Synthetic Humans & Quantum AI: The Future of Humanity

Is your business ready for a world where AI agents act, adapt, and make decisions for you? Today on Digital Disruption, we’re joined by Global Chief AI Engineer at PwC, Scott Likens. Scott Likens serves as the Chief AI Engineer at PwC, overseeing both the Global and U. S. teams. He leads the AI Engineering and Emerging Technology R&D groups, driving the firm’s strategy across AI, blockchain, VR, quantum computing, and other disruptive technologies. With over 30 years of experience in emerging tech, Scott has helped clients across industries transform their customer experience, digital strategy, and operations. He began his career in software engineering during the early days of the internet, working with major multinationals to apply a localized lens to global digital and innovation trends. Scott’s diverse technical background spans advanced analytics, digital architecture, AI engineering, and innovation. During his time at PwC, he has lived and worked in both China and the U. S., serving as a global technology leader and advisor to key clients. He is a regular speaker at international conferences on emerging technologies, including AI and generative AI, blockchain and crypto, IoT, quantum computing, and advanced robotics. Scott Likens sits down with Geoff Nielson for a look into what’s actually happening across the front lines of AI and innovation. Scott shares insights from the edge of tech, from AI agents and embodied intelligence to quantum computing and synthetic identities. He explains why most enterprise AI efforts fail to scale, how to think in innovation “horizons,” and what separates real value from hype. He touches on many topics including, how holographic AI and digital twins are already reshaping communication and the skills, and structures shaping the IT organization of the future. In this video: 0:00 Intro 1:55 GenAI hype vs. real Value in the enterprise 4:20 Embodied AI and the rise of holographic humans 6:00 Multilingual synthetic avatars 7:30 Deepfakes, trust & the role of blockchain in authentication 9:00 Responsible AI 12:15 Innovation is moving faster than trust 14:00 Speed or scale? 16:00 Defining true innovation vs. incremental tech 18:00 A Framework for emerging tech 20:30 From quantum to satellites: What’s next 23:00 Digital Twins, IoT, and Bipedal Robotics 25:30 AI at the edge 28:45 AI agents in action 30:20 Legacy system modernization without rewriting code 34:00 Enterprise use cases 36:30 What business leaders get wrong about tech 39:00 Moving from pilot projects to organization-wide impact 42:30 Balancing speed, risk & innovation in enterprise ai 44:00 How PwC enables innovation without losing control 47:00 Why “waiting” is not an ai strategy 48:15 The most important investment is your workforce 50:00 Upskilling, hiring, and culture shift at scale 52:00 Quantum, cryptography & the real threat timeline 54:30 What’s next for leaders and innovators Connect with Scott: LinkedIn: https://www.linkedin.com/in/scottlikens/ X: https://x.com/ScottLikens Visit our website: https://www.infotech.com/ Follow us on YouTube: https://www.youtube.com/@InfoTechRG

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Geoff Nielson?

  Hey everyone. I'm so excited to be sitting down with Scott Lycan, who leads AI Engineering and Emerging Tech R&D for PWC. What's cool about Scott is his experience across the emerging tech landscape and actually implementing them in an R&D capacity. What I want to do today is see if we can pull back the curtain on the research and development actually happening with these technologies right now and learn what secret capabilities actually exist. And maybe even find out what he sees on the horizon and the next horizon. Let's find out.

• 0:34Geoff Nielson?

  Scott, thanks for being here. Maybe just to start, for people who don't know, can you tell me a little bit about your story, what you've been up to in your career and what you're doing right now?

• 0:42Speaker 01

  Yeah, so it's been quite an interesting career. I mean, currently I'm the Global Chief AI Engineer at PWC, so based here in the US, but looking after everything AI engineering around the world. As a big firm, that means a lot. Part of my job is also looking at emerging tech, so research and development beyond AI. And obviously, AI is the word of the moment, but there's just a ton of innovation happening, whether it's quantum computing, blockchain continues to grow like crazy, virtual reality even with synthetic reality being supercharged by AI. My career started in software. Actually, I was studying to be a pilot and to pay for flight school, I was working at a software company and it just happened to be one building one of the first internet browsers.

• 1:27Speaker 01

  And for me, that really was this glimmer of imagination of what could happen in technology. So I started early days of the internet, building a browser, transitioning into really just the next wave of everything. So living through the browser and the internet boom, the e-commerce boom, the social media boom, the cloud boom, big data boom, and now what seems to be more of an emerging tech and AI wave, which has just been amazing. So really interesting journey.

• 2:00Geoff Nielson?

  No, that's awesome. And one of the things that I thought was so cool about your story is that you've had this kind of broad exposure to a whole pile of emerging technology that you've worked with organizations to work on the R&D side of as well. So I'm curious, from your vantage point right now, Scott, like what is most exciting to you in tech right now? Like what's on your radar as the technologies we should all be looking at or thinking about?

• 2:26Speaker 01

  So no doubt, no doubt AI is the bulk of the conversation. And I think that finally has turned the corner in the sense of at the executive level, at the board level, you know, really pervasively throughout an organization. Everyone understands AI is a lot more real. And you think about it's 70 or 80 years of research and development. So it shouldn't have been that surprising, but I think we've been through so many cycles of promise, but not, you know, value. We're seeing that value. And with that investment, it's also supercharged innovation across the board. If you think about some of the challenges of AI, it needs tremendous amounts of energy. So there's tons of innovation happening in energy and energy transmission and green energy, you know, really good things that are going to benefit beyond AI.

• 3:09Speaker 01

  But I mentioned a few, I think one of the big areas we're focused on is what's happening in quantum computing, whether it's around cryptography or just quantum computing itself in the sense of how it will support AI in some of the needs ahead of us. So a lot of excitement for me, and I think there's been a lot of progress just in the last two, three, four years in quantum. We've seen a lot of breakthroughs. I continue to monitor blockchain in the sense of how it's becoming pervasive as an infrastructure, not, you know, in the crypto world and investment, stay away from that. Maybe personally I don't, but in an enterprise world, you know, I think blockchain is really maturing in the sense of payments and tokenization of assets, both digital and physical, and

• 3:52Speaker 01

  giving us a really strong foundation that we just didn't have before in this way. I love how AI is infusing into the creative areas, so in multimodal, so generating, you know, a video and human image and likeness, even into holographic endpoints. And I think the next wave that we're really focused on right now is kind of the embodied AI. So the physical connection of AI to robotics and the Internet of Things, which again continues to grow behind the scenes. So now infusing AI at the edge, embodying it into something physical. I think that's, that's probably a wave that's going to hit us here, maybe this calendar year in a big way, if not already.

