Andreas Welsch: 0:30

Today we'll talk about how agents are disrupting business, and who better to talk about it than someone who's actively working on innovation management and disruptive innovation. Christian Muehlroth. Christian, thank you so much for joining.

Christian Muehlroth: 0:43

Thanks. It's a pleasure. After seeing you in person a couple of weeks ago now, also being on this digital format thanks for having me. It's great.

Andreas Welsch: 0:50

Wonderful. it's been a while since we've or since we've crossed paths digitally. I think it's almost two years ago that you invited me to your show. So it was great seeing you in person a couple of weeks ago and now having you here. But not everybody might know you already, and if you don't know Christian yet, you should definitely give him a follow on LinkedIn. But why don't you tell us a little bit about yourself, who you are and what you do.

Christian Muehlroth: 1:15

Yeah, sure. I keep it short. I'm Chris. I'm the CEO of ITONICS. We are a Software as a Services provider. We build software basically. And what we build is a platform for enterprise innovation and technology management. Which means we help companies actually make use of new technologies and create inventions and innovations and bring them to market to accelerate growth to be more resilient in the time of disruption. And that we do with a software first approach. And that's why we also talk about agents today.

Andreas Welsch: 1:47

Exactly right. There's so much talk about innovation in general. But very few are doing it well. So I'm sure you're getting a front row seat at what's working and what's not working. So I'm really excited to have this conversation with you. In good old fashion, why don't we play a little game to kick things off? Let's see. Sure. Fantastic. When I hit the buzzer, the wheels will start spinning and when they stop, you'll see a question. I would love for you to answer with the first thing that comes to mind and why, in your own words. Okay. Okay. Are you ready for What's the BUZZ? Let's do it. Okay, here we go. If AI were a color, what would it be? 60 seconds on the clock to make a little interesting. Here you go.

Christian Muehlroth: 2:38

I say if AI were a color, it would probably be white. The reason is because white contains all colors. So I think that resonates well with AI, which is more, in my opinion, a general purpose technology. That probably can take on any task in the digital, but also maybe soon in the physical realm. But also it needs something to channel through like, a prism needs a prism to be useful. Data ontology policies. Agency tasks, intention, ideas, whatever. Yeah. Unfocused, it's white. Yeah. It's a clear, yeah. But then focus through laser focus. It becomes a color and then also useful in practice.

Andreas Welsch: 3:24

I love it. What an awesome answer. And what a great segue to talk about innovation too, right? You really need to channel it through it. Something almost like a catalyst. Otherwise it's just there, but it doesn't do anything. It doesn't do anything meaningful for you. Beautiful. Just like that, right? Innovation also isn't something that just magically happens and also doesn't happen momentarily. How does innovation typically occur and, what comes together now in this wave of AI agents?

Christian Muehlroth: 4:00

Yeah, it's so interesting because if you think of the major innovations first of all, probably it's great to distinguish between invention and innovation. I know it's been done in literature and everywhere else, but I just think it's interesting. And important to do it specifically in the age of AI because AI was not invented in the recent months or years. The foundation, specifically the physics, but also the machine learning algorithms of the mathematic and the statistics and the algorithms they're based in, decades ago in, in groundwork that was being done decades ago. But so that was the invention. But the innovation now came in the recent years, which made things much more useful. That's because of the processing power for sure, but that's also because of the electricity needs of the data centers, the scale, the user interface and so on. So these innovations, sure, also in algorithmic, but also in delivery of the AI actually made it much more useful in the recent years. And that, that's still a little bit so sometimes confused in practice the difference between invention and innovation. And I think right now specifically with AI, we see it very clearly. What's an invention and what is actual an innovation that's useful to the market?

Andreas Welsch: 5:19

Yeah, so when we met a couple of weeks ago, it was part of a panel discussion and you mentioned some parts of innovation management. And in innovation theory, we started talking about these waves, these

5:34

innovation waves or Kondratiev Waves based on the

Andreas Welsch: 5:38

Soviet economist. And I don't hear a lot of people talking about this, by the way. It's also in the book because when I took innovation management at university, I was quite excited to learn about it. But maybe you can share a little bit about what's up with Kondratiev Waves and how they now fit into this innovation cycle.

