Preparing for 2026: Digital Threads, Integrated Data, and the Future of Asset Management

Okay. I think we are live here. Let's see. Yeah. All right. Kind of give I was going give him one more minute, but it looks like we're twelve zero one. So, yeah, I think we'll go ahead and get started here. Yeah. All right, everyone. Welcome. Thank you for all. Thank you all for joining us today. Appreciate you being part of this session. Yes, session will be recorded. So yeah, this is our our presentation on preparing for twenty twenty six digital threads, integrated data and the future of asset management. My name is Josh Sparrow. I'll be your host for today. I am a marketing manager here at Brightly Software. And before we jump in and before I introduce our speaker for today, I just want to go over a few housekeeping items. Number one, you'll be muted during during today's session. But if you have any questions or experiencing any technical difficulties, please do submit them through the Q and A function you should see at the bottom left corner of your screen. If we have time at the end, we will try to get to some of your questions. However, if we're unable to, we'll be sure to follow-up with the responses to your email afterwards. And you will also receive an email of the recording today afterwards. And lastly, if you can provide us with any feedback on how we did, things you would like to see in future webinars, we would, of course appreciate that as well. So now, yeah, we are just going to jump right into it. Like I said, our speaker today is Brian Bell. He is our head of strategy, SVP and head of strategy here at Brightly Software. Brian leads our corporate strategy and M and A efforts, and he brings more than two decades of experience in the asset management industry. He's also someone who I've seen has a real deep passion for the topics that he'll be talking with you about today. So I do think we're in store for a really great presentation. Without further ado, Brian, I will pass it over to you. Thanks, Josh. Really appreciate the intro. Very happy to be here today. As Josh mentioned, I'm the Head of Strategy here at Brightly. I have been working in this space for over twenty years in a variety of different industries and geographies and asset classes and various opportunities to see how the technology has really changed the way that we manage infrastructure and operations in the asset life cycle space. And wow, what an exciting time here in twenty twenty six, early twenty six as we are going through yet another kind of S curve of technology change and shifts. That gives us more opportunities to both lean into the technology to help us figure out how to use that in a way to manage our asset portfolios, but also, you know, figure out the challenges that come along with it and avoid the risks and whatnot. So while we won't be able to touch on every possible topic area, we've curated a list of things that we're watching. We'll touch a little bit on some of the things that we identified last year and give you an update on some of the trends that we saw in twenty five, and then also add on some additional ideas and concepts that are heading our way here in twenty six and and what we can do about it in in the future. Real quick, so as I mentioned, twenty twenty plus years in this space. I do lead strategy and corporate development here at at Brightly within Siemens. And I don't know if this is on there. Yeah. Fun fact. I I still own my mother's first car. So I am the steward of a a vehicle that's been in our family since nineteen sixty eight. So just to give you a sense of some of my hobbies and passions, family one, but number two, really enjoy kind of some vintage things and enjoy driving that around. It does still run. So going back to twenty twenty five, let's think quickly about where we were. And the short version of this I think is all of these things are still relevant in twenty six. And I will give you some some views on how things have evolved and changed. I think we were fairly accurate in some of the predictions we made, and I think you'll you'll find that some of these are relevant and probably recognize what we saw last year throughout throughout the year as you made your way through twenty five. You know, AI was certainly a hot topic, as it is today, probably even more so. But, you know, as we were thinking about AI, we knew that there would be evolution there. There was a trend early in the year when we were thinking about how AI would evolve and be applied to our industry. The indications were that we would start seeing AI get more tailored for specific use cases and that we would find ways that AI could be applied in specific ways. So it's not just going to something like Chat Chat GPT or Google Gemini or one of these large large language models that are kind of generalized use cases, but looking for specific application of AI. Absolutely happened. We'll talk about more about how we saw that be delivered throughout twenty five. Digital twins, a concept that we really started talking more about over the last twelve to eighteen months. Kind of a scary concept in some sense. There's a lot of expectations and assumptions around what it means to use a digital twin. How applicable is it across various industry contexts? Definitely continues to be a topic of conversation. And I think you'll see interesting development from the ISO organization that there is a maturity curve with this and that we can talk more about where you enter on your journey towards a digital twin. Third, we talked about the need to manage the evolving demographics of the workforce and using digital tools to help us bridge the gap between all the knowledge that was leading organizations with, you know, in creating various workforce demands and challenges. How do we use our digital tools to capture that knowledge and move forward into new ways to operate with maybe a different composition of worker skill set, experience, etcetera. Certainly saw that happen in a lot of different contexts and we'll talk more about that. And then, you know, just