The Infrastructure Technology Podcast: Digital Delivery and Innovation with Chris Harman and Jay Wratten, WSP

Gavin Jenkins (00:04): 

And welcome to another episode of the Infrastructure Technology Podcast. I'm Gavin Jenkins, Senior Managing Editor of Roads and Bridges. And with me we have Harlee Hewitt, Associate Editor of Roads and Bridges, and Brandon Lewis, the Associate Editor of Mass Transit. How are you two doing today? 

Brandon Lewis (00:23): 

It's Tuesday and you guys know what that means? 

Gavin Jenkins (00:27): 

It's podcast day. 

Harlee Hewitt (00:28): 

We need a song for that. Brandon needs a song. 

Gavin Jenkins (00:31): 

Oh, aren't you the singer? Aren't you the singer? 

Harlee Hewitt (00:33): 

Oh, true. Right, so it's incumbent on me. 

Gavin Jenkins (00:37): 

What we should do is just get one of those computer animated tuneups of Brandon and saying no--you know how the internet does. Alright, so it is Tuesday, it is podcast day, and today we have a really special episode. We're going to be talking to Jay Wratten, who is the Executive Digital lead for WSP and Chris Harman who is the Vice President Director of Digital Delivery and Innovation at WSP. And before we get to my interview with those guys, let's just talk about WSP for a second. WSP is a massive engineering firm. It might be the biggest, can you guys hear my dog in the background? I'm so sorry for that. He's a loud border collie. Anyway, WSP is a consulting firm that is everywhere from its website. It currently has 73,900 employees. That is more employees than most places, have people, residents in their city and active projects on all continents, 200,000 and over 9,000 clients served. So this is one of the major firms in our infrastructure space and they're just an impressive company that really cares about infrastructure and technology and so it is a no-brainer that we would have them on our show. Brandon, can you speak a little bit to their involvement in the mass transit space? 

Brandon Lewis (02:25): 

Yeah, so they work a lot with transit agencies. They currently have projects with the likes of Metro in Texas along with Denver RTD in Colorado. And they work to build transit facilities, bus yard, light rail, street cards, every aspect in sort of the mass transit industry. 

Gavin Jenkins (02:48): 

Wow. I mean it's a huge company and I'm getting this off for the Wikipedia page right now because I am a talented journalist, but WSP stands for Williams Sale Partnership and it was established in 1969 in England by Chris Cole, Jeffrey Williams, John Sale, and Jeffrey Middleton. And today it is just a massive company that is involved with a lot of projects that deal with roads, bridges, and mass transit in the United States. And without further ado, let's get to my interview with Jay Wratten and Chris Harman, take it away. 

(03:38): 

Jay Wratten and Chris Harman from WSP. Welcome to the Infrastructure Technology Podcast. Gentlemen, how are you doing today? 

Chris Harman (03:47): 

Awesome, so glad to be here. Thanks Gavin. 

Jay Wratten (03:50): 

Yeah, doing great. Excited. 

Gavin Jenkins (03:52): 

Alright, so now before we came to this, our listeners heard me introduce you to my co-hosts. I probably did a terrible job introducing who you are and what you do, especially in compared to my co-hosts who always nail that aspect of the show. So why don't we begin by breaking down a little bit more in depth of who you are and what you do for WSP. Chris, let's start with you. 

Chris Harman (04:18): 

Awesome. Yes, thanks very much. I'm Chris Harman and I guess if you had to listen through that bio, I apologize. It's way too long. It always embarrasses me when it gets read. The short version is that I look after digital delivery for the transportation infrastructure business for WSP in the USA. That means a lot of different transportation clients nationally, a big group of people doing different things and half the time I'm focused internal trying to make us deliver better use BIM and 3D and all that usual stuff. And the other half I'm working externally trying to really drive clients to do new things, trying to sell new ideas in places and win new and exciting work. 

Gavin Jenkins (05:00): 

All right, excellent. And Jay, tell us about yourself. 

