Bridging The Data Divide: Accelerating Technology Adoption in Construction

Good afternoon and welcome to this webinar, Bridging the Data Divide: Accelerating Technology Adoption in Construction. This event is brought to you by Engineering News-Record and sponsored by InEight. Hi, I am Scott Seltz, publisher of ENR, your moderator for today’s event, and thank you for joining us. If you’ve read McKinsey’s Accelerating growth in construction technology report, InEight’s latest Global Capital Project Outlook, or numerous articles on the estimated $50 billion invested in AEC tech between 2020 and 2022, it’s clear that technology adoption and maturity are finally picking up. However, over half of the industry still hasn’t embraced connected data across their projects or have a clear data strategy. We’ll discuss why organizations are lagging, common mistakes to avoid, and how to choose the right tech while shifting delivery models at play. Now let’s get to know our presenters. Catie Williams leads Vice President of product development at InEight, focused on digitizing the industry and being a champion of change management and business process standardization for contractors, owners and engineers.

Catie currently oversees several application products that drive productivity growth in the construction and engineering industries. As a leader of Hensel Phelps Innovation entity Diverge, Thai Nguyen pioneering a new approach to sourcing, evaluating and deploying disruptive solutions for the industry. With over 20 years of experience in the architectural and construction industries, Thai’s vision is to leverage technology and collaboration to empower Hensel Phelps to innovate the industry and to build the future. Aaron Toppston is managing partner of GS Future’s early stage venture investment fund focused on technology for the built environment. The fund is supported by GS Engineering and Construction, one of the largest contractors in the world, and a member of the GS group, a large Korean conglomerate. In addition, GS Futures has a second fund focused on biotech, energy transition, and consumer technology. Now, I’ll be back at the end of the discussion to field your questions to our presenters that come in throughout the webinar. So don’t forget to submit them in the Q&A section of the webinar console. Now I’m excited to turn it over to today’s presenters, Catie, Thai, Aaron, take it away.

Catie Williams:

Okay, so let’s get started. Thai and Aaron, thank you so much for being here with me today. I thought that where we should probably start is with the term connected data. So what do we even mean by that term? I think that’s probably a little bit of a buzzword and maybe a newer term. So I thought that that would be a good place to start is what does that mean to you? So Thai, I don’t know if you want to give your thoughts and when we are talking about connected data and we throw out a stat like half of the industry is still lagging and doesn’t have connected data, what do we mean by that? What does that mean to you?

Thai Nguyen:

No, that’s a great question, Catie. Thanks for having me on this webinar. I think for us it’s many facets. I think I’ll tackle one where really today and with the construction space, many of us have a very bloated tech stack. Right. What does that mean? It means we have a lot of solutions in place for different things and within that another data or silo of data. Right. And so that’s a piece of it as well. Today with our day-to-day operations we’re signing in and accessing many different solutions and within that, having to go to different systems for different pieces of data. And so in reference, that’s a piece of it as well.

Catie Williams:

Aaron, what are your thoughts about connected data? How would you define that term?

Aaron Toppston:

Sure. Catie, appreciate you hosting today. So when I think a little bit about connected data, I like to think of the analogy of pre-con estimating versus operation, right, and the connection between what do we think we’re going to build and what do we build. Right. And we’re all familiar with that concept. Data has similar silos between what do we plan for, what are we gathering live in the field, and then how quickly are we making decisions with it? And each, depending on what we’re gathering, whether that’s estimating information, drawing and design or realization of safety, quality and operational components in the field for productions, how all that information is stitched together to have a logical and clear path for decision-making is what we think about when we talk about connected data across the lifecycle for this conversation of building work.

Catie Williams:

Yeah, I love the highlight that it’s really not about the technology, it’s the business process that connects it all together. And Thai, you mentioned that there’s tons of technology, and I don’t want to put words in your mouth, but there is so much technology, but it is really about understanding the business process and how it starts to meld together. And the data really, there’s all different, the pieces all are connected, not to use the same word in the definition, but right, it all starts to flow together. And so you really have to have that understanding that there should be touch points and what are the handoffs between these different business processes.

And when you have everybody working without knowing what someone else is doing, then you have those silos and then we start to have breakdowns and challenges. So I’m curious what your thoughts are on why such a big discrepancy in the industry, why 50%? I mean that’s pretty significant that you have such a significant part of our industry that still doesn’t have a data strategy, doesn’t have connected data. What are your thoughts on why that is? Oh, I’m sorry,

Thai Nguyen:

I can jump,

Catie Williams:

Whoever wants to yeah, sorry. Go ahead.

Thai Nguyen:

It’s a good question. I don’t think there’s an easy answer. And ultimately, like I said, as you look at different areas of our business, I think we optimally always kind of look at what we do in terms of really data flow. Right. How does it start, how does it start to build up at the job sites from procurement through estimating to the job sites? Aaron alluded to scheduling. Today we schedule, but really we don’t leverage different data sources to get additional insights. Right. So if you had schedule and you’re creating a schedule, if you can leverage on your production engineering data within that, you start to look at quantities from estimating, right, through the BIM, putting that information together, it drives towards a great schedule. Right. And so again, I don’t think we’re there yet. We’re working towards that.