generally, how do we make the best use of AI? Probably far and away, I think the research is now demonstrably showing that the quality of your data and the information that is being used and leveraged by the AI tools is the number one success factor for whether or not AI is gonna deliver the results that you are expecting as you as you think about deploying AI components and things within your all of your your toolbox. So we'll go through each one of these topics in a little bit more detail just to show you the progression from twenty five to twenty six. And then as I mentioned, we'll touch on a few more topics that we're seeing standing here looking into the future that we can add to this conversation. So AI agents are exploding. AI tools are being adopted across all enterprises. I picked one statistic. You can find a lot of different examples of this and it seems like it changes on a weekly I mean, I've seen numbers from the mid fifties to the mid eighties, and depending on your industry and context, you'll see different ones. But I think suffice to say, almost everybody is using AI in some form or fashion, And we are seeing how AIs are now being tailored for specific use cases to solve specific problems and even starting to move to environments maybe in the edge. When we think about edge devices, you know, it's a transformation of of traditional IoT devices that it's not just there to collect and aggregate information or apply some rules. We're actually seeing that the language models and other forms of of AI intelligence can actually be packaged in a smaller form factor that is moving out into the remote field environment. So what does that do? It's gonna increase the power of information collection, aggregation, decision making at the edge and maybe offload some of the computing requirements that we have within the data centers. But all in all, it's helping us get better quality data, more relevant data, associating it to other points of information and feeding that into our systems of record. So exciting to see that. You know, lot of folks in our industry are placing bets and deploying functionality and features around this area. We're doing the same thing at Brightly. This past year we announced a relationship with XOI to how we can use AI models to improve data quality, fill in gaps in your asset records, which ultimately leads to better ability to drive capital plans and make predictions on proactive maintenance strategies and other things. So major improvements we're seeing in the accuracy and comprehensive nature of data. At the bottom line, the quality of data being so important to AI, the barriers to improving the data are really coming down given the ability to drive these smaller models. Next, digital twins. Really interesting. You know, we we talked about this at Siemens and Bradley for for many years, and I think in the industrial segment, particularly, I'm sure if you're, joining us today from a manufacturing environment, you you hear a lot about it, think a lot about digital twins. We just featured this with our CEO, Roland Bush, at at CES and what we're doing with Microsoft and NVIDIA on industrial digital twins. But, you know, apart from these super complex environments, there's still an opportunity for everybody across the asset management industry to take advantage of digital twins. I mentioned earlier that the ISO in twenty twenty five published a new standard around defining what maturity model looks like for digital twins. And so there are levels to a digital twin. I think a lot of times we just think about it as a single term that defines some big complex hairy thing. But I think what's important here is that you can enter into your digital twin journey at a variety of different places. And given the ISO model, it helps us give a framework for where you enter, what's required. You can go from static replicas with specific sets of data, maybe a three d model, maybe not. But you start in one place, but then you move up this maturity curve as you connect more systems together, as you bring in more historical data or you can do simulations on things, you know, and there is a kind of a, an ultimate end goal that you would love to get to aspirationally. And hopefully, you know, we're seeing some people and helping some people get to that point. But the concept of a digital twin is more than just that that future end state. I guess that's the point here on this slide is that, it doesn't you don't have to have, you know, a perfect holistic solution end to end ready to go turnkey just to start your digital twin journey. I think, realistically, we see organizations starting this journey. We've got a couple of case studies around this, a couple of things in the industry, not even necessarily to Brightly specifically, but there are many examples where people have identified a specific problem set, a specific set of data, and then they can start building some basic modeling, get on that journey and deliver real outcomes. Shanghai University used a series of open source tools even along with some of their connectivity to hardware and other data sources to achieve a nine to twelve percent energy reduction. We're seeing many K-twelve schools within North America leverage AI modeling tools such as our Origin solution to piece together information, move up that maturity curve, and build a digital twin of their capital needs and their asset infrastructure. So I guess I just really want you to take away that it's not one big bang thing. There is a journey here. There's a maturity curve. I encourage you to reference the ISO standards around this, and it's something you can start on today. Workforce crisis continues to be a challenging situation for all of us. And if you're monitoring the incredible infrastructure kind of super cycle of capital expenditures around AI infrastructure, the build out of data centers and everything that's coming along with that. Where are we getting all these workers? I mean, it's the same types of skill sets in many cases that we are really struggling to find to fill jobs in all of our environments