Christian Muehlroth: 5:59

Yeah. I think it's so interesting, and I love that you have it in your book. I'll totally recommend the book, by the way. It's, really great. And the, pure fact that you already have conative waves in there I know. Makes it a great book. Yeah. And I think it's interesting because ev the, theory is that everything. Really everything in life, not only technology, but everything goes in waves. Yeah. Things go up, things go down then, they go sideways for some time, but ultimately they go in waves. And that's if you look back in history and the history of technology and then also society, that actually has been the case. You always know in retrospect what these waves are. For example the steam economy, electricity, then the internet, these are all considered big waves, big technological waves of change typically. And then after technology, typically big societal and also work transformation and business transformation follows. But of course it takes some time. And by the way, many of these actually stem from military and defense applications like the internet. So it's, pretty interesting to also see what those folks are up to. But that's the theory and the interesting thing about it that I think time is getting compressed right now. So the length. Of each wave, maybe back in the 18 hundreds, something, it was recognized to be about 60 years, 70 years maybe. But these waves are getting shorter and the reason is pretty obvious is because if you can stack the technologies on top of each other, so the previous wave or the previous waves even support. The next one we need electricity for the internet. We need the internet now for scaling AI and so on. So they built upon each other and that's why we feel that everything is accelerating these days.

Andreas Welsch: 8:00

To me that is fascinating, right? Especially the layering of things. And, if you've been in the industry for a while, you have seen this, right? Whether it was your dial up modem and cable now on your phone and whatnot and, all the services that that enables. Now, to your point, add data, add AI on top and, whatever is next. So it also, it feels like things are moving indeed faster, right? We've been talking about this for, a long time and you observe this probably in your own lives too, but it's sometimes getting to the point where it's not only overwhelming, but where you feel disrupted and maybe where your business feels disrupted. And what are you seeing when you work with companies who are looking at innovation management and, who are, for example, looking at software to manage that process. Why do companies fear this, kind of disruption? And what do you think now with agents, this disruption really drives?

Christian Muehlroth: 8:55

Yeah it's, very interesting because you have if, you consider this on two axes, right? So you have the accelerating technological change. That's very true. And every, as you say, I 1% agree, everybody feels it. Everybody can observe it right now. But there is also second aspect to it which is that specifically large enterprises and big corporations, they know very well that the larger they get, the slower they actually get, and that's a problem. Because then you have basically a, a gap between the accelerating technologic change. Things get faster and faster Over the years, we've specifically seen it with the foundation models over and over again improving at a, impressive rate. But then at the same time, we see organizations always running behind because they have processes, they have politics, they have, some policy stuff and which basically leads them to be slower in the adoption rate. So the bigger they get, the more processes, the more politics, the more policies they have, which means ultimately that they actually slowed down. So you have an exponential acceleration in tech. You have actually an algorithmic rate of change of those large organizations. They're getting slower and slower. So that means that the risk of disruption is pretty real because it's even for, those organizations, it's even a super exponential, it's more than an exponential rate of change because they get slower and that's where the risk is.

Andreas Welsch: 10:31

So we've seen these examples time and again, right? Kodak who dismissed digital cameras and we believe people still want to take pictures in analog and film and things. We've seen Blockbuster who didn't see Netflix coming. We had who, Nokia, who didn't see the iPhone coming. All of these. Are those the kind of examples that we should be looking for in our own organizations, or are there other examples? For me it's not quite that obvious. And by the way, it's only obvious in hindsight what they, yeah.

Christian Muehlroth: 11:08

And that's the main point. It's always, unfortunately, it's always obvious in hindsight. But on the other hand, I'm surprised that we, as with me the collective of organizations that are on the market. we we don't, learn too much from history because there is tools and methodologies and ways for example, there is corporate foresight. And I'm, not, saying that organizations should engage in year long scenario creation techniques because that's probably an outdated way except for your public agency. But specifically private companies who cannot look forward right now, 3, 5, 7 years. It's, rare. To be able to do that. But there is systems, algorithms, and ways how to understand signals of contin of change. How you capture them, how you interpret them, how you bring them in the organization, and how you also use them for decision making. And I'm, always surprised because in, in, specifically. Turbulent econom e economic times, which we in 2025 might have right now, depending on the region where we're in right now. But then specifically companies deinvest in those capabilities and they go back and focus on the core business. They actually should be the exact opposite thing. I know it's hard in these times. But specifically in those times, you should watch out for the next thing, for the next move and have a very rigid strategic intelligence. But that's not what they do. So they deinvest in stages where uncertainty is the highest. Where actually in those stages they should invest a lot in exploring potential future pathways.

