Navigating the Nexus of AI and Human Expertise in the AECO Industry

JEREMIAH OWENS: All right. Welcome. You have our safe harbor statement up. Get the business out of the way up front here.

All right. We want to just welcome you to our class BES2424, "Navigating the Nexus of AI and Human Expertise in the AECO Industry."

This is going to be a high-level overview. We're not going to get into the weeds really at all. We're going to give some examples of some use cases.

We're going to look at some definitions, try to have fun with this. We really, really appreciate you guys joining us for this next hour. Hopefully, you can take something away from it maybe to spark-- it sparks some thought, some passion, imagination, what have you.

And again, at any time during this presentation here, if you feel like you need to leave for any reason, please do so. Your time is valuable, and we respect that. So just do it in a respectful manner, and we should be good to go.

All right, so real quick, I'm Jerimiah Owens. I work with Burns and McDonnell as a technology and innovation consultant. I've been with Burns and McDonnell for three years now, almost four. I've been in the industry for 24 years, which is hard to believe, almost 25. And so I guess I started out in 2000, late '99 or early 2000 as a drafter in the civil structural design world at a small, small firm.

And through, I guess the first 10 years, I did-- I wore a lot of hats-- and so civil designer, structural designer, CAD manager, design coordinator, and then eventually BIM coordinator, field tech-- I did some special inspection-- observations. Just I guess a couple of fun facts is I have my South Carolina contractor's license, residential contractors license, which is a passion of mine. However, I haven't had a lot of time lately to enjoy that.

But I'm also a proud father of a 15-month-old baby boy. So that's awesome. That's been a blessing. And as you can imagine, that's kept me kept me quite busy. So I'm going to let Nate introduce himself.

NATHANIEL COOMBS: You do not look old enough to have 25 years of experience, Jeremiah. But yeah, congratulations again on the kiddo.

So yeah, I'm Nate. I am a senior business consultant with Autodesk. Burns and Mack is one of my largest accounts that I work with on a regular basis. I've been working with them pretty heavily over the last two years as one of their lead consultants from the Autodesk side.

My focus is on data analytics and construction. I do a lot of reporting and AEC work. I've obviously moved a little bit more into the A and the E space with companies like Burns and McDonnell.

I went to the University of Vermont, with my backgrounds in engineering. I'm based outside of the Boston area. Actually, came to Autodesk via the acquisition of Assemble. So I always like to mention that, holds a special place in my heart.

Like I said, the other than-- other than data analytics and construction, being a LEED AP and that sustainability is a big focus for me and a big passion. So if you're not finding me here working on the consulting stuff, I'm probably in the garden, traveling, taking some pictures, or losing golf balls in the woods. So that's me.

So our agenda today, we've got a great slate of slides to show everybody and a really cool concept. I like that-- AI is for sure going to be a massive topic at this AU. It was a big topic at last AU. I can only imagine it's going to be even bigger here. So I'm excited to share all that we have with the group today.

Obviously, we've just kind of done introductions. We'll do a little bit more of that about why we're here. We want to talk about design professionals. A big message here that we're trying to send is that AI is going to impact our industry in ways that are unforeseen.

And I know a big focus of Jeremiah's is making sure that engineers don't lose that special sauce. So we want to talk about those design professionals, what makes them special, what makes them unique, and the skills that they should focus on keeping as they start to build their relationship with AI over time.

We want to give some good use cases of AI in the AEC space. People are already using this as-- I know it's a hot topic now, but AI has been slowly working its way into the background of your tools for a very long time. We want to talk about opportunities, challenges, to my point before, some long-term effects. And then obviously, we want to wrap this up with how this is going to be disrupting the industry and how we transform in a way that's beneficial for all. And then we'll close it out with a few closing thoughts and give everybody on their way.

JEREMIAH OWENS: Awesome. Awesome. Thanks, Nate.

