Microsoft Corporation (MSFT) Earnings Call Transcript & Summary

We still -- we see the attendees rising right now. So I think everybody's joining us a bit late, so we'll just give another minute or so just to make sure everyone can join. Meanwhile, enjoy your cup of coffee. We'll be up shortly. I'll make a cup of coffee real quick. That's great, especially so early in the morning for you, Sarah and Salla. Thanks for joining us so early. Okay. It's indeed 12:00 noon already. Again, I just wanted to thank everyone for joining us today. We have an exciting webinar ahead of you. Again, I want to introduce myself. I'm Anna Liza Montenegro, the Director of Marketing of Microsol Resources. And I'm also joined by our Marketing Manager, Miriam Schrier, who will be monitoring the question-and-answers box. So if you have any questions for our panelists, you can post them very shortly. Again, I wanted to say good morning for those joining us, and for those joining us in the East Coast, good afternoon to you. And Microsol Resources actually is doing a great spotlight on the latest innovations on design and construction tech perspective. This is actually a continuation of our spotlight on the latest innovations in construction and design. And I wanted to bring the thought leaders at the forefront of those innovations. We're excited to discuss the new and exciting technologies that are shaping the architecture, engineering and construction industry. In fact, today's tech perspective is online conference series, and yes, that is a curious word series. This is the first of many. It is going to be about leveraging digital twin technology. And before we go ahead to our presentation, just wanted to introduce -- for those who are unfamiliar to us, we are an Autodesk Platinum Partner and have been a proud partner for -- of Autodesk for over 30 years. In addition to that, we are also partnered with numerous technology companies, and we offer our clients support and the resources they need, including the various companies you see here, from Bluebeam to McNeel, who brings you Rhino and [ Skip ] for a virtual reality solution; Chaos Group for your visualization software; and many others you see here. So if you're interested or want some additional information about any of our technology partners, feel free to contact us at [email protected]. So on that note, I just wanted to let you know, TECH Perspective basically started about 8 years ago. Since then, we've held TECH Perspectives at the Wall Trade Center in New York, in Kimmel Center in Philadelphia and Boston Convention Center. This is our President of Microsol Resources, Emilio Krausz, and the different locations here. And last February, we held TECH Perspective of the Seattle Art Museum, and today, online from my home. In fact, last year, in Seattle, we were lucky to be joined by Salla Eckhardt at Microsoft, who was a presenter then and also going to be today's presenter. But before we do that, we just wanted to let you know, in addition to TECH Perspective, we are also in the middle of hosting our What's New with Autodesk 2022, so you can see the latest features. And if you have any questions, we highlight the latest features and enhancements. The next one is actually on the 20th about What's New with Revit 2022. We also have -- on April 27 and May 4, we're going to be talking about the new Autodesk construction cloud. And we had positioned this to separate them. If you're a designer, you might want to join the 27th instead of the one for construction firms or construction topics on May 4. Also, I just wanted to let you know if you're interested in receiving a link to the recording for today's webinar or interested in getting your AIA continuing education unit, please send us an e-mail to [email protected], and we'd be happy to send you a certificate of completion. Or if you want, send us your -- e-mail us your AIA number, and Miriam will get you those certificates and learning units. And again, we had started this conversation, as I said, because digital tools quickly are proving to be a key strategic accelerator for digital transformation. And we want to unlock the value created by the Internet of Things, artificial intelligence and analytics. But we wanted to talk what is really the buzz about digital twin. What is it? What elements define it? When can and should you apply this technology and what makes it so powerful? So these questions and more, we'll be discussing it today on leveraging digital twin. And we are bringing this conversation with Microsoft as we talk -- as they talk about the develop -- how they develop the digital twin approach. In fact, they are members of the Digital Twin Consortium. We will also be joined by CallisonRTKL on how it informs the design and using the data gleaned from the model, and how do you make adjustments on prediction versus reality; and also Stantec on how it will affect building sensors and operations from room occupation to technology used to control temperature and air quality. And on that note, I wanted to introduce the first panelist of our presentation today, Salla Palos Eckhardt of Microsoft. Salla, all yours.

Thank you, Liza. And thank you, William and Sarah, who's joining in this panel. I look forward to learning about your side of the industry on how you leverage digital wins. And I work for the Internal Real Estate and Security at Microsoft. So I'm bringing it in, hopefully, a real estate owners' perspective and also, like a real estate investor-developer perspective and my own opinions on digital twins. And this is an image that I use just to kind of formulate in my own mind that how might we, as a real estate owner, start leveraging digital twin as a framework because it's not a single tool that you can just go and buy off-the-shelf in the market and hope that it's the silver bullet for resolving all your problems with the built environment industry. But it's more of a framework or a methodology that then has different tools to support developing the digital twin. And I'm taking the end-user perspective into what the digital twin might be for you or it might be for me and thinking about what are the business cases and the use cases for delivering this. And that way, when a real estate owner is commissioning digital twins and asking the architects and engineers to deliver the digital twin, that it's an educated commissioning, an educated ask and not something that is just adding burden on the designers and engineers and the digital contractors as well without the end in mind or the outcome that is then valuable for everyone. So just to kind of like summarize what this framework is about in 10 minutes. Looking at the lower-left quadrant, where you're dealing with an environment that might be able to be low cost, but it's also low performance because it might be an existing city block, for example, or city neighborhood and tying this into the ambition of major metropolis or even smaller regions in developing smart cities. And digital twin could be an aviation infrastructure. There is an example from when I worked and lived in Finland that I was leading the Finnish team on EU-funded smart city program that was aiming at improving the energy efficiency of existing buildings in