E21 Integrating Emerging Technology & Innovation ft. Lars Haus Olsen
September 2024
84 minutes
E21 Integrating Emerging Technology & Innovation ft. Lars Haus Olsen
September 2024
84 minutes
Stream on:
How can companies stay ahead of the curve in a world of constant technological disruption? This episode features Lars Haus Olsen, an expert in innovation and emerging technologies. The discussion focuses on how businesses can effectively integrate new technologies to drive innovation and growth. Lars shares insights on navigating technological disruptions, fostering a culture of continuous learning, and leveraging digital tools to enhance productivity. The conversation also explores the importance of adaptability and forward-thinking strategies for staying competitive in rapidly evolving industries.
Key topics include:
0:00-07:53 Introduction To Lars Haus Olsen 07:53-13:11 Inventiveness Versus Innovation 13:11-17:14 IoT In Commercial Real Estate 17:14-24:08 Hype and Practicality of Generative AI 24:08-42:51 Importance of Data Integration for AI 42:51-44:44 Clean Data and Accurate Data Outputs 44:44-47:36 Limitations and Risks of Generative AI 47:36-01:08:27 New Technology on Productivity and Change Management 01:08:27-01:23:43 Technology Investments and Innovation by Senior Leaders
is a global, leadership-strategy consulting company. 3Peak creates the roadmap that aligns behaviours, relationships and Functional Human-Systems™ to achieve your business strategy.
Co-Founder holds a Ph.D. in Neuroscience, and did extensive research in Consciousness, Trauma and Physical, Emotional & Mental Health in various Institutes and Research Centers around Europe.
Co-Founder is one of the most sought after therapists in the world, mastering diverse modalities and opening wellness centers in Istanbul, Santiago, New York and Berlin. Her approaches bridges transpersonal psychology, meditation, bioenergetics, family- and business-constellations and more.
Co-Founder has extensive experience advising Fortune 50 and FTSE 100 C-Suite Executives in leadership, strategy, team dynamics, and organizational change. Before coaching, Mino worked in finance, management consulting, and mergers and acquisitions (M&A).
Mino Vlachos: Hello and welcome to the 3peak master leadership experience. My name is Mino Vlahos and I'm the co founder of 3Peak Coaching and Solutions, where master leaders build healthy systems. Our company provides coaching and team workshops to executive leaders. Today, I am joined by Lars Olsen. Lars has spent the last 15 plus years working with emerging technologies and innovation across multiple industries. In the last ten years, he's focused on emerging technologies in the commercial real estate space. He's worked across the spectrum from helping tech startups to get their solutions out to the market and get adoption, to working with large commercial real estate players, helping them understand which new technologies are worth buying or investing in. So I'm very excited to welcome Lars to the podcast. Thank you so much for being here, Lars.
Lars Haus Olsen: Thank you, Mino. And we're excited to be here and have this conversation with you.
Mino Vlachos: I really love the topic we're going to talk about today, which is all about innovation, technology and how to really infuse that into companies to begin with. I know this is something that we've talked about personally before. If you're thinking about, okay, I want to know what's out there, what is innovative technology, what is emergent technology? What are some of the ways that you are able to stay in touch with all the evolving technology out there?
Lars Haus Olsen: Yeah, it's a good question because it can quickly become overwhelming, at least today when you have, you know, a lot of good blogs and podcasts and websites and news sources that obviously do cover technology and emerging technologies. And there's, you know, luckily there have, there's a more and more sort of niche stuff that you can focus on and find the stuff that you're looking for and not be that overwhelmed. But I will say trying to keep tabs and we can maybe get into that a little bit more later when we talk about process and how we can go about actually filtering through all the different technologies that are out there. But in general, I think trying to know and understand and be aware of everything is really tough. And I was kind of lucky that when I started working with emerging technologies in the real estate space specifically, that was very new, like ten years ago. So in the beginning, I could just keep a spreadsheet with a handful of blogs and sources where I could find the, these emerging technologies and new solutions and, and talk to the companies and try them out and essentially keep tabs of everything. But quickly that kind of spiraled and, and, and there's just so much when something works and there's a demand for something, you know, that there's just going to be a lot of supply as well, very quickly. And so, yeah, the short answer is there's so many sources and places you can go to find solutions. But honestly, these days, the solutions find me more than I find them, because when you've been around for this amount of time, you essentially have, you know, I've been to all the trade shows for many years, and, you know, with LinkedIn, you know, these tech startups and so on, they are really proficient and good at lead sourcing and finding their buyers. And now being one of those buyers or helping buyers vet those technologies, they tend to find me, which makes my life a lot easier. But, yeah, that's kind of how I go about it today versus how I went about it ten years ago, finding technologies.
Mino Vlachos: Yeah. So I want to be careful because it sounds like you might almost be encouraging. Cold emailing, cold calling. I don't want to overwhelm you, but.
Lars Haus Olsen: Yeah, for sure, I've been on the other, I started out in sales, so I've sold a lot of emerging technologies as well. So I definitely appreciate and respect the game, so to speak, of sales. And I find that fun. And when someone approach me and have a really strong pitch and good value propositions that fit with what I'm looking for in this moment in time and can show and demonstrate value very quickly, I love that, that I'm a sucker for, for a good sales process. So, so, yeah, bring it. That's all good.
Mino Vlachos: I love it. I've seen a similar journey to my own. The more that I do sales or reaching out to people, the more I almost, for the first time in life, enjoy being pitched once in a while. So there's a reciprocal. I was like, I can't just be pitching people and not be open to being pitched. It's not, that's not fair.
Lars Haus Olsen: Right. And I think personally at least, you know, I think it's easy to kind of, when you get a bad pitch or you get something and, you know, respond negatively or, and so on. But I try to either, if I don't, if it's way out there, I don't, you know, I typically, I try to respond every time and just say, you know, I'm sorry, I'm not your target audience. Good luck. Because just any response is better than no response. When you're on that other end, just knowing that you at least missed your target will be helpful. So you can cross that lead off your list or that prospect off your list. So I try to kind of pay it, pay it forward like that because I just remember how painful it was being on the other end and just have all these unknowns floating out there. But it is, sometimes it's hard not to respond in sort of a snarky way if you, if you feel like they're being super obnoxious or aggressive and they're way off their mark, but I don't really do that. But it is tempting sometimes.
