NDE 4.0 Podcast Transcript
Episode 20 — AI in NDT
Our Guest: Shawn DuBravac – ASNT 2024 Keynote, NYT Bestselling Author and Top 30 Futurist
Nasrin Azari (00:12)
Hello and welcome to Floodlight Software’s NDE 4.0 podcast. In this series, we interview various experts in industry 4.0 concepts, ideas, issues, and technologies as they relate to non-destructive testing and inspections. This show is designed to explore the biggest challenges and opportunities for the future of NDT guided by some of the smartest people in the industry. So be prepared for a thought-provoking discussion and to learn something new in the next 30 minutes. Hope you enjoy the episode.
Today, we are joined by Sean DuBravac, an economist, futurist, and bestselling author who guides leaders through uncertainties, distilling complex technological shifts into clear, actionable insights. Whether on stage delivering keynote speeches, advising Fortune 100 companies, or collaborating with nonprofits, Sean is the go-to for those who want to thrive amid rapid transformation.
With a deep history in tech and economics, he’s the trusted voice when industries need to make sense of disruption. Sean is an internationally recognized thought leader and keynote speaker having recently keynoted ASNT 2024. He is also the author of the New York Times bestseller, Digital Destiny, How the New Age of Data Will Transform the Way We Work, Live and Communicate, which explores how the world’s mass adoption of digital technologies
portends the beginning of a new era for humanity in business. Welcome to our show, Sean. Thank you. The format of this podcast is that I’ll pose five questions to Sean designed to dig into some of the most meaningful and interesting aspects of NDE 4.0. In today’s discussion, we are going to focus on the most impactful trends in the NDT industry, AI and digital transformation and how leaders can navigate this complicated landscape.
Shawn DuBravac (01:41)
Glad to be here.
Nasrin Azari (02:03)
But before we get to that, I’d like to start with an introductory question for Sean to set the stage for our audience. Sean, can you tell us a little bit about your background and your journey into the world of AI and digital transformation?
Shawn DuBravac (02:17)
Sure, so I have, as you noted, background in economics and finance, have a PhD in economics, but spent most of my career in technology, working around emerging technologies and the things that we’re playing out. And so I tend to sit at that intersection between economics and technology and then societal shifts and business change and how all of those things are coming together. My focus over the last 20 years has really been on helping leaders frame the future, understand the forces that were at play, and then prepare both themselves, their organizations, and ultimately their workforce for a future that’s emerging.
Nasrin Azari (02:59)
Well, I think that there are a lot of leaders in this industry that are going to be very interested in what you have to say. So thank you again for coming on board our podcast today. And let’s go ahead and start with our question number one, which is some organizations struggle with digital adoption due to legacy systems or business culture. What strategies are future focused industry leaders using to accelerate transformation.
Shawn DuBravac (03:32)
I think one of the big challenges with digital transformation is we tend to start at a very specific point in time and we try to build every solution that we’re going to possibly ever want or ever need based upon the existing technology at that point. the struggle there is that the technology always is evolving. It continues to grow. It continues to improve and get better. And so I think it’s important to take a more adaptive approach when you’re looking at these types of transformations rather than think of them as like a one-shot game where we’re going to try to do everything and build an entire roadmap off of what we know today to be true. Start by solving very specific problems in sequential order. And what you’ll find is that over time, the technology improves, the way you approach some of those problems might evolve. Obviously you want everything to be cohesive, you want it to work together. And at the same time, I think it’s important to be adaptive in how we approach these things rather than thinking about it as one single big large investment. I think you really wanna take a stepwise approach as you go through these transformations and recognize that it’s never is over. You’re never fully implement the solution that you’re constantly iterating and constantly working towards a better outcome, a better infrastructure, and ultimately better workflows and processes.
Nasrin Azari (05:10)
Yes, definitely. I 100 % agree with you there. I mean, we see that with our customers and we encourage our customers to have the mindset that digital transformation isn’t a one-shot deal. You’re always going to be digitally transforming from now going forward. And I imagine that you might encourage companies instead of looking at like a 10 year horizon, let’s just look at like two years for now, because in two years things will change. And that might be hard for people that are used to having these five year plans, these 10 year plans. What do you think?