Andreas Welsch: 12:49

So how does that then play out with Agentic AI and I, feel we're still early by the way. I saw Gartner's generative AI hype cycle and generative AI moving into the trap of disillusionment. Great. That means. Relief is near. People are figuring out what this can really do beyond shiny objects and throw away proofs of concept. But with agen AI, do you think this is really going to be that big of a disruption as some of the industry players and marketing departments are hyping it up to be?

Christian Muehlroth: 13:22

Yeah, I think there is two, two things. One is the agentic AI is going to move us closer to an abundance economy because of the collapsing cost that's behind it. So right now agents are ki like interns that never sleep. They can be super smart like PhD level in certain domains in general they consider them as interns because they need oversight. But because the, labor cost trends towards zero or whatever, you pay to these AI specifically for some bridged tasks or some, mundane tasks. You don't have a headcount constraint anymore. You actually have a throughput constraint. And sometimes maybe even a lack of ideas. Okay, what should we do next? Very practical. For example, in. You can do much faster. Market scan preparations even coding of course. These models are so great at producing software code. Yes. They might not know all your code base yet, but it's brilliant. Yeah. So that's in the dig digital space and also in the physical space. When we have less boundaries on land. You can create, I don't know, maybe even cheaper physical production, higher buildings faster production of facilities or whatsoever if you now do this with robots or machines, whatever this is. So that's the first thing. It moves us through to abundance, specifically for labor. And that is in combination with some, something that we call. Amplified intelligence or AI is amplified intelligence because humans still set the intent. We are, we still have the agency. But the AI agents, they expand your capacity, sometimes even your capability to execute stuff speed, precision and maybe even latency. So two things are interesting because they amplify human intention, and if you're really good at something, you can use AI to really 10 XA hundred x. What you're doing. And at the same time, it gives us specifically for mundane tasks, basically abundance and labor. And that, that's really disruptive.

Andreas Welsch: 15:47

I've been thinking about this quite a bit and that's a lot of the conversation right now. Also if you look at media and what CEOs are publicly saying, especially those that are leading or want to be perceived as leading. A lot of this conversation is about we are reducing the number of people. We're not hiring people because we want to look at AI is this the right, and the only way to look at this or, why is nobody saying it saying about we're giving you agents so you can 10x or 100x what you're doing. What's your perspective there?

Christian Muehlroth: 16:21

I think you're spot on. You're really spot on because the truth is probably both. Yeah. The truth on one hand is if you consider AI and agents as a way to automate specific. Labor specific tasks then yes there will be people being laid off. We, already see it specifically. For the tasks where a text-based AI we, currently just think about lms. There are different ways of artificial intelligence, but right now we focus a lot of text-based AI, so large language models and the like. And there is just so much you can automate with this sort of intelligence. So it's the truth. But at the same time everything that requires human agency and also human creativity, human ingenuity. And these things will remain and probably the people who already are pretty creative, pretty have a great level of ingenuity agency. They now have tremendous opportunity to make a career, to make a business and to bring this to life. So it's, both sides.

Andreas Welsch: 17:51

So, then where do organizations fall short? We know they should embrace this over short or long, they will eventually look at this even if it takes a couple years like we've seen with cloud or with mobile and other trends. But where are they failing in adopting this AI right now? And what's your recommendation? What are you seeing?