Mino Vlachos: Yeah, I totally hear you. So one of the things that this is going to be a bit of a tangent ish, but I'm going to go into the academic space for a moment and then we can circle back to kind of the technology space. So what we've seen in the academia space is that the amount of total papers that are being published is going through the roof. It's skyrocketing. So there's a lot of research being done. But then when they looked at the actual innovativeness of those papers, there's actually a pretty big decline in how much innovation is happening in the research space, in the innovation space. And so there's this trend of a lot of activity, a lot of noise, and then innovations going down. And so when I think about the technology space, and I would love to hear if you see it differently, I have the sense that there's a lot of inventiveness. So we're inventing a lot of stuff, but it's utility or truly innovative kind of capacity is probably not as much as the hype. What have you seen in terms of the inventiveness versus innovation? And then the second question, which we can also do a follow up, is around, well, how do you start to process and filter what is truly good versus just a lot of hype or buzz around it.
Lars Haus Olsen: Yeah, no, I think that brings up and something that I see a lot of, actually, and I think for, you know, real estate and commercial real estate, especially when you have innovation that's targeting an industry and that industry in particular, that is historically been very slow to adapt to new technologies and so on. And what we've definitely seen is that as soon as someone gets, you know, they have a good idea on paper and people buy into the concept very quickly, all of a sudden, there's just a rush, too, because there's so much money in commercial real estate, you know, because they look at these funds and real estate companies that have billion dollar contracts and billions of dollars in assets and so on. But at the end of the day, the margins in the business, like, for instance, in the, in the operating of commercial real estate, the margins are typically very, very narrow. So there's a huge disconnect between the technology industry that typically operates with massive margins. Right. Especially on the software side, you make it once and then you keep charging for it. That's a simplification, of course, because it requires a lot of resources to maintain it and further develop any software. But the difference is still very big. Where on the software side you often operate with 30, 40, 70, 80, 90% margins in some cases, right? Whereas on the commercial side of operating commercial real estate, you're operating with two 3% margins, right? So you then have all these players that they have a good idea on paper, they get some good feedback from their target market. And so many people are willing to, from VC's to real estate investment funds themselves that are due to pouring money into all these tech startups that are then trying to provide these solutions. But getting people to actually find or finding value in these solutions and implementing these solutions turns out to be really, really difficult in many cases. So I agree with, you know, what you're saying in academia like that the, all the research shows that there's a lot of inventing happening, but there's not a ton of innovation that sticks or that there's application on it for all of these inventions, right? And I see that every day and I've myself like what I've been doing the last, especially the last five or six years, specifically what I've been doing is take, you know, assessing merging technologies, trying to, you know, find a use case for it and then piloting and trying those technologies out in a, you know, semi controlled environment. And you know, that could be anything from a software that helps, you know, the backend of the operating or, you know, hardware that's a physically placed into buildings that will count people that walk through the building and so on and so forth. And you know, it's easy to create use cases for a lot of these technologies, but deriving value and proving ROI and I'm sure we'll get a little bit more into the specifics around that. That is very, very hard, especially when you're dealing with a very small margin type of industry and business, because investing in these technologies often cost a lot of money first to buy the technology itself and then to implement it. Make sure you have adoption and make sure that people are using and taking advantage of the new technology, especially when a lot of these folks have been used to doing fine without the technology for forever. That sentiment I live and breathe and experience every day. So I see a lot of innovation, but there's honestly very few things that we can really get a lot of value out of, to be honest. But at the same time, the clients of my clients are demanding technology. You know, all the rfps that my clients are responding to about, like, what are you doing with new technologies? What new technologies can you provide us with? And so on. So in a lot of cases, we, you know, we provide suggestions around technologies that we can experiment with and try out and pilot and, you know, so there's definitely a lot of technology out there that do have value, and there's a lot of innovation that is super legit, but there's definitely a lot more hype than true tangible value that's being brought, I'd say.
Mino Vlachos: And as you think about what you'd like to pilot, have you seen any patterns in terms of, like, winners and losers? Like, what are the things that you might say, hey, there's a chance this is worth a pilot, or you might go to a conference, or someone might message you, and you'd say, I've seen through experience that this might not really work. We're not going to pilot this kind of emergent technology.
Lars Haus Olsen: Yeah. So, for instance, a lot of things, you know, a lot of technology that I've worked with in the last couple of years have been IoT, specifically, and there's, you know, a lot of good IoT technology out there, especially for the commercial real estate and especially after the pandemic, when we've seen that occupancy levels are much lower in an office building, for instance. So, for instance, we have seen a lot of value and a lot of demand for building operators to understand what kind of usage the building has. Right. So what are the occupancy levels? What are the true occupancy levels? How many people are using the building on a day to day basis, which area of the buildings are they using, and so on and so forth, so that the operators of the building can reconsider the way that they maintain and operate the building. Right. From the janitorial staff to the engineering staff. A big office building, a 50 story office building, is a super complex, almost breathing organism that you can't just have the bathroom sit unused for weeks or months on end on the 12th floor, the pipe's gonna dry out, there's gonna start smelling and things like that. You know, the building has to be in use constantly, and all the parts of the building has to be used constantly. So, for instance, the technologies that we've seen, that's been super helpful to both the tenants and the occupiers of the buildings and the people operating and owning the buildings have been to really put in occupancy technology and occupancy sensors that really reads, you know, as I said, you know, how many people are actually in on what days, and, you know, can we spot trends? Can tenants themselves, you know, consolidate to using, you know, parts of the floor that they have leased versus the whole floor? Because as humans, is easier when, you know, maybe you work from home on Mondays and Fridays now and then you go into an office on Tuesday, Wednesday and Thursday. But when you go in and you have a big office, you may try to find a seat that has some privacy if you're in an open office environment. And that means that you're then the only person that sits on a whole row of desks versus having five people on the same row of desks. So then everything needs to be clean. So that's going to cost more. And in janitorial services and things like that, the technologies that we've seen a lot of value in has been these occupancy technologies that gives a better understanding of exactly what areas of the building is used, how much is it used, when is it used, by whom is it used, and so on and so forth to service the building better. On the flip side, the technologies that we've received a lot of hype around and has especially been these sort of tenant experience applications, they're called. So there's been hundreds of millions of dollars that's been invested, maybe even billions, into all these tenant experience apps. Everyone has had these ideas that everyone that works in the building is going to have an app where they book a meeting room, where