\n\n Maybe Matthias, you can start for us. Basically, why industrial AI and why now? Why are companies really looking at adopting these solutions? Sort of what problems are they facing? What are they looking at industrial AI to help them solve?<\/p>\n\n\n\n Matthias Loskyll:<\/p>\n\n\n\n So if you look at industrial companies today and where they are, it’s like a perfect storm. They still have the requirements they’ve been facing for decades. It’s about high quality of their products, it’s about efficiency and cost saving and production. But in addition, now they are facing lack of skilled labor as a big challenge. So in the end labor shortage, workforce shortage.<\/p>\n\n\n\n But also, they have to get their production more resilient because of all the supply chain crisis going on, on a regular basis in the meantime. And they really have to get more adaptive in their production to unforeseen situations to additional product variants that they have produced. So it’s really these old challenges, known challenges, but also the new ones coming up. So it’s really the time to act for industrial companies now to use AI to really get to the next level of productivity.<\/p>\n\n\n\n Conor Peick:<\/p>\n\n\n\n And so, Matthias, you bring an automation perspective to this. And Samir, from your side, it’s more of the engineering perspective. So what do you think from an engineering perspective, maybe some of the same challenges that Matthias just outlined or?<\/p>\n\n\n\n Samir Desai:<\/p>\n\n\n\n Yeah, we see some of the same with a different flavor on it. So let’s pick the demographic issue. We’re seeing a lot of change in the type of users that our customers have. We’re having the older generation move out, the newer generation coming in, and the newer generation is very impatient.<\/p>\n\n\n\n And for them, they want to really quickly learn the tools and do their tasks. So how do you really bring these new users on board? Some of those are the challenges they’re running into. They got data today that is completely fragmented in lots of different systems of records, if you would. And when they’re building their products, this data from different systems need to come together. So they’re kind of seeing that.<\/p>\n\n\n\n And the last part that we see is hyper-competitiveness. Their competition new, not just local, but global competition is moving really, really fast. And they’re saying, how can we cut short our development times, our engineering time so that we can take our products much more faster? So all these things are coming back together a perfect storm for our customers. And that’s where they want to be able to use AI.<\/p>\n\n\n\n Conor Peick:<\/p>\n\n\n\n Yeah, that perfect storm. So maybe some of that innovation cycle speeding up, perhaps similar in the automation world as well. So maybe as we can jump into a bit of an AI introduction here for some of the listeners who maybe are not quite as familiar with the technology as you guys. Maybe really quickly, Samir, you could give us a rundown on consumer versus industrial AI, some of the different requirements, the different needs.<\/p>\n\n\n\n Samir Desai:<\/p>\n\n\n\n Sure. So when we say consumer AI, go back December 2022, ChatGPT. Kids to their grandparents, everybody’s using it, but what are they using it for? It’s primarily around text, videos, images, things like that, which is ubiquitous all around the place. We were using Google and YouTube for that. Now we are using AI for all those things.<\/p>\n\n\n\n When we look at industrial side, what our customers do, yes, they have documents and they have images and they have videos, but that’s not their primary type of data. They’re dealing with engineering, design engineering, manufacturing, simulation, manufacturing operations. That is a very complex type of data that has to be dealt with. And so that is where the big difference comes in. And we don’t see in the consumer marketplace what are known as those modalities for this complex engineering data available.<\/p>\n\n\n\n So that separates us, which is where we start getting into our strategic elements around industrial foundation model. It’s all about building models that deal with the engineering, the manufacturing, the operational data that can then be used to build AI features on top of that. So that’s the real big difference in terms of what is the type of data that our customers use for their products.<\/p>\n\n\n\n Conor Peick:<\/p>\n\n\n\n Right. I see. And presumably, there’s also a question of risk involved as well. I mean, in the production environment, an AI doing something that you’re not expecting or not ready for could be potentially extremely costly or damaging to a production facility, presumably.<\/p>\n\n\n\n Matthias Loskyll:<\/p>\n\n\n\n Yeah. Actually, besides the topics that Samir mentioned, so the different data modalities, we need models understanding the language of engineers, so to say, or the language of the industry, that’s an important aspect. But besides that, just think about where AI acts in industry. It doesn’t act in the digital world only. It also acts in the real world. We are actually operating factories using AI.<\/p>\n\n\n\n And if you think about this, this means there can be safety issues because of wrong predictions of an AI model. There can be massive downtimes or standstills of a production line, which can cause a damage of millions and millions of dollars. So it’s a different thing than I always try to explain to my family what I’m actually working on. So I always say, just think about recommendation of your favorite movie or using AI to translate some text.<\/p>\n\n\n\n If something goes wrong, it’s not a big damage. If something goes wrong in the industrial space, we have a huge issue. So it’s all about reliability. It’s about deterministic systems that engineers are used to and how do they then interact with an AI system, which is by definition non-deterministic. So how do you really integrate that into a reliable, trustworthy manner that can be used for industrial production use cases?<\/p>\n\n\n\n Samir Desai:<\/p>\n\n\n\n If I could just add one thing on top of what Matthias just said, there’s this key difference of language of the internet versus language of engineering and manufacturing. And that’s where the main separation happens on the consumer versus industrial AI, the risk elements engineering and manufacturing is a science. It needs to be deterministic. If something goes wrong, then the car or the plane or the machinery that our customers are manufacturing, there’s going to be a huge problem there and we cannot risk it.<\/p>\n\n\n\n Conor Peick:<\/p>\n\n\n\n Certainly. And Matthias, you mentioned this aspect of it that AI is in some ways is inherently non-deterministic. So it’s a fascinating way of thinking about how do you build it into systems in a way that manage that risk, I suppose, for you. It’s a really interesting marrying of this technology that can maybe be very powerful and predictive and help you maybe knit together patterns you couldn’t see otherwise, but doing so in a way that you’re not going to open yourself up to too much danger of a wrong prediction or something like you mentioned.