today, whether that's in facilities, whether that's industrial, whether that's infrastructure, even health care environments are losing workers to this infrastructure build out for AI. We can look at many statistics here, but they're paying more. They have a huge gap in filling the workforce to build out this infrastructure. It's such a high priority item. And it feels a little bit, you know, you can look at it in some ways and say, is it a bubble? Is it, you know, are are we reducing the the the guardrails to risk and other things? There's so much money that's flowing into this space and they're willing to overpay. They're willing to really extend because it's kind of an arms race. Right? So we're having we recognize that our our customers and the market in general is having trouble filling electricians, plumbers, network engineering, HVAC technicians, liquid cooling and all sorts of things that are going into these environments are really putting a challenge on filling jobs. What does that mean? So yes, this is a trend that has accelerated from last year to this year. But in our view, knowledge capture is one of the really important components of your infrastructure from an IT landscape, from an operational technologies landscape. You know, it's more important than ever that you have systems that not only give you easy ways to capture information, organize information in your in your maintenance management systems or your capital planning systems, your energy management systems, but also augmenting that and organizing that in a way so that it can leverage things like natural language search or natural language addition, maybe voice inputs and other things so that your entire workforce, if there's a higher risk that you might not be able to retain the knowledge base that's in your workforce today, how do we make sure that we're getting information possible in every interaction with your asset environment to collect information and collect that for the future so that you can build these comprehensive knowledge bases that become the data foundation for future systems and future tools and AI and and the modeling that's gonna be done in the future. So, again, data is the common theme here as we think about all these challenges. And to my point, you know, AI needs the data. We touched on it last year, but, it it is the unlock. And in our view, what we're trying to achieve here is a digital thread. It's it's an interesting concept. But when you think about the life cycle of any particular asset from the time that it's built or constructed or acquired, going through kind of a commissioning phase or onboarding or installation or integration into your systems and then an operational phase and utilization phase of those assets And then moving on to forward future looking capital planning, expected renewal replacement cycles. And there's a feedback loop at various points of that cycle that go back in, maybe even all the way back to the construction phase. What if maybe in a built environment, what did we learn from the way that we built and operated a set of facilities that impacted our capital plans that we would want to reincorporate into our planning for the next building or the next area of operations. We can create this this this loop of information that's that's reinforcing and helpful to this entire asset life cycle. AI is going to accelerate and improve the way that we move from segment to segment along that asset life cycle, but it has to have consistency in the quality and availability of data. So that thread of information that flows through that whole asset life cycle is increasingly important when we have systems that are beyond just human ability to read and report on things and piece things together. But as systems become more integrated into this entire asset life cycle management process, we think about agents getting involved, the kind of the thinking agents within the box that that piece together information, they have to have access to that really high quality data. So building these digital threads really increases the ROI of those of those investments, and it's really no longer an option. It it is it is the greatest predictor of your of the success of your AI investments as it relates to the asset life cycle management, and really it's gonna pay benefits, in everything that you do going forward. So apart from an update on where we were from our twenty twenty five trends, I want to take a look at a few new things that are on the radar in the ALM space. And one is specifically a deeper dive on AI and maybe an element of AI. And it's really this notion that agents are really starting to take hold. And this is not unique to asset life cycle management. You know, I I humorously here include a picture of something that you've probably heard of if you follow any of the AI social media or or X posts and things. You know, this this notion of clawed clawed code, clawed bot, there's there's all sorts of examples of this, but that's one that's really been very popular in the, you know, zeitgeist recently. And and the deep track here is is the Ralph Wiggum reference. There's a short story. There's a Simpsons character that people have associated to a particular looping of agentic work to help you get a lot of things done while you sleep. But the point being agents are definitely moving forward here. We're starting to see more things happen autonomously or semi autonomously. You can think about how you prompt the various AI tools. The more comprehensive the prompts become, the more powerful the agents can be and the more they can get done without having to come back for questions and answers. So this is a macro trend across really the entire world and it's gonna touch every industry. But there are gonna be elements of this that are going to find their way into our industry. And so again, having information that is organized, well structured, connected to other related important data sets is going to just accelerate your ability to adopt and then make use of the value of these tools. We are starting to see this creep in