they order lunch, where they have their dry cleaning, where they have a dog walking service, where they have packages delivered to their offices. And God knows there's so many. So those are the kind of things that, yes, works really well on paper, but at the end of the day, what we've seen, there's no one's using these apps. So then the developers of the apps, they've tried to, quote unquote, force people to use the apps. For instance, in a multifamily setting, it's easy to force people to use those apps because you say, hey, you have to pay your rent on this tenant experience app, okay? So you're already using the app once a month at least. And then there's maybe other services. Maybe you have your packages delivered to whatever part of the building that they allow packages to be delivered. And then you get a notification through that app. So it's easier to drive adoption in sort of a multifamily setting. But on the other side, in an office building setting or a commercial real estate setting, it's much harder because the tenants themselves, they don't pay rent. Unless you're maybe a wework one man van or something like that. What we've seen is, okay, those applications have tried to force adoption through that. They have to use the app to maybe access the building, you know, okay, so you're using the app every day to scan at the turnstile and get in, and therefore, you're also gonna book your dog walking services at lunch through that app and so on. And I think maybe the pandemic certainly impacted that because there's just a lot less people. We're seeing, best case scenario, in a market, maybe 50% occupancy in office buildings. So that's a pretty big hit that they're taking on their sort of potential customer pool and operating pool. So all those models that were built to underpin the value of these tenant experience applications that we're going to do, all these amazing Rev share business models where, you know, the building operator and provides this tenant, but would get back 100 times what they're paying for it because of the rev share models for all the partner businesses they would put on these apps, we haven't really seen these things take off and provide any significant value at all. And, yeah, starting to see more and more of these companies go under. And even though I said with the occupancy sensors and IoT sensors have brought a lot of value, what we have seen is that, you know, without getting too specific on technical requirements and so on, is that a lot of these companies, they build these sensor technologies, and they're trying to target everyone, like, you know, the. The owners of the building, the tenants of the building, the different service providers for the buildings, and so on. But you can't provide a single dashboard that visualizes that data that's useful to all those different consumers or end users, because someone who owns the building wants to know occupancy levels and things like that, whereas someone that operates the buildings or provide janitorial services wants to know which conference rooms were used in the last 24 hours so they can clean only those conference rooms that have been used. So they're not cleaning the conference rooms that haven't been used, because that's a waste of time and money for everyone. And, of course, the users of the actual tenants or occupiers of the building have yet another set of analytics that they probably would be interested in looking at. So what we've seen with a lot of these hardware providers is that they're trying to cover the cost of the hardware. Hardware is super expensive and difficult to make. So they're trying to, you know, get their recurring revenue and their income from the software subscription that goes along with, with the hardware, but there's just not enough, like they can't provide, you know, quote unquote dashboards for all the different use cases. So what we worked on is more like trying to be completely hardware agnostic and building our own independent data layer that can take information from anywhere. And then we customize the outputs for, specifically for the different types of end users. That's really the only way that it's viable and feasible, and that costs a lot of money, requires a lot of resources. And what we're seeing is that a lot of these hardware companies are either consolidating or going under and, or being forced to white label others, other sensor providers as hardware. So when I'm starting, you know, I'm getting, you know, let's say in a year I look at a couple dozen different hardware provider, hardware providers and half of them will use the exact same sensors from some other hardware provider that have figured out the technology. They just put their own sticker on it and create their own dashboard that's proprietary and their secret sauce. So I'd say those are like the very specific examples or two opposites of something that on the hardware side, with the occupancy sensing stuff, you have value. Very straightforward value propositions that have been demonstrated to bring and have value, whereas on the other hand, you have these applications, tenant experience applications in particular, that had a lot of promise, a lot of hype, a lot of investment, but really hasn't lived up to that expectation. And you won't know until you try and fail or try and succeed. So it's been an expensive lesson for a lot of us in the industry trying to figure out which technologies do bring value and which do not.
Mino Vlachos: For those listening, can you explain a little bit IoT Internet of things? A little bit, if someone isn't familiar with this part of technology, what IoT is and encompasses?
Lars Haus Olsen: Yeah, for sure, for sure. And I should probably have done that upfront, but essentially think of IoT is kind of a catch all for everything from your smart fridge to your smart sprinkler systems to your occupancy sensors in our people, accounting sensors to, you know, anything, any piece of hardware. Meaning, you know, a fridge can be hardware, any sort of physical object that you or someone or something interact with that is connected to the Internet and typically sends some type of data to a cloud instance or that you're sending something to that piece of technology. For instance, if you have a smart lock on your front door and you have someone that is coming to water your plants while you're on vacation, you can open your app, open the door within a specific timeframe for your neighbor to come in to water the plants. That is an Internet of things thing. And then on the other side, on the more extreme side, in a large data center or warehouse, you could put vibration sensors on every piece of sprinkler system, h vac, any type of piping or ventilation fans and so on. And then you can detect any sort of unusual behavior in the vibrations and then send an alert to the engineering team that will come and have a look at a faulty fan. Or, you know, or you can have a water detection, water leak detection sensor if there's any flooding or spilling or from a clogged toilet, things like that. We even put sensors into soap dispensers, into trash bins in airports, so we know exactly the soap levels and so on. So we can be more proactive in the way we go about making sure that, you know, the end users of these spaces have the best possible experience. And all of these things are connected to the Internet. And that's what we call, you know, Internet of things. So it's kind of a very broad term, but, yeah, it's kind of in the name. If it can be connected to the Internet and it has served some type of utility, I'd say it's an Internet of Things item.
Mino Vlachos: Yeah, I was going to share that when I think it was around 2016, I was working with a CEO of a pretty large cleaning company. So they make the products that clean a lot of these large sites like airports. And back at that time, 2016, she was already talking about Internet of things and putting in sensors. And, you know, I had never really, at that point, considered Internet of Things IoT. And so everyone was really talking about how visionary she was, and it's really, I think, come to pass. And this, you know, as an end user, you go through the airport, and I don't know how many thousands of people go in and out of airport bathrooms on a daily basis. And if everything is great, you kind of just don't think about it. You only start to think about it when things are dirty, when things are not working. And yet we don't consider how much effort must be put into cleaning these bathrooms at such a large scale with such large volume, and to also do it in a way that's also efficient for the company, organization, whatever the building, whatever we define as the entity where, yes, you could have, of course, someone just standing there 24/7 cleaning, but it's not the most efficient allocation of resources. Right. So it's pretty incredible what IoT can do.