<\/p>\n\n\n\n Matthias Loskyll:<\/p>\n\n\n\n And this really depends on the use case, how critical it is. Sometimes it’s okay to have a certain non-determinism and work on statistics and predictions of an AI model and work with that. That’s fine for many use cases, but there are some that go into the actual control of a machine or of a production line or even AI-based safety applications. That’s also what we see coming up more and more.<\/p>\n\n\n\n So then we really talk about and embedding into I would say something like a sandbox or into some guardrails that are more rule-based maybe even, or that at least can be certified to a certain level and you can make sure that the outcome of the AI model is more predictable and more deterministic to really be able to use it in such critical environments as well. It’s similar to autonomous driving age actually. We have similar challenges there in industrial production as well.<\/p>\n\n\n\n Conor Peick:<\/p>\n\n\n\n Yeah. Thinking of that safety criticality and making sure that the system doesn’t step out of its bounds or something to that effect. So maybe as we keep thinking about this AI introduction in a way, there’s obviously lots of different AI models, maybe different AI applications that can be used in engineering and production environments.<\/p>\n\n\n\n Things like maybe people will have heard of predictive, generative, agentic, physical AI, all these different terms, right? Maybe you could give me just a quick, Samir, maybe a quick rundown of are there different benefits or maybe different advantages of these various models or systems? Yeah. How do they inform perhaps a engineering process differently?<\/p>\n\n\n\n Samir Desai:<\/p>\n\n\n\n Fair question. So AI first of all is not new, right? Generative AI is new. Predictive AI has been there for three to four decades. And if you look at in the Siemens portfolio, we have been doing predictive AI for a long time. Now there is an increasing value going from predictive to generative to agentic. Predictive, as the name is suggesting is it’ll say, “Hey, this is what could happen and this is in there.”<\/p>\n\n\n\n But generative is saying, “Well, it’s not only we are predicting it, we’ll help you create that. We’ll help you generate that information for you.” That takes it up to the next level. And then beyond that, agents are coming in with agentic AI and saying, “We will assist the engineer in creating that.” So now the engineer is free to do their primary task, which is doing engineering and design engineering and manufacturing engineering and it’s taken away repetitive actions and tasks they have to do.<\/p>\n\n\n\n So there’s always an increasing level of benefit that they get out of it. Now we use this in different ways on the engineering side of our software portfolio. All of the above elements are used in a way that when a design engineer is going into our mechanical or electrical design software, we got copilots in there that they can quickly start chatting with them and say, “Hey, I want to do A, B, C. How do I do this thing? I want to create an electrical diagram. I want to create a 3D geometry with so-and-so. How do I do it? But not only how do I do it, can you go create it, do it for me, that I’m going to review and say yay or nay, or I’m going to edit it in there.” So we start doing that across our portfolio. There’s several examples of how we take advantage of all these pieces for us.<\/p>\n\n\n\n Conor Peick:<\/p>\n\n\n\n Fascinating.<\/p>\n\n\n\n Matthias Loskyll:<\/p>\n\n\n\n I think there’s one thing which is really important. It’s not always about following the hype and not everything should now be agentic or generative. We see a lot of use cases actually out there in industry where predictive AI works best because these are specialized models, solving specialized use cases.<\/p>\n\n\n\n So that’s a perfect fit and there’s no need actually to go for the hype topics, but that’s something where we really also have to consult our customers sometimes to clearly say, “Okay, for this problem, you don’t even need AI. For another problem, you could use predictive AI, which will work perfectly fine. You just have to train it based on certain data to make predictions then. And for others, there’s really the need for generative and agentic AI approaches.”<\/p>\n\n\n\n Conor Peick:<\/p>\n\n\n\n That’s a really good point matching the tool to the problem that you have and it’s right tool for the right problem at the right time and you don’t just throw the AI blanket over every problem that you have and expect that it’s going to work out. But as interesting as this has been, unfortunately, that’s all the time we’ve got for this episode. Please tune in again next time as we continue our discussion with Samir and Matthias on the Future Ready Podcast.<\/p>\n\n\n\n Siemens Digital Industries Software<\/strong>\u00a0helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator<\/a> business platform. Siemens\u2019 software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today\u2019s ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries.\u00a0Siemens Digital Industries Software<\/a>\u00a0\u2013 Accelerating transformation.<\/p>\n","protected":false},"excerpt":{"rendered":" AI isn\u2019t a new technology yet, today, AI is taking off like never before, finding countless new opportunities not just…<\/p>\n","protected":false},"author":82958,"featured_media":8933,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spanish_translation":"","french_translation":"","german_translation":"","italian_translation":"","polish_translation":"","japanese_translation":"","chinese_translation":"","footnotes":""},"categories":[1,13763],"tags":[12,194,11,2,4,175],"industry":[],"product":[],"coauthors":[2461],"class_list":["post-13272","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","category-podcast-transcript","tag-artificial-intelligence","tag-digital-transformation","tag-digital-twin","tag-digitalization","tag-simulation","tag-xcelerator"],"featured_image_url":"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/19\/2022\/12\/Siemens-LDA-Visuals-RobustEcoDesign2-1920x1080px8_original.jpg","_links":{"self":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts\/13272","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/users\/82958"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/comments?post=13272"}],"version-history":[{"count":1,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts\/13272\/revisions"}],"predecessor-version":[{"id":13273,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts\/13272\/revisions\/13273"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/media\/8933"}],"wp:attachment":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/media?parent=13272"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/categories?post=13272"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/tags?post=13272"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/industry?post=13272"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/product?post=13272"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/coauthors?post=13272"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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