And real quick, before we move on, the image to the left is an aerial of our world headquarters located in Kansas City. And that's a nice little lawn area that we take advantage of. I'm pretty proud of that. I think it's a very nice facility. I just wanted to give that--

NATHANIEL COOMBS: I'll call out the putting green in the top left. I've been to your office a lot of times, and I've always wanted to chip and put on that green one of these days. We got to get out there at lunch time.

JEREMIAH OWENS: Yeah, absolutely. And there's clubs right inside the building there. I've found that out.

NATHANIEL COOMBS: Ooh, no need to bring my own clubs. Wow.

JEREMIAH OWENS: That's right. All right, so introduction-- so kind of as I approach this class in preparation for it, one thing that I'm kind of passionate about is making sure that we mentor the younger staff, and we don't, like Nate said, we don't lose that special sauce, so to speak. And so I wanted to take just a few minutes to look and talk about some of the phrases that's in the title and some things that we may encounter along our way.

We're discussing the convergence of artificial intelligence and human expertise. And I think over the years, we've seen major technology disruptions. We've had hand drafting to CAD. We've had CAD to BIM, and now AI is the next disrupter. It's offering new ways to automate tasks, predict outcomes, and optimize design processes.

But really, why AI can handle data and repetitive tasks, human skills like creativity, ethical judgment, and complex problem solving, they're irreplaceable. So coming together a little bit in my mind was the phrase or the challenge-- I guess the one that stuck out the most was we really have to get ahead. We really need to say, OK, what's the balance? It's going to be finding the balance, leveraging AI to enhance our capabilities without losing critical human skills that make us unique.

As we move forward, we'll need to adapt and embrace AI as a tool to complement. It complements what we're doing. It doesn't replace our expertise. So hopefully, we're going to explore a few of those things throughout this presentation and hopefully help some of you guys.

Again, high level, but maybe some of you are already down this road at your firm, and at the end of this, we'd love to hear from you. So please, please come up to us. We'd love to meet you.

So AI is the next disruptor. Again, CAD to BIM, BIM to CAD, CAD to BIM, hand to drafting, all that good stuff. So it's going to augment. And I think one thing at the bottom there, it is going to shape the future. So it's not just changing the tools we use. It's redefining the skills required for engineers and architects.

NATHANIEL COOMBS: So this doesn't quite hit the same in a digital experience as it does in person. But a show of hands to the room. How many in this-- people in this room can give me a formal definition of what artificial intelligence actually is? Yeah, Jeremiah.

JEREMIAH OWENS: I was just going to say--

NATHANIEL COOMBS: You have the background.

JEREMIAH OWENS: Right. We can't see them. Yeah, we can't see them, but we know they're there. So they can--

NATHANIEL COOMBS: Yes, but I always like to do this at the beginning of my presentations when we're talking about a topic like this that's really high level. I've done it before with sustainability, because when something gets so big, it becomes very polarizing. And then it almost-- you start to have the Kleenex effect of AI.

And I always like to boil it down to its simplest form, which is the capability of computer systems or algorithms to imitate human behavior. So this is important. Like all of what we're talking about here and everyone's fearful about AI taking our jobs and how do we collaborate and build a strong relationship with that going forward. But it all starts here.

I also would call it that word imitate. At least at this point in time, like AI is only as smart as we make it. And we can use that to our benefit. Now, everybody is not capable of knowing every single little thing that you can find on Google, and it's using these things to supplement our own human intelligence and add a little bit extra on.

So when we look at well-known industry solutions, there's a whole bunch on here that I haven't listed, but and that little flower-looking icon in the center is probably the one that you recognize the most, ChatGPT. You better believe I asked it plenty of questions while we were making this presentation. Autodesk AI, I'm 100% sure this will not be the first time or the last time you hear about this in your few days in San Diego or even on the digital experience-- Newmetrix, OpenSpace, Siteaware Technologies, even things like ECC and Autodesk Construction Cloud.

You'd be blown away at the number of AI solutions that you're probably using on a regular basis that might not even be on the front end. ChatGPT is a good one where it's AI. You know you're interacting directly. You're interfacing directly with the AI. For something like Construction Cloud, where you go to Construction IQ, it's not as-- it's not as direct interaction, but you're still taking advantage of AI resources to develop analytics and to give you trends and ideas and mitigating risks, stuff like that.