• 4:36Geoff Nielson?

  Yeah, no, that's super cool. And it's something, to be honest, I haven't had a lot of exposure to. So you know, I'm curious, what are you starting to see emerge there? Are there specific use cases that people are talking about or investing in and kind of the physical side of AI? And I'm curious as well, like holographic AI, that's something, that's a phrase I have to be honest, I've never heard before. You know, what does that look like?

• 4:58Speaker 01

  Well, I'll start with that. We've created a synthetic version of me, Synthetic Scott, almost two years ago. And using AI, I can capture my name, image and likeness. So I can capture myself now and really use AI to have someone interact with the synthetic version of me. And that can be done through a screen. So we can do that through video. But one of the more interesting advancements is holographic boxes. So I can have a three dimensional version of myself, sounds like me, looks like me, moves like me, uses my hand movements, my facial movements. But also giving me the power to speak any language and interact in any language and have knowledge that the carbon based Scott doesn't have, right?

• 5:39Speaker 01

  We can program through large language models, information about any topic. And not only that, but we have the foundational training of the large language models. So putting this holographic representation of me allows me to maybe go to a meeting without having to go. And I've done that. And I've had a meeting in Korean, which I don't speak, but Synthetic Scott does. So it's just maybe a different endpoint, you know, tying together, kind of converging the AI generation, the multimodal version of me. The voice match, the image and body match, the movement match. And really, in the last two years, that went from me as an avatar, so a cartoony looking avatar didn't really look like me to

• 6:22Speaker 01

  4K representation of me in live video. And it's really kind of changing that pattern of how people interact. So not just typing a prompt, just speaking to me in any language and having me respond. And it's not actually me. So that one's pretty interesting. I think it's kind of changing that that human machine interface quite quickly.

• 6:41Geoff Nielson?

  And if you can just, yeah. Yeah, sorry. Let's stay on that for a minute because I'm curious from like from a capabilities perspective. Like, have we passed that uncanny valley? Like, do I need to be worried about worrying about like, am I talking to Scott right now? Or am I talking to an AI avatar of Scott? Like, is it that level of quality we're seeing?

• 7:01Speaker 01

  Absolutely. I think we've passed that in the sense of a video screen, for sure. There's technical ways to understand if it's generated or not. But to be honest, right now, the human, it's beyond human comprehension. And there's some pretty public cases of Teams meetings, you know, video meetings where it's not actually the person, because the voice can be mimicked. I think with three or five seconds of my voice, it gets very accurate. And with anything beyond that, it gets to a point where humans won't tell the difference. Now, machines can still tell the difference in the sense of using AI to detect AI. But I think it's going to be really hard to tell for humans. Now, when we get into holographic, you know, there's still a little bit of interaction delay. It's not specifically real time back and forth.

• 7:46Speaker 01

  But that, again, I think that's a solvable problem. And it'll get to the point where it's really hard to tell whether it's me or not. So we're looking at, you know, can other technologies help us understand and create that trust factor? And maybe that's where blockchain comes in. We can authenticate and understand, is this an approved version? And there's another layer to it. So I can capture my image and what I look like and speak like. So, once I've done that, my team can have me say whatever they want. So it's not only the image, it's actually the content. So we have to actually authenticate, is the content approved? And the way I deliver the content? So a pretty interesting and difficult problem to solve.

• 8:26Geoff Nielson?

  Well, and that authentication piece is really interesting as well. And it seems like something we need to fast follow on, right? Because now we're in the realm of deepfakes, it sounds like, right? Like, is this an approved Scott? Or is this, you know, a nefarious Scott who's trying to get me to sign over, you know, a million bucks for, you know, God knows what?

• 8:45Speaker 01

  That's right. And this has come up, obviously, in Hollywood and IP protections around image and likeness. So there's a lot of people thinking about how to not only secure it, but then is there a commercial model around it? Because now I can get out to more places than I could physically, right? I don't have to fly, I can save travel and carbon. There's opportunities with it if it's done responsibly. So that's really where our focus has been, is that responsible use of the technology.

• 9:12Geoff Nielson?

  Right, and responsible is a word that comes up in a lot of the, you know, that comes up in a lot of the, you know, the presentations that you've been giving. And I'm curious, like, what's your, what's your worry level with some of these technologies versus excitement levels? Because, you know, we talk about nefarious uses, but even, you know, you mentioned Hollywood, you mentioned IP as well. How concerned do we need to be that, you know, are we empowering people here? Or are we potentially, you know, robbing people of their agency in some capacity?

• 9:44Speaker 01

  I'm probably the ultimate optimist, so I'm, I think I'm excited in general. I think there's a lot that we can do now to put the guardrails in place. And I've said this before, with, with the boom of Gen AI, it's the first time I've seen the word responsible or trust built in early in the conversation versus let's figure that out later. So I do think I'm hopeful. A lot of people have taken responsible AI as a fundamental pillar to everything they're building. There's, there's definitely a worry that nefarious people will use this technology in a bad way. But I think we've seen that through, throughout the decades of every technology. And it's always been a kind of a constant battle. So as long as we're, we're all aligned in the beginning that we, we have to do this responsibly.

• 10:29Speaker 01

  We're implementing those things because they're, they're the right things to do. I think we're in a better place than we typically are, but there is a lot of work to do. There's unsolved problems in the sense of how fast the technology is moving and how fast can we make sure it's done responsibly? There's a lot of intellectual property rights questions out there that haven't been answered, either by regulation or by legal court cases. We've seen some, but there's still a lot of gray area. So not solved, but I'm, I'm hopeful that people want to do it the right way and that we're all, we've all started thinking about that in the beginning, which is different, a different pattern than other technologies. I said in the early days of the internet, we weren't thinking about securing it. We were thinking about growing it, connecting it.

• 11:13Speaker 01

  And if you think about the e-commerce boom. Really, the pivot point to that was kind of this this escrow version. If I'm going to buy something, someone's going to escrow my money until I get the product because I didn't trust it. Right. And that was an add on. And then then we started to build in more controls and securities. So now e commerce, everyone trusts. But in the early days, no one did, right? You didn't know if you're going to get your product. So I I do think AI's generative AI wave has been different, but still a lot of hard problems to solve.