Jay Wratten (05:03): 

So I've been at WSP a while, I never intended to be a lifer, but 20 years, a long time at the firm. And so gotten a roll up through the industry, worked in different parts of the business, but currently I lead our strategy in digital for the US business, and that sits across all our market sectors. So what are the opportunities that WSP has to solve problems for all of our clients using technology. Where can we invest to improve that? And then how does that shake out globally with my colleagues in other parts of the world, with our global strategy team I sit on that and work with them on what are the commonalities, the themes that we might be able to solve globally. 

Gavin Jenkins (05:40): 

Excellent, excellent. Well Jay, then this first question has got to be for you then tell us what are the trends that you're seeing in 2025 for digital delivery? 

Jay Wratten (05:52): 

Well, maybe before I jump into 2025, it'd be worth reflecting slightly on where we've come from. Digital delivery is not a new topic and Chris and I could both talk our readers, our listeners ears off on all the cool things that we've seen happen over the last five, 10 years. But I think in 2024, it won't be a surprise to anybody that AI really came onto the stage. We saw it in 22 and 23, but really in 2024, I think you saw an industry pickup. So we saw a couple of major themes in 2024, a real focus on data underpinning the opportunity for this technology. And we're going to talk a little bit here on the podcast about how that's rippling happen to our projects. We saw AI tools starting getting rolled out in our software vendors and our workflows. So you saw the likes of Autodesk and Bentley and Esri signaling really strong to the market that AI is a core part of their platform. 

(06:46): 

And then we also saw our clients starting to mature their view of like, well, what is the opportunity that AI is going to present us in solving actual problems that our stakeholders have, improve road safety, reduce carbon, increase efficiency. In 2025 If we build on that as a sort of starting point, I think a couple of things excite us. First of all, we're starting to see our clients move to an implementation phase. We're seeing RFPs come out on the streets from departments of transportation and other clients like that saying, Hey, help us build a tool that leverages AI that we can start to test out. Secondly, we see a real shift in the types of data that they're asking from us in what we give to our clients. So an acknowledgement that data will underpin, as I said in 2024, is rippling into the expectation of our projects. And then lastly, I think in 2025, you're going to see the major players start to lead from the front. It's our opportunity to define how AI impacts our industry, and it's not something that we want to let simply disrupt our industry. We know the problems our clients have, we know how these domains work. And so I think you're going to see in 2025 firms like WSP partnering with those AI providers, with those digital providers so that we can create positive outcomes for solving problems in this industry. 

Gavin Jenkins (08:16): 

That's excellent. Thank you. That was a great answer. I want to build off of that and talk about WSP workflows. How are those workflows starting to adapt to new AI capabilities? And Chris, I guess that would be for you, right? 

Chris Harman (08:34): 

Yeah, absolutely. I can definitely kick us off there. I think what Jay said is one hundred percent right, and it's those language models that really made us feel like our heads were spinning like AI whiplash for an entire year has been what seems to happen to me. And I'm in the business of change. I run around trying to get people to change and now things just feel like they're changing so fast. So at the start of last year, we really started using it and the models are really, really good at making that next step at giving you a good answer and making you feel like, wow, this actually knows what it's talking about and what we're seeing, I mean the RFPs that Jay's talking about and the implementations is really taking those models and applying them to a domain knowledge space. Some clients say, 'Hey, we have all of this information, could we use a model to help us answer questions out of it?' 

(09:27): 

And the answer is yes. That's what we're doing. We're seeing it over and over and over again. We're taking all of this information, this knowledge that we have and trying to organize it so that we can get to answers faster, we can be more productive. And in our field, I think especially in BIM, where I came from, the promise of more productivity, better solutions has been the thing that we've been saying, but it's really built on a data-centric approach. And until now, I don't think we've ever felt so validated. Suddenly our information is important. So the way we're using it is we're having to pull more and more information out of the models, the designs, and then we're seeing more and more opportunities for that in delivery. So we can take a language model and apply it to codes and standards or approaches that we've taken in the past or previous designs and really get good information out of that. 