So you'll surely see more and more of these as the industry goes on. And I just thought it would be-- we're going to give-- we're going to get into some examples here. And I thought kind of giving a high-level view of some of the more well-known tools in the industry would be helpful. So I think that's to you, Mr Owens.

JEREMIAH OWENS: All right. Again, like Nate said earlier, show of hands. Hard to see you guys out there online. But show of hands, how many people in this room would say they actively use AI in their work day to day like currently using it?

NATHANIEL COOMBS: Yeah. And I would say to you on that, I would almost say in the direct way that I was referencing before, not just using it in the background, but whether it be ChatGPT or whether it be employing a specifically employing an AI solution. I know I do pretty regularly these days.

JEREMIAH OWENS: Absolutely. Yep. It's going to increase with time. Yeah, so as we discuss this integration of AI into our industry, it's critical, in my opinion-- I think others share it-- to understand what makes a good design professional.

What makes a good engineer? What makes a good architect? And by defining, again, your group, defining these skills and characteristics that set us apart or set you apart as top professionals, we can better understand how and where AI is going to bring value to our workflow and also find out where it's going to fall short.

So the next couple slides, we're going to just look at and illustrate what makes a good design professional. Again, high level, there's probably 20 more of these that we could have added. But again, by identifying the human strengths, we can more effectively determine how to balance AI-driven automation.

We can say, OK, well, we know that we're strong here. We're weak here. Can we bolster that with AI where we're weak, or do we need to pull back AI and just go straight with our current workflow and our human expertise? Again, this will help us-- should help us avoid the overreliance of technology on technology and focus on developing our younger professionals. So I think that's critical. We don't want to lose track of those skills, and we want to build additional skills to complement our current ones.

So again, up here, you'll see a paragraph or three. I'm sorry. You'll see education. That's important.

We just put a generic BS from an accredited university. That's super important. Working experience-- you really want to have a really good understanding of those things, like site work, problem solving, troubleshooting.

Then your special skills-- ethical and professional conduct. That's a big one. And I'll speak on that a little bit later on. And that's really in the conversations in our groups here.

I know Nate and I, we've talked about this at the inception of this is really what is the responsibility of the engineer? Well, it remains the same. What are the ethical and professional conduct responsibilities when dealing with AI? Obviously, AI is not going to have it. So there's a lot of legal questions even out there in the industry as you attend some of these councils and associations throughout our industry, you'll hear that kind of repeated.

There's lawyers. I'm sure many, many lawyers looking at this on a legal standpoint. And really, the ones that kind of separate the human from the AI. We wanted to highlight some of those, and I think site inspections and field work would be a big one, although AI is moving in that direction as well. But having a human boots on the ground, interacting with the materials, seeing things, using those critical thinking skills on the spot is very important.

Another one would be collaboration and communication. We don't want to get so isolated from our human counterparts that we forget what it's like to interact with someone. So collaborate and communication is one. Again, AI can help with those. But think at the core, those will remain a human task. And again, there's one ethical and professional conduct-- that's a big one-- attention to detail.

AI is right now it's attention to details pretty much whatever you tell it to be, critical thinking. And then my favorite or the one I'm passionate about is the passion in the mentoring of the next generation. We want to make sure that we're pouring into the next gen, that we're explaining the history of things and these transformations, that we're not just forgetting our history and what the industry is going through over the last 25 years.

NATHANIEL COOMBS: Whenever AI does develop passion, I'll be scared.

JEREMIAH OWENS: That's true. Yeah, and maybe we see that in our lifetimes. Maybe not. But I definitely want to make sure that we're not forgetting where we come from, so to speak, with some of these tools.

Another show of hands, I'll take it. How many people in this room would describe themselves as a good design professional or a great design professional? Yeah, and in person, we can say, oh, we see some liars out there in the room.

NATHANIEL COOMBS: Oh yeah.