• 11:43Geoff Nielson?

  Yeah, yeah. And, you know, it's one of these things where, you know, my sense is, and I'm curious if you're seeing the same thing, Scott, like, people are struggling more and more to keep up with like today's gen AI technology, let alone like what's happening every, literally every week that comes out. And so i- it's exciting. And I'm, I'm really glad to hear you say that like responsibility is starting to be baked in. And I love, I love that optimism, right? Because it's fair. That's, you're right. That hasn't always been the case. So, you know, that that is a nice kind of glasses hat full approach.

• 12:17Speaker 01

  Yeah, I think the the pace is the problem right now. And there's a great author, Rachel Batzman, who said, speed can be the enemy of trust. As things move faster, it's hard to trust them because the ground is moving underneath us. And AI for the last three years has been exactly that. It is moving faster. There's more innovation happening than I've seen really in my career, which is a long, a long time. So it's exhausting to keep up. But there's also this excitement around this innovation and the invention that's happening. And in general, we're going the right way. You see people, you know, thinking about the mission and ethics of things. And there's obviously some examples where they're not. But in general, like the whole industry, the whole set of innovation is, is really trying

• 13:00Speaker 01

  to be accretive to what we're doing in business, in society, for ourselves personally. And I think if we stay focused, we stay in alignment, we need, we need some regulation and guidance and guardrails. But in general, the technology, it's, even though it's moving fast, I think its trajectory is good and it's positive. And, you know, we're seeing benefits across the board.

• 13:21Geoff Nielson?

  Right.

• 13:24Geoff Nielson?

  You talk about speed and how fast this is all moving and generally moving in the right direction. You know, I wanted to throw at you a quote that I got, I think from a colleague of yours, Dan Priest, something like that. But he had said, you know, speed is more important, scale is less important, innovation is the most important. Can you kind of, you know, unpack that and what it means for people who are, you know, participating in this race?

• 13:48Speaker 01

  Yeah, I think it's trying to call out this, this pattern is different than we've seen. Again, if we proxy back to the internet or e-commerce or social media, the cloud, big data, they follow these similar patterns where there was some innovation, but then it was all about scaling it to get to be the biggest and to own everything. And we saw that, of course, with the social media, all kind of conglomerating into central apps. With AI, the innovation keeps you ahead. And that's hard sometimes for big enterprises. Innovation is something they say they invest in, but it's really not the core of the business strategy. And I think because this new generation of AI really does affect every aspect of

• 14:31Speaker 01

  an organization, you have to stay, maybe not at the edge of innovation, but you have to keep up with it. So that's where speed means how fast we can change our organization, our workforce. How can we scale our people at a very different rate? If we give them a tool today, it could be different in one week or two weeks. And that's just not a kind of emotion that most organizations understand. So we think that the innovation, that gives you that roadmap, that view of what's coming next. And again, you don't have to do every single thing, but you have to understand the trajectory it's moving in, especially with generative AI. There's been really amazing inventions that, you know, can be infused right back into the organization. And that's where speed matters.

• 15:12Speaker 01

  And we're a big organization, 150 or so countries, you know, 375,000 people, that's a hard, you know, workforce to change overnight. But for us, it was, we saw the benefits. So moving fast with our people, giving them access to that new innovative technology. And then we got the scale. So it's kind of that pivot around, scale will happen.

• 15:34Geoff Nielson?

  Right.

• 15:34Speaker 01

  Versus.

• 15:34Geoff Nielson?

  You back into the scale versus front loaded.

• 15:37Speaker 01

  That's right. That's right. As you start to get the business to understand where we can fundamentally reinvent processes that maybe have been around for decades or hundreds of years, even depending on the industry. Reinventing those are hard, but if you're innovating at, at speed, the scale will happen. So it's a little bit of a different pattern that we've seen in big technology revolutions.

• 15:59Geoff Nielson?

  Right. Can we, can we unpack that word innovation a little bit?

• 16:03Speaker 01

  Yeah.

• 16:04Geoff Nielson?

  You know, it's, it's a word that, that gets thrown around a lot and I'm, I'm certainly guilty of that myself. When we're talking about innovation here, you know, what, what does that mean in the context of an organization or, or leaders? Like, what, what are we doing there? What, what's kind of the outcome of the process?

• 16:19Speaker 01

  So really it, it depends on where you sit in an organization because there's innovation happening. As the engineer, for me, innovation is technical invention. So there's invention, AI and, and the innovation on the back end of that is how I, I apply that into the business to actually change something fundamentally. So a lot of the times technology is an incremental improvement. I'm going to get five or 10% better, you know, efficiency based on this technology. To me, innovation is not doing any of the, the patterns we did before, but getting a better output. And that's where we really work with our clients to say that the innovation in a process is reinventing it. If you did 10 steps

• 16:59Speaker 01

  yesterday, doing those faster is not innovation. Do two steps today with AI filling in the gaps and getting a better output. That's innovation. So there's business model reinvention, which could be innovation. There's technical invention, which could drive innovation. So it's really a combination of, of how we apply that into a business. That's where we see the, the benefit and the results. To me, that's the big eye innovation.

• 17:23Geoff Nielson?

  Right.

• 17:24Speaker 01

  Small eye innovation is more invention. There's pieces we're inventing that didn't exist or we're, we're using inventions out in the market in open source or with our partners. The innovation happens when we're changing something fundamental to the business. We're, we're getting to market differently. We're creating products differently fundamentally. And you see the, the business impact happens then.

• 17:46Geoff Nielson?

  Got it, got it. And I understand, Scott, in your role, you know, you mentioned engineering. This is sort of what your team lives and breathes, right? You guys are, I don't know if I'm allowed to call it a lab, but you've got kind of your, you know, innovation, emerging technologies, kind of engineering group. Can you give me a little bit of a peek behind the curtain? Like, what does that look like in your world? And what are some of the things you're working on, either internally or with other organizations?