Lars Haus Olsen: Yeah. And I think it's a true, and this is where innovation has been a true game changer and is affecting valuations of big assets. Assets meaning, you know, different types of commercial real estate types, from airport to warehouses and so on. And in the way you operate these buildings, to your point, like we can now with these types of technologies. Instead, let's take a step back. Like in the past, you would hire a cleaning company or an engineering company, or, you know, anyone that helps service the building, makes the building run. You would agree to a set of predetermined service intervals, cleaning intervals, and so on and so forth, right? You clean the conference rooms or the bathrooms x amount of times a day and so on. But now you can be more dynamic in that approach, right? So you can use all these sensor technologies to then only service the areas that needs to be serviced and when they need servicing the most, then on the backend, a lot of the things that we're working on now with predictive maintenance, we call it, and it's AI. But AI is also a very broad term these days. But we're essentially working with these models that will, based on trend data, help us analyze when a piece of equipment may fail so that we can go and replace it before it fails. Because it's always more expensive to fix things retroactively instead of fixing things up front, essentially. Right? That's why you take your car for an oil change every six months or every year, or every five or 10,000 miles, depending. Know what kind of car you have? Of course, if you have an EV, you don't need to do that, but, you know, with an internal combustion engine, you need to service it regularly, at regular intervals. You're not. If you wait until the oil lamp is flashing, that may be too late. Right. And so it's kind of the same concept, but now we're trying to be even more predictive and at the same time also improve. So, to your point, as you said, with the CEO that you've been working with, you've now seen that a lot of places have these smiley face buttons that provide feedback on the end user experience. One of the things that we're doing now is experimenting with putting in these screens and displays and everything from airport restrooms to conference rooms and office buildings and so on that have those smiley faces so we can correlate the user experience with the occupancy levels. Is the user experience poor because there's just a heavy load of use on any particular space, or is the experience poor because the soap dispenser is always out, or the toilet paper is out, or the lights out, or the toilets overflowing, whatever it is. And then you can provide instant feedback through these displays. So we have now dashboards that we provide airport operators with, where they can monitor real time and pull daily, weekly, monthly reports. That gives them insight in how they can improve the operating of the airport and improve the experience of the people using it. So that's, again, very useful and tangible values that can be derived from investing in these types of technologies, for sure.
Mino Vlachos: So this question is really just about one of my biases and pet peeves. So I want to tell you kind of my bias, and then I want you to correct me or tell me your opinion. But right now, there's so much, again, hype and buzz around kind of the generative AI, the chat GPT LLM, like, large language model, kind of part of AI. And every company I go into, it's like what everyone's talking about, and everyone's trying to solve this problem of the GPTs and how we're going to automate the workforce, and there's a lot of talk about it. And yet, when I look at statistics in the United States, about 4% of workers are actually using something like chat GPT or claude or any of those models on a regular basis, and yet 95% of the conversations are about this thing, and it's dominating every conversation. There's a lot of, again, fear and optimism and all this stuff. What I'm. I don't know if it's a worry or just a thought, but, like, I'll say it's a worry, maybe, that in this conversation, we're kind of missing that there's so much other technology that's also developing at a rapid pace and also might be reaching a more mature phase. For instance, like, IoT feels like it's in a more mature phase than, like, chat GPT, like, right now. So do you feel like there is kind of a. Is it like we're in a hype period? Do you feel like it's actually warranted? Is there overshadowing of other kind of cool emerging technologies? That maybe should be getting a little bit more attention right now.
Lars Haus Olsen: Yeah, no, those are, I think there's for sure a lot of hype, but I do think there's a lot of promise. Personally, I don't really, you know, I was very excited about, like, everyone, you know, all the promise that chat GPT brought and thought my life was going to be, you know, so much easier and so on and so forth. But honestly, I don't really use chat GPT myself. I've tried to use it and. But personally, just for the work that I do, and it just isn't super helpful other than summarizing maybe some long research paper or bring out some, or that I can. I've experimented with putting, you know, things that I've written into it and ask it to do better or find faults or, you know, stuff like that, that I think a lot of people have tried using it for. But for me personally, it hasn't brought a ton of value, sort of the generative AI that we see from these things, but at the same time, we have experimented with, and we think there's a lot of promise for this technology on the business side. And I think, as an example, we have a challenge in the commercial real estate industry that a lot of the, we have a lot of data. We're producing a lot of data. The buildings produce a lot of data from the building systems to the people in the building, and so on and so forth. Right? So there's so much data that's being produced, but a lot of this data is siloed in a lot of different systems. So you may have a building management system or a BMS that's housing a certain set of data. You may have a CMMS system that does all the work orders for the building that exist in one silo. Then you have the occupancy and sensor technology, vibration sensors, and all those things that's gathering data in another silo. And then you have the tenants and the users of the building with their tenant experience app or whatever it may be, for the HR data or whatever other. And these datasets are powerful if they can be brought together in certain ways and leveraged, but getting those pieces of data into the same pool and then be massaged and abstracted and apply business logic to that data and algorithms and so on, is very, very hard. And getting just different systems to talk together is very, very hard. And getting the data sets to make sense to each other is very, very hard. So, for example, let's take a building engineer and a building operator. They have what would be super powerful when it comes to predictive maintenance. And this is where we've experimented a lot with AI because we think there's a lot of promise there. For example, you have all these building systems from the h vac. So the ventilation, the heating, the cooling to sprinkler systems, ingress egress systems, alarm systems, all of these things. You have all of this data, of course, that's being corralled and gathered and stored somewhere in some type of cloud. But at the same time, you have building engineers that are going and checking on these systems that, you know, write stuff down by hand, or maybe they even have an iPad where they write down, you know, when they make their rounds and check on these building systems and do their diagnostics and logging, you know, pressure levels on pipes and what have you. Right? And the rotation at which the fan is spinning and things like that. But each individual engineer will describe the same problem in a different way. So if you have ten building engineers that are looking at a fan or a ventilation systems and they are experiencing the exact same problem or anomaly in the behavior of the system, they may use ten different ways in describing that particular behavior and issue. So then, okay, so we may be able to tell the LLM or the AI system, like, if XYZ is happening, this is the prescribed fix. But at first, you need to then teach the system that there is ten different ways in which this particular symptom is being, you know, described or discovered and entered into a system. And just a sheer challenge in getting everyone on board to using standardized ways of describing the same thing and problem. And even, you know, we've looked at uploading, you know, a lot of all these pieces of equipment comes with some type of service manual and, you know, user manuals, essentially. And you can upload all of those and, you know, say that, you know, after so and so, however much time has passed, you probably need to replace a bearing here and there so you can teach the system all those things. That's a pretty straightforward way of getting to sort of a predictive maintenance schedule, if you will. But there's just so many band aids and fixes that are being put on these systems in a case of emergency or in an outage or some creative engineer who probably had a better solution to the problem than the instruction manual dictated, but then they've used parts that may be third party. So what we've then tried to do is look at, okay, accounts receivables, are accounts payable, and go into all the. Because now everyone is using software, right? Everything that's being purchased is being recorded. So we're looking at, okay, can we take all