an existing neighborhood while developing the foundation for that neighborhood to be developed into a smart city neighborhood. And if I had, had digital twin, it could have been a really great ideation infrastructure for engaging with the citizens that were living in those buildings, that were moving and using the amenities in that neighborhood to really discover that what might people actually want and how might we actually deliver those experiences. And that way, have that quick testing and prototyping of ideas before then going forward with the designers and engineers. And that way, take that avoiding up costs, avoiding unnecessary investment mindset into developing and improving what already exists. And digital twin, in my mind, could be a framework and a platform for really engaging with people that are not necessarily industry experts but have a stake in the game. They might be the end users. They might be the decision-makers that need to understand what it is that the architects and engineers are then designing and developing to improve. And where is the aviation infrastructure is used? We can still continue using the digital twin framework for continuing the development. And if we move over to the right lower quadrant, the invention catalyst, this might be a regional project development that you might be dealing with a greenfield project somewhere that is very underdeveloped, might not have any infrastructure existing or it might be a greenfield project, a brownfield project or even a [ black field ] project that you're dealing with something that is completely like low performance, no performance at all. It might be that it doesn't have any infrastructure existing, hence, you're looking at very high cost of investment and long project lead time into developing something that you want to develop into that region. So it might be something that, when thinking about smart cities again, that there's a lot of construction boom happening in certain regions in the United States or in certain regions globally. And the main driver is that there is a very vast urbanization happening in regions, and at the same time, people want to have affordable housing. Companies are looking for affordable office space. So there is a lot of regional development happening in regions that haven't been developed earlier. And that way, the digital twin could be the digital prototyping platform for inventing that how might we actually deliver something completely new that with traditional methods speaking, it's very high cost. It might cost billions of dollars to develop. So it's a big position to make, but the region itself is very low performance. So you don't have existing infrastructure to support for the development. So with the digital twin, if you can already kind of predetermine the total performance of the environment that you're trying to develop and then simulate it using the digital twin as a platform and analyze it and that way spearhead the design and engineering workflow, design -- sorry, spearhead the decision-making of the real estate investor, developer, owner to provide better decisions that then don't backfire during the design phase and read into the iteration rounds of changing what is already designed and developed or changing things when things are already being manufactured or fabricated or built and assembled or arriving at a situation that everything is built, but once the environment goes into operation phase, it continues to be high cost. So overall, digital twin could allow us to prevent those scenarios from happening and being more of a platform for a collaborative project delivery and transparency of communication between the experts and those that are funding the project or the end users of the final project. Then if we move over to the higher -- the upper-right side quadrant, innovation platform. This is where you might be dealing with existing smart campus area like we have in Redmond that we are redeveloping and refreshing our original campus region. The digital twin might be an innovation platform. So you already have a build environment that is very high performing because it's been maintained, and it has end users, and it's fully running. But overall, those environments [indiscernible] operating those improvements continue to rise. And it's -- when you think about real estate operations and ownership and how much there is need for, considering the renovation debt that continues to accumulate once the buildings are handed over and the operation space start, there is a need for understanding that when is the optimal time for tenant improvements, and when is the optimal time for retrofit and renovation. And those are the very large spectrum of investments. So if you're only dealing with new paint coats and renewing carpet, that's very low cost. But it only -- it lasts about maybe 10, 20 years of the technical life cycle of the building. And within after 10 years, you're already starting to look into some of the retrofits because things start to break down. And then when you reach the 30-year point, that's when you're looking at the first round of big renovation. So over the technical life cycle of the building or the life cycles of the building, you're looking at reinvesting the same amount of money into the renovations and retrofits as you did for the original greenfield project. And that's something that shouldn't be forgotten with the real estate owners. And the real estate owners should have a more open dialogue with the architects and engineers who are the subject matter experts on how they actually designed and engineered the building to perform from the social performance point of view. So the end users -- from the end user experience, they plan and develop based on what the real estate investor-developer wanted originally, but then also understanding that -- what is the technical performance that the building was engineered to, to cater to the end-user experiences. So using digital twin as an innovation platform, you come across as more educated and more meaningful real estate owner discussing with the industry experts, and they sit off on a path to success. And the overall idea is that we continue improving the total performance of our [indiscernible] environment and not destroying also the natural environment in that process. And then when we move over to the left quadrant, higher quadrant of the chart, this is where we are in the perfect world. This is the unicorn of digital twins, that you can use that as an intelligent tool to continue to optimize and harmonize the performance of your building environment every day. So you can then leverage the AI and machine learning algorithms and all these intelligent components of digital twin to help people make educated decisions and be more proactive about maintenance and predictive about operations and that way, continue driving the built environment as low cost as possible but as high performance as possible. And that's where the unicorn idea comes, that it's not usual that you can have very high performance at very low cost. It's usually high performance, high cost or low performance, low cost. But with the digital twin, we are looking at a paradigm shift. Thank you. Anna Liza, back to you.