• 18:14Speaker 01

  Sure. If I think about the process we take, we think in horizons. So Horizon One is taking some emerging technology and applying it into the world with ourselves or our clients that we could make an impact today. It's still probably the first time it's been done, but it's a tangible idea and a tangible technology. So implementing AI agents is Horizon One. We know we can do agents today. We can have multi-agent build outs. Those agents can work together. They can supply some opportunity to a business process or efficiencies within the business, et cetera. Horizon Two is trying to understand what's happening in universities

• 18:58Speaker 01

  or in research labs or our own lab, building something that probably isn't finalized. And that's where I'd say some of the quantum work we're doing is in Horizon Two. We know that we can start to optimize, do AI optimization better with quantum. Now, it's still early because there's not a scaled quantum platform. There's some options, but at the enterprise level, it's still not ready. So that's Horizon Two. Horizon Three is looking further out. And that's maybe an area we're looking at, what's happening in space technology and communications and things that may not apply to the enterprise in the next year or two. But we know that that's going to spawn invention and innovation that we can use.

• 19:38Speaker 01

  So trying to understand how to apply maybe what seems irrelevant, making it more relevant to enterprise business. So we think in those patterns. And then within my team, we have deep research and development done in different areas. A lot of the times it is skill-based. So if we're in quantum computing, you have to have quantum understanding. There's specific skills and math that you have to understand. If we're talking about satellites, that's a different skill. That's, you know, how do we actually build a physical satellite and sensors? And so we start to kind of pattern the skills with the horizons. And in the, the art and magic of it is, how do you then bring that into an enterprise relevancy? So where, where, where would this help an enterprise, a big insurance

• 20:23Speaker 01

  company, or a retailer, or an oil and gas company? So we have to have that sector lens. So we have a many to many funnel in the sense of what we do in our work. We work very agile. So we have very small pods of people focused on delivering in a week or two weeks, not long drawn out research projects, because we have to understand if there's going to be value there, and we want to then go scale that. The other aspect is, we, we kind of have these incubators. So once we get to a point where we see there's some value, we've tested it with a client or two, we want to incubate that into a business that we can bring out and scale to many more clients. That then takes, you know, traditional skills, the change

• 21:05Speaker 01

  management and the program management, the, you know, the skills that would mean we could take this to a lot of clients and help them find value with this, this new idea. And that's, that's the hardest part. You're selling something that didn't exist before. And, you know, I think about going back to blockchain, you know, it was probably two years of experimentation and, and working with clients on, on ideas that really didn't exist in the market. And now it's exploded. And, and there's, there's timing of that with the administration change in the US and what's happening with the, the price of crypto. Like, there's a lot of reasons now is the time, but now it's exploding. And we're seeing a lot of interest. And, and that's where that incubator just starts to scale.

• 21:48Geoff Nielson?

  Right. Well, and I expect in your world too, Scott, like the, the hit rate at this stage of the game just cannot be a hundred percent, right? Like, it's always going to be fractional. There's going to be wins and losses. And that's kind of a built in model of what you're doing.

• 22:01Speaker 01

  Well, it's interesting. What we find is when things don't hit, it's it's likely because they're just ahead of their time. And I and I look back and we pull things off the shelf from four years ago that was just pure research. And now is ready because there was maybe some dependencies in the data we needed, or the compute we needed, or the physical hardware we needed. But now is the time. So it's it's almost like we we look at our backlog from previous years and say, oh, wow, this one, let's let's pull it back out. It's ready. So we don't throw anything away. Because we've learned something from it. But sometimes it's timing. Is the market ready?

• 22:36Geoff Nielson?

  Right. Are there any of those that you've pulled off the shelf in the past, you know, 12 months or so that, you know, like, any technologies whose time has finally come or is coming?

• 22:46Speaker 01

  Uh, one that comes to mind is digital twins, which is a concept that have been around for a long time. They were always really bespoke and and one off. And now with with generative AI, helping us interface into digital twins with with the amount of data we have coming from IoT devices. So the the data now is there. From physical, the physical world, like digital twins now is one of those that we've we we've researched systems dynamics models for for a decade. And now you're seeing real value, you know, in in enterprise usage of those. So that's one. A lot of the AI work is is had been done in the research phase, and maybe that was a horizon three, and now it's really accelerated.

• 23:31Geoff Nielson?

  Cool. The the digital twin piece, you were kind of now backing it into it from the other side, but we were talking earlier about these kind of physical manifestations of AI. Can we come back to that conversation for a little bit? What what are we starting to see there emerge in terms of capabilities and maybe also as use cases?

• 23:49Speaker 01

  So the one I love is is bipedal robots. So robots that stand and walk like humans and and you've seen an explosion of bipedal robots in the last two or three years. And in 10 years ago, there was some very well known labs that were building one and, you know, you you can see some some videos, but now there's an explosion. And part of that is the generative AI. The natural interface in the sense of how humans interact with them so we can speak in natural language, but also voice to action, you know, they're able to now use generative AI to have those robots learn and you're seeing explosion of intelligent moving robots that applies into the drone, the drone space, which in the US at least had really softened over the

• 24:34Speaker 01

  last five years. We didn't hear a lot about drones. There was regulatory and licensing issues, but now indoor drones, land-based drones, those kind of, to me are all the same thing. We're embodying AI. We're using the power of both generative and good old AI together in a physical instance. And that's where it becomes a lot more real. You know, we can fundamentally change manufacturing and warehousing and logistics. Self-driving cars are embodied AI. They have AI happening all throughout them. You're starting to see a lot in the defense tech world. So not an area that, you know, we do a ton of work in, but just the amount of invention happening there. So from a drone's perspective, both in water, on land and in air. So that is

• 25:18Speaker 01

  all in the backs of this, this AI explosion and generative AI and just the reasoning and thinking that these models can do has really advanced what's happening in embodied AI. And then in a more micro level, the IoT world having edge AI embedded. So not just a sensor that's kind of dumb, just sends a reading. Now that sensor can be much more intelligent and actually make decisions or take action. So now we can instrument the world in a different way. Smart cities and smart districts, of course, smart cars, they're not only generating data, but they're actually taking action and that's because of AI and that's, we call it convergence, the convergence of AI and robotics and IoT and maybe blockchain to authenticate or make payments as things are

• 26:03Speaker 01

  happening. So that's where the real value comes in is when we converge these technologies.

• 26:08Geoff Nielson?

  Right. And are you starting to see some of those convergences in your, like, at that Horizon One level in your lab? Is that still Horizon Two?

