What Companies Should Be Doing With AI and Agents Right Now

Today on the AI Daily Brief, I am joined by Steve Chase, Vice Chair of Artificial Intelligence and Digital Innovation at kpmg. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. To join the conversation, follow the Discord link in our show notes. Hello friends. Excited to do an interview show today departing from our normal format. Steve Chase is someone I've gotten to know over the last year. He is deep in the AI space. His role is technically the vice chair of KPMG's artificial intelligence and Digital Innovation Innovation organization, which as they describe, involves leading a firm wide initiative to integrate AI into every aspect of the business. And what I've seen and what makes Steve have a particularly interesting and unique perspective is that not only is he thinking about how AI helps KPMG do their jobs better because of KPMG's role as a professional services company that works with clients who are also thinking about their own AI transformations. He almost gets to have a second perspective on the AI transformation as well that's informed by all those customers and conversations. Steve's background is on the consulting side as well, having previously led KPMG's US consulting practice. We talk a lot in this conversation about lessons learned from 2024, the state of play for big enterprises in AI, and of course quite a bit about agents. It's a great conversation. So without any further ado, let's dive in. All right, Steve, welcome to the AI Daily Brief. How are you doing?

I'm doing great. I'm excited to be here.

Yeah, happy 2025. Lots of great stuff to talk about. You know, I'm sure we're gonna run up against time very for, for background context, for people who aren't familiar. I gave, I gave a little bit of an intro to you before, but just, you know, tell us a little bit about your, your role at, @ KPMG. What, you know, kind of the landscape or, or how, how that helps you kind of understand the landscape as we dig into the.

Conversation. Yeah, so my title is the Vice Chair of AI and Digital Innovation and I've had a lot of different jobs at kpmg. Really love this particular one. At this particular moment. My job is to think about AI and other digital transformation or other digital capabilities that can lead to transformation both of our business and our clients business and help navigate that journey that we're going through to take advantage of these new innovations at scale and at.

Pace. Yeah. So one of the things that I think makes your perspective extra valuable here is it's Almost like a two for one. Because you're thinking about AI innovation transformation in the context both of this big large company that you're working with as well as the plethora of clients that that company has to go serve, which a little bit of a different stance than I think a lot of folks who just have one of those.

Perspectives. Yeah, I mean, listen, I think that it's partly because professional services firms are, you know, have a lot of opportunity with these technologies. So a lot of our clients are also interested in our journey. I wouldn't say they've always been interested in what we're doing internally, but they're really interested right now because, you know, we are, as we say, we're heavily, heavily impacted by the opportunity, both potentially positively and potentially negatively. We've certainly seen, we've certainly seen that. So, so then as others are thinking about framing their journey, they're very interested in how we're doing our journey as well. So that's just been, it's been very symbiotic in that.

Way. Yeah, that's great. So I think most of our conversations could be very forward looking, but we're still in January. I think it's a good chance to kind of broadly catch up a little bit. You know, overall, what were your biggest learnings around AI, whether it's in the context of the enterprise or just generative AI more broadly last year and you know, maybe, maybe to get refined. Is there anything that you thought about generative AI coming into 2024 that didn't play out the way that you had.

Imagined? I'll give you a market perspective. I actually thought there was going to be more regulatory response, some more court cases and other things that didn't happen last year. We were sort of expecting the emergence of some type of AI certifications or other things that, just more in the enterprise, you know, I'm going to be more in the enterprise space, but like enterprise would be asking for that or, or whatnot. And that just really, it wasn't something that we had seen on a, like a little bit more sort of like something I experienced. I, you know, I always say we were giving access to supercomputers on people's desktops. And I've been surprised, even though it's, it's something that we've been, we're talking about right in the beginning about how hard people change is. I've been surprised by the resistance to like people picking that up and just immediately gravitating to using it. And so, you know, it's like it seems so self evident and yet there's, there's a resistance in there and so kind of leads you to that narrative around enterprise AI is. Adoption is really challenging, right? It's really challenging.

Topic. Yeah, it's fascinating. I mean, you know, this is something that you and I have talked about in the past and obviously we think about a lot. I think that the, to the extent that there's a positive with it, I think that it's a great reminder that as fast as the technology is moving, there is a certain amount of human, social and organizational inertia that, that gives us a little bit of a chance to catch our breath and adjust. Yeah, that's the upside. The upside of what's challenging for a lot of folks who are trying to drive, you know, adoption a new opportunity with this.

Technology. Yeah, absolutely, absolutely. It's interesting also that the number one request you hear from users in our client work and what have you is it needs to show up where I am, like, like it needs to show up in my workflow. And it's funny because one of the things, probably the first presentation I gave on this topic was about how effective this was in sealing up gaps in between workflows. Right. Like it's just so useful as a thought partner and what have you and to work on things where we haven't built systems. So it's funny, it's like a funny thing about like, well, you know, emerging in my.