in some ways, anything from robots and devices that can do automated inspections in an agentic way, make decisions, filter data, advancing beyond just the traditional rules based engines that that have been applied in the past. We're gonna start to see this this evolve in twenty six and beyond. We can think about copilots and agents that are working in the background, recognizing maybe resource conflicts or challenges with how we planned our maintenance schedules or the availability of particular assets when we think we might have a need for them in the future. There's gonna be more and more of this behind the scenes back office and in some cases, black box type approach to agents that we'll have to understand and then incorporate into our workflows. But just recognize that this is coming. We need to level our understanding. There's going to be cultural and organizational changes and training that we need to do, but we will all need to embrace it. This is kind of the in some sense, Pandora's box has opened, and so there's going to be governance issues. There's going to be, again, cultural issues, But the common thread, making sure that we have the right data that can be utilized by these things. We're seeing a shift in ESG and sustainability. There have been if you go back over the last five, six, seven years, there were massive investments and bets placed across the landscape on sustainability. Many different approaches have been taken on how to address the regulatory requirements that we see across the globe really. There's always a political component to this. So as various administration priorities change in various geographies, we do see that there's either an ebb or a swell in certain components of this. But when we take it down into some of the more tangible and near term things, what we have seen is that there appears to be a preference for a tire integration of ESG and sustainability information and decision making into the operational environment and as well embedded into some of our planning tools as well. So the standards are still there and there's still a requirement. And I think it's no surprise to anybody that there are certain geographies in the world that are leaning in more so than others. But it's not to say that the the requirements are not there or that the underlying trends in climate and energy are not still present in our market. But how we're responding to this and incorporating our adherence and compliance and requirement to report out and integrate these things into our day to day operations, we think is evolving a bit. And so what we're seeing is that there's a greater need to pull in those tool sets and information and reporting to make it more tightly integrated into things like CMMS and into things like AIP. And that teams that are operating these tools every day are having a greater request and obligation to report on the elements of ESG and sustainability or energy consumption in the context of operations and planning. So again, you can't report what you can't measure. So making sure that we have the right tooling and applications that's collecting this information and incorporating it into those day to day decision making practices. For example, you know, what is the carbon impact of making a particular maintenance decision? You know, what is the appropriate routing that I should do for a vehicle with a with a crew on it so that we're optimizing, you know, energy consumption or carbon mitigation or a variety of other other factors. So we do see that as a trend. And then a third element looking into twenty six and beyond, we do see that there are some trends of stabilization around the uncertainty that we've seen in the market back in COVID, post COVID time period. There was a lot of uncertainty about how are we gonna use the massive built environment that exists across all of the markets. Office space clearly hugely disrupted by changes in work patterns, utilization of space, reformulation of high rise buildings. You know, there's lots of different examples of this. I think it's stabilized more so in certain markets than it has in other markets. There's still, you know, frankly, some uncertainty in the office space. But as we work through these challenges, you and your teams are right at the front of this having to deal with answering the questions. Well, what do we do now? Now that we're starting to get some stability into patterns of use, maybe we understand a little bit more about who's going to be in a facility, which days of the week or when are the times that we come together for major activities. And we understand how that might change the way that a higher educational institution is used or office environments or retail environments or elements of manufacturing, even though manufacturing tends to be more of a all time, if not twenty fourseven type of environment. But we are starting to see some stabilization in these usage patterns. But then it leads to the question of, okay, now what? You know, what if we feel like we've got some more conviction in in what we need to plan around, then how do we do that? And so what information is required of us as a operations team? How are we gonna feed that information into the other stakeholders within that conversation? What can we get out of the the tools and the systems that we have where we've been tracking information? Maybe what do we need to do to collect a little bit more information? Are there additional records, that we wanna update? Are there, other adjacent software applications that we wanna integrate into this the stack? And then how do we feed that into our planning tools going forward? So understanding the condition of your asset portfolio, usage patterns, maintenance costs, and total cost of ownership of of your infrastructure are all gonna be really critical things to bring to the table when we're all called together on that biannual or annual basis to reassess how we're making investments and prioritizing our planning for the portfolios that we all are responsible for. So let's take a short pause and just do a temperature