• 26:16Speaker 01

  No, absolutely. I'd say the first wave for us was in the IoT world where we're seeing the ability to have much more intelligent sensors, which then create better data or take action, which then we can build into an optimization of energy, of water, of people, you know, that absolutely been rolling that out. The bipedal robots, still probably Horizon Two, you know, there's experiments, those still seem to be a little bit out, maybe a year or two for scale, but there's some individual use cases you're seeing. And then I think Horizon Three is this space and what's happening, you know, beyond just our, the world that we walk around in, and it's a broader, a broader connection from a communications perspective,

• 26:59Speaker 01

  from a sensors perspective, et cetera.

• 27:03Geoff Nielson?

  Right. The space stuff is interesting, and, you know, it's got me thinking about the 20th century in general. And, you know, I'm not an expert in this area, but my sense is there's been a lot of use cases where, you know, DARPA, or the military, or, you know, these public R and D institutions came up with these advanced technologies, and then they were kind of brought into more, you know, if I can call it civilian or commercial purposes. Is that still a trajectory we're seeing, or is it almost the reverse now, where it's more kind of commercialized R and D labs? And then, like, where is the space tech coming from, I guess?

• 27:40Speaker 01

  Well, I think there's a lot more private industry that is, you know, inventing things related to space than we had before. It was, it was really driven by, you know, governments and the, you know, the investment that they had to have. But now with the lowered cost of, because of AI, the lowered cost of product development and, you know, engineering.

• 28:05Speaker 01

  There's a lot more private industry and I think it's amazing and there's some great examples without naming names, you know, many private companies launching rockets at a pace that governments can't even come close to. And you know, that that competition, I think will create, it will, it will be to the benefit of governments because they're going to have to keep up. And it's to the benefit of us because it's, it's technology we all can see and understand and start to figure out ways to proxy that into, you know, enterprises. So I think it's amazing. And I think, you know, that's just an example of how that innovation will lift everyone together. Lift, you know, see what I did there into space. But the, you know, low orbit satellites and the communications and sensors gives us more data and, you know, it's just a

• 28:50Speaker 01

  kind of a self fulfilling cycle where more data equals better AI and better AI equals better products and better ways to get more data. So it, it's really starting this flywheel that we think is, is amazing for business.

• 29:03Geoff Nielson?

  No, that's, that's awesome. And it's, uh, I mean, Horizon three is such an exciting space for, you know, just trying to, just trying to figure out what's next and what's, you know, what's next after next, you know, in, in terms of back to Horizon one for a minute. Are there any projects, you know, you can talk about either that your team has done in the past year that are ongoing now that, you know, might surprise people that, you know, technology is kind of here or that you're excited about.

• 29:29Geoff Nielson?

  That that may not be on people's radar.

• 29:32Speaker 01

  It, it might be on the radar, but just to give an example of, of how real it is, the agentic AI era that we're living in. This is something that we, this was Horizon one for us last year, even though it probably wasn't a term people were using. There were, were not any, if, if only a few agentic frameworks. But we started in the world of software development, knowing that agents could help engineers just build better technology. And now we're at the point where we can scale agents with our engineers, not only to generate code, but to, to look at regulatory obligations, to look at obviously security obligations. Can, are we meeting all of our safety and responsibility?

• 30:14Speaker 01

  Requirements and empowering our developers to put, you put out output at, you know, two, three, five X of what they were doing. So for us, it's been a huge enabler. And those AI agents, you know, just a year ago, were probably not on anyone's radar. And now we're infusing them into everything we do and thinking about fundamentally transforming legacy technology migration. You know, looking at old mainframe software and languages, nobody knows. Being able to use AI and agents to unravel something that's 40 or 50 years old, that's been running perfectly. Modernize it and maybe not have to have humans recode that, right?

• 30:57Speaker 01

  Have the agents do that for us in a way that we trust it, that we have a comfort on the output. So that's one that was last year, probably Horizon two, and suddenly boom, it's here, it's at scale. We're doing this across many other functional areas beyond software development. Thinking about, you know, customer service and finance and operations and human capital and starting to really scale that out. We launched something called Agent OS, which is our ability to work across all the agent frameworks in any cloud. You know, in a much simpler way so that agents can can work autonomously together. That's rapidly happened and I just think that's an example of how fast these horizons could move. When the innovation is understood and scaled.

• 31:42Speaker 01

  And that's, that's happening throughout almost every sector in every function.

• 31:47Geoff Nielson?

  And I've got to compliment you for a second on the branding of Agent OS because that's

• 31:50Speaker 01

  Yeah.

• 31:51Geoff Nielson?

  Beautiful, beautiful branding and right away you're like, oh, I get it. That's cool. Why doesn't that exist? How, like, how ready for primetime is that right now? Is this something that people are already implementing? And the legacy modernization piece, I don't know. Like to me, to me, it's quite interesting because it's such a gnarly wicked problem. Maybe some other people think it's boring, but are we starting to see people actually implement this? Or is it still talk right now?

• 32:16Speaker 01

  Now, so, so first, the agent OS is, is ready at scale. We're, we, so we, we take a kind of an approach that if we can drink our own champagne, use it ourselves, we're very complex organization. This is something we had to build out in necessity, to your point, it didn't exist. So thinking about how agents across frameworks can, can speak in a standard way, share memory, you know, all these things that didn't exist, we built that. But we are rolling that, we roll it out internally. We've been on this journey over the last eighteen months for our own AI chassis. Again, hundred countries at scale, has to be safe, secure, protected, confidential data, et cetera, et cetera. So, once we get through that pattern, then it's ready for our clients. And that, that is the point we're at. We're, we're working with many clients.

• 33:01Speaker 01

  This is something that we can, we can bring in, accelerate in a, in a week or two, have agents up and running and, and start to see business value. So that, that's ready. Legacy modernization, you know, obviously is, is a huge area of cost for clients. The industries that have been running on mainframes for a long time, or maybe they even transition off that. About a year, maybe a year and a half ago, I said, you know, I could see a world where we don't even have to move the system. With a world of agents, could we keep the system isolated? And if I think about a specific like life insurance where that policy runs for thirty, forty years and has to, you know, you have to trail those off. Could we just isolate it? Have an agent that's intelligent enough to speak to the old system and the new system? Could we build a new system?

