Engineering the Future: HDR’s Mitch Dabling on AI, Infrastructure, and Human-Centered Transformation

Dylan Cosper:

Welcome to the Infosys Knowledge Institute podcast, where business leaders share what they've learned in their technology journey. I'm Dylan Cosper, Infosys Knowledge Institute research program manager. And today I'm joined by Mitch Dabling, technology director for the Water Business Group at HDR. Mitch has deep experience in leveraging emerging technologies like AI and large-scale infrastructure projects. And today, we're discussing how HDR is embracing digital transformation and the growing role of AI in engineering and infrastructure. Welcome, Mitch.

Mitch Dabling:

Hey, thanks for having me, Dylan. It's exciting. I mean, I love talking about how technology is going to change our civil engineering industry. So excited to talk with you today.

Dylan Cosper:

So HDR is known for these large-scale infrastructure projects. Can you tell us a bit about how you lead at the intersection of these large-scale projects and digital transformation?

Mitch Dabling:

At HDR, technology is no longer just becoming a tool set that our staff use to deliver infrastructure projects. It's really the backbone of how we deliver and bring value to our clients. Everything from the software that we leverage to emerging technology like AI, machine learning, it's really becoming more and more embedded into project delivery and even final products as we start talking about smart communities and internet of things that is becoming embedded into all of our critical infrastructure throughout the world. And so, you know, it's really an exciting place to be because as we go through this technology journey, we have a lot of engineers that need to understand the technology and understand the capabilities. We have clients that are at all different points along that journey. Some that are, you know, have digital transformation offices in-house and are really focusing on things. And some that are just beginning that process, even just, you know, digitizing their assets or an inventory of their assets. And, you know, we're really focused on meeting our clients and our communities where they're at and then walking them along that journey with us as we try to add value by leveraging the technologies that are coming out.

Dylan Cosper:

How is HDR using AI today? And what opportunities are you seeing currently for AI to reshape infrastructure planning, design, and maintenance?

Mitch Dabling:

I mean, it's interesting. It's obviously a hot topic right now. I think it's always important to remember that it's not as new as we think it is, right? I mean, there's a lot of buzz around large language models and the like, but machine learning has been around for a long time. At HDR, we're really trying to identify those opportunities where the technology can add value to our staff and then, like I say, ultimately to our clients. And for the most part, our clients, they center around communities or utilities that are trying to build, maintain, and operate critical infrastructure. So, some of the ways that we've seen technology that I find really exciting, specifically AI technology, addvalue is condition assessment work. We've done this on dams where we've actually taken drone imagery throughout a dam and taken video and been able to compare over time if there's any significant deterioration in that dam that we need to proactively prevent or, proactive maintenance. I think that that really adds value, right? Traditional way of doing those inspections is being on a rope, climbing around the dam and trying to measure different cracks or different potential imperfections in that dam face. But this allows us to do, basically, like I said, add more value because it's a more automated process, but it's really more accurate and detailed process, right? So that's been something that we've been doing with some of our clients. We've also leveraged some tools where I always like to think about wastewater inspections. It’s not a very glamorous job, Dylan, but we have, think about your community at home, right? You have miles and miles of wastewater pipelines underground that are collecting everyone's wastewater and taking it to a centralized treatment plant. That is a lot of assets that need to be inspected and maintained. They need to be cleaned. They need to identify flaws so that they can be potentially replaced. And the traditional way of performing that exercise is putting a camera down the pipeline. And then we had often multiple times with multiple different people watching that camera footage and trying to identify different flaws within that pipeline. We've now been able to leverage machine learning, machine vision that can do at least one of those passes so that we can identify those flaws more consistently, more accurately and ultimately be able to process more footage of these videos. And that's really exciting. Again, the way that adds value is, I don't think many people would argue that watching wastewater inspection footage is not necessarily a high value activity. Obviously, the goal is to get value there, but instead we can focus our efforts on the true high value activity, which is prioritizing the replacements or designing replacements if there's flaws in the system. Helping a utility identify where the weak points in their system are so that they can prevent failures before they happen. And again, taking kind of that mundane monotonous activity and trying to instead leverage our human capital towards things that are high value and require a higher level of thinking is, I think, really exciting.

Dylan Cosper:

What risks and challenges are you most mindful of when including AI into engineering and client projects?