Gavin Jenkins (10:21): 

Alright, so last month here at Roads and Bridges, we had our Texas issue. Every February we try to make that issue about what one state is doing and how they're leading the way and how they're changing and reshaping their infrastructure within that state. This year we focused on Texas and TexasDOT has a really fascinating strategy when it comes to AI. They're improving road safety and driving decisions on the road through AI. Chris, can you talk a little bit about whether we are starting to see the use cases trickle down to RFPs in the traditional sense? 

Chris Harman (11:11): 

Yes, I think we can. I know Texas did an AI strategy and I'll probably yield to Jay on that one. I know he knows a little bit more about it. And I also think for a long time in the consultant realm, we thought of ourselves as being a lot more future focused. We had the better tools, we had the newer workflows and we thought of our DOT clients and partners as being stuck in the past in a sense hindered by their own structure and bureaucracy in some senses. And Texas, along with most other states right now, is a perfect example of a state that is kicking our butts, right? They are really doing it and we are trying to keep up with them. I can say from a delivery standpoint, they kicked off their how they want projects delivered kind of training sessions yesterday with A CEC and they're really pushing the bar. 

(12:05): 

I don't know that I'm going to say a specific RFP, but I can tell you we're doing several projects in Texas where they are just requiring us to use modern delivery formats. They want models, they want information models, they want us to use latest tools and standards. They're really pushing their consultant base to start designs that way because they know they can continue that through construction and really use it beyond. But if you don't mind, Jay, do you have anything, I know that you really looked at that Texas AI strategy and document they published. Do you have anything you want to add to that or maybe build on what I said? 

Jay Wratten (12:43): 

Yeah, first of all, that was quite a Christmas present when Texas rolled it out. I think we really appreciate it and yes, we'd been kind of tracking and be aware of it, but having that sort of insight into how your clients are thinking about applying technology to solve problems, we're in an outcomes business. Like yes, we build roads and bridges, but the outcome is improve safety, better traffic flow. So how is our client thinking about solving those problems helps us think about the steps that we take on projects and how we could support that better. I had a chance, so I'm going to tell a quick story. So I had a chance to go to India, Bangalore earlier this year, and I'm riding along in traffic and Indian traffic has different rules than American traffic. So that was fun in and of itself. And so we're sitting in traffic while I watch, there's no lane markers, but roughly four lanes worth of traffic trying to turn while those same four lanes go straight. 

(13:47): 

And a gentleman had taken manual control of the intersection, a traffic cop, and he was flicking the lights and sort of guessing on timings. And we finally muddied our way through that and got to the next intersection, which flowed very well. And my driver said, 'this is one of the new AI controlled intersections'. And what I discovered is in Bangalore from intersection to intersection, depending on who's running it, you've got a guy manually controlling an intersection and an AI platform, adjusting timing and signaling based on traffic flow, camera vision prediction and so forth. I think our industry is a little bit like those two intersections. We have some projects that were started at a certain point in time that are very traditional. And then you have these new projects that we're doing that sort of incorporate say where TxDOT wants to go. I think an opportunity for us is to try to be that bridge between the two and for guys like Chris to say, 'hey, this project might not have thought about AI, but the next one you issued did. So how do we incorporate those thinkings into the project that we're currently doing so that it's future ready so that it can leverage those new tools? Chris, I'll throw that back to you. How do you push that into a project that was already underway when we know the technology is there? 

Chris Harman (15:10): 

I think that's maybe where the whiplash comes from because we can do it today by using language models and applying them. I think a foundational piece to use Texas as an example, WSP is involved in delivering the GIS in transportation, the applications, which is a GIS program nationally. And that's essentially just to help state departments of transportation understand how data could flow from design through to construction and into operations into GIS. Because these are two completely different data schemas and there's no thread connecting the two. And Texas we are delivering that as well. And this is a foundational piece because on our current projects, what we find ourselves doing is saying like, well, should we update this workspace to include the new name, the new schema for these things? How could that help this project deliver? And sometimes that's a lot of extra work and sometimes it's not much. 