Shawn DuBravac (05:50)
No, I think it is very hard. think it is very hard. And it’s hard and difficult because we look at the existing technology infrastructure and the existing technology stack, and we say, okay, we’re gonna build from here. And it’s hard to leave things out of the roadmap in a way. You wanna have everything really specified. You wanna have a very specific plan. And so it’s hard to say, hey, we’re going to take a more fluid approach to this. I like to think of it in terms of real options. What do I have to do today in order to take advantage of some opportunity in the future? And that might not mean mapping directly to that opportunity, but building and putting things in place that allow us, when that opportunity arises, that then we’re well positioned. so I think it’s important to recognize that all of this is built upon previous innovations and previous iterations of our technology stack. so, I think one of the challenges in the past has been getting interoperability between different technologies. And I think that’s becoming easier. Obviously, AI is both this huge opportunity and this huge unknown. And so in AI, we still don’t know exactly where we’re going to net out when it comes to AI. We’re seeing tremendous gains. We’re seeing a lot of new developments and we really don’t know where this might end. And so with that in mind, you really can’t be path dependent on your investments. You have to think forward, but also be agile and be flexible and be able to recognize that we may need to pivot if the technology changes and if our needs change. And as a result of that, we need to make sure that we are making our investments over a time horizon and not thinking this again as kind of a one shot. We’re going to build a one big project, but recognize that we’re trying to build an infrastructure that supports us in a future that is still uncertain.
Nasrin Azari (08:08)
Yeah, and the technology I feel like is changing so much faster today than it did, you know, even two years ago, five years ago, 10 years ago, that our mind shift definitely has our mindset definitely has to shift towards things are going to be different in a year. And instead of making maybe hard decisions, maybe it’s making some soft decisions. And instead of deciding on an end point, deciding on a path, like I’m deciding to go this direction, not necessarily sure that I’ll go 10 miles at this direction. I might change part way through and shift a little bit. So I think that’s really interesting. think you’ve kind of hit a definite real point there. Let’s move on to, yeah, go ahead.
Shawn DuBravac (08:57)
just going to say, I think there’s a balance there too, because as much as you want to move in a direction and you want to be constantly reevaluating your decisions, you also don’t want to end up in decision paralysis where you’re constantly second guessing your investments, you’re constantly second guessing what you’re building out, you’re constantly reevaluating. And so there is this fine balance in this is the direction we’re going and we’re going to continue to reassess, reevaluate and iterate around what we’re trying to do and be stuck in this constant paralysis where you’re constantly second guessing, you’re constantly changing directions. And so there is a fine line there and that can be a very challenging thing for leaders to do. Certainly it can be challenging for workforce to live through. And so there is, I think, a very delicate balance there that you’re trying to find.
Nasrin Azari (09:50)
Yep, definitely. Let’s move towards to question number two, which is as the NDT industry pushes toward greater digitalization, what do you think NDT will look like in 10 years? For instance, how do you think it might evolve beyond defect detection?
Shawn DuBravac (10:08)
I think if you look at where we are in history broadly, I think we’re on the cusp of the next big transformation. moving from digitization to datafication. And I think that has really big implications for every industry, especially for an industry like NDE. And if you’re going to just step back, you can see what this process looks like. So you go back to the late 90s and the early aughts, and we were still living in a pretty analog world. Even as late as 2000 in the US, only 40 % of households had home computers. Only 3 % of us were on broadband. so, you know, we forget that time where we weren’t relying on digital technologies. Then as we moved into the ATS, we saw in pretty quick succession, we began to replace all of our analog products with digital ones. And that’s really been, I think, one of the defining trends of the last 25 years is this move from analog to digital, I think we’re now moving into the next age where we move from digitization to datafication. We’re gonna take advantage of all these digital technologies. Many of them are sensing the environment and so we’re capturing all of this, I call data exhaust off of all of these systems. And now we’re gonna try to figure out how do I take this data and make sense of it? How do I deploy this data in new and useful ways? Are there other things that I can do with all of this data. And so when I think about the NDT industry going through this next big shift as we move through what I think will define the next 25 years, I think it’s in some ways building upon what they’re already doing, providing the services that they’re doing, but also empowering their customers in entirely new ways. And so the service offerings that I think they’ll be offering over then the next two decades as we go deeper again into this shift to datafication will be significant. think you’ll find that they will, rather than perhaps just doing certain tests and reporting on those, I think they’re going to be much more deeply integrated into what a business is doing. They’re gonna look, the services that they provide will be different, not only broader and more things that can be assessed, but also the way that their clients are going to use the data that they’re capturing and providing, think will be significantly different. And so I think it will create entirely new workflows, but also as a result of that, it’s going to unlock all of this new value for their customers. And I’m pretty excited about what that might look like.