check. We try to do a poll during these webinars. How prepared are you feeling? I mean, it's overwhelming to think about the workforce challenges and technology challenges and incorporating AI and making sure that we do it within the regulatory constraints and guardrails of our organizations where we serve. But how how prepared are you feeling? So, Josh, I don't if we wanna kick up a poll here. Let's take a minute. Let's do a quick, quick pulse check. Are you very prepared? Are you you know, you've got this stuff licked, you're fully embedded in AI, you've got perfect data sets, you know everything you could possibly need to know. I don't feel that way. I don't think everybody else does. But odds are that you have spent some time thinking about this and that you've been hearing from partners and vendors and internal stakeholders and people are trying things on their own. You were running some some copilots, various things. Odds are you've at least dabbled in some of this. Or it's okay. Maybe you're early on this journey. Maybe you're feeling a little bit overwhelmed. Maybe you need some additional support from higher ups. Maybe you need some more budget. Maybe you need some more knowledge on the staff. But it'd be good to understand where everybody is on this journey. I'm gonna take another couple seconds here. Got some great submissions here. Appreciate everybody contributing. Alright. I think we've about tailed off. So, Josh, can we click over to the results? Alright. Well, this is about what I would expect, I think. Great to see that there is a portion of our audience today that's feeling very prepared. I don't think that means necessarily that you've got it all in place and figured out yet, but, it does indicate to me that there is some percentage that is kind of leading the way, and and making the right plans for how they tackle the future. But I think, I think everybody should feel pretty good that somewhere in the middle here, is is the the critical mass of our audience. You know, you you probably experienced it in some form, maybe even just as a consumer of, in your personal life. You know, as as Google moves you to the AI mode as as opposed to the traditional search mode, you're starting to see the power of of AI and and and the importance of data. But, we are all on a journey here. So hopefully, you can take some solace in in this and, and be encouraged to keep moving forward. And for those that feel very unprepared, I would just give you a word of encouragement. I mean, this is incredibly overwhelming. There's so many different headwinds against us from a technology workforce, regulatory, financial. It is okay. And there is a path forward, but hopefully we can give you some ideas about taking initial steps and working with what we have to move forward in this new and exciting world. So comparing briefly with with last year, we've seen an uptick, I think, in very prepared from from three to four. Very unprepared, I think, has has moved down if we go back. So that's that's really good. You can see movement. So last year, eleven percent felt very unprepared. This year, it's only a little over seven percent. And so somewhat prepared. I think we got a duplicate there, but still have the critical mass in the middle. But I think we are seeing some shift from twenty five to twenty six on where people are feeling great. So in twenty twenty six, a little bit of a summary statement. Digital transformation in our view is much less about the specific tools, but it's really about how you integrate them. And the key to integration of all these tools is building those digital threads. It's about the data that's flowing through them. It's about establishing standards for what information you're gonna collect. And there's every industry that's attending this call today has a different set of standards likely. There may be an ISO standard. There may be something like, you know, haystack or some tagging mechanism that might give you something that's more building specific. But establishing what your principles are as an organization or organizing your information and then assessing where are you? How much of a gap is there from kind of basic understanding of your data to if I had a perfect world and can wave a magic wand and I could fill in these additional thirteen fields which would unlock all this additional value for me, let's do an assessment. Let's figure out what do we need to do to improve these data quality and availability within our systems. Interoperability is gonna be a huge differentiator. It's more than just nice to have. As we touched on with things like AI agents and other related systems, the data being so important as the digital thread through all these things, it requires that the systems that you're adopting have the ability to interact. Do you have the ability to get access to the data that is within these systems or is it siloed? We we talked about this for a decade, but, you know, having information locked up into siloed systems is going to be a huge problem for you as we advance into this this crazy dynamic future. And, you know, we we talk about AI a lot. I don't know. You know, it is certainly an exciting thing. There's some potentially some fear and trepidation around it. It's not all about AI. I don't wanna go way overboard with that just because it's a popular term and it's getting so much mindshare. But even in the day to day tasks, the ability to connect systems together with or without AI, the interoperability of your systems and making sure that you have that consistency of your data on ontology, how you organize and structure your information is really important. So collecting, validating, ensuring you've got high quality data is gonna reap major benefits for many years. I mentioned what we've done just in the past year with a partner of ours, XOI. We are seeing how the application of their tooling and AI models is really making a difference for, again, that data quality ensuring the completeness of asset information. And again, this is not for it doesn't solve all problems for all things, but