(16:11): 

And the mindset we're trying to take to it is by incorporating updating and connecting our projects throughout, we're going to see benefits. They always play out in the end. So if we have an existing project and we have the ability to use new tools or a new workspace on it that embeds that data across, then we're going to see the same value out of it, then we're going to be able to achieve that. And these things last a long time. I think one of the mindset changes I really want us to see as an industry is that just because you started delivering this project this way seven years ago does not mean we have to stay with it now. I mean the days of holding onto your software platforms and your processes until the project is done are over. You can upgrade, you can migrate. This is how things work now. And so that's I think the big shift is to get people over that kind of hesitation about diving in on something new after they've started and really push them for it. If it's starting up, it's easy. We can say the client wants it or we can say, this is just how we're going to do it. But making people feel comfortable with that step over the edge I think is a big one, especially when something's ongoing. 

Gavin Jenkins (17:23): 

Let's shift gears a little bit and talk about 2D to 3D. Alright, so when it comes to 2D to 3D data as the main deliverable ties to theme in 2024, without data you have no AI. So in a world where our deliverables were PDFs, now the data model, talk to me a little bit about the AI tools that need to keep you on top of all of that. 

Chris Harman (17:57): 

Jay, do you want to start that? 

Gavin Jenkins (17:59): 

Yeah, let's flip a coin and heads Jay starts and tails Chris starts 

Chris Harman (18:09): 

I won't give it to 'em. So in a world where we've been trying so hard to shift to 3D, a data model is fantastic for me because we had been saying parametrics is so much better for producing plans. You have to do 3D, this is the best way to do plans. Your plans, production's going to be so much easier, so much more accurate, so much less rework. But the truth is that if you embed information into that parametric object, and just to give everyone an overview, if you're kind of like, I don't know what he's talking about, imagine a column between the floor and the ceiling. If it's parametric, I could say it's square and it's 12 inches by 12 inches. In the old world we would have to draw a 12 by 12 square and then extrude it between the two floors. 

(18:52): 

In today's world, we say there is a column between the floor and the ceiling and it's 12 by 12 square and it's made from steel. And if I want to, I can change it to concrete, I can change it to circular and I can type in 16 as the diameter and it will automatically change. It just updates. And then if I raise the floor three feet, it automatically extends itself up. That's magic. And you can imagine why that's magic for making plans. So we've been pushing this for so long, but now people are saying, wait, that's concrete or steel, I need to know that for my sustainability calculations, I need to know that to get costs, I need to know that because I want to understand when it was installed and what type of concrete went into there and does it have rebar. Those are all questions that you can also include in the model as parameters. 

(19:40): 

And so suddenly this 3D thing, we've been talking about 2D, 3D and this idea about creating plans, which by the way is how we were building things in ancient Mesopotamia. So I don't think we should feel like we've modernized construction by doing 3D because it's still off plans. So we're really moving forward now, we've actually done it whereas for the last 20 years we've been banging the 3D drum. We've been talking about plans. Now we're saying, well, if you want a data model where a column is a column is a column, you don't care what platform it came from. You don't care if it's Autodesks or Bentleys, it's a column and it has information that you think is important. And our clients are starting to say, I need to know where all my columns are. It's a bad example, nobody cares about those. But you get what I'm saying. 

(20:25): 

This is the magic of where we are right now. We've kind of passed through this 3D as a better way to do plans and into this world where our clients are saying, I'm interested in actually maintaining that thing. I need an intelligent object that I can extract information about it. How am I going to get that? And that's where having that foundational data model is important. And then once you have all this information, AI can do predictive maintenance, AI in rail, we're starting to see a lot of our clients asking for predictive analytics on track and rail wear, right? This is all information that artificial intelligence can give to us now. It's really kind of allowing us to do that forward looking and in order to look forward, we have to be able to produce some information about what we have. And that's this kind of like finally we did it, we come through with something actually useful. And I don't mean to diminish all that artwork. Sorry that sounded bad. 