But I think to your question, it’s really hard because I think it’s such a fragmented industry and that term gets used a lot, but it’s true. I think you realize the amount of startups in our industry today, there’s over 3000. There’s a lot of solutions for, again, a lot of the gaps that we have within our workflows. And so as you’re bridging those gaps and you’re using different solutions, if anything, that cause more complexity and more fragmentation in industry. And then I think for us it’s trying to have a strategy, I think overall from beginning to end, how we capture, how we manage and ultimately how we surface it to our people for those types of insights. For us, it’s critical.

Aaron Toppston:

Yeah. And I think the headlining stat, right, half folks don’t have connected data or data strategy is that it’s a difficult statistic to digest because order of magnitude, 10% of the world’s economy touches construction and the construction related manufacturing materials, execution of work, development of real estate. Right. So it’s a massive industry. It’s incredibly local. Right. What it takes to build here in my hometown in Chicago is quite different than Birmingham, Alabama. Right. And so that fragmentation from a macro environment has cascaded into I think a lot of point solutions as well from how do we generate views, data and frankly the use of technology to generate that data within this sector as well.

Thai Nguyen:

Yeah. And Catie, I’ll like to add,

Catie Williams:

Well, it’s interesting you say that, oh, go ahead. Yeah, go ahead Thai. Go ahead.

Thai Nguyen:

Aaron made an interesting point. I think with a lot, like with every project that starts, there’s lack of standardization. When I say that, I mean we have different owners, right, and very diversified portfolio. With every owner really, they have a vision of what they want and really within that, sometimes they dictate what solutions we use. And so if it’s a different project management solution, we’ve got to adhere to that, but not knowing we’re bringing our own tool bag of solutions right? Within that, our trade partners have their own tool bag of solutions, and so it just continues to compound. I think one of the things that I think can’t get us out of those kind of revolution just because again, everyone’s kind of bringing their own data solutions and within that I think it just again continues to fragment and cause more complexities with every one of our projects as well.

Catie Williams:

Well, it’s funny that you both say that because my next question was going to be, well, do you think having more standards in the industry and more cross sharing across organizations could help, but maybe not because, right, like you said, every owner has different requirements, every contract is different. But I am curious, do you think that if there was more cross sharing, because if you compare other industries and it is hard to compare industries because the business you do is totally different, but if you think of the financial industry or insurance, there are similarities and they do do some cross-sharing of what they do. So they establish best practices, they have whole organizations so that there’s this cross-sharing of information. Do you think that that type of thing, there are some, like CII for example, does that help then create better data standards so that everyone’s starting to do standard business processes that would then have this ripple down effect from a data perspective?

Aaron Toppston:

Maybe I’ll take that.

Catie Williams:

No, that’s fine too.

Aaron Toppston:

So let me take that one first and I’ll do another analogy. So for those on the phone that do work in public infrastructure, we know that federal DOT has a set of standards and for let’s say I-beams, right, just to make something up. And then each state may have a slight modification to what’s an acceptable precast concrete I-beam. Right. Those standards exist for our built in work, right, the work we’re all here to do. When we look at the side of the coin around data, I think one of the challenges is the conversation of who owns it. Right.

The DOT owns that I-beam when it’s done being put in place and maintained for 30 plus years. Who owns the data is a very different conversation. I think there’s a very strong argument that the owner of the project owns it. It’s their money that procures the project. The data is where the project exists, therefore is there a good argument there? But on the flip side, I think there’s a very good argument that everyone individually owns it, right? Without the work of the specialty trades, without the work of the GC, without the work of the independent QA, QC firms, each of those bullet points generates what’s going well and what isn’t going well on an individual project, which ultimately rolls up into better decisions for the next project. And so I think that lack of clarity on ownership is unique to our industry. That doesn’t exist as much in some other industries.

So I think standards are going to be very difficult to enforce and there’s limited incentives to enforce them. Rather, I think our industry is often best served by folks being motivated by the benefit of organizing your own house. Can this truly help increase profitability, increase the execution and operations of my firm? If the answer is yes, folks will want to organize data well and share it appropriately, versus I think the more centralized approach is particularly challenging in our space.

Thai Nguyen:

Yeah. And I think one thing to add Catie to that is I think we see many projects, our owners are very prescriptive on the how. I think if there’s less prescriptive on the how, but again, more criteria on what actually is the output for alignment, I think there’s value to that. And when we say standardized, it’s again a very broad statement, but there is I think some common ground. So less prescriptive on the how, letting really the project teams figure that out, but really on the output, it should be adhering and to what Aaron says there, if the owner does own the data, we can definitely find some common ground for sure.

Catie Williams:

No, I think that’s great. I mean, that’s a really great way to look at it and I love the comment about ownership and that being unique to this industry and yeah, I’d love that call out. That’s really good. Maybe shifting gears a little bit just to how you talked about the benefits of your own house. Can you think of anything tangible? Not to put you on the spot, but if you were to compare or think of something, if you have a data driven strategy or just you’re using the data to make decisions, having that versus not, can you think to a time where maybe on a past project where you didn’t have that versus when you do the benefits that that drives, like any tangible examples of that? Again, not put you on the spot to come up with something. I’m just trying to, oh, go ahead.