Thank you, Salla. I know we will have a lot of questions about how do they achieve that unicorn shortly. But I wanted to encourage everybody else, if you have any questions for Salla, for Bill or for Sarah, feel free to go ahead and type your questions in the box, and we can ask them shortly. So on that note, we heard from Microsoft's property owners' perspective. And I wanted to pivot the conversation from that perspective to an architectural design perspective. And on that regard, we're going to go to Bill and over to Chicago, if I'm not mistaken.

Yes. That's correct. The weather is actually unseasonably fine. So it's a pleasure being there. And thank you, Salla, for teeing up my portion of the conversation. It's so germane and aligned with this notion of digital twin, not just being a mechanism for efficiencies and operational management and those kinds of things, which are critically important for sure. From our perspective, there's this other element of design and utilizing the information in terms of past data sets as well as ongoing performance information to help shape whatever design happens to be on the table, aggregating those other sort of insights so that it continually informs a new design. So -- but first, I wanted to like give a little bit of structure around what we mean around digital twin, right, because the definitions are definitely a little bit sprawling, and they're often really unique to the industry that you're in. So this is a diagram that actually, our parent company, Arcadis, has been working on for a couple of years. And I think it's really, really applicable to how we, in the architecture arm, really look at the digital twin and the meaning of what all the -- how all the information kind of comes together. So we kind of break it down into 4 different levels, the first being sort of digital ready. And the diagram sort of illustrates because our parent company operates across these different business areas around infrastructure, environmental remediation, water and buildings, which is the area, of course, where CallisonRTKL practices. So in that first area, when we look at digital twin ready, that sort of first -- the lowest level of its maturity, it's often work that we're already -- we've been doing before digital twin became a really emergent buzz term. And it really means about working on a design in a much more meaningful way than just sort of lines on a screen or a piece of paper, that everything has attributes, everything has meaning, that there's geometric information, whether it's digital or physical, aligned with all of its metadata and attributes about how big something is or what materials are and those kinds of things. That's sort of its lowest level. And being able to prepare a model so that it can be connected with other sorts of data and then real-time data capture and monitoring systems through IoT, that's sort of the first step. A lot of the work that we do sort of exists in that realm. But as our own business evolves and as the technology evolves and new opportunities arise, we move into these next -- these next levels that are not only available to us but also as a lot of clients and regulatory bodies are now sort of leaning towards in terms of the direction of digital twin. The next thing about informational twin is where it's more about sort of the predictive modeling and performance analysis and those kind of things and where you introduced -- understood and known data set parameters about how much sunlight is coming in, how much heat is being lost and those kinds of things, and then we can test that against our model. It doesn't reflect an actual real-time assessment or analysis of how the building or the asset is performing, but it at least gives you that first understanding of what we predict or how the design should perform. And as you go down the -- or up the -- in this case, up the hill in its maturity, we go to operational twin, where we are actually utilizing IoT, depending on the criteria that you're really designing around, what are the important metrics and data points. All those kinds of things get sort of connected, not only to the digital asset as it's being designed, but they are integral pieces in the built assets so that you create this constant feedback loop of understanding the performance and measuring it against the predicted. And finally, the sort of the crown jewel of it all, right, the future and ideal state is the connected twin and -- which is more akin to the idea of the smart world, smart cities, where you have multiple different operational twins all connected into an ecosystem where data is not only shared, but it continually informs and continually improves the performance of, not only the built asset in and of itself, but how it's being used by people, I think, by actual human beings. And so Anna, can you go to the next slide, please? So from our perspective, on the design -- through the sort of design win, there are both these aspects of this sort of the digital and the technology piece as well as the understanding of the human experience and this idea of happiness and wellness. Human centricity is this thing that is a really kind of hot topic right now. And I think it is actually really, really closely aligned to this idea of digital twin, right? So that we have a number of assumptions around design, whether it's architectural design or product design or process design within a city, that there's a lot of intuition that drives that, a lot of information, legacy information, that is maybe -- some is less quantifiable than other, let me just say it that way. And -- but now we have an opportunity to really understand where our predictions actually meet with the actual performance. And so again, Arcadis and CallisonRTKL look at digital twin this process through an infinite loop, right? So it's a constant feedback loop where the understanding of what human experiences are, how we understand that information, how we capture it, how that informs design and then how we simulate it, right, to drive that prediction. And then, of course, that gets fed back into the actual physical twin or the physical aspect where things are tested, right? So information and performance criteria then gets reported back. And at this point, right, because of the level of maturity, that information is -- often informs the next design, right? But as we look towards the future about this idea of personalization, right, that's ubiquitous across all industries, the idea that modular construction or modular space or multiple use space can be informed and impacted by information being sent from the physical twin to then offer quick solutions for renovation, change of process, whether it's a 6-month construction process or it's simply an alteration of space used as a result of modular components. These kinds of things are -- creates a system of constant feedback with -- where we're not just talking about performance as predicted through IES or ClimateStudio or these kinds of softwares that are excellent in terms of predicting what -- how a design should perform. But the true meaningful part, if we think about social impact, human wellbeing, human centricity and sustainable impact really is where kind of the rubber meets the road, right? What like -- what are the actual efficiencies that you get from building performance? How are people actually using a space? And how happy are they? And what is the level of wellness that they are? All of these things can be achieved through this framework of the digital twin, not just around the reporting mechanism for operations. And so I think Salla, one of the things that I think you brought up really -- that's really important is this level of maturity and appetite for the clients, right, and on the client side, whether they're real estate or developer or a health care system owner and those kinds of things because, obviously, at this stage of digital twin, there is a cost, right? There is a cost that we don't -- that isn't associated with, let's just say, traditional building components, that it's a little vague right now about like what is the true value that you're able to get back in return. We understand these things intuitively, right? But there just isn't enough data to really fully illustrate that. But good thing is like we're human beings, right? And we have logic and we have intelligence, and so we can extrapolate a lot of these things. So really, there's -- like we also think about our clients and then the experience around design and the opportunities of utilizing digital twin at a really sophisticated way or in a simple way, and that's really dependent on the client, right? And it really -- it's not even about like how much money they have to spend or what their expectation is in terms of owning the asset itself or just building it to then flip it. So if we think about clients and appetite in that way, we think about it in terms of like these sort of visionary clients, right? Like -- and I would say that Microsoft is definitely in that category, where they're thinking well ahead, not just in terms of financial performance and fiscal performance, but really about sort of the human experience, the overall portfolio management and its impacts to the entire business of Microsoft, right? And so if we think about the things about how it improves creativity, how it improves wellbeing of employees and, thus, fosters on higher productivity and those kinds of things, all of that we know drive innovation, right, and -- which is one of -- I mean that is the asset of Microsoft, right, is its innovation. And so their visionary sort of perspective comes first, and then the other criteria about its applied efficacy and the cost and those kinds of things sort of fit into that goal. The next set on the spectrum is more sort of these pragmatists, right, that they understand the value. They understand its meaning societally, environmentally, all of these other things, but they are sort of bottom line thinkers, which isn't necessarily a negative thing, right? None of these are kind of bad or horrible categories. And the last one is what we consider more emergent thinkers, right? So they're just getting into it. And they're sort of understanding that they're hearing a lot of this stuff. They understand the importance of data and those kinds of things but not really sure how it impacts their business. And so I think that depending on the clients and where they kind of sit on that spectrum, right, because it's not really a set of buckets, that's where the opportunities for design and design improvement through digital twin really exists. So it's not necessarily important that you had set out a strategy that every one of our clients have to do a digital twin and those kinds of things. It's more important to understand what part of digital twin and what part of that feedback loop is important and most valuable to the client. And the last thing I'd leave off with is that if we really think about digital twin, like most of the visualization around digital twin shows like an entire complex building or a campus of buildings, and there's all this data being reported in donut drafts and charts and line graphs and all these things, it's really intimidating, and it often leaves people to think that like, I have to have everything figured out, and everything has to be kitted up and monitored and all this. Otherwise, it's not a real digital twin. And of course, that's simply not true. And like all things, right, there's the -- that's the goal, right, indeed. But to understand whatever the problem is that we're trying to solve from a design perspective, the digital twin part is a microcosm of the larger assets, right? So maybe we only need to investigate and monitor a heat mapping of a corridor or a nursing station or something like that. Maybe we do need to understand the humidity or the heat gain or heat loss of the building. But being able to work with a firm or designer around the aspects that are most important to you is the critical piece, especially around this level of maturity that we have around digital twin. So with that, I'll hand it back over to Sarah. All right. So I'll hand it to you, Anna, sorry.

That's okay. But I actually wanted to bring in and loop in the conversation, obviously, from an owner. Again, you heard from Bill about their insight on the design perspective. And we -- when we -- we're talking about this initially, we wanted to bring in -- who do we bring in to talk about the building performance, the engineering perspective of it? And Salla had volunteered Sarah to talk about that aspect, bringing in a perspective, not just from an architecture, but also from an engineering perspective. So on that note, let me just -- switching on to Sarah's slide shortly.