JEREMIAH OWENS: So next, I think we're going to share a quote. I'm going to share a quote with you guys that Nate and I found really interesting. It's by Albert Einstein.

"Imagination is more important than knowledge. For knowledge is limited to all we now know and understand, while imagination embraces the entire world and all there ever will be to know and understand." So that's a pretty cool quote. Nate, you want to add anything to that?

NATHANIEL COOMBS: Yeah, no, I just think it really hits on the undertones of our message here. It's the idea that AI is a tool, and like, there's still a value in using your own mind for your work instead of just relying on artificial intelligence and really just like the idea of imagination. It's a hard thing to lock down, but a lot of the best innovations in our industry come from the imagination side of this, not knowledge.

So it's an important thing that makes us human and kind of makes us unique from artificial intelligence. And I think that it's something that we have to latch on to heavily as we feel out in artificial intelligence in the years to come.

JEREMIAH OWENS: Awesome-- great points. All right. We're going to get into some use cases. Again, there's many, many, many of these and even more possibilities.

So well, a few-- I guess, four-- not a few, but four that we wanted to highlight. And think one that's near and dear to my heart-- safety. Here at Burns and Mack as employee owners, we will make sure that we're living a safe life, not just on the job, on the job sites, or at client events, but every day in our personal lives as well. And so AI is starting to help with safety monitoring and risk prediction, and I think that first one's really cool. I'm just going to read from it.

It says, hey, it's using computer vision and machine learning to analyze videos and photos from the construction site to help identify unsafe practices or conditions. I think it's really cool. I think that's a really cool one.

One that we may already be using throughout the industry is the project management and scheduling AI portion of it. And there's some tools, like Alice and some others out there, that will help make us more efficient with those tasks. Another one I think is pretty cool-- and again, the first thing you think of is, oh, no, job displacement-- autonomous construction equipment. But where my mind goes first is back to safety.

So maybe there's a hazardous environment, and we don't want humans in there doing this work. It's safer for a robot. It's safer for AI to drive those machines, operate those heavy equipment.

Well, there's a bulldozer, whatever, skid steer, tractor, whatever it may be. I think is a really cool use case for AI. So there's some H2S gas. There's a really steep embankment. Maybe there's a chance for cave ins.

And, I live in South Carolina, and we just went through Helene. And there's a lot of mudslides, a lot of unsafe environments that some of my co-workers, my friends are in. So having that option to program those machines to get out there, operate in those unsafe environments, I think is really, really cool use case.

And again, predictive maintenance is another one that I think is going to really shoot up, in my opinion. The use of it will increase dramatically because you really want to keep an eye on your maintenance cycles, your lifespan of certain equipment.

Hey, if I wait to let's say the recommended is six months, but you have the data from hundreds and hundreds of equipment vendors and operators that say, well, you really if you wait to six months, you're really pushing it. 3, 4, 5 months is better to extend the life of that equipment I think is another one that AI is really going to help us with. So those are just a few use cases that we wanted to highlight for you guys today. And I think Nate's going to take the next one.

NATHANIEL COOMBS: Yeah, I want a deep dive into a couple of these. And I mean, my intent of wanting to go through some use cases here was to do two things. The first is to explain and show some areas where we think and we agree, me and Jeremiah, that AI was putting into really good use, the use that I like to see that is good for everybody. Everybody wins. And the second that this stuff has been around for a long time, and there's definitely situations where it's already mature enough to be used in your day-to-day life.

So when I was thinking about which one of these use cases I wanted to go through, I kind of felt this one felt close to home. I went to our Toronto office for the first time last year, and I walked into that place. And it stopped me in my tracks when I walked in the front door. And like I thought to myself, this place is weird.

And like, not in a bad way, but it just felt different. I couldn't figure out why. Not one wall really felt that straight. And it was the layout was just like unconventional, I guess is the best way to put it. And come to find out.

I was on a tour later that day, and the entire thing was designed with generative design. And if you're not familiar with that, it's one of Autodesk solutions that helps our people using our tools like Revit to quickly use AI to explore a wide range of designs options. You can provide a whole bunch of parameters. You can give a whole bunch of constraints, and it will think through all the potential options and then rank them for you based off of which ones meet your criteria the best.