that data put into the large language model and teach it? Okay, so this was the company that we used for this particular fixed. They use these particular parts and so on and so forth. So very quickly just fixing a sprinkler system or an h vac system or surveillance system in any type of building from an airport to an office building to a school to a hospital. And, you know, as you think of all the different types of commercial real estate that's out there, from hospitals to educational facilities, you know, you can start imagining how unique each of these different types of buildings are and how they're, you know, they all have their own type of systems. Like a surveillance system for a school may be very different than for a hospital versus a data center or nuclear facility or, you know, whatever it may be. So, yeah, to get back to your point, getting use of AI on paper is very easy, very easy to create use cases and value propositions and so on. But in reality it's very, very, very hard. And we're working on it and we still see a lot of promise and we're investing a ton in it. And I work with a lot of startups that have good ideas and with these building operators and real estate funds and so on. And there is a lot of value. But I almost say, like right now, you need to have a pretty clean set of data. So, for instance, real estate brokerage, they can probably use, or I know they're using a lot of AI technologies to better understand valuations of buildings because, you know, you just have clean financial data to plug in that will be sort of same for, you know, markets and building types and so on and so forth, so, and occupancy levels, all of these things that underpin the valuation of an asset. You know, those metrics are always kind of the same and described in the same way. And you kind of have a common denominator that you can assign a dollar value to a lot of these metrics. So in those instances where it's a straightforward, clean input, you'll get a clean output, but it's a cliche that you're only as good as the data that you put in when it comes to these things and, but that's just a hard truth. So, and I think just the last point on generative AI, especially when we think about how that technology works, you know, it's, it's essentially just looking at historical records and trying to predict the next word and then the next word and then the next word or the next line to draw and things like that. Right? So it's prone to hallucinating, which, you know, a lot of people have seen in the news that, you know, you ask the Google AI Gemini about, you know, pizza recipe and it's suggesting to put glue on the pizza or that you should eat rocks to, you know, harden your teeth or whatever. You know, there's been so many obscure things and this is, I think, just proof of when you have really weird data that you're teaching the system with. Well, you're going to get really weird outputs. Right? When you're training your LLM based on Reddit forums, yes, you're going to get a pizza recipe with glue. That's just the way these things work.
Mino Vlachos: Probably some racist comments to go with it.
Lars Haus Olsen: For sure. For sure. It's, it's hard, but I do, at the end of the day, I think it's, there's a lot of promise, but there's a lot of hype. And I think when it comes to innovation in general, it is very easy to get carried away with the hype. But that's why it's so important to have sort of a sober process and a method in place that, you know, gets you, gets you to at least like some type of conclusion if the technology or the innovation effort actually brought any value.
Mino Vlachos: Yeah. What it reminds me of is I'm going to reference part of. Nate Silver is a statistician. He does a lot of forecasting with sports, elections, and he has a book called the signal and the noise. And one of the things that stood out to me is that when we look at major technological breakthroughs, typically when they come out, actually, there's about a decade of lost productivity all the way going back to the printing press in the 15 hundreds or computing, personal computing, that kind of really started to come out in the seventies, into the eighties, there was a decade where productivity fell really low and then skyrockets. So there's this kind of boomerang effect, because in the first ten years, we actually don't know how to apply certain technology. We don't know how to get the best use of it. And so it's a tough thing because we actually need folks to go experiment and figure out what does it look like in reality? How do we make the best use of this technology in reality? That there's a lot of trial and error and there's a lot of failures and there's a lot of wasting resources in that way. But once we start to understand the use case, then productivity skyrockets way more than it was the ten years prior. And so my feeling with the kind of, I'll just, my last thoughts on the kind of generative AI chat GPT kind of is we're, what, a couple years into kind of more mass use. And I understand there is a lot of both excitement and concern, but my hypothesis, if this is in the same vein, is that we're going to spend probably 510 years trying to figure out how do we apply this. It's also improving. Day by day, new models are coming out, so the hallucinations are going down, the capacity of these systems is going up, but it might take almost ten years for us to figure out how do we really use these in a constructive way. So I'm not surprised. I also don't use it that much because I'm like, it's not that useful to me right now.
Lars Haus Olsen: Right. And I think from a sort of a philosophy perspective, what I think is really interesting now is that when chat GPT was launched, everyone was floored by the technology. It's shocked and so on. And. But now we're in the middle of this super fast evolution, but we're already completely desensitized to all these major leaps of advancements that these systems are seeing, right. So we're kind of brushing off, you know, there's a new chapter that is like, exponentially better than the previous one, but it still can't walk my dog for me. So, you know, you know, I can, you know, correct my papers a little bit better, but it's just hard to see it. So I think we're just slow walking into this sort of, I don't know if we're going to have AGI, you know, any, any time soon, like a sentient piece of software, but I don't necessarily think we'll know when we have it because we're already now in this evolution. And I do find also what you were saying about productivity is super interesting because I do think that's something that we're definitely seeing every day with technology that, you know, the promise is a lot of productivity, but getting people to using the new technology or getting people to use new technologies is one of the most unproductive things I've often experienced. And take, for instance, like, all these, like, collaboration tools, like the last, there's one client that I've been working with now for three and a half years, and I think I've had, had to use at least a dozen different collaboration softwares. And every time there's a new software, there's a new implementation team that comes in that's going to help everyone get trained on the new collaboration software and so on. And I don't know how many hours I've lost to sitting through onboardings and trainings and then six months later, no one's using this particular productivity. And we're back to emails and just 90% is just emails and 10% teams messages, right? So it's just, when it comes to technology and productivity, there's for sure, you know, productivity gains to be had all over the place. But it's so much for me at least, like change management. You can't have innovation and investing in innovation without change management. Proper, good change management, because getting people to use new technologies is kind of the hard, everyone loves trying it, but changing their day to day behavior and using the technologies. I mean, I have by now lost count of how many times I've talked to a client, been like, okay, this type of technology we think will increase productivity or help you perform or things like that. And then everyone's on board, everyone from the end user to whoever signs the checks and everyone's on board. And we have an implementation plan and a training plan, adoption plan, all of those things. And just as I said, with going through that on these collaboration softwares, getting people to use new technologies, period, is just really, really hard. Even when it makes their life easier, it's really hard to change human behavior. So that's why I think change management, instilling this mentality into the people that you're trying to change their behavior. Like there has to be a, there has to be a reward, obviously, but an incentive for them to, to use it instead of, you know, maybe it's a stick and a carrot, but it's like, and even when you do everything right, and as I said, it makes the end user lives easier, just changing people's behavior is so very, very difficult. And, and that costs essentially a lot of money and resources because you have to stick with it way past when you're, well, you would just want to give up and go back to the way you used to do it because everyone's pissed, everyone's tired of, you know, learning the new ways and so on. But yeah, so getting adoption is generally just really, really hard for new technologies. And, you know, there's a lot of things you can do from, you know, VC's love to talk about gamification, just gamification. Gamify your software and everyone will use it and so on. But there's just, yeah, a lot, a lot more to it, for sure.