• 33:45Speaker 01

  And not move the old system? Do we actually have to, to modernize it? And I think we're starting to see people look at different approaches. One is we're seeing right now on the legacy modernization, can we use AI to actually unravel what's there? You know, you're not going to find the original coders. It's maybe in a language, you know, of course, COBOL, but maybe INSEMBLY. Like, there's languages that are really tough to find experts. Well, AI can help us unravel that and create, you know, the, the, the new version of that. Maybe not even in code, maybe it just gives us the, the agile stories. And then from there, we go code it. But if we could do that in a day or two or a week, that's a huge difference from the old, the old approach.

• 34:26Speaker 01

  We're definitely seeing that. Let's go after those legacy systems. Can we extract what's there using AI and then build it new? That's one approach. I think another approach is can we leave it and can we create an agent that's intelligent enough to interact with the old system and the new system and and bridge that? And I think we'll start to see some of those patterns. That's not at scale, but that's one of my hypotheses around where agents can really play a strong role.

• 34:49Geoff Nielson?

  No, it's it's it's super, super exciting. And, you know, I'm sure, as you know, like, a lot of these legacy mainframe systems, like it's they're limiting the pace of innovation for organizations, right? It limits the speed of effectiveness. It it impacts what they can do. And so for for organizations who want to to go down this road and and maybe more broadly, organizations that are looking to implement agentic AI across their organization. What's the role you see corporate IT playing here? Do they need to be leading this? Is this happening around them? Is it replacing them? Like, how do they? How do they fit into the puzzle?

• 35:28Speaker 01

  Well, of course, we'd love to help those clients, but I think I think part of the challenge is corporate IT has to do this upskilling that, you know, of course, we've lived through. They have to adapt the pattern and and the the way they approach these problems. We can't just go after the the old maybe waterfall way or hybrid agile way where we're just creating a two and three year transition. I think they have to change that mindset and build those new muscles around how these tools can help accelerate that. And we have a tool called Code Intelligence that'll go in there and and extract all of the the way the code works and generate, you know, the the new stories. It'll generate all the documentation, create a little website. That that's something that the corporate IT has to kind of change the mindset. And some of that is the

• 36:10Speaker 01

  building those muscles around what Gen AI can do, what it can't do, you know, it it doesn't do everything. Where it can play a role and be more efficient and and helping their team actually have much more output.

• 36:20Geoff Nielson?

  Right.

• 36:20Speaker 01

  So I think that's the transition. It's more of this human problem. The technology is available. There's vendors like us that'll help you, but you have to change the mindset. I think it also affects budgeting and strategic planning. You know, if I see a five year plan on technology, I just shake my head and say. How do you know what's happening in five years with the pace of change today? Not that you shouldn't have a target vision, but the reality of technology is going to be very different every year versus maybe every five years before. So I think corporate IT has to adjust that pace that they're working at and the way they bucket these activities and just change the skill set as they go along.

• 36:59Geoff Nielson?

  So when you look at the IT organization of the future, you don't see it being replaced or displaced. You see a different skill set than we have today.

• 37:10Speaker 01

  I think, yeah, I see it adapting. I see, I'm already seeing a little bit of a change in the traditional organizational models in the sense of moving to much more, you know, you call it whatever you want, but pod structures of multidisciplinary technology and business together. So I think the org model changes a little bit and the skills have to change because you have to take advantage of AI, of course. But no, the role is there and that's, you know, we've been studying jobs. We'll publish our second version of the AI jobs barometer here, I think in May. And what it shows is that it's not taking jobs away, it's changing the tasks in those jobs and it's changing them faster than we thought, but it's, the jobs are there, they're

• 37:52Speaker 01

  just different and the people that are adapting and using AI, they're actually making more money. So there's a good story there and it's creating new jobs, of course, and you know, it's been accretive. So we haven't seen this drop off. There's some jobs that have gone away, but in general, it's just what those job titles do versus the job title going away. So I see that throughout IT and other areas of the business.

• 38:17Geoff Nielson?

  So to put a little bit of a spin on that, and I'm curious, first of all, if you'll agree with this or not, but you know, what I'm hearing and what I'm seeing firsthand is that, I mentioned this earlier, we have these capabilities now that seem to be in many ways far outpacing organizational abilities to adopt them. And I mean, just to say it bluntly, it seems like organizations are just not going fast enough to be able to embrace these technologies. And so I mean, I wanted to ask you like, what's, what's going wrong out there and what do organizations need to be doing differently? Or what do they need to be, like, what's the mindset piece? What do they need to be doing? What do they need to be thinking about, you know, to better get ahead here?

• 38:56Speaker 01

  I agree 100%. I struggle even with my own teams. I want to go faster and it's really a human problem. I say biology doesn't move as fast as technology. You know, we have to, it's a cultural change in an organization and each, each org is different. There has to be strong leadership from the top about the pace. And that means you're breaking the budget cycles, you're breaking the planning cycles, you're breaking strategic, you know, organizational cycles. That has to come from the top, but I'll tell you over the last two years, I've been in front of more board of directors and more C, C level across the board about this topic. So I think that's been solved. And I think at the, the, the lower levels and you know, people coming in out of college, they're, they're using AI. It's kind of this middle level that

• 39:41Geoff Nielson?

  Yeah.

• 39:42Speaker 01

  They look at it as, as a threat. And I, I think that's just the wrong way to look at it. If you lean in, you say this now gives me a superpower, it, no matter what I'm doing. If I can enable my teams to do more, it's not about getting rid of our people. It's about producing more products. It's about getting to more markets. It's about being more efficient than my competitor. That's all upside. So I think we have this weird problem where the top and the bottom really align that this is a good thing. And the middle is saying, whoa, whoa, this changes my world. I've been doing this for 10 years, 12 years. Yes, and it's going to change and we'd like you to change with it because it's a better outcome for everybody. So it's a human problem and I think, you know, how you communicate that, how you create the change management around that is really important.

• 40:27Speaker 01

  And I do feel like it's a little slow and we've seen some of our clients really just over the last year, change and go fast. Ironically, a lot in the regulated industries because I think they see so much opportunity and they've been really doubling down on the investment. But a lot of it's about workforce training and the communications around it, that this is, this is a good thing in, in, in general for everybody. And giving them the tools and skills now that the challenge is the training changes all the time. You know, it's, it's like, how many tools do you have? And, and as we, we roll out our own tools, we struggle with, how do we communicate this? It changed again. I just gotta say, we gotta tell them that that's the new world. It's gonna change every month. It's not something one and done. These tools are gonna get better as, as the AI gets better.