there's a set of use cases and set of asset classes where we're really seeing this as not just a proof point, but really it delivering value and ROI for customers who are starting to combine these interoperable systems, as we said, and making sure that the data flows between them, they become self reinforcing. We can improve the way that we identify and get assets into a system and then use tools and partners like XOI to enhance those and provide even more value as part of that overall asset life cycle chain. We talked about digital threads, one off integrations kind of between two distinct platforms is one thing, but the real value starts to show up when we connect across the entire life cycle of an asset. Going from the design and build phase, if you do the commissioning phase, all the data that's being collected along that journey becomes valuable downstream. And so the more we can do to keep consistency and quality and availability of that data and keep integrity of that data throughout that digital thread will benefit your organization, other constituents, you know, stakeholders that that you're trying to share information with both inside and outside of your organization. It gives you more context for the whole life cycle of a particular asset and really going to help you gain more insight, reduce the number of kind of manual workarounds and reduce the number of less confident decisions that you can have when you can really look back and see the entire history and the predictions for an asset on that digital thread. I want to just reiterate again, AI is not going to be able to deliver reliable insights until the data is consistent, connected, and in the right context. We do believe the digital threads are the way to do that, and it requires a unification of historical, real time, and then driving predictive models and forecasts down the road. I think everybody's probably experienced hallucinations within AI. That's kind of the fancy term for AI that kind of comes up with stuff in response to a question. A lot of that's driven by trying to stitch together and put filling gaps within the information that it's accessing. Just like all of you do, many of our searches have moved from just a direct request response from a search engine. We were starting to use AI models to help, you know, guide us and and feed information. But, you know, I'm sure you've experienced it as well. I I've experienced it even as we we talk about some of these topics here. You can put in some concepts. If you went into an AI model and said, tell me about the future of the application of AI in, I don't know, food and beverage manufacturing equipment. It might come back with some examples that sound incredible. And then you say, oh, that's great. I would love to double click that. Show me the exact case study or the sources or the resources that you use to deliver that. And maybe you get a response that says, well, I've even seen it where it kind of admits. I kind of pieced together these things and made some assumptions, but the initial result that it brought back was highly confident. And so we had to be really discerning and push back. But again, why is it doing that? It's because it's filling in data gaps. It's making assumptions that it just assumes that you might not care about necessarily. So it goes back to data. It goes back to the higher quality data, the more, greater quantity and quality and in the right context that we can feed into these AI models is going to deliver the right kind of outcomes that we can be confident in and then take with us when we go to speak to other stakeholders in our organization. We're seeing examples across a variety of different industries. I just point out a couple here within the industrial sector, and we're seeing folks like like Falconry and Sensei and NanoPrecise and others on using AI to deliver new insights. It's really unlocking all sorts of different capabilities, augmenting the digital twin, delivering predictive maintenance plans, classifying assets in different ways, automating the process of diagnostics, doing things agentically and outside of the human workflow that becomes a force multiplier to those of us that are able to leverage and use these systems. So exciting to see that. Just wanted to mention a couple of examples of that. And then, you know, final, you know, I've mentioned this many times, but the digital threads start stacking on each other. And so you can identify in various workflows within your organization, you know, how you can draw a digital thread through the multiple systems that can work really well together, but then you get a compounding effect. You know, if you start laying them all out, you know, if you just drew it on a whiteboard and you said, well, this digital thread delivers these kind of things and it requires this kind of data. And then maybe this kind of digital thread delivers these kind of things and this kind of data. But then you start seeing the interrelation between the digital threads and said, oh, well, if I can get to these three stages of this first digital thread, that gives me more options. You know, I could go deeper on on the existing digital thread, or maybe I could take some elements of where I am here and jump over into an adjacent digital thread and start building a compounding effect to to the value that you're you're building through all this connected data. So again, that's gonna help you build more trustworthy models in the future, build out that digital twin, and deliver better results for your your asset portfolio. Another quick poll. Again, I wanna go back that the ISO standard is telling us that digital twins is not just one future perfect end state. The term is really expanding to address multiple stages of an asset life cycle and a digital twin maturity. So I'm curious, does that resonate with you? Where do you feel you are on digital twins today? Like, you feel like maybe you don't quite understand it well enough, you're still exploring it, it's on your roadmap, or maybe you're in a much more complex and data rich and intensive and