Aaron Toppston:

Yeah, I’ll jump on that. So for clarity, my role now is I run a venture fund, so I’m not in operations, so I want to call out that looking to my previous life experience to maybe offer some observations. And in that one of the, I worked at a large GC, and when you think about GC work, the most valuable asset is staff. Right. You’re talented and capable leadership in the field and in the office. And that their ability to make the right decisions on a given day or with a given set of circumstances, I think is incredibly valuable to the clients and to the outcome of the company. How do you decide how and where to put your best staff? Right. Why do you staff design build project differently than you staff a rip and read project? Why would you staff a certain owner differently than you staff another, right, private work, public work, et cetera?

And a lot of that decision making is intuitive, but I think data helps articulate how you allocate staff from a leadership level. And I think data strategy around something as simple as RFIs and observations by client base can be uniquely helpful in how you bid the next project that otherwise is maybe a little gut check only historically in our industry. So it’s a very small tactical example, but hopefully one that’s relatable.

Catie Williams:

No, absolutely.

Thai Nguyen:

No, absolutely agree with Aaron. I think that’s such a, I’m going to spend hours on this conversation, this aspect of it, right Catie? I think we look at it across our enterprise and even as early on as when we start to look at work, new work. Right. If it’s medical, really having that data that how are we doing in that region, in that specific area for the last five years, who have been the best performing trade partners with this approach with medical and what type of contract are we steering towards? So all that having it, I mean I think day one has been ingrained in me. I think as builders, everything that we do supports really that job site. And so whether it is looking at how we’re chasing work through what Aaron just said there about resources to the design iterations. Have we seen this situation before?

Can we access that data to kind of leverage on that historical information that got us through some of those pain points? We don’t have enough of those, that information easily accessible today. It’s hard. We’re getting there. We have tons of data. Today is really trying to figure out how we get that and getting to the point where we’re not reacting and we’re being able to trend and be proactive. Right. So it’s what Aaron says there, it’s through design, it’s through construction where today trying to get in front of a lot of this stuff, whether it’s costs, it’s schedule, making data actionable, and for us, safety is, it moves the needle in every aspect of what we do, getting more trends in place, right, specific to incidents and how they’re happening.

Can we improve on these different things? We have tons of processes, but I think today that data is captured but then not given to our people early enough and often enough to make it actionable and make it be able to be corrected. And then even into QA/QC, I mean that’s a whole nother aspect of catching things early enough through our installation process, progress tracking, things like that that I think today with data connected provides those insights and gives our people the best information so they’re able to actually be proactive and not just reactive.

Catie Williams:

Sure. Maybe, so switching gears, because we’ve talked quite a bit about the data and not a ton on the technology, and I don’t really want to make this focus completely on the technology, but the technology plays a role in helping assist in the connection and the integration. And so I’m just curious your thoughts on the challenge that introducing the technology side on making it a reality of having the integrated and the connected data and maybe your experience bringing those tools into an organization, the change management that’s required. What are your thoughts on the technology and how it plays a role in bringing the business process and the connecting of the business process to life?

Thai Nguyen:

Aaron, you want this one? Do you want me to jump in?

Aaron Toppston:

Why don’t you take it first? I’ll see [inaudible].

Thai Nguyen:

Yeah, there’s no easy button, Catie. It’s disruptive. It can’t be very disruptive I think in that change management process. So we do have today, I think within Hensel Phelps, many ways that and tools that we approach that with. I think part of Diverge and standing up Diverge is really to kind of get in front of things that can be disruptive and then positioning Hensel Phelps in the best way to absorb these things, right? So when you say technologies, is it robotics, right? So I’ll stay in that for a minute. With really the change in environment today, the shortage of labor, finding skill trades in remote regions, we have to start to look at alternatives to that. But what does that do to our environment?

And so we don’t want it to just start to throw robotics right on the job site. We want to start testing it, right, and understanding really the pros and cons of it, how our people react to it because I think the people aspect is huge. I don’t think we pay enough attention to that. So when a robotics shows up on site, does it make them feel threatened that their jobs are going away? So there’s that human factor to it as well.

And then the useful cases of robotics, can it do those things that are repetitive, mundane, taking the human out of harm’s way. And so we want to do all that evaluation before they land at our job sites. I think in the past we didn’t have many opportunities to do that, but today I think being more strategic upfront and planning those things out, creating onboarding programs, creating an educational program. And then a change management system where we start to understand really how the human interacts, how they’re responding and adjusting accordingly I think as we start to deploy technologies on site, because I think you have to have a plan, but you have to be agile enough where you can make adjustments to make it more impactful at the job site and listening to our people.

I think for us, I think we don’t do that enough, but we’re doing better. We’re getting better. We’ve got, again, systems in place today that understanding really the rhythm of our job sites. What are people feeling on their day-to-day, that helps drive the priorities and allows us to categorize and then focus on the things that matter. And then when our people see these things, I think they’re understanding that we’re listening to them, they’re feeling that they’re being rewarded and their day-to-day is going to be less cumbersome. So the people aspect I think I’m trying to convey is such a huge component of all this and managing that process. The technology, I think we’ve all gotten pretty good at today where we’re able to roll in new solutions, but I think again, being mindful of really people and the process is something that we’re definitely focusing on.