Mino Vlachos: Yeah. Just even a personal example. Like, you know, we have, I have two other co founders. There's three of us, three founders of the company. Like, I use HubSpot as a CRM to manage our sales and marketing process, essentially. And one of my co founders, I love him, but he keeps reverting back to Google sheets and it's like driving crazy because I'm like, we're three founders and we can't even align on one technology to use to manage leads. And I've been, so this is three people, right? And I've been in companies that are 300 people where I used to work, where passing any new technology, it seemed like it was an impossible task. And then I've been in big companies, big corporations, consulting, and they're actually quite innovative and they're actually moving into new technologies. And that's like hundreds of like a hundred thousand people. So you can have a small company where you would think theoretically, okay, it's only 300 people. Like, this should be an easier task on paper to put a new technology, a new process. And yet there's way more cultural blocks than this giant corporation that has something in its culture or formula that's enabling technology adoption. So what have you seen that helps a company actually integrate innovation and actually get with the times, regardless of size? Have you seen something that supports that process?
Lars Haus Olsen: Yeah, I think in general it's really hard to change anything unless you have buy in from all the stakeholders. And I mean, that's kind of such a consulting cliche and obvious thing, but I think it's obvious for a reason, right? So for instance, at a large, you know, one of my clients now has 150,000 employees, right? So large enterprise. And what we've seen often is that, okay, there's a ton of buy in from for a certain solution underground level. So the people, you know, certain piece of technology that will be super helpful in a day to day increased productivity, efficiency gains left and right, we've piloted it and so on, but then all of a sudden there's just a lack of appreciation or understanding at the top level or at a much higher level. And at a company like that, a mid level could be pretty far away from the boots on the ground, right? So at some point in that chain of command, there's just someone that doesn't, you know, for lack of a better term, doesn't get it, right. So they have all their folks saying like, this will improve our day to day, this will and so on and so forth. But because this one person or this group of people, they just don't see it and they don't have, because they have a thousand things to consider and, and they're busy themselves, right? So, you know, spending time and learning about this particular technology or change, they just don't have the time or the resources or appetite or interest, right. So, so that it dies on, you know, right there and then. Right. You can, and you can have it the other way around. You could have, you know, from, from the higher ups. There could be, you know, strategic reasons for investing in a certain piece of technology. They can see all the benefits because they have, you know, the, they have the helicopter view, the birds eye view. They see how all the different pieces connect. They see the bigger picture and they totally understand, like this will have the impact and they have all the decision making power and can drive this through, but no one went and got the buy in from the boots on the ground. So they're just feeling like they're getting this new process or new technology shoved down their throat and there's a lot of pushback because they haven't been educated on why it's helpful to them and why it's useful. So, you know, let's say the company invests, you know, a ton of money into this new technology, but there wasn't any good strategy for educating and getting the on the end users on board and giving them time to ingest and digest and find ways of incorporating and utilizing this new technology so it ends up not being used. So I think for the first step is to make sure that everyone's aboard, everyone's understanding, everyone's willing to, quote unquote, pay the price if that's signing a big check, taking a big chunk out of their budget and investing in the solution or spending a little additional time doing tasks that they could normally do much quicker, but, you know, in the long run, they'll, you know, value this piece of technology and change. So I think that's just really being often overlooked and, you know, especially at large corporations. But this can happen at small companies as well. And, you know, often in, in the world of real estate, it could be, you know, a small firm that has, you know, 25 people. It may be, you know, a third generation family that owns and operates and have, you know, a few hundred million dollars worth of real estate assets under management. So effectively they're working with all these third parties that help run all of these assets. So maybe, though, that they don't have all these people on their direct payroll and they don't have the power of them, but they're trying to force this change in which they are operating onto a bunch of third parties that they have no control and say over, other than that they hold the, you know, the agreement and so on. So just being strategic about, yeah, going out there, making sure that you have buy in from everyone, that everyone understands what, what it's going to take because you can't just take, you know, whoever sells you the technology, they're always going to make it sound really easy to adopt and use and get up to speed and so on. But in the real world, it's seldom. Yeah, that easy. It's easy to make something sound good on a pitch deck in PowerPoint, but, yeah, getting real people in the real world to change is very, very different.
Mino Vlachos: Yeah. And I caveat my next question by saying, like, I really love technology. Like I, to adopt whatever is the newest, like, I really love experimenting. So this question is not coming from a, I really have an optimistic lens on technology. And what we're hearing is that there's also times where it sounds really easy on a pitch deck, like you said, but it might not match reality. And I'm wondering, like, something that struck me earlier in the conversation is that you're purchasing potentially hardware or even software, and these companies are either folding or consolidating. So, and I was like, oh, wow. So you could take a risk, buy from a startup and then tomorrow they don't exist anymore, but you've incorporated or tried to integrate it into a pretty large enterprise. What are the kind of risks or downsides of being a little bit more on the early adoption side of things?