So the idea here is to generate, analyze, rank, evolve, explore, and then integrate. And a lot of things go into this. In terms of the design-- Jeremiah, you can go to the next slide here.

What do we think about? AI is only as good as its inputs. I don't know if anyone's ever used ChatGPT and tried to get a very specific answer back out. I don't know about you, Jeremiah. I have to ask that thing generally.

Like if I'm trying to get something very specific, I probably got to ask it two or three times and tweak my question just to get it exactly what I want. So the more inputs, the more things it understands about what you're looking to constrain, the better it can serve you and give you outputs.

So for us, as I was doing some more research about how we designed this office, we took into consideration the building size. We only had three floors.

How many occupants are we going to have? What is the preference between workstations in terms of not too close to people get distracted but close enough that they can collaborate? And how do people work? Are they heads down alone all day just grinding out code, or are they collaborating on a project and on a whiteboard and working in a conference room?

General buzz, I like-- I really like this one. Office buzz and energy-- it's hard to explain, but everybody knows exactly what I'm talking about when I say buzz-- daylight productivity and then used outside-- pretty simple stuff. A lot of the same stuff you see here is also what we take into account in like LEED buildings too. So it's just health and wellness of occupants. How do we make a space as optimal for everybody to be productive, but also to enjoy themselves?

And I thought this was cool. You can forward one here at a time here. So I just wanted to give some examples of the technology at work.

So this first one was adjacency preference. You can see it looks at the distance between workspaces. And if someone needs to go to a bathroom or someone needs to go into the kitchen, where do they walk? What is the most optimal way for the most people to have like access to everything they need?

We look at daylight. As daylight comes across the windows-- I remember correctly, only one side of this office was outdoor facing, as you can tell by this animation. So how do we optimize that lighting and not put partitions everywhere so that the back half of the office is dark, and the front office is too bright, that you have to put the shades down and how the facade works for things like that?

And lastly, productivity. I thought this was an interesting one. They describe it as minimizing visual and audio distractions at each desk. This is obviously getting harder and harder with people being able to pull up their phone and scroll.

But it's just interesting. I'm a very easily distractible guy, so this is an important one for someone like me. If If people are walking by me all day, I'm never going to be able to get things done.

So I thought that was really interesting. And I want to look at it a second use case here, to which this one's even more close to my two, kind of hits close to home. And this is if anybody was happen to be in my panel last year talking about Autodesk Takeoff, this is a story we shared there. One of our colleagues in the industry, who we'll thank here in a moment, they shared a story of using ChatGPT to support their estimating and carbon calculating process.

So they are a design firm. They do not have estimators on staff like many design firms don't. EC3, if anybody's familiar with the embodied carbon calculator, it's a free tool to use, and it integrates with a lot of our solutions.

But it's very good at what it does. But it requires very specific inputs and naming conventions to get you what you need. So there's this gap between models and how things are named and how things are annotated to what information EC3 needs to output a total carbon cost or a carbon value. So you need to be able to align those names. And an estimator would know this, but without that knowledge, this group really struggled.

And Woolpert really struggled to align their vision, like their information with the carbon database. They gave me the example of they're from the south. They call drywall stucco.

But in the AC3 database, it's called Siemens cementitious finish. So it's like it's those differences that if you're an estimator, you might know them. But if not, it's really, really hard to get where you need to go.

So how did they use ChatGPT for this? I thought this was the most interesting thing ever. They mess around with this idea of roll adoption, and they fed ChatGPT all this information about the EC3 database and all of their coding systems and basically asked it to adopt the role of an estimator.

And from there, basically, the idea was here's the common name. I need stucco. And basically, I'm going to give you the common name, and I need you to give me the technical name as it relates to EC3. And I also need a breakdown of all those material components in a table.