Yes. No worries. Well, my esteemed colleagues has keep me up very well. We've defined what digital twin is, where the ideal position is to be in the future, the unicorn status, if you will. And we've talked about the different levels to adoption, understanding that as with design, when it comes to anything digital, you kind of have to plan with the end in mind. There is no one answer. So a little bit of background on me. I'm a discipline leader, building digital practice at Stantec. I've been in the industry for about 20 years, 4 of those with Stantec. I'm a gadget keeper, occasional gamer, avid reader, recovering musician and serial hobbyist. So I'm delighted to be here. So what I'm going to talk about today is how the data can be leveraged. More often than not, like Bill and Salla have already aptly pointed out, it's confusing. It's confusing. Where do I even begin? I think most clients like us have a lot of data. Over time, we've siloed it all over the place because we all have our own ideas of logic, reason and practicality. And more importantly, a lot of our clients and owners have been working with people like us, and we all have different standards. And so as you're getting into this process trying to decide, okay, what does level 1 or step 1 look like if I want to start leveraging digital twin? The biggest question you have to ask yourself is what can I even do with that data and establishing your own priorities, as Bill mentioned. I mean, really, it's understanding what questions you want to ask and have answers that determines what type of data and how detailed models and things like that need to be. So I'm going to talk a little bit about how that data can be leveraged, and I'm going to talk about some of the challenges and just some things and considerations to keep in mind as you start to begin your journey. Obviously, you have 3 experts sitting right here, and I'm graciously volunteering each one of us to answer any questions, not only here, but definitely reach out to us should you be venturing and embarking on this journey in the near future. So asset management, we already talked about that. So we talked about systems performance, taking a look at how they measure against how it is they were designed, helping inform the future design or, more importantly, that next asset that you have. But you can keep track of any data. You have to think of it as garage in, garbage out. Any information that you put in this environment where you'll tend to -- you can extract. If you want to know the last time that you change out to paisley carpet in one of your buildings or how many buildings have paisley carpet in the first place, you can do that. If you want to take a look at your systems performance, monitoring and maintenance and how to do a facilities management [ client ], you can take a look at that. You can do systems performance. You can compare and contrast the systems, maybe understand what the correlation is between each of them, why they're doing what they're doing or, more importantly, how are you leveraging all of your space, and why is one building more of a money sink and operating more inefficiently than another. You can go through and do that and do the validation of intended versus actual performance. Maybe you want to start training your building. Maybe you want to start training your building and its systems to operate totally autonomously so that it's more adaptive to existing conditions and, more importantly, that you are prepared for that postpandemic response. Hopefully, we don't have another one. But if we do, you could use that information and a digital twin environment to do some scenario analysis. So maybe you want to take a look at and study how your HVAC systems compare and contrast to your acoustical sensors, so that you're looking at population density and having to increase your outside air in order to accommodate that. This could do that. You absolutely could. So it's training a system to proactively and autonomously adjust for the changing conditions. But like the sample that I happen to have here on the slide is walking a client through this particular journey. More often than not, we find the clients and trying to take that first step, really want to understand, more importantly, what they have and how they're leveraging it. So in this particular instance, you can actually use digital twin, not only to just take a look at your system performance or take a look at your assets, all very valuable, but you could also do it for master planning. And so in this instance, this is a health care client. So they had a combination of owned and leased space. They want to go through and then validate their existing plans based on industry predictions of a rise and increase in elder care and pediatric care. But there's a lot of different dependencies that happen there and a lot of different informational needs. So that's where things get interesting, too. There are many different consumers of the information that come out of digital twin. So the type of reporting and the things that you're looking at are going to vary based on those needs. So in this instance, specifically with health care, the client wants to just take look at -- based on our owned and leased space, do we have the ability to expand and accommodate in our existing footprint? And if not, what are some things we could be doing differently? And also, hey, maybe there's an opportunity to take a look at how we're leveraging that existing space. So with each 500 beds that you want to add, there's a contingency or some parameters that include a quotient of nursing, nutrition and parking. And so what they wanted to do was be able to validate that. Are we teed up for success in the future? Is there the ability to contract? Maybe we're just not leveraging our space as efficiently as we could. So we built a solution to help do that. Again, it's all about what are those questions you're trying to ask and answer. Some other things that you can do, wayfinding, so you can take a look at how you can leverage different types of visualization technology. This is incredibly important when it looks at your patron, end user, employee engagement, emergency evac and response, personal office signage and environmental controls, bit of general signage to make sure that people know exactly what to do in the event of an emergency. And then Bill already touched on this, too. I mean another opportunity, too, is to take a look at health and happiness and how does that weigh in with all the other environmental controls that you have to have going on in the building. Another use case is security, right? So we -- I think, obviously, I do, as a very large self-appointed nerd, the more efficient and cooler technology gets, unfortunately, the more demanding I get as well. And we like to have things instantaneously. We like to have all the information in our fingertips all the time. And we don't want to carry a bunch of cloud cards or access batches. We want our phone to contain nearly everything. So what is that digital twin solution that you have to be working with that integrates with all the mobile devices? There's a lot more information that you can service and a lot more functionality that you can incorporate. So integrated security or visitor management, lockers, parking, building space and access, vehicle charging or, heck, the building reacts as you walk through it, how great would that be? All of these things are absolutely possible once you start getting into the digital twin environment. So now that I've told you that, they're like, "Great, Sarah. That's awesome. Where the heck do I even begin, right? And then more importantly, what are the things that I need to keep in mind as I'm trying to embark on this journey because that's a lot of stuff that I could do, which is great. So what are the things that I need to be aware of as I start considering this path?" And so some of the challenges that a lot of earlier doctors are running into isn't just the assisting of data and trying to figure out where to begin. That's certainly a portion of it, but it's the democratization of data, right? So we all know that data has value, not the data itself necessarily. I mean there is an argument for that. But how to leverage it is really the competitive advantage. But it's value to each of us. And there are very good reasons that people wouldn't necessarily want to openly share that. So understanding what questions you're trying to ask and have it answered will really tell us what type of data it is that you need to get access to and start having those conversations sooner than later. I remember how we were trying -- I remember when I first started my career, anyway, that we would charge people for drawing any kind of record drawings and things like that. We're starting to see that with data as well as people are trying to figure out what is that value proposition as a result of sharing information. And then, of course, there's a data privacy security and other regulations around information, data share and storage. Then there's data normalization or curation. So any system is garbage in, garbage out. You get a lot of duplicated information when you're importing model geometry. And then, unfortunately, because there are multiple points in the continuum, to Bill's point, and when do you start, and when do you bring this information in, and not a lot or not all content, it's actually within all of the models. So sometimes it depends on what your goals are. But when you begin to bring these models and this data into the solution, it can be very, very dependent upon what those goals might be, right? And while there are very many schools of thought at this time, we're still developing standards and industry benchmarks for what a digital twin is and how we organize that data and then how it should perform. We also have the interoperability of disparate systems. Anybody who's been in this industry for any great deal of time knows that most of the applications that we tend to use don't actually play nice with each other. And then on top of that, a lot of our buildings, these smart devices have their own proprietary interface, their own APIs, their own software. They don't actually all talk to each other. So it's just something to keep in mind. A lot of contractors are switching, which is great, but it's just a consideration. So where to begin? I think the biggest challenge is taking a look at your data, understanding who your different subject matter expert roots are. So what are those different informational needs? In the use case that I presented, we had not only the facilities management, security, but also the executive team and the Board taking a look at this data. And so the different types of reporting that we would produce out of that would be different in each one of them tailored to the individual's needs. And so I think the biggest, most daunting task is understanding what is the digital twin, what can I do with it, and then out of all of this data, what do I really need to care about. And it is very, very overwhelming. But there are people who can help guide you to the process, and I just graciously volunteered a couple of them. And so when you're deciding when to begin, what to include and, more importantly, who should be part of that conversation based on what your goals are, you need to understand that you can only expect to leverage what you include. So you can append data. It doesn't always necessarily have to be embedded in the model. But it's more about defining what it is that your specific needs are as short- and long-term goals. And then like I said, not only do you have interoperability, but a lot of the applications and then, frankly, the way that the industry process works, you don't necessarily include all of your information, specifically with systems, in your actual design models. There are quite a few reasons for this. So figuring out that dovetail and that handshake early on as you're beginning to start planning new projects, legacy projects or so -- or facilities, there's a way to handle that. But as you're beginning to kick off new projects, having that conversation as you're going through the design process with experts so that they can tell you to what degree to model, when to model it, a lot of the LOD, MEA documents out there, try and cover this, not necessarily from a digital twin perspective, but when do I need to start [ sewing ] sensors in my model so that if I want to interact with model geometry and have that data connection as well, that it's actually contained within. And then the last question I usually get is when do I start? Well, now is good. The reality is that we've been waiting a long time for this technology to catch up to us with the understanding that we had to reach a level, what was it 3 or 4, Bill, on the step chart? So where you're looking at truly connected building. So you have that virtual model connected live with the data. But the reality is you have to start somewhere. So maybe you don't tackle your entire campus or your entire real estate portfolio from the beginning. Maybe you start small. Maybe you'll leave the existing buildings as they are for now, but take that next project as the opportunity to start digging in. You got to start somewhere, and the technology is here. We're no longer awaiting. At this point, if you don't jump in with both feet, you will get left behind, and you're missing out on the opportunity to leverage all of that information to help drive your business forward. And that is what I have. So I'll hand it back to Anna, and hopefully, you guys, we have got the [ grey cells ] greased and running, and you're ready to ambush us with some questions.

There are a lot of questions that are coming in to each individual one of you. And I just wanted to kind of like remind everybody that today's webinar is actually 1.5 hours long. So we did expect that we're going to be getting a lot of questions from you, so just keep them coming and just type them in the questions box.

And again, this question is, I guess, to either Bill or Sarah. Somebody was asking, "What does this digital twin basically affects? And how does it help with carbon footprint? And how this digital twin help with sustainability?"