Nasrin Azari (12:55)
Yeah, I agree with you. think one of the really interesting things related to the digitalization trend and datification trend is that NDT is basically all about data collection and data analysis. And there’s tons and tons of data from what I hear with a lot of folks that I speak with is that there’s a lot of data that’s being collected and just a very, very small amount is being acted upon. And it’s that, let’s make use, like you say, let’s make use of all of that other data that’s being collected and basically ignored for the most part right now. It could be so valuable in a lot of different ways. And I think this is gonna be a really, really exciting time for NDT and a scary time too, because I think the industry is very traditionally slow to adopt. That’s the other thing that I’ve learned over the years is that it’s an industry that is more slow to adopt changes than some other industries that might be all in on AI. think it’s gonna be a little, I think there’s gonna be some road maps maybe from a perspective of people not wanting, people feeling like it’s moving too fast, perhaps.
Shawn DuBravac (14:17)
Yeah, I think, you know, I like to say technology moves slowly until suddenly it doesn’t until suddenly it’s upon us. And that’s, think what organizations often feel is like, they’re surprised by how quickly things are moving, even though it had been building for years. I think that’s where we are right now as we make this transformation into a datafication is you’re going to start to see more requests from your customers for a wider variety of data for different types of data. In some instances, you might already be capturing that data and just not utilizing it. In other cases, it might be an entirely new request. You’re gonna start to see, I think, requests from your customers and your clients who want data at different intervals. They want different types of data delivered in different types of way. And these are all signs of this move. And recognizing that as you rise to the occasion and as you’re able to provide these new types of data or these new types of data in new ways, I think you’ll see that you’ll become much more embedded and integrated with your customers, with your clients. And so that starts to really change the value proposition that you’re offering as well. So I think there’s a lot of opportunity in all of this, but as you noted, it can also be a scary time as we try to understand how does this change the underlying business and is this a valuable transition and valuable opportunity for us?
Nasrin Azari (15:53)
Yeah, definitely lots of opportunity in a good way, I think. And I really liked the idea of the relationship between the receiver of information and the provider of information. So in this case, in our case, the asset owners and operators and the NDT experts, the relationship actually becoming stronger because they can provide more value throughout the whole life cycle and having NDT services become more integrated into the entire process is, I think, very, very positive. So that’s exciting. And let’s move on to our next question, which is along the same lines. It’s always fun to predict the future and there’s always the instinct to future proof, which brings us to our next question, which, what principles should executives consider to ensure that digital transformation and NDT remains relevant in an uncertain future? I think you’ve touched on some of this before.
Shawn DuBravac (16:54)
Yeah, mean, building off of some of the things we were just talking about, I think it’s also important to recognize that this isn’t just a technological shift, it’s also a cultural shift. And so you have to bring your workforce with you, you have to move them forward. And it really isn’t the technology that is the struggle, it’s often the workforce and the dynamics that play out there that end up being the true struggle in any technological transformation. It’s ensuring that workers have the right mindset, that they have the right skill set, that they have the right tools available to them in order to move forward with this. And so we so often look at just the technology when we’re looking at technological transformations. Leaders really need to make sure that they’re also bringing their workforce along and that they’re providing the right, not just, again, technical training, but also they’re providing the cultural shift required to make these technologies successful.
Nasrin Azari (18:00)
Yeah, definitely. think that’s been another trend of the topics that I’ve talked about with other folks on the podcast that there is this, lot of the roadblocks tend to be in the whole culture aspect and the difficulty that humans have with change. And I think that’s going to be tested quite a lot because in the near future, the upcoming future, the short-term future as digital transformation, as we talked about, it feels like it’s speeding up and it feels like AI is speeding up. It feels like the impact is speeding up and changes are speeding up, which means our business models and our business operations are going to have to be a lot more adaptable, as you mentioned. And I think that’s probably going to be the biggest challenge is just getting people comfortable with how quickly changes are happening and becoming comfortable with that constant possibility that things could be different, slightly different today than yesterday and how quickly that will change. I think that’s kind of something that’s been studied by a lot of psychologists is how difficult change is and it’s definitely gonna test us over the next few years, I think.
Shawn DuBravac (19:23)
Especially when the skills that you’re using or that you’ve relied on in some cases for years starts to shift and to change. So what you’re actually doing is changing. I don’t think it ever moves away from your expertise and your skill set that you’re bringing to the table, but the way that you leverage that expertise, the way you deploy that expertise does start to change. And so it’s making those subtle shifts in how we do work, how work flows through our organization, how work flows throughout your individual day. All of those things start to change in small and subtle ways. so being able to recognize those shifts and take advantage of those shifts, I think are important for everyone in an organization.