Christian Muehlroth: 18:13

Yeah so, we are seeing, first of all, that there is no redesign of legacy processes. So that means you just take inefficient processes and you put AI on top of it. But if you, I don't know, if you automate a shitty process, you get an automate shitty process. Basically. Yeah. So that, that's that, that's not. So you need to design your processes to work in this new area. And if they really don't make sense then, you shouldn't be automating it. For example the, in, in Germany we are digitizing our public processes to a large extent dispute. Trying since a couple of years. So what we do is basically we, had all these forms that you have to hand into some public agency to get some stuff and you have to write physically write on those forms. What we did is when we digitized this process, so we just put the forms on the screen. But we never we, never think about does it actually make sense to have a form? For example, if you, I don't know if you if, you have a newborn, child. Yeah. You still have to hand in a specific a specific document to request some things from, the government. But the government already knows anyways that you have a child because there are other processes registering your child. So why should they actually have to fill the form? Why couldn't the government just do this automatically as they know I have not your child. So we, just took the same process. Digitized it and made them form on the screen. And if organizations do the same now with AI, have legacy processes and just try to automate them, it's probably not a good idea. So it's very expensive. Yeah. If you do this that's one of the things. So just reconsider what makes actually sense and it's a great opportunity right now to do it. And the leading organizations do it.

Andreas Welsch: 20:05

Yeah. But it costs money and it takes time, and we don't know if it works. And I still want a paycheck in a bonus and a raise. Yeah. Yes, innovation, but please, not too much and not too much at the same time. Is that something you encounter or you perceive?

Christian Muehlroth: 20:24

Yeah. It's, the truth. 100% because. It's comfortable. Yeah. Specifically, being in an organization may, maybe you're in there for 10, 10, 15, 20 years. It you have a comfortable job. Why change? If there isn't pressure? Unfortunately, and there can be positive and negative pressure for sure. So it, it goes both ways. But if there is a certain tendency and an aversion to change. But there is also people who really want it. We, again, we see both in, in big corporate, we see people that say, ah go away with this stuff, or maybe I can set it out or wait it out, or, I don't know, just see what happens. We see this a lot. But to be, to be honest, we also see the opposite. We see great people who have agency, who have ideas, who wanna push forward, who also wanna make a career. And for them that's a perfect time to grab these opportunities and really deliver impact in those organizations.

Andreas Welsch: 21:24

So you have super progressive and excited and tech savvy. Folks on your team or in your organization, they want to try this out. They say, yes, I want change. We need to do this differently. And then you have the other side of the spectrum who say please leave me alone. I just want to collect the paycheck and be here my nine to five, and then have a life outside of work. And by the way I'm retiring soon, or Yeah, to your point, and I like change. How do you bring these two together? And is that maybe even the solution to making progress but still taking everybody along on this journey?

Christian Muehlroth: 22:01

Yeah it's a great question and I think to some extent the decision makers who actually decide which way to go, they just need to be honest with themselves and with the organization. I think it is a very noble goal to say, let's. Get as many people as we can on board. I think it's probably the right way to do. But that's theory. And, then there is practice and you, always have people who just do not want for specific reasons. Maybe some can, some don't want it doesn't really matter. But that's just normal. And I think while this is maybe a ground truth, specifically senior executives and decision makers, they should. Then rather go with an 80% solution and say, we need to move on. This is the path. Everybody who wants gets on board and the others. We need to see what we should can do for them and how we can help them basically get along. But, as much inclusion as, is needed. Perfect. Do it. But you also need to think in terms of the enterprise. And as we discussed in the beginning, we have this disruption risk and it's imminent. It's really here. So maybe go with the 80% solution and do whatever it takes.

Andreas Welsch: 23:16

See, I'm wondering if, some of that is also more culturally relevant. I know Germany has a very democratic process in companies and we should talk to many people and make sure everybody's heard and included and so on. And to your earlier point, I think that can a lot of times slow your business down as well. Yeah. I feel over here in the US, it might not be that long of a deliberation. We see the opportunity, we see the potential. We also see the pressure. What happens if we don't do this if we don't adopt this? And I get the sense people are moving, businesses are moving relatively fast on many of these topics. Certainly not all. But maybe culture plays in into that too, I don't know.

Christian Muehlroth: 23:58

Yeah, for sure. There is times where a more democratic approach might work a little bit better and there are just times where a little bit of more straightforward approach might work better. It's like a pendulum that swings, it depends on the situation of the industry or the company of the enterprise. And for both, have a reason to exist. But you just need to know when to deploy deploy, which approach and at which time.