Gavin Jenkins (21:21): 

No, no, no, no. It was great. And it's so crazy about AI is that some people use it to build roads and bridges and I know people who type in their dreams and get an interpretation of their dreams in the morning. So I mean AI, the opportunities and the possibilities are endless. Jake, do you want to weigh in on what Chris said? 

Jay Wratten (21:47): 

I agree with Chris. The thing that I think is fascinating about what's happening with those models is I believe there's going to be a network effects that's going to occur as we get better data on individual projects and you start to combine the projects. And what do I mean by that? If you imagine the world Chris painted, right where we were issuing 2D drawings, we've distilled a ton of information out of the design process into a flat set of information. That circle represents a light, that line represents a pipe in the model. All the information about is there. That's amazing because now we can look across every project, well how many pipes are typically in this kind of a project? Those are questions we can't ask in a 2D world. So you can say what does typical embodied carbon look like in a section of road in Kentucky? 

(22:41): 

And you can look across all the roads that have ever been designed there that have that information. That's the network effect we're starting to unlock and I think we're going to be able to start providing much stronger insights that drive our engineering decisions. A lot of it is institutional right now. We know this is the right way to do something, but we're going to be able to ask much better questions and that's going to be the exciting part. That's where AI is taking us in general. But for the a EC industry that shift from a plan mindset to a data mindset unlocks a ton of opportunity. 

Gavin Jenkins (23:14): 

Well, can you talk a little bit about the responsibility then of large a EC firms in this new digital landscape? 

Jay Wratten (23:23): 

Yeah, absolutely. And Chris, feel free to jump in. It's my view that we have a strong responsibility. We know the domain we are in many cases the experts are even undisputed experts in how these pieces of infrastructure should be built to run and maintain our cities safely and the companies that are developing AI models, that's a general purpose technology. It applies as you said, to dreams as well as it applies to buildings or to bridges. So it's our responsibility to shape how it's applied in our industry and I think our clients are also expecting the large AUCs to make that investment in developing these tools, in partnering with these companies. I'm a strong proponent that if we partner, we can certainly solve new problems. And I was telling Chris as we are prepping this, I watched, this is a different industry, but I'll tell the story quickly. I watched a YouTube last night from Veritasium great YouTube if you guys are into science, and he talks about protein structure mapping and the history of protein structure mapping. And the thing that really stuck with me is that in the 40 or 50 years that we've been trying to map proteins, humans have mapped something like 140,000 protein structures. DeepMind mapped 200 million protein structures in one year with AI, not a small shift. That is not a two times improvement, that is a light life-changing opportunity that they're creating for the science that's based on that. What is our responsibility as a large a EC is to find those opportunities. Chris said that we've been building buildings the same or roads the same way since Mesopotamia. I think that we need to start to question whether that will be the case and how do we explore those new things and we as a large firm have the responsibility because we are large enough and the other ones like us to direct an and budget to exploring those opportunities. 

Gavin Jenkins (25:37): 

Excellent. So Chris, are we seeing a place where AB testing approaches this year and we'll be in a position here in 2025 where in the new way? Well, the new way overcomes the old way. Is there an opportunity to test out the software in executing client projects? 

Chris Harman (26:03): 

Yes. I think when I think about AB testing, I also think for a large part, engineering work is iterative. I think maybe, and I'll try to tie this back to the last question too. I think a lot about standard of care. I'm a professional engineer and I think a lot about how do we prove that we've met our standard of care as a large firm, but as any firm really our duty is to society and the public and then our clients and then our company and then ourselves. We kind of work backwards that way. And if our duty is to society and we're designing public infrastructure, which is by and large paid for by tax dollars, right? We are stewards of the public's money and we're building the infrastructure that they require to live and if it fails catastrophic things can happen. I mean, that is something that we don't want to go into lightly and if we're just going to use an AI model and let it spit out everything, I don't think we would can say we've met our standard of care, so we can't just throw everything in there and wash our hands of it. 