Aaron Toppston:

Maybe,

Catie Williams:

Do you have any other thoughts Aaron? Oh, sorry, go ahead.

Aaron Toppston:

I’ll try to hit one quick one here with Thai, because Thai hit it pretty well, which is change management is 99.5% human and 0.5% technology. Right. And when you think about, let me just do an example for general contractors. I tend to think through numbers and if you have 100% of a company’s revenue as the size of the company. Right. Overhead and general conditions, which is your money for people is order of magnitude under 10%. Let’s just do 10% to make life easy. Right. When you look at that, that 10% of cost is being used to deliver everything in the field, all of the operations from HR and legal, et cetera, everything to run a company.

Technology is a very small percent of that 10%. Right. So technology must be incredibly easy to use for that to move the 100% bucket of how can I get more cost-effective, more thoughtful in executing that 100% revenue. If technology isn’t super easy to use, it creates a much more challenging case that it can be effective. So I think listen to people, understand what the needs are, and make sure it’s delivered clearly, succinctly, and shows actual results. Otherwise, it’s challenging to scale.

Catie Williams:

So if someone listening that hasn’t done, like maybe they have a lot of siloed systems and they’re interested in taking more advantage of their data and reducing redundancy, right, where would you suggest they get started?

Aaron Toppston:

I am going to give the not venture capitalist answer, which is start with data you already own.

Thai Nguyen:

Invest in a VC. That’s what Aaron’s going to say.

Aaron Toppston:

Look at the data you already own that’s well organized. What data do you already own that’s organized and that you trust? Right. So my old company, the first stop there was finance and accounting. Right. That was the most trusted, reliable data because it ultimately, it’s pretty meaningful to the lifeblood of the company. And if that’s the lowest hanging fruit, how do you go up tiers of information that you trust? Data you don’t trust equals more effort to get it to a point where you do trust it. So I think just prioritizing on trust is typically a good approach and that requires a lot of collaboration and leadership and prioritization. But yeah.

Catie Williams:

Yeah, I call that the captain obvious use case, which is not a PC, but I agree. Whenever I’m asked similar question, that’s my suggestion too. Start with something that you already know works, everybody already believes in, because then the output, right, everyone already knows what to expect and there isn’t this need to revalidate and have to run something parallel, right, to prove it and all of that, right? You’ve already built that baseline, just like you said, but I think your answer’s better than the captain obvious response that I always say. But yeah, that’s how I always answer the captain obvious use case. But yeah, I agree. Go ahead, Thai. Sorry I cut you off.

Thai Nguyen:

No, not at all. I think Aaron’s question or answer was fantastic, and I absolutely agree and I think there’s more components to that. I think as we are trying to understand really our journey, it was really first and foremost trying to baseline in our culture I think, understanding the culture and understanding people’s appetite for these things. Right. And having those uncomfortable conversations, I think specifically with all this and having a strategy. I think that’s so important. So getting buy-in, really getting a sense of your culture. Within that, start to understand really what areas are the most impactful. I mean that’s really where you should be prioritizing and making a difference, right? And I think what you said earlier makes sense too, it’s building kind of that momentum as well. I think not trying to tackle everything at once, increments I think is important. And then with all these different things have an objective overall, why are you even testing that product?

Why are you piloting that job or on that job? Right. Is it a fit and overall objective in terms of where you’re going? Is it going to increase your fees, is it going to reduce safety instance? What is that north star? Right. And then from there start to incrementally tackle some of those areas. And as we all know, within the construction space, there’s a lot of opportunities for improvement and it can get very complex. There’s no real easy answer. But again, I think just building that foundation that will allow you to incrementally just iterate and get better. I think that’s important.

Catie Williams:

So my last question before we switch gears to talking about AI, how important do you think the technology is on retaining and generating interest in this industry? I mean, especially knowing the potential staff shortages and things like that. Do you think that plays a large part on the industry?

Aaron Toppston:

I think we should be avid champions of our industry. This is an incredibly strong career for an enormous number of people globally. Right. I think that technology and how it will continue to shape how we build work will create more and more opportunities for a wider set of folks. There’s always work in the field. That fundamentally is a core part of what we do. But I think newer generations in the workforce are native technology users, native mobile users, and we’ll have an expectation and a comfort that modern tools make things like administration and execution of work that much easier. And our failure not to meet those expectations, I think creates a detraction from the industry more than because we use them all of a sudden folks are interested. No, if we don’t use them, it’s a strike against us. So it’s kind of maybe the framework is that it’s table stakes.

Catie Williams:

Got it.

Thai Nguyen:

No, I think that’s,

Catie Williams:

Oh, anything to add Thai? Oh, sorry.

Thai Nguyen:

No, you’re good. No, it is definitely motivating. I think everything you referenced there, I think they’re all factors in pushing forward and embracing. Understanding technology first and foremost in terms of how you’re going to apply it, I think it is motivating many of my competitors out there, even myself and is the industry in general, we have to get better. There’s just still a lot of waste out there that I think through our processes and our day-to-day that we can cut down on whether it’s getting better in insights with supply chain, right, and with how we perform work. There’s so much, I think, and not leveraging technology and innovation to do so. I think it’s something that I know in the last handful of years, it seems like everyone is just, there’s innovation everywhere. I think that industry is starving for this, right? And trying to improve.