• 41:11Speaker 01

  And you gotta be curious and you've got to lean in and, and you gotta have that passion to change with it, but it's a, it's a tough problem.

• 41:19Geoff Nielson?

  You said something there that caught my attention, which is, you know, you alluded to, we give them the tools. You know, to what degree are you recommending that this is kind of centralized? And it's, you know, here, here's the tools. Like, I don't know, the image that came to mind was still like, and obviously this is very old fashioned, but, but, you know, Moses coming down from the mountain with like the tablets, like, by AI versus, you know, it being kind of grassroots, people led, employees at the bottom saying, these are the tools we found. This is what's enabling us. Is it one? Is it both?

• 41:52Speaker 01

  It is a bit of both, but I think in the world of Gen AI specifically, we have a unique opportunity. At the end of the day, one foundational model, you know, whichever one you use can serve the whole organization. So we have a, we have a point of entry to that technology versus the, you know, the previous world of technology. We had a lot of options. There was a lot of competition and anyone could install and use it and however they wanted within their business unit, machine learning, et cetera. Now we have one way that we have to make sure it's done responsibly. It's secure. It's private. So we can't let people use public tools because that means data could leak and it's, it's hard to understand. So there's, there's some business strategy around. We wanted to provide the best tools and we, we did.

• 42:37Speaker 01

  We've, we've provided cutting edge tools at, at pace, but we didn't want to have a hundred tools because then it's impossible to manage the, the risk. So it is somewhat of a centralized enablement. But that tool is so flexible that it's not constraining. It can help a tax accountant. It can help a consulting strategy person. It can help someone doing an audit in our business. Really that same tool is so generally intelligent that there's no constraints. But to us it was, you know, minimizing the risk, giving them enough innovation. And the, the bottoms up is about how I use it, not about which tool is better.

• 43:20Speaker 01

  If I'm this language model or this language model, don't worry about that. How are you going to change what you do every day? That's where we want the innovation. So we open that up. Want to build something custom using the standard architecture, go crazy and let's share that with with your colleagues. So we want that innovation around how to use it, but not necessarily this tool versus that tool. That's what my team can do. And we'll evaluate them in a very deep way against not only our business rules, but the innovation. And we feel like we've kept a good balance there. There's other strategies, but I do think you have to have some controls on it.

• 43:54Geoff Nielson?

  So you provide the sandbox, but they can build whatever they want.

• 43:58Speaker 01

  That's exactly right. That's exactly right. Yep.

• 44:01Geoff Nielson?

  Yeah, I love that. I imagine there is a tension there, though, for your organization or for others where if you're not there fast enough. You know, it's the same shadow IT problem now, you know, a shadow AI problem. If you're not there fast enough, you're you're kind of racing them as they come up with new use cases. And I don't know, maybe that's a good thing, or maybe it's a bad thing, but at least it's a challenge.

• 44:21Speaker 01

  Yeah, no, it is. It has pushed us to to be very fast. When new models are released, we have them same day or next day. Because if we wait four, six weeks, then we do get frustration. We do get people trying to find a way around it. So I think it's made us better and increased our pace of delivery to give them the best thing to then go innovate on. But you got to be in the sandbox. We've got to protect our own, you know, internal IP. We got to protect our clients data. We cannot take any shortcuts there. So while I want to go fast, I can't take any risk in certain areas of our business. It's it's it's just nonnegotiable. So it is a bit of a tension, but I think we still get great. You know, ground level innovation within the the boundaries, the guardrails.

• 45:05Speaker 01

  It's not a controlled environment. It's just guardrails, like stay with the knees, do whatever you want and work with us to scale it.

• 45:12Geoff Nielson?

  Got it. There's there's another word I wanted to unpack here, and this comes up all the time, by the way, which is tools, right? The the tools we're talking about.

• 45:21Geoff Nielson?

  Scott, you mentioned kind of broad, general applicability. Are we talking about enterprise versions of things like Copilot GPT? Are we talking about, you know, more industry and sector-specific tools, or maybe even, you know, business function-specific tools? What tools do you consider to be, you know, part of your toolkit here?

• 45:42Speaker 01

  So I'd say last year was mostly about those enterprise enablement. We've created ChatPWC, which is our custom, private version. It has access to OpenAI models, to Gemini models, to Claude models, if you're the right kind of level, Llama models and other open source models. So for us, that was, you have Copilot in Microsoft, of course, and other tools, where that's, to me, a general use. It's, you know, you're using it on a document or a meeting. ChatPWC was something we built to be general PWC use. We're embedding our data, our processes. We're building agents that do things specific to what we do as PWC, no matter where you're at in the business. That was valuable. And then you had these

• 46:25Speaker 01

  enterprise versions, which were secure, but they were still general. They don't have your data necessarily. So this year, it's moving much more into the world of agents, which is where you get more domain-specific. And, you know, there's probably some areas of fine-tuning or small models and certain domains, but in general, it's more agent-specific to a domain or a function within an industry. So there's very interesting data in this area. We want to create an agent that knows everything about that data. It's not more generally usable, but it's going to be very precise. So we are starting to see, and we're building dozens of those now, to say, this would be used in this specific instance,

• 47:07Speaker 01

  and it's going to be very good, and we'll continue to make it better. So don't use a general model, use a specific agent that was using a general model behind the scenes, but in a different way, so that we get the precision we need within areas of our business.

• 47:22Speaker 01

  And then tools, to your point, is a very broad term. In the world of agents, tools means something. I'm calling a tool, which is an API to go do something versus a large language model, which is doing the planning and reasoning around calling tools, which in effect are APIs to a piece of software or code or another model. So there, you know, it's a, it's an interesting term right now because of the way people are using it.

• 47:48Geoff Nielson?

  Got it. So, you know, to zoom out just a little bit, you know, as you talk to leaders who want to implement, whether it's, you know, a Gentik AI or any of these kind of Horizon One technologies, what's kind of your best advice for, you know, what they should be thinking about or what they should be doing to, you know, break the cycle of slow and actually get to success?