Well, I can address that from -- maybe from a design perspective. So through the process design, right, there's always this -- again, when we talk about measuring or analyzing the design, the information that we get back about what -- how much carbon it uses, what the energy intensity usage is, what the daylighting impact is, all the full dynamics of wind, how that impacts the overall temperature of the building, these are all things that, again, are highly technical. There is credibly sophisticated software design to analyze and predict it. But at the end of the day, it's a model, right? All models are just models, right? And so that's why the sort of -- the proposition of digital twin is so important, is that it is a true marriage, right, of the predicted versus the actual in terms of its performance. So with all other things around sustainability and impact studies, for anyone that's super nerdy about that kind of stuff, myself included, is that we understand that so much of it is prediction based, right? So much of it is based around trying understanding these numbers. Where digital twin becomes really critical is now we have this sort of measurements in place everywhere across the world, across many different dimensions, where sustainability can actually get measured. The things about design with the carbon offsets and those kinds of things will remain maybe largely intellectually in terms of those things. But in terms of the actual performance, energy draw, energy saving, self-sustaining energy loops and cycles, water systems, all those kinds of things, this is exactly where digital twins are necessary to carry out these sustainable recommendations and then ultimately to affect policy. Sarah, I'll pass over you if you have anything else to add.

You killed it. I don't think I can say it any better. I mean you've gone through and validated what we already predicted. And then I think what gets really interesting is once we have enough data, like you talked about before, Bill, it's really impacting that next design because a lot of it is theory. Now it's about validation. And then taking that and being able to look at things like generative design and AI, and having the computer or the system goes through and make a lot of those recommendations for future design, I think that it will get a lot more interesting because you get form versus function. And hopefully, we end up with a good marriage between the 2. But that will enable what I think is the more exciting part, which is where you get to the optioneering. And we just don't have a large-enough data set for that at this time, right? So I think the future as we know it in the next 5 years is going to be very, very exciting, indeed.

It's such a good point, Sarah. And especially about when we talk about the application of machine learning and building out true AIs, those -- the criteria for the algorithms, right? Like -- I mean that's the necessary point that I think that we should definitely let everyone on the call know is that the data part of all this is actually pretty complicated, right? And so I don't want to like -- I don't want anyone to leave the webinar thinking like, "Oh, great. Got it. All we've got to do is just do these. And now we got digital twin. Not what, right?" It's like there -- all of the examples that Sarah gave, these excellent examples, are driven by a lot of data and that it's structured in a way that's usable and ubiquitous. And it's really are meaning to gain insight, maybe like across sectors or across practice areas or building typology.

Climate regions, right?

Yes, exactly. Economic -- like the economy -- local economies become a component. These are all things that just require a great deal of discipline, right? And that's the thing, I think, of -- if you're going to like have hiccups initially going into this realm, that's going to be the first one for sure, is not only the reliability of the data itself -- as Sarah has mentioned, garbage in, garbage out, but having a structured framework and discipline for the data that you use, right, in order to get those kinds of insights, because only through that can you then apply machine learning and develop algorithms that get us to that generative design perspective. And finally, that idea that all of our previous designs and build assets, right, not just the idea of them, can then inform future design and actual construction from an informed perspective, right? And I always have to plug this thing, like there's always this terror -- the sense of terror that algorithms and AI are going to replace human beings and those kinds of things. And like maybe -- well, okay, I would say, inevitably, right, like in a science fiction world. But the reality is like all of these are tools, right, and the tools are meant to drive insight. And also, remember, right, algorithms are written by human beings, at least at the moment, right? So...

And to that one, you just say, I mean it's an evolution. As technology evolves, so do we. And I think what the interesting thing is that a lot of people have predicted it, I think, a long time. I think that the role of the architect, the engineer, the digital practice people, whatever, right, will change dramatically and will get negated by technology. But I would argue that the conversation changes, right? I mean -- so we spend, in AE, a lot of time, modeling, remodeling, modeling again, things like that. And I would argue that from an owner perspective and even from a practice perspective, that's not efficient, financially or otherwise. And so what this does, and I think with the machine learning, AI, generative design, any of these things, it's just the reality is that the conversation changes. Our role adapts slightly, and we better leverage these tools, new and emergent or existing, whatever, depending on where we happen to be at that point in time, to better supplement. So the conversation changes. So we're really, at this point, where we're adding more value, not doing some of these nonvalue-added activities. And I know I just downplayed that as a technologist. Design is extremely important, you need options and be able to take the best one. I'm just suggesting that maybe the computer can do it faster. So no, we'll all still exist, it's just that the nature of our roles and what an architect is and what an engineer is might be very different in 10 years. So -- and then from a business perspective, we can handle a higher capacity of work without compromising quality and certainly more efficiently. So that passes on the savings to the client and they should be pumped about that.

Totally agree, Sarah. And there is, right, a natural sort of segmentation between sort of existing value from traditional services and those kinds of things and new value, right? And that's what I always think about technology, right, is technology surfaces and exposes the new value, right? And so as you say, right, like as services sort of evolve and shift a little bit, there's this whole new bucket of new value, right, for engineers and architects, right? So I totally agree. I think the fear mongering around the demise of our professions are a little premature, right?

I think so. And I like the fact that you hit onto the data portion. I mean one of the examples that I like to give people is just like, okay, who the heck doesn't have a camera roll at this point, everybody has them. They're not organized. We take hundreds of pictures of things that absolutely don't matter. And we are totally going to get back to it at some point and clean that bad boy out, but we never have time to do it. Now multiply that by like 1 million, right, because as an organization, most of us have a lot of data and it's not structured. Each one of us has information squirreled in our honeycomb hideout. We have a lot of proprietary or legacy information and those industry best practices and things like that, that are captured in the minds of some of our more senior individuals, et cetera, et cetera. And so it's like, okay, well, where do I start? So that's why trying to suggest that if you guys start simple and you take a look at what are the big questions, a couple of big things that I really want to better understand, what are those questions that I have, am I using my space appropriately, could I be looking at master planning. So out of all my owned and leased space, am I prepared for the future? Or more importantly, how do I react to the next major event, if you will, and is there a tool that will allow me to do that? Well, this is the place to start. And so it's starting small. I think it's going through a session and trying to plan, even internally, what are those types of -- what are those different informational groups. If we did have the utopic or unicorn state [ who ] we use to make better long-term plans, but what are the short-term steps in between? What's the minimum amount of information that I need in the next week or 2 that could probably help me? You're not going to get it a week or 2 if you don't have structured data. But you'll get close, right? So I think that's one thing. And then the other thing that I want to bring up, too, is more of the security portion. I mean, obviously, we get lots and lots of messages that's just popping up on every kind of social media every day, right? Like, oh, my goodness, this morning, if you play Call of Duty, watch out when you're getting Steam invites because you could be hacked, right? Like -- so it's every single time we turn around, there's something when it comes to security or a data privacy issue. And so one of the other things that we've been noticing, too, is that a lot of these buildings will get smart devices in them. But a lot of times, the clients own IP, won't allow us to access those smart devices directly or they shut them off automatically. And so that's another thing to take into consideration. So when I'm talking about the multiple different stakeholders or informational groups that need to participate in the conversation of digital twins, especially if you're going to go to the unicorn status, there are a lot of different subject matter groups that need to be brought into the table from the client perspective, your IT team, so that we can't talk about security and privacy. Then there's decisions to be made if do you want to manage your data moving forward? Do you want somebody else to manage your data for you? Do you want it on-prem? Or are we cool with the cloud? So there's a lot of different things that have to come into play. And so I think if you start going through those basic questions, starting with the data first, understanding questions, focus on the questions, then get somebody else to help you navigate the data part, that is the best place to start to tee yourself up for success. Being specific about what it is that you want, the questions you want to ask and answer, and then you can start figuring out, okay, now where did I hide all this information or where is it scrolled away. What have we got next, Anna? More questions?