I think we’ve done so many things the same way for so long, we’re really not seeing a lot of changes. And I think with many of these factors that you just referenced earlier, that it is motivating almost everyone to kind of put some strategy behind innovation, putting a focus on it, and it’s really creating more efficiencies in our day-to-day and insights on data. I mean, I think you can’t get out of a conversation without talking about AI and what it’s actually doing for industry. So I’m excited for this next section as we transition over.

Catie Williams:

Great. Thank you. That was a perfect segue, Thai. So yeah, I mean obviously AI is a huge buzzword right now. I mean, I don’t think you can have a conversation with many people without bringing up like ChatGPT and, but there’s a foundation that’s required to be able to start predicting what’s going to happen if you want to predict productivity or if you want to predict if you’re going to have a schedule delay. And so knowing that we have 50% of our industry that doesn’t have a data strategy or doesn’t have connected data, I’m curious where you think our industry is at, where do you think we’re going? What do you think the big use cases are for our industry? So maybe just starting with AI in general, what do you think the main role is and use case that you see AI playing in the construction and engineering industry? We can just start there and Thai or Aaron, if you want to go first.

Thai Nguyen:

Aaron, why don’t you tee us up?

Aaron Toppston:

All right, I’ll tee us up. So let me just,

Catie Williams:

Okay.

Aaron Toppston:

Take a step back on AI. Artificial intelligence is the evolution of frankly applied statistics, right? Just a much more macro scale. And it’s a tool right? And one really important thing I think we should remember is although ChatGPT and other widely available LLM models are incredibly versatile tools, it takes a user to understand what are we trying to achieve with this tool, right? And Thai and I were spending time together and I gave the analogy of a power drill, right? Most of us do not articulate what wiring is inside a power drill, what’s the weight of the magnets, et cetera. I don’t know how everything is put together, but I do know how to use it and when to use it.

And so AI is a similar tool that we have to use appropriately. And I think when we use it appropriately, what it does the best is find the most common answer given a dataset. It tries to find the media, it doesn’t find fringe cases. And people in our industry, I think understanding what is that edge case, what is that corner case versus what’s not is really important because there are an enormous number of snowflake opportunities in building anything.

And so the area that I started to get really interested in AI is on things that are highly repetitive and highly time-consuming from an administration size. We could talk about data normalization later. We get a bunch of data how to get into one spot that’s organized to use it for other decision making, including closer to edge cases. AI does a really good job of that kind of stuff. How do you do first pass estimates? Things like easy takeoff of architectural elements and a drawing starts to get really useful at that, this level of T-shirt size, right, type of work. Does not do a great job when you want an absolute answer. And so that’s where I think the tool has to be used as part of a much larger effort to execute administrative and highly time-consuming work quicker, but shouldn’t be used to rely as a replacement to thoughtful professional decision making.

Catie Williams:

I didn’t want to cut Thai off, so I didn’t know if he was going to start jumping in.

Thai Nguyen:

Oh, I’m sorry. I’m sorry Catie. I thought Aaron was pausing for a thought.

Catie Williams:

I just keep having a habit of doing that to you. So I,

Thai Nguyen:

No.

Catie Williams:

No. No, it’s okay. I agree, Aaron, so but go ahead, Thai. Sorry.

Thai Nguyen:

No, I absolutely do. And I think for us it’s really where do you start I think, with all this, right? AI’s been around for a while now. It’s just in the last handful of years, you can’t get around it, right? So I think the reality for us was understanding and putting some governance around it, meaning how do we use it, right, within that today we know it’s all powerful, but we don’t want to be putting anything that’s IP into ChatGPT for instance. Right. And so within that being guarded with our IP, but then not being afraid that not to try it and test it on certain things within our enterprise and really how we perform work. Like even as early as when we start to how we chase projects. Right. Today with every region, we have a lot of RFPs that go out using AI to help us help manage that process.

And what Aaron says today is a lot of times the work is getting stuff put together that is sometimes mundane and monotonous, but can we get and build our responses in a way using AI that gets us 80% there and that our people are really fixated on really. What’s going to differentiate us on the shelf from the competition, right? Focusing on the things that matter. Instead of spending all that time trying to figure out where all that data is, what’s the last 10 that we submitted specific to aviation? Right. Can we lean on? So getting that information together to our people quicker, letting them focus on the things that matter. So it starts as early as that. I think as you go through design, Aaron brought up a great example there. Throughout construction, the way AI I think I’m excited about it is really, I referenced earlier about capturing a lot of data we do on every job site.

How we manage it can be improved. But ultimately at the end of the day, really I think as AI is starting to, when we do 360 video walks on the job site, can it start to recognize objects within that tying those objects, understanding mechanical versus electrical, can it tie that to schedule that we already have built out? Can it give us more automation using machine vision at the job sites because the human can only see so much as many people you have on site. Having that back check and understanding a daily progress of systems being installed, recognizing safety conditions in the field at some point, right, where it’s starting to understand leading edge, starting to understand with certain elements that we have in our job sites that while that might not be too safe, you might want to put an eye on it and get the human in there to make that decision.