(27:07): 

I think we're going to have to take the classic human in the loop more like human augmented intelligence. So augmented intelligence, we're going to have to make sure that we understand where the data or information is coming from and that we are watching it as we're using it and turn that into a real productivity shift. I mean, Jay talked about going from, I forgot the numbers, 200 million in a year, which is that massive increase. We need productivity support our clients, the public needs productivity support. This is an absolute way we must do it in terms of making sure that it works. We do that with everything. We do that with every design. We do that with every set of design calcs. I don't know many engineers that just trust it because the software says it. There's always going to be a requirement for ensuring that you've done your standard of care. And there's also a lot of heuristics in this. There's a lot of rules of thumb that goes into engineering. And one of the big challenges for Jay and I is to start digging those out from those engineers, all that tribal knowledge and pulling it out and also feeding that in to better augment, to better level the playing field to better increase productivity across the board and safety and all those other benefits, but to do it in a safe way. 

Gavin Jenkins (28:31): 

I think you got it. I think you got it. I got a few of the things I want to hit before I let you guys go real quickly. We haven't yet talked about digital twins, and so tell me, does digital twins need to prove its value? That could be for either one of you. I think probably as back to you, Chris, 

Chris Harman (28:55): 

I'll start maybe and then Jay. I do think it does. I mean I think that the term is I actually ask clients now. I'm like, can I say digital twin here? Some people want to hear it, they love it, and other people don't say those words. In my presence, it can be that divisive and I don't think it's really, it's not really digital twin. I think that the people who don't want you to say digital twin around them want you to call it what it's right. What was the example I gave earlier? Like a track railway predictive maintenance model. If it's that's what it is, then call it that. Because that's value. That's a positive outcome you're providing to your client. If you call it a rail system digital twin, everyone's kind of like, okay, that's great. How much does that cost? And then you were like, you can't see me, but I've got the pinky to my mouth when I say $1 million. 

(29:46): 

And they're just like, what am I getting? But if you said you get predictive rail maintenance, then they're like, oh, that's good. I need that. Right? Either way, somebody could call them both a digital twin. But I think for our clients, we're going to have to start proving what the outcome is that we're achieving for them. What is this thing that they're getting? What's the return on the dollars they're spending on this? And I'm still bullish on them. I think that there's a lot of stuff in that, but we got to be careful to actually call it what it is is typically how I kind of push that to make sure we're getting there. 

Jay Wratten (30:16): 

Yeah, I mean we are in the outcomes business and I think the challenge digital twins have is that it's not clear what the outcome is. And where I think the industry has possibly taken a misstep on a few occasions is that you get lost in the tech. It becomes a shiny object that looks cool, but no one really sees how it's generating value. So when you ask, do digital twins have to prove out their value? I think Chris is spot on. What outcome is it creating? Enabling what is it solving? And if we stick to that, the digital twin is an enabler of that outcome a hundred percent. But if we build it, if it's a sort of build it and they will come approach, I think that's where our industry has gotten in trouble with albatrosses or whatever that no one really understands. 

Gavin Jenkins (31:06): 

I have one last question for you guys. VR, is it ready for primetime? 

Chris Harman (31:15): 

I've decided that it would be good to go on the record and say something you don't think is going to happen this year because I'm a long time VR AR lover. I think extended reality is the future. I'm a true believer in that, although more augmented, I think we're going to be interacting with the world through our phones or devices or glasses or something, but that information is going to be persistent and we're going to be able to see it. And I was like, we're just not ready. I still don't see it enough and I'm like, I'm going on the record 2025. It's not going to happen this year, but I still think it's coming, but not this year. I was going to go on the record. Do you know what news story broke today or yesterday? Microsoft has said they're divesting all in their HoloLens too. 