Yes, we do. Sorry. Yes. And then one of the questions was really more about a case study, they wanted to point out, well, it's great to hear about the journey, but they actually wanted to find out how digital twin actually been in the building before and what kind of feedback it is for real life performance. And maybe I'll start with Salla. I mean how's Microsoft's building?

Right. So we've done a few prototypes of the kind of digital twins that we, as a real estate owner, would want to use for maintenance and operations. So overall, it's now figuring out what are the components that we need to have. So like what Sarah and Bill said that you have to be invited as a real estate owner to really understand that what we want to get out from digital twins because if you're trying to outsource the development of digital twin, it will be outsourced to whatever caters to the outsourced organizational or personal needs. And when you think about the AEC industry and their use of digital twins and what operations and real estate owners want, they are completely different types of organizations. So we, as a real estate, owner, we are part of a finance organization. So we think like accountants and we think like finance people. And then when -- having discussions with Sarah and Bill, they think like an architect and an engineer. So how do we marry those together and then make the digital twin solution, something that supports them in developing the future outcomes of our build environment, while we're still looking to operate our legacy portfolio on a daily basis, maybe using the same platform and the same components. So that's something that we are currently kind of discovering and having those transparent discussions that how do we actually cooperate together and start breaking down those silos that traditionally exist, and then have the discussion that where do we host the data, who accesses the data, who is the manager of the data, do we want to do it ourselves or do we want to outsource them. And that way, figuring out who are the stakeholders and how do we make it and data secure as possible, because we have a very high standard into data security and all data management. But then again, as a real estate owner, we want to be buying services rather than performing everything ourselves. It doesn't make sense to like silo everything inside our own organization, but really think about sustainability from the socioeconomic point of view, that how do we actually work with the local vendors that would generate the business for them and gain the benefits of those services for ourselves.

I would add to that, from our perspective, the -- as I sort of mentioned earlier on, there is a lot of hype around digital twin, and there's a lot of good applications of them currently, particularly around O&M, right, and FM. And I think from our perspective, our real case uses that we have at CallisonRTKL is -- so we have sensored up a number of our office spaces. We're kind of going through this testing and proof-of-concept for ourselves. And we've taken that information, and we're building out our own just sort of crude digital twin platform using Microsoft Azure digital twin framework. But as well, our interests are in also visualization of the data. And so we have a pathway out also using -- from the digital twin hub, we have a pathway out using technology from Spatial Flow that exports it and imports it directly into the Unreal Engine so that we can use [ engine ] testing and these other things to sort of give us different types of insights. When it comes to actual clients, the closest we have in terms of real-time uses, we actually have a technology group, a client-facing technology group that has been specifying and designing spaces with sensors for years, right? This has been going on -- for IOT, the early stages of IoT in terms of improving building performance, whether it's measuring community or managing elevator or transport in a more intelligent way. But those things haven't yet been aggregated into a platform yet for -- at least for these clients. But that's precisely what we're -- why we're doing internal experimentation and looking toward -- as Salla has mentioned, looking to collaborate with external partners that have more experience and have a little bit more leverage in that space to sort of help accelerate the offerings that we plan to give.

Sounds great. And also, one of the other questions was really more about when do you start the digital twin concept. Somebody was asking, "Well, is it even possible to do it from a conceptual phase? And how do you assemble the information across different information models?"

So unequivocably, yes. And in fact, the sooner that you start thinking in terms of digital twin at the beginning, the easier and the better your outcome will be. I mean, Sarah, you mentioned that as well, right? It's like really understanding what you want to get out of going through this process, because it is worth, right? There is -- there are specific things that you need to do in terms of data restructuring, interoperability with your software, having framework and the standard for the data that you're using. Like all of these things require -- there are explicit amounts of work. And so the sooner that you can inject that into the overall process, the better. And some of these things are going to seem mundane, right? Like they -- like if you're using -- if you're still inheriting like 3D -- sorry, Civil 3D files or AutoCad files, and then you have a designer that's using Rhino or SketchUp or something like that, and then you have your production team using Revit, I mean whatever happens -- or I think you have a performance-driven design team that's utilizing Prime Studio or IES, you have all these different sources of information and they're handling different types of geometry and attribute data that's assigned to the geometry. And so like in order to really, really make effective use of it, you have -- like all things in life, right, you have to have a plan, a really good plan. The sooner you have the plan, the easier it is to make adjustments over time. The deeper you go into the project -- the process, whether it's design, engineering or construction, as we all know, as the deeper you go into the process, the more difficult, the most -- more costly and the less impact that those changes will have, those little interventions. So from the get-go, I always advocate, if you really want to do this and if you need to do this and the client wants this, it has to be done at the beginning.

And speaking of the G&A, I mean I just wanted to find out, somebody was asking real more realistically, right, that the benefit really is great for the owner side. They felt the artificial engineering firms, at least from a question from [ Plumen ], he said that many didn't allow them to increase their fees, the design fees. Bill's laughing. Well, it produces higher standards and more detailed deliverables. So how is digital twin different? The benefits seem to be tilted towards the owner and the GC, while the design firms want to find out what are the required services for the level of development and additional services. So do you want to talk about cost and benefits, Sarah and Bill?

Sarah, why don't you go first so that you didn't spoke last.

Sure. So cost and benefit from just the owner's perspective or are we talking AE? So for AE's perspective...

From AE, yes.

Well, I would like to think, mostly doctors they have a Hippocratic Oath, that we as designers have something similar, right, where we look holistically at the design and we're trying to do what's right for the client, right for the community and right for the environment and we certainly believe that at Stantec. So from an AE perspective, as a practitioner, I want to know how my buildings are performing and how the designs are performing against the original targets and the theory, right, to Bill's prior point. We want to make sure that we continue to inform our designs, understanding the changing priorities like carbon, I think, should have always been a priority. But hey, much like digital twins, it's becoming more and more popular, right? And so I think informing the next design and making sure that we leverage that information, try to establish industry standards and best practices. And more importantly, start working on what that future technology is that it's going to allow us to deliver design more responsibly, more efficiently in the future is incredibly important. Now from my perspective with what I do and where I work, I want that data. I want all the data. I'll take all the data that you can give me. Why? Because I'm absolutely not only building a digital twin platform that kind of does all the stuff that we talked about today, but it is going to automatically generate designs. We're already working on it. And so we want to be able to provide our clients with a higher degree of predictability more efficiently and at a more competitive price point than anybody else. So the more data that we have, the more lessons that we're able to learn. And it's easier, we're allowed to -- we're able to share that throughout our organization for our collective benefit. That automatically translates to cost savings to the customer and to us, right? You already hit on it. It's not like we're going to get an insane amount of more money or the margins are suddenly going to get bigger. It never happens. And that's the best part about technology, too, right? So the more efficient technology gets, the more that we want and, honestly, the less we're really willing to pay for it. I mean we'll pay thousands of dollars for a phone, but data, what? Right? And so you want to do this. So I mean that would be my 5-second spiel. I mean clients are going to want these things included. It's kind of like them and the translation, or the evolution from like 2D or Sketchup or even hand drawing into the Revit environment or the Bentley environment, right? There is [ savings ]. We know that clients are going to demand it more and more. Our clients are smart. They're very intelligent. They know how they could be using this even if we don't. And so we need to be prepared in anticipation of that demand. So if you aren't doing it, regardless of the margin status, you will need to do it in the future because our clients are going to want it. And if you're not prepared to dance, unfortunately, you're not going to get asked to the party. That would be my simplified version. Sorry, you got to bear with me. I'm beginning to lose my voice. It's allergy season. And unfortunately, I'm allergic to the Pacific Northwest.