And that’s what Aaron said earlier too. It’s really the human still plays a large part in this. There’s no easy button, but that data is going to be much more refined, it’s much going to be more concise, and it’s allowing you to make these decisions that are more actionable than it is reactionary. And so for us, that’s where the excitement is. And I think, again, we can spend so much time and there’s a lot of opinions on AI, but I think for me it’s just really, I referenced it. I think one of my comments was, for us it’s the X factor and all the things that we do, it can definitely booster all that, getting our people better, again, information for their day-to-day.

Catie Williams:

Yeah, I love that Aaron, you mentioned and Thai, that you’re reiterating that you can’t expect the human element to go away. I mean, you shouldn’t just be blindly following whatever it’s producing. I think that’s really important to remember. Right. It’s not just some silver bullet that’s always going to be perfect and accurate. And I think that’s really important to keep in mind because it’s easy to just think that it’s this magic crystal ball and it’s going to eliminate jobs everywhere too. And that’s not necessarily the case either.

I’m curious though, are there any slippery slopes or threats to things that you’re worried about that from a use case perspective that someone might be doing that you’re like, I’m not really sure that’s a good use case from an industry perspective, just kind of to that point of just because you can, doesn’t mean you should. I tried to think about that a little bit in terms of the industry, and I don’t know that I can right off the top of my head. I think some of the safety things could be interesting, obviously because you have some PII data where you have to always be really careful and cautious. But I’m just curious, when you think about the threats, right, potentially of AI, there’s anything you could think of. I really couldn’t think of necessarily.

Aaron Toppston:

[Inaudible]. Do you remember when we were all much younger and in middle school and high school, you would have those yellow books, like the cliff notes and you got an assignment to read Jane Eyre and instead you got that yellow book and you read it, right? And you’re like, oh, I didn’t read Jane Eyre. I read the CliffsNotes. Right.

Thai Nguyen:

Aaron, that’s how I got through college. Just saying. That was my,

Aaron Toppston:

So look, those tools have existed forever. AI is a fancy shortcut. Not doing work has existed for as long as work has existed. And so I think what scares me the most is blanket applications of any technology AI enabled or otherwise with the expectation that the outcome is equivalent or better than professional engagement. And so that makes me, although that sounds negative, I’m actually incredibly optimistic about how can we reuse people’s time to do higher value work and use tools to do monotony and low value add work. And that isn’t indicative of a job function. It’s indicative of things that allow folks to use their professional decision-making stronger and better, whether that’s on executing work better, planning work better, improving how we report on work, et cetera. And that’s kind of hopefully a framework of optimism about it not ever replacing anything, but simply being a little bit of a modern reincarnation of something that’s existed for a while.

Catie Williams:

Yeah, I think that’s great.

Thai Nguyen:

Yeah, I think just not from an application standpoint, but I think I’ll take a different perspective. I think for us, we’re also cautious, Catie. I think as we pilot and we bring in new solutions within our environments, it’s also being cautious that some of them are bringing AI. Right. And what does that mean? Are they using our data to build their AI models? Right. And so I think that’s something that we have to be very cautious on and having that transparent conversation with whoever we engage with to make sure that the alignment’s there and the expectations are there from both sides. Something is foreign to us, it’s new, but it’s something that again, we’re very keen on. And just making sure as we’re making iterations on our approach with AI and as part of our strategy, being mindful of things like that as well.

Catie Williams:

Sure. Yeah, that makes sense. Okay, so we have just a few minutes left before we do our Q&A. So from a future perspective, some emerging trends and innovations, what are some other things that you think are coming? It doesn’t have to be data specific, anything. I know Thai, you definitely are out there a lot with startups. And Aaron, I’m sure that you’re very up to speed on other things that are coming out in the industry. What are some other things that you’d call out that are on the horizon?

Aaron Toppston:

I’ll do a macro thesis. So the intersection between climate and construction is a big talking point for massive investors in the built environment, public pension funds, sovereign wealth funds, et cetera. There’s a lot of focus on what climate tech is helping, whether that’s direct air capture or a lot of other very progressive technologies. I think we’re going to start to see much more pragmatic and concentrated efforts by us in our industry to satisfy client demand, to improve the way we build buildings and operate buildings to reduce both embodied and operational carbon. It’s not next year. Right. It’s going to take quite a bit of time, and it only happens when there’s adequate incentive. Whether that incentive is things like the IRA and federal externalities or client demand or regulation. All three I think will motivate us and I think when we take a step back and think about where are we 10 years from now, I think that’s going to be significantly more mainstream across the United States than it is today.

Catie Williams:

No, it’s really interesting. Thai, do you have anything that you’d add?

Thai Nguyen:

Yeah, I think with Aaron and the macro, maybe I get a little bit into the micro. There’s a lot, right? I think for us, what I’m most excited about is really seeing a convergence of all these innovations today. Excitement for me is really how it’s being realized in our day-to-day, I think with our people. And it’s amazing when you see someone that within their day and it’s a process that takes two to three hours, that they’re able to do it within 30 minutes today with really good information and good tools. I think for me that’s so motivating. I think at the end of the day, I think I referenced we are builders and really that is always my focus is just making that job site better. And so again, the convergence of everything. I mean, we can talk robotics, we can talk AI, we can talk machine vision, we talk AR, VR, all that. I think in its aggregated together. I think seeing that value of all those things in the job site and just giving that as excitement to our people. And within that it is twofold.