Nasrin Azari (20:14)
Yeah, and just something that I just thought of, do you, have you seen in your work that there’s a generational difference in how people adapt or, you know, how quickly people can change their mindset? you, it feels to me that, you know, you know, maybe those of us in the older generation, maybe it’s more difficult for us, but the younger generation, because they’re used to, you know, technology and the faster pace of technology changes that maybe it would be easier for the younger generations? Do you have any thoughts on that?
Shawn DuBravac (20:50)
Yeah, and I see it less as a generational impact and more as a, if you will, seniority impact. Like if you’ve been doing the same type of work for an extended period of time, it’s harder for you to see sometimes that if I were to integrate these new technologies over time, it’s a positive outcome. Because at first, it’s often not a positive outcome. We have to learn the new technology. We’re trying to figure out new processes. So every organization as they’re integrating new technologies might actually see a dip in their productivity. They might see a dip in their efficiency. And for somebody who’s been doing this type of work for a very long time, it’s hard to be convinced that that dip in productivity or dip in efficiency will be overcome and that you’re trying to move to a new trajectory. You’re trying to get your your organization onto a new plane, if you will. And so I think that can be the challenge. Whereas somebody who is newer in the workforce, they often don’t have those same productivity and efficiency type of work structure that maybe somebody who’s more senior does. And so they can change processes more easily because they don’t lose the same amount of productivity that a more senior worker would lose. so that’s the challenge. It isn’t that they don’t see positive outcomes or the potential opportunity through some of these technologies, but they look at it and they go, I’m so efficient, I’m so productive in doing it the way I’ve always done it, the way I’ve learned. These are learned over time. And so they didn’t start out as productive as they are today, but they developed that expertise. I think it can be really challenging for an organization to try to move all their workers at the same time when, you know, maybe that’s not the right approach. Maybe that’s not the right goal, but figuring out how do we leverage our workers in different ways.
Nasrin Azari (22:55)
Yeah, and maybe just making it okay for it to feel like I need to take a step back before I take a step forward or take two steps back before I can move forward. And that is that can feel like a wait, I’m going in the wrong direction. That makes sense.
Shawn DuBravac (23:10)
Yeah, and I think you also as a leader in an organization need to recognize as we’re integrating new technologies or as we’re integrating new approaches that we have to provide some of that space for us. You know what is a proverbial step back? What might look like lower productivity or lower efficiencies? And so we might need to adjust at least temporarily what we are expecting from our employees as they make these transitions.
Nasrin Azari (23:38)
Yeah, that makes sense. So let’s move to question number four, which is how do you foresee AI augmenting human expertise in complex entity inspections and decision-making?
Shawn DuBravac (23:50)
Yeah, I see AI impacting a lot of things in every industry. think where we see it, at least for right now, is we are going to be using AI to help automate some of the routine, mundane tasks across every industry, in every field, with every employee. We’re going to want to start with the jobs that we don’t want to do. And that’s been true for a very long time. There’s lots of examples of AI or robotics in our world. We don’t necessarily call them robots anymore, but if you think of like a washing machine or a dryer or a dishwasher, like those are, those are robots. They’re very well-defined robots that do very specific tasks. And they’ve automated some of the work that we used to do so that we were freed up to go do other things. And I think, think when you think of what AI is capable of and where we’re seeing AI being deployed, we’re going to use it. I think, first and foremost, automate away some of the routine mundane tasks that we do, the mundane analysis that we might have to perform. We’re also going to use it to augment the type of work that we do. Where we see it right now is that it’s helping remove some of the cognitive load of work. so think about some of the work that you have to really think about or that is taxing on you using AI to help augment your journey through that so that it is a little easier. So it frees up some cognitive load that can be deployed elsewhere. There’s so much talk, if you look at education, there’s so much concern that students are going to use AI to cheat. What we find at least right now is that they’re mostly using it to lift some of the cognitive load. And I think that is true also for workers in every industry.
Nasrin Azari (25:49)
Yeah, maybe doing performing research or I love the idea of the AI performing some of those mundane tasks, particularly because in NDT, know that what inspectors are doing, the work that they’re doing is very important. if 90 % of the defects or the product that they look at is defect-free, you know, it becomes really rote and it’s easy to miss something if they get tired or, or they start thinking about other things because the work is, you know, quote boring. so I think in a lot of ways, there’s a lot of benefit to having that augmentation come along in that, let me get all the sort of boring stuff out of your, off of your plate and have you focus on the stuff that’s, that’s going to be interesting and be able to be engaging. And then you can actually get more work done. Most of it’s more interesting. So I think that’s definitely gonna be a mechanism for the future of NDT.