[indiscernible] late for that.

So Bill, did you have something to add?

Yes. I agree 100% with what Sarah is mentioning. And it is indeed challenging, right? And so -- but I think there's a couple of things, right, that like we need to kind of keep in mind. And one of them is that the idea that architects as a profession and engineers who are going to be able to leverage the intelligence built into BIM as a way to earn more fee was sort of -- there was -- for everyone that was involved in those conversations back in like 2005-ish, that time frame, I mean there was a lot of ideas kind of pie in the sky about this is how we're going to get more influence back and all these other things. But I mean there's a reality, right, that as of -- as an end result of the fragmentation of the services we provide and the way that some of that in terms of the responsibility and the risk, right, and the fee has been given away to other people, those are really difficult things to get back, no matter what. And I think that if we look at BIM and if we look at digital twins as a way to sort of claw back some of the things that we've already lost, I don't think we're ever going to be successful at that, right? I think the promise of not just digital twin and BIM and all new emerging technologies that are going to greatly impact our industry is the new value that gets created, right, and the new value propositions, right? Like one of the things that -- as we say like we're competing on fee all the time, right? Our -- and we're not always really good at focusing on the value or articulating the value, particularly the lifetime value, right, of an asset. We tend to like characterize our services around what we understand as our traditional deliverables. And therefore, if we look at digital twin and the value and the efficacy of digital twin as sort of this newly created value that you could capture, it's always going to be diminished, right? It's always going to be sort of in this small sort of bubble of what we think our roles are, right? But the thing we know with all certainty across all industries is that the most successful companies, the most successful innovators are the people that think outside of that little -- their traditional little bubble or swim lane, whatever you want to call it. And I absolutely believe that digital twin and BIM -- because look, for us, right, BIM is the key authoring tool for the digital twin. And so they're going to be intimately related. Whether that be gets driven by automation or still by human designers and engineers, that's a separate discussion. But they're intimately related and so the value are intimately related. And so as we talk about like what we're doing and what benefits that it has for the clients and how we're trying to persuade them that those kinds of things are valuable, as an industry, we need to illustrate what that value is and quantify that, right, and then have different discussions, right, about what our services are, right? So -- and there's an example of the work that we've done for clients where we've used our modeling in digital twin and performance analyses to understand, view, the optimal configuration of towers to get the best view of a certain landmark, which drives up leasing prices and rental prices for those properties. And so the -- we ran these kinds of -- these experiments, and it turned out we optimized the solution and the client had told -- informed us -- I'm not going to mention the number. It was a shockingly high number of how much more they're going to be getting for their rentals. And those are the kinds of things, right? Like as we go forward, from a design engineering services, like if we're bringing that kind of value, our value isn't just predicated on delivering drawings or a model, right, that holds the data. It's about the outcome from that. And I think that from an industry, that's the way we need to be looking at things. And the only way we're going to get there, right, is through this more sophisticated perspective on data and this application of digital twin.

Well, thank you very much. Not only did we talk about like the needs to evolve into digital twin, but one of the other questions by [ Nicholas ] here is like what is the financial cost of investing for an AE firm? They're interested in how they can leverage this and how do they get the leadership to be -- who's cost-conscious to be aware of the benefits from an AE perspective.

Yes. So I think Bill already touched on this from the client perspective. There isn't a flat answer. And the reason for that is, is like -- if you're like, Bill or I, and you're looking at leveraging Microsoft digital twin array, the different functions and features that are available within Azure, it actually has to do with more of what you're connecting, how often you're connecting and pure volume and then a different type of services that you're offering, right? So safely, there's calculators already available, and this is not a shameless plug for Microsoft, I am not getting paid [ and so it's lovely ], but it has no bearing on my comments next. So they actually have this all really well defined. And I think Microsoft is very intelligent in getting ahead of the curve and trying to plan and develop the documentation to make it as intuitive as humanly possible. So a lot of the information about, hey, what is in this Azure suite of services, how could I leverage them, what specifically is Microsoft set up from a digital twin perspective, the documentation is there and there's even a calculator to figure out what your total capacities might be. So if you're looking at like a building, maybe you only want to deal with 2 floors because that's the floor since the client happens to occupy, you're taking a look at and make up some square footage because, honestly, it's pennies after a certain point for the number of devices that you have and how many times you read from them. Because you may or may not do it live 100% of the time, maybe the client needs are that, you only need to pull from each of these sensors like 2 to 4 times a day, right? So it's a penny or a micro penny, I think, for the transactional stuff. I have to look at it again. But I mean -- so let's just say 2 floors, you've got maybe 2 years of historical data and you want to do a live connect, roughly, if I had to do some bad math in my head, I don't know, about $1,000 a month for operational costs and that's not actually buying the tenant itself, right, because you got a plan for the total capacity that you're looking at and then how you structure that might be different. If you're doing it for yourself, cool, you can plan on having a tenant. But if you have multiple clients, you're probably going to want multiple tenants. I think the bigger investment isn't actually the technology involved. A lot of that you can dial in, right? It's just capacities, is what you're looking at and transactions. I think the bigger challenge is finding the people, right? I think technology is easy, but you're finding the right expertise to help you embark on this journey, like what are those questions, what are those things that you know, why do you not know, right? And then more importantly, getting the right people in there is like, to Bill's point and some of the stuff that Salla and I have discussed prior, it's the data, right? So the digital twin, I mean it's a 3D model. It's cool. It's got information in it. For people like me, I've been more interested in the information behind it than the geometry itself for a very long time, right? And so it's sifting through the data. It's establishing a taxonomy. It's structuring it so that you can leverage it and setting that foundation for the future. And that takes a different specialty, right, than we're typically used to handling and purely AE. So you're talking data analysts, you're talking data scientists. If you want to get into the really interesting machine learning AI stuff, which is [ really ] where I'm really interested, I'll give you the data, so I could do more of that because that's the fun stuff, for me that's the stuff that keeps me up at night, gets me super excited, then you need to bring in a specialist for that. And so it's more about what type of digital twin work do you want to target, what is the vast bulk of your clients looking for. For us, honestly, it's a lot more of the analytics and the master planning and then taking a look at the efficient use of space, so throughput, probably logistics or a tech side of things. That's where we see the conversation really starting. It's, okay, let us get -- like let's get our feet wet, let's figure out what this digital twin thing is. We've got the models, maybe it doesn't have everything in it. Let's start answering some of those more immediate questions, I think. And certainly, the pandemic and post-pandemic response in getting back to the new normal is really driving that. And so I think it's finding the human capital, setting very specific goals if you're an AE firm about what type of services you want to offer, and then building that talent pool to help you build that plan, right? I think all the technologies out there, it's finding the talent to help you establish with that plan and that journey and the other players. So that was not an answer about costs, but I -- sorry, I need more data. From a technology perspective, I need more data.