Also, it tracks new potential workers as well. I think many ways I get out there today and I try to educate the universities and the students on really construction today and really how progressive we are with all these things. They had no idea we were using robotics, right, coming out of the CM program. They don’t know that we’re looking at AI. They’re looking at using augmented reality to better iterate designs to better check QA/QC. So really I think that for me is exciting, is really now having that conversation. Before you didn’t have much to talk to from an innovative standpoint when it came to construction. But today I think getting the attraction of new potential workers to our industry, for me, that’s been great.

Catie Williams:

Awesome. All right. I think we’re out of time and ready for questions. So Scott, I think we are ready for our Q&A.

Scott Seltz:

Great, Catie. Thank you. This was a fascinating conversation. Before we handle some questions that have come in through the webinar, I want to remind our viewers that we’d love their feedback. So please take a few moments to complete our webinar survey, which you’ll see on your screen now and you’ll be redirected to it at the end of our discussion. So I’m going to give our audience a little time because I wanted to field the question to you that I was thinking about during the discussion and it’s this, what are some of the common mistakes or pitfalls made when working with connected data?

Aaron Toppston:

Scott, I’d love to answer that one first. I think prioritization is probably a common pitfall. Is there a priority from leadership and from the business to use whatever the connected data is helping you make decisions on? Right. Just because putting all your information in one spot, organizing it well does not create value in and of itself. It’s the output of how you use it that creates value. And I think that that has to be the priority and kind of everyone has to say what we’re trying to do, manage our staff better, serve a new market, et cetera. And without that North star, the rest of it can easily be distracted.

Scott Seltz:

Thai, do you have something add or Catie?

Catie Williams:

I think I’d also throw in there, I would also throw in too the misalignment sometimes of a business process or common definition seems to come up pretty frequently too. So you might think that everyone has a common understanding of what something means, but that isn’t necessarily the case. I see that pretty frequently as well. Once you start to really try to integrate and have a connected platform, that you’re really not all that in sync. And I think that is one of the hardest parts of achieving this true vision of connected data is getting that alignment on what is our real business process? How does the data really flow from point to point in the different pieces of the business process?

Thai Nguyen:

No, and I agree with all that for sure. We have a pretty amazing data strategy team we put together this last couple of years and really I think just seeing really the strategy behind what they’re doing is pretty amazing. It’s really what Catie says and what Aaron says. It’s really getting grounded on really what is data to our company within that, making it a very inclusive process, meaning that you need to talk to everyone involved throughout the enterprise. Right. It can be talent acquisition, it can be our estimators, it can be production engineering. And so having those conversations, right, and some of them are uncomfortable. A lot of them, we talked earlier about change management. Right. A lot of people don’t like change, right, especially when it comes to having to create standardizations and adherence and governance. And so having those conversations and then yes, I would agree, commitment.

I mean that’s got to be absolutely something that starts from the top down. And then within that, really educating our people in a way of why we’re doing this, showing them really the value of why we’re doing this, showing them the end results and not saying that we’re doing this because we are doing this, showing them the value of why we’re doing this, I think is so important. And then that’s how I think you get engagement. You start to understand really how you need to prioritize areas of focus that you need to start with. And then again, to finish it off with what Aaron says, there’s the value that you attain from doing on this as well so.

Scott Seltz:

Well, on that note, this question from a viewer is related to change and they’re asking how ready is the workforce to be digital operators? Right.

Thai Nguyen:

I’ll jump right in. I think years ago, I think in my role it was really pulling, I think really the company towards technology and really just digital transformation. But today I feel we’re getting push. I think people are tired of really the status quo. They’re tired of doing these things that Aaron reference earlier that is monotonous, redundant, and really they see that they can kind of get home earlier that day and see their family. And if they can take that process that takes three hours and make it an hour, right, there’s so much value in that. So I think today, short answer is I feel we’re ready. I think we’re getting, at least in my perspective, in our culture, we’re getting pushed towards that and our people are wanting to iterate and improve and be progressive in terms of how we do everything from our day-to-day so.

Catie Williams:

I just agree with what Aaron said about the expectation is that it’s 100% ready or that it works 100% of the time. I definitely think that’s the expectation so.

Scott Seltz:

Aaron, any thoughts?

Aaron Toppston:

No, I’m happy to do another one. I think it was well covered there.

Scott Seltz:

Okay. This viewers asking, you brought up the new members of the workforce. Any suggestions on how to introduce PMIS to college students? It’s coming from somebody who teaches project management, by the way.

Thai Nguyen:

That must be towards me because I think I brought up that reference. I think it’s just education. I think just getting out there. I don’t think there’s any one best way or ideal way. I think as I get out to industry and having guest lectures at different universities. First and foremost honestly, it’s just getting their attention and making sure they don’t fall asleep as you’re lecturing. But most of the time as they’re engaged and get engaging them. Sometimes I use excitement in terms of showing them really what we call sometimes the sizzle. But I think once you bait them, it’s really starting to show them the value of really the process and really what we’re trying to achieve. Right.