• 48:11Speaker 01

  So I always say waiting is not a good strategy in the world of AI right now. And last year, that was, there was a lot of wait to see which model will win. So that is, that is a bad strategy. So I say waiting is not a great, great starting point. So some experimentation, my big advice is the workforce investment you need to make. So I believe you're going to get benefits, no matter what large language model you use or what hyperskiler you use, you're going to, you're going to see opportunity. We're probably barely tapping the actual benefit available. It's all about the investment in that workforce. What are the skills you need? You know, I, I personally have changed the team to be AI engineers. It's a combination of data science and engineering, which typically we would have in different groups. Now I want everyone to have some data science and some full stack development.

• 48:55Speaker 01

  Because everything we're doing is, is kind of a combination. Well, how do you do that in your organization? You know, it could look different, but the skills you need to, to deliver at pace, to look at, you know, your funding and, and roadmapping models. How, how are you effectively managing this? And then that overall organizational design, you know, is there a central group of experts? Is it a hub and spoke? Couple of different models depending on, on the organization, but you have to start looking at that workforce transformation to enable the, the upside. Otherwise, you're just going to get. Individual efficiencies here and there, they're hard to capture. You're not going to see the big return on investment. The bottom line won't be impacted. You have to go in, pick a business that's, that's willing and has opportunity.

• 49:39Speaker 01

  And team them up with a new way to operate the technology transformation. And that's where you're just going to see explosion of, of, of return.

• 49:46Geoff Nielson?

  I agree with you. I think that, that, that's very much in line with what, with what I'm hearing and what, what I've seen in practice. The, the workforce transformation piece, just to kind of put a finer point on it. Is, is this largely about upskilling current staff? Is it about procuring, you know, external talent, either full time or, you know, working with vendors or suppliers? Or, you know, some combination of the above?

• 50:10Speaker 01

  As the ultimate optimist, I think it's a combination, right? I think there's plenty of great people that are, that want to be upskilled. They want to make this transition and you should, you know, go after them first. There's kind of the, I forget the percentages, but the zealots, they're all in. There's kind of the big middle that's got to be convinced. And then there's ones that are never going to change. So the ones that are never going to change, find different stuff for them to do. Convince the ones in the middle, use the zealots or the passionate to, to help them. I think outside talent always helps kind of be a catalyst to change. And with the, the speed of delivery, I think you need a little bit of that, whether it's a partner or vendor or, or hiring in some of this, this new mindset. We're seeing that quite a bit, um, working with very traditional companies who are, you know, in fact,

• 50:52Speaker 01

  one client said, can you help me write this so it sounds like a, a Google or an Amazon job rec? And, and it's a very traditional industry because they want to attract that, that different type of person. So I, I do think there's some kind of catalyst to bringing in the new mindset, but I think you have to focus on the core. Your, your, your, your team has been delivering something good or else you wouldn't be in business. And taking them through that transition makes so much sense, but it is, it's difficult. You have to plan for it, you have to invest in it. And you have to change things that maybe have existed for decades. And that, that's the tougher part, that cultural change.

• 51:27Geoff Nielson?

  Cool. That's, um, yeah, it's, it's, it always comes back to the human piece, right? As much as we talk about the technology to get it to work, it's the people, the people, the people.

• 51:39Geoff Nielson?

  Scott, I wanted to ask you a completely different question, and you may find this one a little bit challenging, given your kind of moniker as, you know, ultimate optimist. But I'm curious, in your mind, what technologies are overhyped right now? And when I say overhyped, you know, in your language, that may mean what are probably closer to Horizon 3s? Or, you know, deep Horizon 2s that are being marketed right now as kind of, you know, the next big thing?

• 52:07Speaker 01

  Ironically, AI agents, I think, are overhyped.

• 52:12Speaker 01

  Most, you know, to be blunt, and I'm not going to call it any specifics, but I think people are falling in the trap of creating agents like we did RPA, where they're creating agents in a brittle way that is just a step-by-step workflow. And the true power of agents is autonomy and action. And I, you know, I think that's a ways off. You know, the, that's challenging to trust an AI to be autonomous and take action. So are you going to let purely AI commit that transaction to close that piece of business? I think we're a ways off from that. And people are selling it as agents are the answer to everything. And I don't think they are right now. So I think that's one.

• 52:55Speaker 01

  I don't know. I don't know if any other are necessarily oversold at this point. Because we've seen the quantifiable, a lot of the AI stuff, I think, is pretty quantifiable. Some would say quantum's overhyped currently, but I don't know. Again, I see that the trend line on that is very different. So people used to think it's 20 years away, and now some would say it's two. And, you know, it's somewhere in the middle of there, but I don't think that's necessarily overhyped. But maybe under understood, misunderstood, in the sense of how to get there. Different, very, very different approaches.

• 53:32Geoff Nielson?

  Right. Do you have a, do you have a prediction for, you know, Q day or when we get to, you know, the need for post quantum cryptography?

• 53:41Speaker 01

  I don't. I think it's closer than people think. Depending on what you mean by Q day, I think the one we were really tracking aggressively is the post quantum cryptography.

• 53:52Geoff Nielson?

  Yeah.

• 53:53Speaker 01

  Post PQC in the sense of when, when quantum could be a real threat to, to encryption. And I think, you know, that was probably a 2035 date for many people. And I think that's moved in. I think that's moved in. I think more and more people are starting to say that has moved in. And maybe not necessarily just because of the, the Q day is there, but maybe quantum approaches to optimization could cause a new attack factor for cryptography. And we're starting to see some trend lines there. Could quantum inference help AI inferencing, which could maybe create another attack? So maybe there's different attack factors for cryptography that could move that, that post quantum cryptography day in. So we're, we're tracking that quite aggressively.

• 54:35Geoff Nielson?

  Yeah. So if I understand it, there's, there's just some convergences happening here that we can't quite parse out what the impact is going to be, but it feels like, it feels like they're starting to come together in a big way and take off.

• 54:48Speaker 01

  Well, and it's, it's hard because there's, there's much more nation state investment in quantum. And we're not going to ever really know what nation states are doing. They're not going to share that. So it's, it's hard, hard to judge based on just university and enterprise research.

• 55:03Geoff Nielson?

  I really appreciate it. This has been a really, this has been an awesome conversation. I feel like we covered a ton of ground, a lot of really interesting stuff. And it's nice to hear kind of, you know, what, what you guys are doing, you know, in your own group and with your customers, you know, you've got a really unique viewpoint on it.