Andreas Welsch: 24:21

Yeah. Good, point. Yes. So definitely your leadership skills in figuring out when do I invite a dialogue? When is this a decision that move forward with or to your point, an 80% solution that helps us move forward. So before we wrap up, I'm curious here, what do you recommend leaders do in addition to being sensible? What approach they should follow when it comes to leadership, but what should leaders do as they want to arrive this wave of disruption? And instead of being disrupted, what's the one thing they need to do right now?

Christian Muehlroth: 24:59

Yeah, so the one thing that is often overlooked and that leads to AI Tourism typically is as boring as it may sound. Yeah. Laying the platform and the data foundation for it because Mo, most of the AI applications we've seen, they all are based on dirty data which means wrong data sources. Just an example we've seen many companies try and use an experiment with public large language models. For any sort of innovation or research and development tasks. But there's a couple of problems with it. For example, public LMS currently lean a lot towards open web sources like Wikipedia, R and, Reddit. There is some statistics that kind of try to prove it, but for, let's say a concise r and d strategy, our new product development strategy need clean enterprise. Data, that's customer insights, that's patents your own patents to see where you have technological capability, but then also competitor information. You need to take your current r and d pipeline into account. You need to take your current product and services offerings into account, and so on. Otherwise you lean too much into the public LLM data. So we are actually in a very clean enterprise pool to make this usable. Maybe even a shared taxonomy that's, for example, what we enable in atix. So we, we make sure, so we, don't use public lms. We have a different approach. We didn't train our own, but we deploy one specifically in our infrastructure. Data never leaves cynics, it doesn't go to open eye, it doesn't go to tropic, it doesn't go to complexity. It's all within the existing data privacy contract you already have with Atix in that case. So it's much more safe and secure and we can prove that. So that's the thing later. Foundations specifically with the data, otherwise you have journey data and you have just open web source data, which is not really useful in, in the specific r and d and innovation context.

Andreas Welsch: 27:01

Wonderful. Thank you for sharing. Chris, we're getting close to the end of the show and I was wondering if you can summarize the key three takeaways for our audience today.

Christian Muehlroth: 27:10

Yeah, sure. Let's go to the beginning and let's, so the first one is the abundance AI, the abundance economy. So because AI means amplified intelligence, I would look. Specifically towards use cases and empowering people that want the change and amplify their human intelligence with AI, amplify their creativity, give them the most expensive tools. Don't I, understand the approach of a. Rolling out AI to everybody because you want to leverage or leverage everybody and raise the bar for everyone. Okay? But that's a lot of experimentation and leads to AI tourism because everybody tries out some things actually wasted potential and wasted time and money. So my first suggestion, recommendation would be. Think of AI as amplified intelligence and provide the tools to the people who really want the change and give them the best tools, the most expensive ones, because that's 10 Xing a hundred Xing, sometimes their capabilities. And I think this is, one key, takeaway. The second thing is. Take disruption seriously because we've seen it a lot. You've mentioned so many examples. Yeah. We're tired of the Kodak and, Nokia examples. Yeah.'cause they've been decades ago. But I'm pretty sure we'll see many of those examples in the, future. Don't undervalue and don't overstate the two big to fail Disruptions really is really imminent right now.

Andreas Welsch: 28:41

And make sure that your company doesn't become the next Kodak or Blockbuster that will tell for generations.

Christian Muehlroth: 28:47

Yeah you, don't wanna be on that list, really. You just don't want, yeah. Yeah. And then the third thing is get the foundations and the data. It is, it takes some time. But it's not a hard, it's not a hard thing to do, but it's an important thing to do. And if you are doing it and if you have really clean data specifically and everything r and d and innovation and over time you stack more and more technology use cases on top of it and add AI and agents to it, this is maybe one of the most competitive advantages you can get to make most out of your data in the next years to come.

Andreas Welsch: 29:25

Chris, thank you so much for joining and for sharing your expertise with us. It's always a pleasure talking to you and thanks much for the invitation.