Mitch Dabling:

I always like to think of AI as kind of a magnifying glass, right? If the data that's feeding it is high quality, then it's gonna perform really, really well. If it's low quality, then it's gonna expose a lot of weaknesses. And ultimately, when we deliver a product, say we're designing a wastewater treatment plant or a drinking water treatment plant. Again, we are holding professional liability that that's going to work. And AI is not going to remove that professional liability. So we focus a lot on enabling our teams to and encouraging them to validate the sources of their data, to make sure that they're still performing very thorough quality control, quality assurance checks, and when we talk about introducing AI into workflow, we actually explicitly focus on not introducing it into the quality side of the equation. There's a lot of value there in hitting pause and having a manual review process. Instead, let's automate it on those repetitive tasks so that we can focus our time on higher quality or maybe evaluating additional infrastructure options rather than just automating a QC process.

Dylan Cosper:

How do you see the role of leaders like yourself evolving in an AI first era?

Mitch Dabling:

At HDR, we focused a lot recently on technology leadership in addition to traditional business and operations leadership, obviously. And, you know, the goal there is to really support our team members in upskilling and adopting technology. It's leading that vision of what's possible. And technology is an acceleration that I think is surprising everybody. I think about how quickly generative AI tools like ChatGPT and Copilot and the different products that we had available to all of us. How quickly those have been adopted by everybody, not just within business, not just within engineering, but even with personal use. My wife just the other day designed our daughter's birthday cake using ChatGPT. And it's only been around for a few years. And I think about that adoption curve compared to the adoption curves of search engines in the early 2000s of social media around 2010. And this is just at a pace that's completely unprecedented. So I really see us as leaders within businesses, we need to be able to create safe places and clear governance around these tools so that our staff understand what they can be used for, what pitfalls they need to be aware of. We manage the enterprise risk. And with the goal of enabling our staff with technology. We're pretty explicit with our teams at HDR that we don't see a lot of these tools replacing jobs. We see them augmenting our people by replacing repetitive job functions. But in civil engineering, there's a severe talent shortage. Most groups like AWWA and ASCE see that in the 30 to 35% range. So we need to help our staff fill that gap by leveraging the best tools that are at their disposal. So, creating clear understanding and guidance on when they should be using tools, what tools are available, how they can support their clients and do it in a safe way. And also collecting those good ideas. I mean, I won't pretend like us as leaders have every single idea of how to leverage these tools. And so I love being able to connect with our teams and build really unique initiatives so we can take those experiments, ground truth them, create a good business case, and then ultimately roll it out so it can be leveraged by other team members throughout the organization.

Dylan Cosper:

If there was one piece of advice you had for ensuring that the human factor isn't lost in these large-scale digital transformation projects, what would it be?

Mitch Dabling:

It's keeping the human in the loop, right? We talk a lot about that in the AI circles of, you know, as we build out these workflows, we need to make sure that they are purpose driven and they're meeting the needs of society. And that could be, you know, at the scale of one person, one designer, or it could be at the scale of, you know, a whole community and supporting. But I'm a big believer in If we're adopting technology just for technology's sake, then we're gonna fail, Dylan, right? That should never be the purpose. It should be, we're adopting technology so that we can augment our teams, we can find that value, we can focus on that value, and we're keeping the human in the loop so that they are driving the decisions with AI, providing all the really high-quality data and guidance so that that decision can be made.

Dylan Cosper:

Mitch, thank you very much for joining us today. I have thoroughly enjoyed the conversation and I am positive that our audience is going to enjoy it as well. So, thank you.

Mitch Dabling:

Absolutely. Yeah, I love having these conversations, Dylan. I think it's just really important, you know, and I know a lot about your audience as engineers of all levels, right? Not just civil engineers, but different engineers. And I think it's important for us always to remember that AI is never going to replace solid engineering judgment. It's going to amplify our ability as engineers, right? And engineers are always trying to find value in our work and bring that value to our communities. And so I think as we pair our staff with AI and with data scientists and build these really solid foundations, that's going to be really exciting for the engineering community.

Dylan Cosper:

Thank you very much, Mitch.

Mitch Dabling:

Thank you.

Dylan Cosper:

The Infosys Knowledge Institute podcast is part of a collaboration between MIT Tech Review and Infosys. Visit our content hub on technologyreview.com to learn more. And be sure to follow the IKI podcast wherever you get your podcasts. You can find more details in our show notes and transcripts at Infosys.com/IKI in our podcast section. Thanks to our producers, Christine Calhoun and Yulia De Bari and Dode Bigley, our audio technician. I'm Dylan Cosper with the Infosys Knowledge Institute. As always, keep learning and keep sharing.