So instead of having to hire an estimator or outsource this stuff, they were able to basically train ChatGPT to answer these kind of technical questions that an estimator might need to know. I call it stucco. What does EC3 call it? And then also when I ask for stucco, what are the other sort of subassemblies that make up that wall system that I need to account for when I'm putting together a carbon estimate? It's not just the panels of drywall.

It's mud. It's corners. It's paint. It's tape. It's all of the above. And this is true for every single discipline and industry.

So I thought this was super interesting. And this is, in my opinion, one of the best examples of AI in the AEC that I have heard where they did not have this expertise in house. We talk about replacing jobs. They don't employ this job. And all of a sudden, when they want to calculate carbon, they realize they have a gap in their knowledge, and they were able to leverage tools like ChatGPT to fill that knowledge gap without having to go train a whole group of people.

When in reality, they don't need to put together detailed estimates. They can take their models, plug them right into, in this case, Autodesk Takeoff and EC3. They're just missing that alignment and gap of how to name things and how to break down an estimate which they were able to achieve with ChatGPT.

So I thought it was really cool. And particularly, a special thanks to Moses Scott. Moses is the brains behind all of this.

If you want to hear more about this, you can go check out Embodied Carbon and EC3. It was a panel that I did last year at AU 2023. And he works for SNHA, which is a Woolpert company. Just a shout out to both of these groups for allowing us to share this story. I think it's awesome.

JEREMIAH OWENS: Yeah, I totally agree. Thanks, Moses. And I really appreciate SNHA and Moses letting us share this. I think it's a great use case. So we're going to move down and talk about some opportunities from streamlining project management, optimizing designs, and in ways previously unimaginable.

So AI offers us a chance to not only improve how we work, but also to elevate the value we provide to our clients and communities. And at Burns And Mack, this is really important not only to myself, but also us here at Burns and Mack. We really don't make sure we provide our clients with the best product and also improve our communities as well. All right.

So we've listed up again tons of opportunities out there. Again, here's five that we chose. Increased efficiency and productivity-- I think we all know that. I think if you read this one, a lot of us are in class detection, like Nate mentioned earlier, ACC, machine learning, some things like that.

Again, drafting would probably be-- in my mind, there are some tools out there now that help automate, dimensioning, placing views on sheets, Civil 3D comparing cut and field, putting different scenarios, automating different scenarios, like a generative design approach, to see what the best outcome would be.

Am I going to cut or feel? Where am I going to do that? How am I going to do that? And maybe reduce the amount of cut needed for a certain structure, improve collaboration.

Cost reduction, I think is another big one that a lot of us are kind of hoping for, that it helps us reduce waste and delays. And again, that should just trickle down cost savings to the client, cost savings to our stakeholders and then our communities as well.

So innovation-- I think this is a big one-- frees up time for creative thinking innovation. We all hope that's the case that our days are crammed packed with these tasks. And AI is going to help us condense that, shorten that up where we're not spending eight hours doing this stuff and sweating bullets. We're only doing it for four, and then the other four or three or whatever, we're going to use to be more creative, to engage in collaboration and communication on a personal level. And again, I could see it helping with the work-life balance as well as we move forward.

So some challenges-- we know there's going to be challenges. I think the planning and implementation is going to be-- strategic planning is going to be really key. I think having the change management in place in your firm and in your group is going to be critical as well. So let's look at some challenges that we may all face as we transition.

So loss of hands-on experience-- that could be a big one depending on your firm and what you guys do. So again, some of the younger staff may not gain that practical, hands-on experience in drafting and design and sheet layout dependence on technology. I think, one we have to watch carefully and closely that we're not relying too heavily on AI.

High learning curve-- that's going to be a big one. So as we're implementing this technology, our senior level staff and some guys in their twilight-- I'll say 25 years and beyond, I won't put myself in that category-- go back to the old adage, it's hard to teach old dogs new tricks. So we have to be really strategic, very supportive in how we implement these technologies and training as we move forward.

So I kind of shaded out the ethical and accountability concerns. To me, that's definitely one that we have to pay attention to. However, in my mind, again, this is me putting this together in my experience, based on my experience. I see that as being less of a concern because ultimately the engineer, designer of record, every one of them, every one of you guys are still responsible for the work that goes out the door.