Lars Haus Olsen: Yeah, that's a very loaded question. It is. What makeshi, again, you know, having a good vetting process in place doesn't only mean that you're vetting the usefulness of the technology, but also the provider of the technology. So, for instance, that's why a lot of the people that I work with were trying constantly to, you know, if we look at hardware, we want the hardware to be software agnostic. Or if we're looking at software, we want the software to be hardware agnostic if we're looking at those types of technologies. Exactly for that reason, because we have been, I've seen, you know, so many people be burned by this, and I have myself right where you invest in something and then the company folds or gets acquired and absorbed into a bigger platform and things like that, and you can't get the use out of it that you once did. And so, yeah, no, it is something that's really, really challenging. So I think there's a fine line between early adopter and I think if you want to be an early adopter of something, you just have to be comfortable with some level of risk and have, you know, a plan b and a plan circumflex have some type of redundancy in place. Because, yeah, anything can sort of any company, no matter how big and successful the company is and how valuable their services are to some folks, those companies, whatever industry, whatever it is, could go under or could, you know, sacra or experience some type of detrimental, have some type of. Yeah. Failure or whatever it may be. So never place all your eggs in, in one basket, especially if you. Yeah. Are trying to, to leverage and use new technologies. I'm not saying that if you're, you know, if you're looking to buy slack as a company, you should buy teens as well. Right. But you have to have, because you'll have your redundancy if slack goes down, is email and text messages and phones and so on. So you kind of have to think about all of those things and think about what the risk that you're willing to take. I will say on that there is generally, like, it's kind of ironic that innovation is the first thing that gets cut. Like when companies are experiencing hard times, they look at where can we cut cost? And the innovation is often where you cut first. But that is such a short term gain, long term loss, because you could then effectively set yourself up for so much pain later. Right. And innovation is, again, very broad term and things like that. But we've seen time and time again, anyone that's gone to business school have lost track of how many case studies that they've read of companies that were on top of the world and then fell asleep behind a wheel for a split second. And all of a sudden, some fancier, smarter, more agile company came in and started stealing their market share. And before you knew it, they were left behind, you know, so the, I think investing in and maintaining an appetite for being an early adopter, but just being very smart about it and being very deliberate about what technologies you invest in and adopt is just very, very important.
Mino Vlachos: So I'll share a couple stories and then bridge into the question. So when I was a few jobs ago, I worked at Ernst and Young and ey. We used to do, like, budgeting for every client engagement pretty much by hand, quote, unquote. So each kind of analyst, senior analyst was in charge of opening an excel sheet and basically keeping track of all the project financials. So the hours we would work, the run of the project, and I was working with a manager, and we basically, we landed a huge project for us, which was, I think, like a $15 million project which had like 50 people on it. So we made some kind of, again, this was in Excel, so it's not like the most advanced technology, but we made a project in Excel where we automated pretty much all of this where you could, like, instead of spending like 20 hours a week, we did everything, formulas, VBA, so that, like, by the end of it, like, you just copy pasted whatever input you needed into the sheet, and it automatically gave you charts and data and this thing and that. And then in a second company where I worked at, it was a different kind of company, but we were doing tons of, like, report writing or different analytics. And again, I know, like a solid amount of Excel. So I sat down and I get tired of doing repetitive work. I'm a bit lazy. So instead I went and created different tools again to automate these processes or get it to like 80%. And then you go in, like humanity, tweak it, you know, like, okay, the report is 80% done. I go in and I change some of the sentences, make it a little bit more humanistic. And again, this is before chat GPT existed, so that would have solved that period. But in every case where I was making a tool and it was like significantly like with the report writing thing, I would have saved about 50% of my time on these projects with the budget forecasting. Like, I saved the team a lot. And in all these cases, I went to, these are different companies now. I would go to senior management, senior leadership. I would present these tools. I would, like showcase. It's literally built for free by someone who's literally already using it. It works. Other people have tried it. It works. It's going to save us 50% of our time. That's a pretty huge time saving for a service business where it's all human labor, human capital. In every case, it went nowhere. No one really gave a fuck about what I was building, what I was doing. And it was like, I sit there and I'm like, what did I do wrong? You know, how do I not? Did I get the clue? It's an influencing problem. So when you're working with these different stakeholders and, like, you know, something could work or has potential, like, what are some best practices around, like, influencing or stakeholder management or what advice would you even give a younger me that was trying to get these things through?
Lars Haus Olsen: Yeah. And I'm just curious, like, where, like, looking back at it in hindsight, being 2020, like, what. What do you think you could have done differently there? Like, knowing what you know now?
Mino Vlachos: So each one, those two companies, a bit of a different. So with ey, what I did was my manager and I, we did a presentation to our whole team about the thing, but ultimately, we didn't do any sort of actual, like, beyond that. You know, I wasn't that invested in spreading it throughout the group because I was like, look, I made it, I use it, but it's not like I owned a product that I needed to sell. So I was basically like, okay, if you like it, come to me, shoot me an email. And no one did that. So I think in hindsight, if I really cared about spreading this, then I would have to almost go and internally sell it and use it as if it's my product. So in that case, it was one of those, like, I didn't push it at all. In the second case, it was a lot of politics. So there was another team that already had been given a lot of time and budget to solve the same problem and had failed to do so. And so eventually, as I was going up the influencing ladder, they recommended me talk to that team. And that team just basically shut me down and was like, like, no, this isn't. This probably puts our jobs at jeopardy. So that was, I think, in that case, I don't know that I would have done anything different, but it just was the landscape I was in.
Lars Haus Olsen: No. And I think that you're touching on some really, really key pieces there, right. Often it is as simple as. As simple and as complex as company politics. Right. Someone else is invested in something that opposes whatever change or innovation that you're bringing forth and so on. So again, you know, getting the key stakeholders on board or all the stakeholders. Right. To get on the same page and so on, but then also have identify. And you probably learned this, you know, through sales process that you work with, right. Knowing every objection that you will get beforehand and essentially have multiple answers for each objection, right. So that you can be proactive. So you don't even wait for the objections or the naysayers to bring their arguments, but you're showing consideration and thought that you appreciate their particular concerns and so on and so forth. Again, I think it's like that. The human aspect of it, like that human interaction, the time. Some people just need more time to sit with certain changes before they get on board and then not trying to necessarily force it. Of course, at some point, there's always going to be holdouts. There's always going to be someone that's against whatever it may be. Right? So I think finding, you know, a word that's often being used in consulting and popular these days is, you know, having. Finding a champion for your solution and working with that person and being strategic. You know, like, for instance, if, you know, there's one particular team in your case that's already been given resources they've invested in, that they may not want to look foolish because someone came up with a better and easier and cheaper solution to something that they, you know, if you can go to them and figure out, you know, why they fell and kind of bring them into the fold and give them, even though they didn't do anything but make them feel like they have some type of ownership or influence and so on, because often it just comes down to personal egos or, you know, we're all human. So you kind of just have to be, I think, at least just very considerate of other people and, and just try and step into their shoes and, you know, it's kind of, you know, maybe, you know, you. It comes down to feelings. Like, people have feelings, right? So you just want to make sure that, that you're not creating any problems or obstacles for yourself by unnecessarily hurting someone's feelings. That could have been a powerful champion. And I think businesses and business leaders and stuff are getting better at this because in the, you know, historically, you have a strong leader that's just gonna, you know, muscle everything through and, you know, in some cases, sure, that's needed and that works and so on. But I think if you want, you know, a healthy, good environment that everyone is working in and where people were a place where people are excited about change and willing to have change, that means that you need to trust each other and feel like everyone's looking out for each other and creating, you know, that type of safe space. And it could be as simple as, you know, maybe someone who's been at the company for a very long time may be part of a different generation, not super comfortable with new technologies or, you know, maybe not scared of it, but just uncomfortable with the idea of change and then just spending a little extra time with that person and letting that person, you know, say their piece and getting that person on board is just going to be so much more powerful. Because if that person is kind of known to be a quote, unquote Luddite. That's what people call that person behind their back because they're always against innovation and new things and changes and so on. But yeah, just spending a little extra time with those people and make sure that they feel heard. And I think that just goes such a long way. But in today's world, you know, where everything's moving at a very fast clip and people feel like, you know, there's a time crunch for everything and that's very hard. It's a lot easier said than done. But I do think, yeah, just taking the human aspect into consideration can never be overestimated.