(32:00): 

They're getting out of the hardware game. Had we had this podcast yesterday, Gavin, I would've been a genius. So I think that that's proof, maybe that, and I don't think they're out of it. I think they just know that hardware is difficult, right? That's what everyone's found out with the AR thing is that hardware is difficult. The technology is not quite there, but the upside is still out there. I am long-term bullish, but I don't think this is the year I don't see us all adopting it. I don't see it all coming. I mean, unless there's a breakthrough, but I think we're going to have to keep Waiting for that true magic of extended reality. 

Jay Wratten (32:36): 

I think you do see a lot more tech augment people out in the field. So yes, not VR, but the one thing I saw my first pair of meta glasses in the wild a couple of weeks ago and the guy let me try them on because I asked him and pretty cool. But where I actually see, I think we're going to see likely things happening in 2025, is people wearing cameras, site data acquisition, wearable LIDAR scanners. You're definitely going to see that that tech is certainly already here in primetime. And maybe that opens an opportunity for flip down lens, see AR piece in the future. But I do tend to agree with Chris, there's something unappealing about sharing an visor with strangers and that is a major impediment to the VR takeout. Alright. 

Gavin Jenkins (33:29): 

Alright. Well, Chris Harman, Jay Wratten, thank you so much for joining the ITP. Appreciate you guys sharing your thoughts on a wide range of topics related to the technology field and to our listeners in the roads and bridges and mass transit spaces. Any final thoughts before we go? 

Jay Wratten (33:51): 

I think I would end with, I'm a tech optimist. I think if you haven't started playing around with AI in your personal and professional life, you should. I'm constantly surprised by people who haven't and the gap in experience is significant. I'm not saying that you have to spend all your time on AI, but getting a feel for what's out there definitely helps you professionally and personally. Yeah, absolutely. I would definitely agree with that, Chris. 

Chris Harman (34:22): 

Yeah, so I was thinking about WSP and our approach and I was thinking one of the shifts I see is that we are going from knowledge workers to knowledge managers, and there's a lot of information science in this shift. There's a lot of understanding how the world is structured and how information is structured, which I think is something we're good at because we understand how the world is structured. But I think if you take Jay's advice and you play with AI, which absolutely everyone should be testing this out, I think having a feel for not just doing knowledge work, but also kind of managing it and trying to invest in that so you can see future returns on it. I think that that's going to be a real kind of shift both in the people we hire and work with, but also in the skills that we need to succeed. So that's maybe my final thought. 

Gavin Jenkins (35:15): 

Alright, well excellent. I think that's a great place to end. Once Again, Jay. Chris, thank you very much for joining me and I hope you guys can come on Again soon and talk a little bit more.  

Chris Harman (35:30): 

Absolutely. A little earlier next year so that I can get my predictions in time. 

Gavin Jenkins (35:35): 

Alright, well maybe we'll have, we'll meet up in December and we can get your predictions for 2026. I would love that. All right. Excellent. Thank you guys. Thanks a lot and welcome back. That was my interview with WSPs, Jay Wratten and Chris Harman. And now we get to the judgment time. Brandon Harlee, how did I do? 

Harlee Hewitt (36:09): 

You did amazing. I thought. Great questions as always. Fantastic. It was a really interesting interview, if I'm giving my 2 cents. I think it's really telling of the industry and what we're kind of going through in the transformation, the digital transformation as they call it. Because they were saying that they now feel like they have to keep up with the DOTs and what they're doing on the technological side when they used to be the ones pushing people and saying, you should try this or move away from your traditional methods. And now suddenly they're the ones having to keep up with people like TxDOT. So it's a strange and transformative time we're in.  

Gavin Jenkins (37:04): 

I'll tell you what, whenever I was heading into that interview, I did not expect for them to want to talk about TxDOT so much. But wow, did that feel validating for me as the person who eight months ago was like, you know what? DOT is crushing it. Texas is, let's do a whole issue based stood the curve, how bad, how great bad they are, but bad in the good sense, the full sense. But yeah, I mean Texas is awesome. Their infrastructure is incredible and it's very telling, you're right that WSP is trying to keep up with them. Brandon, your 2 cents, what do you 

Brandon Lewis (37:45): 

Think? Yeah, again, Gavin, I don't know if you could have a bad interview. You're just so talented.  