You can't say, well, AI did it. You can't use that.

Same thing as if an intern was doing executing the task. You'd still it. You'd still review it. You're still you're still responsible.

Now, ethically some plagiarism type stuff. That's a whole other concept conversation. And then also how it's going to really affect litigation moving forward and errors and omissions type things could be a conversation as well.

So the accuracy, like Nate mentioned earlier, we all ChatGPT is not the most accurate. It's pulling from a database, so you really have to have a conversation with it. And like we saw Moses and his team do, he said, hey, adopt this role. So you're putting ChatGPT in a specific role within the industry or in the world.

Slow adoption may be another one for a lot of us, and the reasons for that may be cost. It may be those training requirements. It may be the disruption. And we're going to talk about that.

We talked about it earlier is AI is the next disruptor. So it's going to be a disruption unto you in your current workflow and progress. So it's going to have it's going to take a little bit of finesse, so to speak.

And again, we have no idea what the long-term effects of AI will be. However, we did want to talk about a few things. And again, you'll see roles, roles, roles up here. I think that's heavily on people's minds that, hey, is it going to take my job? How are we going to do it? So it will have-- or I foresee, and I think, agree it's going to have an impact on entry-level roles.

Increased emphasis on soft skills-- that's another one. Continuous learning and adaptation-- we mentioned that earlier. And the redefining of traditional roles, learning new skills transitioning. And we're going to cover that in a slide coming up.

Our creation of new specialized roles-- so not only are you going to have to-- it's going to reshape the way we're doing work now. It's going to add additional roles to our everyday workflows. And again, let's talk about it as a disruptor. And yes, AI is the next big disruptor for a lot of us in our industry.

Automation of repetitive tasks, data-driven decision-making, enhanced creativity, and efficiency, new roles and skills. And I'm not going to go through all these. But it is transforming our industry, much like CAD did in BIM.

In the past, it automates repetitive tasks like class detection, scheduling. It's hopefully going to free up our professionals for more creative high-level work. AI's data-driven insights also enhances decision-making, so we're going to get-- we're going to see a lot of pros and a lot of cons, so to speak, during this disruption.

So I'm going to go through this table real quick. And I think Nate wants to speak on this as well, because him and I both thought this was a really good slide. Again, high level, it kind of shows the history. Like we want to remember where we've come from, so to speak, and where we're headed as well.

So here in this table, first column on the left is disruptor. The next is key impacts. It had the skill set shift and then new roles created.

So the first one, board to CAD-- so digital drafting replaces hand drawing. That was the key impact during that transformation, and some of us have gone through that. It's a little before my time.

And then the skill set shift was transitioned from manual to digital skills. And then new roles created-- CAD operators, digital designers. And then the next one a lot of us saw-- especially me, this was in my era-- Cadmium, going from 2D to 3D. And the key impacts were integrated 3D modeling and data-rich environments were supposed to replace 2D drawings. That was the key impact skill set shift-- data management, cross-disciplinary collaboration.

And I think I saw this as I kind of was reviewing this slide, and I was like, cross-disciplinary collaboration? Who wasn't doing that before? If you're not collaborating, what are you doing? But I think this is specifically speaking to that 3D environment. The clash detection came on scene. We were able to look at constructability, have constructability review meetings, things like that.

So that was the enhancements and skill sets we saw during that transition. New roles-- BIM managers, BIM coordinators, modelers, et cetera, et cetera.

AI-- it's the one we're on the brink of now. And what we foresee is key impacts, automation, predictive analytics, generative design, and then hopefully to optimize the entire project life cycle from cradle to grave. Skill set shift will be tool management data science, which I think is a big one. I think that's going to be some topics of conversations for a lot of firms.

Algorithm interpretation-- again, new roles, AI specialists, digital project managers, data analytics, and I think-- analysts, sorry. I think really making sure the team, the traditional team we have now may be made up of senior engineers, mid-level, junior drafters, designers, potentially document control. And I think in order to make this transition, you're going to have to introduce data scientists and data analytic professionals into your group. So I think, Nate, if you want to talk on this, if you got anything.