I like your answer, Sarah. And I want to chime in on this, that it's not necessarily the cost of like what is your CapEx cost today that you are putting into something, but what is the cost of not doing it, then what is going to be in 5 years or 10 years from now when you don't necessarily even exist anymore because you didn't take action. These processes, they take long time to resolve. And all the discussions, it's not something that happens in 1 quarter of a fiscal year, it takes longer. It's that intimate loop journey that Bill had in his presentation earlier today that I would be worried about the cost of not taking action today.

I could not agree more with you, Salla and Sarah. I just firmly agree with all that. I would say that, particularly to Sarah's point about the experience, right, and the expertise that -- within your firm, different organizations, different sizes have different ability to bring on different types of expertise. But I think then to dovetail with what Salla is saying, there is an impending cost of not diversifying your capabilities in these areas as a practice. It doesn't mean you need to bring on full-time data scientists that are PhDs from MIT and those kinds of things or comp sci people out of Stanford or Berkeley, but you need to have some semblance of that if you're going to be moving forward. And that's not just dependent on digital twin, right? Or -- so there is a shift in the dynamics of what we do. Not just architects, not just engineers, but for owners as well, right, that there's an element of efficiency and optimization to reduce costs. But to be competitive, right, there are new solutions that can only be driven or gleaned from utilizing this kind of technology, and the point of view and the perspective required to implement this within your organization, right? So I totally agree with Sarah. I think the actual cost of the technology to buy sensors, either utilize a platform like Schneider, right, you can buy IoT from Schneider or Siemens and just use their IoT platform and then feed that into something else for digital twin. So those costs are actually pretty marginal, right? It's like getting the cost of people that really know and understand what it takes to do these things, that's the more significant opportunity cost. At least that's been our experience.

Well, and I mean to Salla's point, I mean, Bill, you just said you're developing a solution. I've got -- mine has been in progress for a while. So that's 2 major competitors that will be raising something TBD, which means that's the forefront. So yes, I agree with Salla. I mean there are far more opportunity costs as a result of waiting or not adopting. And I think the important thing, too, is if you're a smaller firm, you don't necessarily have to do the digital twins delivery yourself. I mean there's always -- even Stantec, Callison, it doesn't matter. I mean we always take a look at each project trying to do what's right for the client. And so it's make mine or partner every single time. And so maybe some combination of that is really what's right for you or what's right for the client, right?

Yes. Exactly.

And then the results, yes, I graciously volunteered us to answer your question in the near future. I think I also volunteered us for a follow-up session, just seeing how the questions that were raised here and really getting more of deep dive, which is the stuff that I really like. It really gets me excited super early in the morning. So I think there's a lot more resources out there to help you guys navigate what that process looks like. And then from the owner perspective, too, understanding what it is that you can be asking for. You don't have to do digital twins or jump into systems management performance right away and trying to get out one system to rule them all and then looking at the comparative analytics as a result of that. You can dial it back really, really far, look at ASIC mapping and just taking a look at how you're leveraging that space versus your master plan and take a look at how you're leveraging your real estate portfolio. And maybe there are other ways that you can think of to leverage this data, like from a profitability perspective or something like that, to Bill's point, so that it's -- the potential uses are nearly limitless. But now is the time to start making the plan, which is actually -- go ahead.

Yes. And then -- Sarah, it's that you don't have to like go all in like right now immediately. But as long as you and leadership are having a formulation period to conceptualize the strategy of what you want to do, right? But you can have the conversations, that's -- this is the time to do it.

Yes. What's right for Stantec might not be right for you, right? It all depends on what the mass client base is looking at. So who are those owners and those clients, who are the most savvy, who do you anticipate is going to have that need, and even start having a fact-finding conversation with them. Sometimes, honestly, owners don't know what they want. They're -- that there's this BIM thing or is this digital twin and they're pretty sure they need to have it, but maybe they don't know why. And there are going to meet other clients you know exactly what they want because they probably sat on this call or one of the other ones that Salla has been on, and they've been learning a lot. They know how they could leverage that data and they have definite ideas. But that will help you plan for the I future.

That sounds great. And we just also like the other follow-up question regarding the cost, right? What somebody was asking is the size of the project correlating with the cost, which really isn't. It sounds like it's really more about setting up the infrastructure and the staff and...

More so, it's more how they want to leverage it, right? So it's -- the technology itself has capacities. I think where it's really, really interesting as a lot of clients are like us, right, and so we don't necessarily trust everybody with our data. And why would we think that, it's our competitive advantage, right? And so maybe your client doesn't want to leverage a cloud solution, right? There are a lot of people who don't. And so they want to keep all the data on-prem. Then what do you do? Are you going to help them build that solution so that they can then control it themselves? Then there's a lot of training, education and maintenance support that might go into that. And that's why it's so hard to cost. It literally has to do with what the client's endgame and objective is. And I mean the good news is that that's a really good opportunity because you can absolutely rightsize it to meet with the clients' needs. Yes, it's a tough one because there is no one answer. So we have clients that want things in the cloud. We have clients that want things on-prem. Some people want to run things themselves. Other people would rather not have to worry about it because that isn't their core business. And so it really depends on the client. That's why I can't give you a number, it could be $7,000 or $7 million. But the good news is that if you're already in some sort of parametric modeling environment, you probably have a lot of the content and the information that you need. It's everything that appends that. So all those smart devices that then need to correlate to those components within the digital twin, that's where it gets interesting. So you're looking at the geometry every day. You could literally do a box that contains all the information for like a bathroom core and not show any of it. But the reality is, is that we need to be able to tell that story because people perceive information in a different way. So more often than not, you're probably going to have to add a couple of additional devices, but it's not a showstopper. So I don't think the bulk of the spend is on the model side if you're already in that environment. It's more about figuring out what data you need to leverage, getting it organized and then making sure that you set the tone and the expectation for all future incoming information.

Fantastic. And also, just wanted to maybe like put a pause on that because, as Sarah did say, we are planning to have a part 2 of this conversation. So that was my biggest news. And actually, that date is actually on May 19, which is a Wednesday, sometime next month. We will be sending you another e-mail to invite you all to join us again at the time. And I just wanted to let you know that I know it is a lot of data that both Salla, Sarah and Bill had shared with us today. So I wanted to let you know that we are doing a recording of this, and we will be sharing with you in a few days. So if you are interested in this recording or learning unit, then please e-mail [email protected] and myself or Miriam, actually, will be returning that link for you. On that note, I know Salla has -- you have a hard stop. So I just wanted to be cognizant of the time and also be aware that we do have additional questions that we haven't answered. But hopefully, this is something that we can continue in the conversation next month and let you digest the information that our panelists shared with you today. So on that note, on behalf of Microsol Resources, Salla, Sarah and Bill, wanted to thank you very much for a fascinating conversation about digital twin and how the AEC firms can leverage that and help the owners as well. I wish you all well. Keep healthy and keep safe. And we hope to see you again in our next version of the webinars. And stay safe, everybody. Thank you.

Thank you. Hope to see you again.

Thank you.

Thank you.