integrated environment. Maybe it's a very well known, utilized, broad term that you have and that you feel comfortable with it. So just curious where everybody is. Let's a quick pulse check on this. And I know everybody's, you know, expectations of what Digital Twin means to them is gonna be a little bit different. Maybe you're just piloting a specific use case. Maybe you're just saying, hey, I wanna focus on a particular type of an asset, collect as much information as I can about it, and then start modeling or or running some analytics around that one particular thing. Maybe it's not, you know, pervasive throughout your entire workflow or your entire asset portfolio. You know, I don't know. I think we'll we'll get some interesting responses here. I think there was just to get skip ahead. There was a question about the the ISO standard that we were mentioning, and it is ISO three zero one eight six, which was released in twenty twenty five. We can maybe send a link or a note to that in the the comments, but I did see somebody inquiring about that. Three levels of maturity on the on the digital twin there. All right. We've got about roughly about the same number of folks that came in on our poll last time, so we will go forward here. Okay. And I my interpretation of this is a couple things. One, really positive to see that forty percent of our audience is at least exploring and learning about it. At least we know that there's you know, it's out there. It's maybe a concept you've heard. Maybe you've you've associated to some specific use cases. But totally not surprising that it's not on your road map. You know, this is, as I say, like, with the introduction of the ISO standard, it just shows us that we're starting to get the language to talk about it and that the definitions of what a digital twin is, how we can make use of it across different industries is evolving and changing. And it's not just something that's stuck kind of on the manufacturing floor or in industrial environments. I think we will see this question evolve in future years about how we interpret digital twins for our own organizations and make use of it. So good to see some pilots out there using it in a few cases. And hey, almost three percent of you are using it broadly across your organization, so would love to hear maybe in the comments or just in the feedback about how you're embracing digital twins as we go forward. A couple of final use cases and case studies just from our experience. We've seen Children's Health, in Dallas leverage AI and analytics, namely using some of the Brightly solutions to secure significant investment from their finance teams. I think we've talked about this and mentioned in the past, but, you know, millions and millions of dollars of additional funding to meet the needs of their capital plans, you know, all driven through higher quality data that's available, driven through things like data collection, better practices on work order collection, how to more efficiently collect and integrate data into their asset registries, and then how does that feed the capital planning and AIP tools in the future. So then it creates a totally different conversation, helps drive different decision making, and clearly there was a great result here for children's health. Also seeing the University of Adelaide Down Under with developing their long term roadmap for how they view the renewal and replacement cycle relative to their building portfolio. Again, using AI simulation modeling, developing forecasts that help them really track the deterioration of that portfolio, making sure that they stay within an acceptable range, and then doing really interesting and deep deep modeling on the the datasets there so they can avoid high risk failures in their mission critical facilities. So what can you do in twenty twenty six and beyond? Just coming to the end here, a couple of things come to mind. There's clearly a range of opportunities. There's unlimited things you could be doing and trying out. We don't have the time. We don't have the resources to try everything. Pick something, get moving, pick either your highest, most painful problem and see what we can do about, again, what is the data that's available in this context? How can we collect it in an efficient way? How do we organize it? Are there standards that we can leverage? How do we make sure we're talking a consistent language around that data? So then we're laying the groundwork and building kind of the cornerstone of our digital twin and and being prepared for the future. So, you know, pick something to act on and and start taking incremental bites, as we move forward. Number two, I would encourage you to, you know, survey your current software landscape. You know, figure out where does interoperability exist? What are the barriers to interoperability between your existing solutions? Are there opportunities to integrate solutions within your tech stack that you maybe haven't taken advantage of or maybe aren't even aware of? The second part of that is who can help you get there? Who are your partners or who is pulling together these digital threads in the industry that can give you that perspective that has been there and done that or has the ability to tie together some of these seemingly disparate systems, whether that's kind of the the OT and IT convergence, you know, the physical hardware, physical environment with the digital world and the digital solutions. How do you find the right partners that can pull these things together alongside you and help you accomplish your business objectives? But do that survey, make sure you've got the right stack in your toolkit. And then the third key thing I would say is organizationally, what are you doing to prepare your teams? How do you consistently expose them to and give them access to these exciting new tools, but in a way that is controlled safely and in consistent with your business guidelines, your, you know, organizational mandates, security and cybersecurity requirements. You know, it's so easy to open some trapdoors and some backdoors and