I think the strategy behind it. I think for me again, short answer is just continue education and just having that dialogue where you’re able to engage with them and connect with them I think in many ways. For them it really elicits a lot of engagement that they want to ask more questions and again, they want to better understand really what we’re doing in construction and how they can be a part of it. So it’s worked fairly well as of late. And again, hopefully I answered that question.

Aaron Toppston:

I agree with Thai.

Catie Williams:

I would say too, oh, oh, sorry.

Aaron Toppston:

Yeah, I was going to say,

Catie Williams:

I would just,

Aaron Toppston:

All right, I’ll hit this point really quick. I try to go on a project side as much as I can. It’s always as a guest and those are the very best days. Right. And you go over to a forum and you say, when’s the last time you looked at the full schedule? What about the free look, look ahead. Why don’t you look at this other one? Asking those questions, engaging, learning those barriers, I feel like those are hugely valuable and it kind of creates this concept of technology to reality. And personally I wish I could do that more often, but it’s a great, great set of experience and awesome opportunity for your students.

Catie Williams:

And I was just going to throw in that I work in education on the side, on the data side, but I am seeing a trend of more partnerships between corporations and education. And so if there’s a way to partner with a software vendor or something like that, I mean that’s a great way too to then create that relationship between either the vendor or a way to get those job site visits because I agree that that seems like the best way and maybe that’s an opportunity as well. But I’m seeing a lot of that direct partnership to then try to create that funnel of students to then those organizations as well.

Scott Seltz:

Great. A viewer asks, and you touched on AI of course during your conversation, but this viewer is asking, could you explain some of the ways in which InEight has integrated AI into their applications?

Catie Williams:

Sure.

Thai Nguyen:

That’s for Catie.

Catie Williams:

I’ll take that one. Sure. I mean, we’re definitely trying to be cautious about how we do it as well to be sensitive about using customer data. But we have put in different things from a schedule perspective, working with productivity data and then partnering with customers as well to do some one-off engagements also. But I would say we’re still pretty early on in our journey also about, like I said, working with the customer specifically to look at their data. It’s definitely something we see as a strategic opportunity for us, but we’re cautious as well to not just go blanket, put something in the application and then have our customers think that that’s the only thing that they use. But we do have some things already built in that’s baked into the application that you would use as a guide or to use along with your other reporting and information. But we’re still very much early on in the journey and continue to work on that.

Scott Seltz:

Great. I think we have time for one last question. The viewer’s asking, entering from the data software tech space, are there established entry points to construction space when looking for impact opportunities and viable problems to solve?

Aaron Toppston:

Okay, let me see if I can take this one. That question sounds like someone who’s strong on software development engineering coming into the construction sector. And so when we look at investing in startups for at least for GS Futures, oftentimes what we try to understand is if there are two founders, does one of the founder understand how to build product and does one understand the industry? And if it’s more of a product oriented founder, what we often do is say how have they put in time? For example, I gave earlier meeting folks in the field, interviewing and identifying consistent issues as a way to inform product strategy and then a lot of iteration on MVPs. I really admire what Thai does in feedback to a number of founders along with a number of peers in our space that we’ll offer direct feedback kind of on an early iteration of software to say, Hey, I understand the pain point you’re going for, staffing or change orders, but here’s the iteration that doesn’t make sense.

This is why it’s very difficult for me to convince my project manager to use this or for the project manager to convince the specialty trades to use something. And so I think it’s that very iterative nature of a lot of conversations is the best way for this industry to kind of grow your definition of a problem and not feel overly confident that you have it held. Because more and more and more conversations will help reveal if you’re on the right scent for that trail.

Thai Nguyen:

And I think to add to that, I think a lot of times I think in my conveyance with the startups that we work with, it’s many times, instead of trying to bring a solution to a problem you may think exists, engage really first with a notebook and really understand what the problem actually is. And I think if your solution is targeted towards that, then it just will naturally kind of just develop, right, and create that partnership. But I think too often we’re, I mean, we’re inundated with solution providers where a lot of times they miss the boat because again, they think, they assume on certain things and pain points.

And the reality of really at the job site level or the project level, it’s different. Right. And so really having a good understanding of really the environment, I think is so critical. And to get that, honestly what Aaron said earlier, there’s a lot of opportunities to engage with the construction space today. I think everyone’s very engaging, willing to kind of support and provide feedback. I think that environment does exist and definitely take advantage of that because I think that will help you better understand really the direction that you’re iterating to and ultimately what your MVP needs to be as well.

Scott Seltz:

Catie, anything to add in closing?

Catie Williams:

Nope, I’m good. Thank you.

Scott Seltz:

All right, good. Well, unfortunately that’s all the time we have for questions today. Please join me in thanking Catie Williams, Thai Nguyen, and Aaron Toppston for their presentation and today’s sponsor InEight. If you have any additional questions or comments, please don’t hesitate to click the email us button on your console and we’ll share those with our presenters so they can respond directly to you. If you didn’t have a chance to fill out it earlier, you will be directed shortly to our post event survey. So we look forward to hearing from you about how we can make our programs work better for you. Please visit enr.com/webinars for the archive of this presentation to share with your colleagues as well as information about our upcoming events. We hope you found today’s presentation to be a good investment of your time. Thanks again for joining us and have a great day.