Gavin Jenkins (37:55): 

That's the ultimate praise, Brandon. Our listeners can't see this, but man, did you roll your eyes when you said it. 

Brandon Lewis (38:05): 

I did feel like that. The interview was great. I was thinking way back, and Harlee, you sort of mentioned this about how the technology, and this is one of the things that we're talking about, it's coming so fast that I think that the AI implementation and everything that's going on, not only in your guys' industry but the master transit industry, we all knew that this was eventually coming down the pipeline, but it's here now. And I don't think anybody was expecting six months ago for us to be surging something on Google and to have 20 different answers already populated by AI. 

Harlee Hewitt (38:46): 

Right? No, and I loved that piece that Jay put in about the intersections in India where you're driving down the road, you come to one where there's a million people in different directions, no lanes. One man in the middle doing manual traffic control, and then you drive halfway down the road and it's an AI intersection. I mean, that's kind of like what we are dealing with. We're kind of living in two realities. But you're right, it's here now. It's here now. 

Gavin Jenkins (39:23): 

I'll tell you how great AI is though. This past weekend I was in Ohio and we're recording a couple of weeks in advance, but there was a huge storm coming barreling down on that part of the country. And I was in Columbus, Ohio, and my girlfriend and I had to decide, are we leaving tonight or are we leaving tomorrow morning to get out of here and beat the storm? And the first thing we went, we put both just sitting there on our phones. Both of us were just like, we got to ask chatGPT. We both asked ChatGPT, should we leave tonight or tomorrow? ChatGPT gave us really sound advice for beating a storm. 

Harlee Hewitt (40:07): 

Wow. See? 

Harlee Hewitt (40:16): 

What did it say? 

Gavin Jenkins (40:18): 

Oh, it said that basically it was just like, it'd be better to leave early in the morning, which was the right call? It was the right call. We left and the time that we left, we were basically an hour in front of a snowstorm. 

Harlee Hewitt (40:35): 

Wow. It was right. 

Gavin Jenkins (40:37): 

And so I was able to get home, pick up my dog and get back to my house. And then basically within minutes there was snow on the ground. 

Brandon Lewis (40:47): 

Wow. There we go. See? And now you're here. Safe today recording the podcast. 

Gavin Jenkins (40:52): 

I am. I am. I am. Well, thank you so much to our listeners for listening today's episode. Thank you again to Jay Wratten and Chris Harman from WSP, a really great company and just one of the leading engineering firms, not just in the country, but on the planet. And it was really great to get to talk to them. And we also want to thank Endeavor Business Media, our parent company that allows us to chit-chat on the clock like this. And it's a great company that produces many podcasts. So, take a listen. And also I want to thank you two. You two are awesome. Thank you so much. Thank you. Yeah. Alright, so next time we'll be diving into an interview that Harley has with HNTB, another great engineering firm. So please stay tuned to the Infrastructure Technology Podcast. 

Brandon Lewis (41:48): 

Wait, before we go. We have to mention, we have a podcast email now. 

Gavin Jenkins (41:55): 

Oh my God, I forgot about the podcast email. Oh my god. Brandon, thank you so much. Dear listeners, please email us at [email protected]. Email us, tell us what you think. Tell us what you want more of. Tell us what you want less of. Tell us how you got away from storms driving through the night or early in the morning. Tell us who we should be reaching out to for interviews that actually, we haven't even asked that on some of our earlier episodes. I'm getting emails from really, really great people saying, Hey, you need to talk to this person. You need to talk to that person. I'd tell you before I got this job, I don't think I even knew how to spell Endeavor, but now it's seared into my brain. Alright, with that, I thank you for listening and we'll see you next one.