NATHANIEL COOMBS: No, I'll echo Jeremiah's sentiment when-- he was the brains behind this kind of CAD to BIM to AI thought process, and this really resonated with me. I spent a large portion of my career trying to convince estimators to use models for quantification instead of 2D documents, and I think it's very similar to CAD to BIM. It's kind of aligns perfectly with that.

And honestly, like some of them still aren't there yet. Like it's definitely not universal that people are using the estimators are using 3D models to put together estimates. And so how do we deal with it when all of a sudden, AI's already here, and we haven't even caught up to BIM yet. But I just think it's a very kind of logical let's look to the past to see what worked and what didn't work when adopting a completely new disruptive technology.

And like I said, BIM is still in the midst of its adoption. It depends on where you go in the country. It depends on your project size, industry. BIM isn't even universal at this point for someone building a house. And all of a sudden, you're going to drop AI on their lap, and they got to figure out how to use it.

But it's just a shift in skill set. It's a shift in role focus. As Jeremiah's called out, it there's opportunity here, not risk. I mean, I guess there is risk, but generally, I think the opportunities outweigh the risks if people are thoughtful about their approach.

JEREMIAH OWENS: Absolutely. And I went ahead and changed slides because what you were talking about leads into this is connecting. It is it's not a standalone disrupter. We've had these in the past. It's going to build off of our current workflows.

Again, going back to what we said earlier, it's not meant to replace us. It's meant to enhance us. And it's up to us to forge and mold it into what we want it to be.

It's not up to AI. Some clients may even influence it a little bit. But it's up to us as an industry, the industry professionals, design professionals. It's really up to us to mold it and shape it how we want it to be in the future.

And again, the bottom here, it says every disruption from manual drafting to AI has expanded what human professionals can achieve. I think that's true. I really do.

And AI is another tool to boost human expertise, not replace it. So again, just kind of solidifying that theme throughout our presentation here. And again, I think this is a final show of hands. How many people in the room felt uncomfortable during their transition from CAD to BIM, or who hasn't made the transition yet? I think in person, that's going to be interesting to see.

NATHANIEL COOMBS: Yeah, no, for sure. I think it's interesting too. Change is not easy. I know. I know.

I know Burns and Mack like a lines to change management principles. They're a huge part of my life.

These things are uncomfortable. You should feel a little uncomfortable. That's a good. Like uncomfortable isn't always a bad thing.

As long as you understand what you're doing, what the goals are, that you're supported by your organization and your management, the being uncomfortable is good. It's no reason to just quit and not move forward.

JEREMIAH OWENS: Yep, absolutely. I think in conclusion here, I think some key takeaways for me as I step through this presentation was embrace AI as a tool.

Understand that it's not a replacement. Recognize the importance of human skills. Let's recognize that, hey, we are important. What we're doing now, every day to day is super important.

And we're still going to be there to critical think, ethical judgment, all these things, creativity. We're going to be there, adapt, and evolve. That's a takeaway. We've got to be able to adapt and evolve. So navigate the challenges wisely.

And I think last for me is just seize the opportunity. Seize the opportunity for growth when you can, advancements, innovation when you can, when it makes sense. It's exciting. It's even exciting to talk about.

So in conclusion, again, AI is not a threat, but an evolution. It's not a threat, but it's the next phase in our digital transformation. And then finally, I'm going to read this verbatim here because I think Nate and I both agree it's super important. We like it.

Each major technological disruption from CAD to BIM and now AI has sparked fears of job displacement, but has ultimately led to the creation of new roles and opportunities. AI should be viewed as a tool that enhances creativity, boosts productivity, and reduces errors.

The key for young engineers and architects is to continuously adapt and learn these evolving technologies, just as professionals did with CAD and BIM, and AI like its predecessors, is simply the next step in the industry's digital transformation journey. And think we have a quote to end on. I'm going to let Nate take this one.