and put really sensitive business information into these AI models. It's so tending to do because you wanna see what this new exciting thing that was just released or maybe an open source project can do to this set of data. But be careful, please. We're already seeing where some of that stuff is backfiring on people that are exploring and with good intent, but without the governance alongside it that's so important and required for your organizations as a stakeholder and steward of your business information and business intelligence. So I'd encourage you to download our twenty twenty six asset life cycle report. I appreciate you being here today. It'd be great to spend time with you. Hopefully I didn't go too far over my time slot. But Josh, you want to take it back over and help us find our way out? Yeah, absolutely. Brian, thanks so much. We did have a couple of questions in the Q and A. Brian, we have about ten more minutes if we wanted to just I wanted to ask you one or two of them. One of them the question was, do you see this type of software being used in BAS in the future? Yeah. Absolutely do. BAS is an interesting space. Think I alluded to earlier in the in the slides. I think we're gonna see more intelligence going into the edge devices. I think you're gonna see a wide range of solutions to how AI is applied to the BAS space. And I think it's going to be very context dependent as well. So I think you're going to see different solutions meet different needs for different verticals that maybe have different levels of tolerance, for what lives on the edge or what lives in the cloud. And I think there's gonna be different workflows that show up within those different environments. Mean, I think, some things we might see pushed more so to the edge and other things I think we may see, you know, get aggregated into the cloud. So, I think I hate that I hate giving the answer. It depends. But I do think that there's gonna be a variety of ways that we solve those problems. But I think in summary, BAS will evolve significantly, and I do think we're going to see a lot of innovation in that space, you know, because it's an exciting place to there's so much information that's available at that physical access point where it's physically connected to our our buildings and the the equipment that's helping us manage those buildings that there's a huge opportunity to do things with all that information. It's overwhelming today, and that's one of the challenges with BAS in any implementation. What do you do with all the things you have access to there? I think there's been a lot of attempts, and some great solutions in the market, but, that's certainly going to be an area of innovation in the future as we go forward. Awesome. There were a few other questions tied specifically to Asset Essentials, some energy manager, some of our products in terms of future AI integration. I don't know if you want to speak to that or if we want hold off and and, you know, speak to that at a later time. Yeah. I I think I can make some general comments. I'm not a solutions engineer, and I I certainly don't sit on our product management team or a software engineering team. So I don't wanna get myself in trouble either. But I think thematically, you will definitely see examples of how AI is both built into your workflows, helping you as a kind of a force multiplier to get more things done in a more efficient way with higher quality of information, making it easier to to, you know, use tools, whether that's through information that kind of appears through its connectivity to other systems or maybe it's enhancing the way that you input information through better asset capture or maybe even using voice and other things that can help you collect data in a more streamlined and painless way. But you're also going to see ways that other systems make use of these core systems of record. I think it's, you know, maybe extending that thought a bit. There's all sorts of hypotheses and speculation about what is the future of software. What what is gonna happen when, you know, quote unquote, somebody can build something in a weekend that does it solves a particular pain point for them and and feels like a a replacement for something they have. Ironically, I I saw somebody commenting, you know, who do you think OpenAI uses to manage their HR software? You know, it's probably something like Workday or, you know, one of the major HR platforms, you know, While yes, probably in some point time you could use Cloud Code or something else to build out any sort of interface to solve and feel like you're building some specific software solution. There are reasons why we have large established enterprise software businesses that have the depth of experience and knowledge and data and expertise around those systems of record. And yes, there will be different ways that we interact with that information and different ways that we engage in different user interfaces that'll emerge, whether that's within the context of maybe an AI agent or something else. But its core, I think you're gonna see more often than not that the value of the data, the models, the governance, application of business rules, and the, I'll say, vertical intimacy that many of these software stacks have is going to be really foundational to making use of all this exciting new technology that's coming along, whether that's IoT or or AI agents or or what else. It's kind of a thematic thing. I think you're going to see things pop up certainly within Brightly Solutions and within the other players in the market. You're starting to see that already. But it's an exciting time to be in the software space with the emergence of AI. Awesome. Well, I think that's everything, Brian. Thank you very much. For everyone who attended, you know, thank you all as well. As I said, we'll be sending out a recording to your email, And you also get in, you'll you'll get a survey if you can provide us any feedback. We would appreciate that. But, yeah. Thanks, everyone. Have a great rest of your day. Thanks, everybody. Have a great day.