Mino Vlachos: For my last question, because our company, three peak, we really work with more senior leaders, typically like vice president, general manager, C suite. So if you consider that audience, and today we started talking all the way from how to find innovative technology, how to filter and actually figure out what's going to work, all the way to the more organizational dynamics of how we influence and integrate that innovation into an actual company. This can be as broad or as specific as you want, but if you think about that kind of more senior level audience, what advice would you give them about emergent technologies and innovation?
Lars Haus Olsen: Yeah, again, like, I think being very deliberate about why you're considering any type of new technology, emerging technology, why you're considering investing in it and being honest with yourself. Like, are you looking for an easy fix to a problem? Are you looking to outsource a problem? Are you trying to make life easier for yourself? Or are you trying to make change that is going to have a positive impact on the whole organization, I think is very, very important and unfortunate because my wife, she's a leadership development coach. So I get to overhear a lot of these conversations. And I think that helps me a lot in my work as I'm trying to get buy in from leaders to invest in emerging technologies. And again, back to that human aspect, all these leaders human as well, right? So making sure that, and often, you know, the higher up you get on the ladder, there's, there's maybe a bigger ego that that comes with that. And in some cases, you know, maybe rightfully so, because these people have maybe earned, and often they have earned right there their spot, because they've worked hard and invested a lot of personal time, money or whatever it may be into getting where they are. But at the same time, I think for those folks, it's very important to check their egos and they're often surrounded by people, especially anyone that's trying to sell them a solution. So they're kind of surrounded by yes people, right? And we all know, without getting into specifics, you know, different types of leaders in the world that we're seeing, you know, as they ascend to more and more power, they surround themselves with more and more yes people and they live in these echo chambers. So if you are a person of power, right, and that could be, you know, someone that runs a company with 30 people or a department, the lead that oversees 200 people, whatever it may be, right, a lot of these folks won't have people that push back on their ideas and their says because, you know, they're scared of their own livelihoods or, you know, these leaders could be the nicest people in the world, but because of the dynamic power dynamic that exists, people will censor themselves and check themselves, you know, before pushing too hard. So, so I think, again, just that you can overvalue, overstate, like the awareness component. So I think for, you know, those leaders, just like, as I said in the beginning, like, are you just being honest with what you're trying to achieve with this change, with this investment, with this technology? And then on the other hand, the people that are then trying to deliver on these things, that they take all of the stuff on the Flip side into consideration is very good.
Mino Vlachos: The final thoughts I'll leave the audience with on our end is one of the pillars that we work with in three peak is change readiness. So we're really big advocates of creating adaptability within your organization, and that looks DifFeREnt. So a small organization typically can adapt to quicker because there's less processes, less people. If you're running a huge empire, like, it takes a lot longer to actually move that whole ship. Like, I was talking to an executive in ExxonMobil and I was in a cynical place and I was like, look, at this point, Exxon can just buy any small startup and either kill them or integrate them or whatever they want. And what he was saying is that the biggest risk for Exxon is just how slow it would be. So even if in ten years, the right startups do emerge in a renewable space, they might not act fast enough or be able to actually incorporate that into their company fast enough. It's like an oil tanker. It's such a big ship to turn that even if they can buy anyone in the space, it might still not be enough. And so this is something that we kind of try to encourage people, is to create an adaptable organization, which means that often innovation can come from fringe, either from external kind of startup space or there's some employee somewhere sitting at some desk that, like me, might just be a little lazy and wants to make something that makes their job easier. How do you identify those pockets of innovation and integrate it into the core, the machine of the company? And that, to me, is one of the pressing issues of the 21st century, is that process.
Lars Haus Olsen: Yeah, absolutely. And I wish I had all the answers for that, because stuff we're working with and seeing every day. And I think. I think. I think you brought up something really interesting there. The external innovation, if you will, that someone external brings in. But there is so much innovation that happens within organizations, and it could be, like you said, you know, someone like yourself who had a good idea, that you saw a solution and you made it happen and you put it together and then tried to get people to use it. And I think fostering, as you said, the change readiness, fostering a culture where no ideas are stupid and everyone feels heard and that they can bring things forward, will create an environment and a climate that will be much quicker and better at adopting to change, for sure. So, no, I really like that sentiment, for sure. And, yeah, can't overstate the importance of just instilling the right culture and mindset into an organization to handle change. Well.
Mino Vlachos: Yeah. Lars, thank you so much. I feel like you're doing the good work, which is supporting organizations that could be conservative to actually refresh themselves and innovate. And like you mentioned, with a lot of things like the. In real estate, ultimately the end users are benefiting because we get to go to airports with cleaner bathrooms. And that sounds pretty freaking amazing to me. So I just want to thank you for your expertise, for spending time with me and sharing all your knowledge on this space. I think it's something that it's only going to become more and more important for organizations.
Lars Haus Olsen: Yeah, no, thank you, Mino, thanks for. Thanks for having me, and thanks for letting me step onto my soapbox on multiple occasions and, you know, just go. Go on and on about these things that I spent a lot of time thinking about and working with, obviously. So, no, really enjoyed this conversation, and I look forward to seeing you next time. Yes.
Mino Vlachos: And to our listeners, thank you so much for tuning in. We'll see you again soon. Thank you.