Shawn DuBravac (26:57)
I think the other thing is as we automate some of these routine tasks, as we automate and scale some of this work, it does free us up to do much more complex tasks, things that can’t easily be automated, things that can’t easily be made to become a routine process. I think the workload in NDE in some ways might get harder with AI. The paradox that I think exists with AI that yes, we’re going to automate some of the work, but the work that you’re left with is going to be much more demanding. It’s going to be much more difficult in some ways, going to require much more thought. It’s going to be much more complex environments that you’ll be using your skill set in. so I think in weird ways, you know, again, this, this paradox is that as we integrate AI into your work, it’ll actually will require you to do more, not less. And you see that playing out in other interesting ways. Like if you look at self checkout in grocery stores, for example, like it didn’t get rid of the bagging of your groceries, it just changed who was doing the bagging of your groceries. so AI will play out, I think in that same way. it might change the type of work that you’re doing and as a result I think the work that you will be doing in the future will be much more complex and much more demanding.
Nasrin Azari (28:31)
That’s a great segue to our final question, which is with AI and automation taking on more tasks, kind of those more rote tasks, what skills will be most valuable in the NDT workforce over the next few years and how should companies prepare? So kind of given that state, how should these companies best prepare for that feature?
Shawn DuBravac (28:53)
I think what they need to recognize is that as the work becomes more complex, the work that humans are doing, as it becomes more demanding, that there is a real return to the expertise and the skill set that they bring to the table. There is a real return to their cognitive capabilities. And so as you’re building this workforce of the future, you need to make sure that you’re also preparing them to be able to handle some of these demanding environments, some of these much more demanding tasks. We’ll see that also because of the spreading of the work, know, we’ll move beyond the confines that we exist in today. And as a result, every time we’re pushing the envelope of what’s possible in the NDE industry, it will require humans to step in, to handle those new environments, to handle in a new complex problem that historically might not have been able to do, that we wouldn’t have been capable of doing for any number of reasons. so building out that expertise, building out the cognitive capabilities of your workforce, I think is very, important. Recognizing that what will be demanded of them in the future will be a lot more than is true today. we’ve seen that over time in every industry. Digital technologies created a way for us to do more. But as a result, it isn’t just that we’re doing more, it’s that we’re often doing much more complex tasks because now we have technology to enable us to do some of those complex tasks. you really see that playing out in every industry.
Nasrin Azari (30:37)
I think it’s really a very exciting future. And I hope that our listeners and folks in the field will look at it as more of an opportunity than, I mean, know, like we talked about earlier, it is a little bit scary, but there’s so much opportunity and it feels to me like there’s so much more positive and so much more to gain than there is to lose or to be scared about that I’m hopeful that folks will take on your advice and adopt a more flexible and adaptable mentality and also help their teams kind of take that step forward as well.
Shawn DuBravac (31:17)
And I think there’s a role for everybody in every organization because even the most senior employees who might feel like, I’m going to retire before this becomes an issue for me, they have a lot of expertise to share with their colleagues and their coworkers. so I think one of the great challenges that every organization faces today is how do we take all of this great expertise that our most senior, most experienced workers have and scale that across our other workers. think technology can help play a role, but at the end of the day, those senior experts need to make sure that they’re also sharing that expertise to bring the next generation of workers along in the industry.
Nasrin Azari (32:02)
Definitely. Maybe we need to ask AI how to make that happen the easiest, right? Awesome. Well, this was a really, really fun and insightful discussion today. Thank you so much for being with us, Sean. And for the listeners today, if you’d like to learn more about Sean’s work or read his bestselling book, Digital Destiny, How the New Age of Data Will Transform the Way We Work, Live and Communicate, you can visit shawnduBravac.com. I’d like to remind all of our listeners that we welcome feedback as well as nominations for future guests. So please send any feedback you have through our contact us form on our website, www.floodlightsoft.com. Any final parting comments, Sean, before we go?
Shawn DuBravac (32:57)
It is an exciting future. There is a tremendous amount of opportunity. We’re going to see a lot of change. But in that change, I think there is a lot of opportunity for growth. So I’m excited to see what happens to the industry over the coming years.
Nasrin Azari (33:15)
Awesome. Well, thanks again, Sean, and to the audience for joining us and see you all next time. And that’s a wrap for today’s discussion. Head on over to our NDE 4.0 podcast page for more interviews like this one and reach out if you have any questions, feedback, or ideas that you’d like to share. Thanks very much and have a great day.
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