Workflow. Yeah. So this actually you segue great, perfectly into a set of questions that I had around kind of the state of AI adoption in.

General.

Yeah. You know, what are, what are some of the biggest and most common challenges that you remain? Obviously we're talking about this, this, you know, just the adoption itself, utilization. But, but is there anything beyond that, that, that you guys see or encounter fre that you know, is holding things back or just, you know, is, is a, is a challenge that, that you know, a lot of organizations are.

Facing. Well, there's a, so there's a long list of those things. Right. I mean, I suppose you'd expect a consultant to say, say that anyway, but here's a couple of, here's a couple that, that jump to the top, right, is the technology moves so much faster than people are moving that anything that somebody believed about it, you know, especially if they stuck, they, they, you know, they, they started out with it and then, oh no, it didn't really do that much. Well, six months later it's so much better, you know, keeping up with that and people well you know, they just, they don't, they just don't move as fast as the, as the tech does. So there's one, there's, that's one, two. You know, most you, you, you inherit everything that's great and everything that was on your to do list in these programs. Your enterprise transformation, right. It affects every part of the organization from the, from the front office where you support customers all the way to the back office where you do your accounting and whatnot and the operations in between. And so when you do enterprise transformation, it's everything you're good at, maybe you wish you were better at. And one of the things a lot of companies wish they were better at is data knowledge management, what have you. And AI is, is really valuable out of the box but it gets a lot more valuable as we start to get your data into it and what have you and how many then understanding the data data use, the availability access. Most people are managing their data managing, they have it reasonably well managed but not available to the AI. And then you know, I think that we're talking about a new function around responsible use that needs to be very flexible that helps create guardrails around what I'm going to do, why I'm going to do it, how I'm going to do it. I think that most organizations that for them is legal and risk but not opportunity and like you know, so there's just a mind shift change. Nathaniel, when you think about like, like how am I going to think about ethical security, like the whole range of these things and design it into the program that I'm doing so that, because one of the, I mean I'm just going to finish on one of the number one things. People just are not always sure what they're allowed to do even when you tell them I want you to work on this and you know, I think sometimes it's because of the mixed messaging that comes up in terms of the kind of training they get or what have you around. Data privacy and other things are super important issues that need to be, need to be netted through. Those are the things that need to like those are the guardrails that need to let you go faster, not speed bumps that try to slow you completely.

Down. Yeah, I think so. I completely agree. In fact I have long thought early last year there was that interesting Microsoft LinkedIn study that found huge portion of people that were using AI weren't telling their companies about.

It. Yeah.

Right. And to me my, my speculation and they had some, some data analysis around this. But but my thought was that probably a big portion of that was people who were exactly in the situation that you just described where they just weren't quite sure what they were and weren't allowed to do. And once you use these tools, boy, you do not want to go back to the old way that you did things. And I think a lot of people probably just, you know, they were trying to do it in good faith, but they weren't sure how to do it and they just didn't want to be told that they weren't allowed to. I think it would maybe made some progress on that front. But it's.

Still. I know you quoted the poll survey that we did like the last quarter. You know, you highlighted that point. I think you highlighted that point, which is that the perception of leadership of the people who were interviewed for our poll survey said that they, you know, that the executives were the main users of AI in their organizations. And that's not our take on it. Our take on it is and all, you know, when we instrument the systems, the demographically, the more recent hires, the younger organ, you know, younger folks in the organization tend to be the ones that are actually the bigger users. So anyway, that's, that's. But maybe not talking about it.

Right?

Yeah. Even in organizations where, where you, where you've said, I want you to do this, you have to keep like hammering the message and hammer in the message because, you know, schools are really struggling with this, like, well, AI is cheating. And I know you quote Professor Ethan Malik a lot talking about like, well, you know, school can be better with AI in it. But. And so we, but yet there's like, kids are petrified to use AI school. Right. And then we get our interns come to KPMG coming out of college, and they're like, they're not really prepared right now. They're coming. They're like, wait a second, I'm told that this is cheating at school and you're telling me we can't be successful if you don't use it. It's just an interesting, interesting dichotomy. It's just like in that, that dislocation that this period that we're in right.

Now. Yeah, it's a very, very liminal period. And those are historically the hardest for people to deal with. Those in.

Betweens.

Yeah. This gets me to another question, and there may be no real answer for this, but is there, you know, to little over two years now from the starting gun on Gen AI with, with the launch of Chad Beat, is there a Normal now is that, you know, the average place that an organization that you see is. Is there table stakes in terms of where people are in the client journey or is it still so broadly distributed that it's hard to, hard to pin that.

Down? Well, I think it's like with most innovation and change, we're beginning to separate into a group of leaders, maybe fast followers and laggards and where there were very few in the leader category, ones who were moved past and maybe put a little definition around that. So ones that have an actual strategy, they've put leadership in place, they've defined the program and its set of goals and outcomes that they're looking for and have moved into scaling. I think that. So I think there's starting to become some norms there. But I'm surprised in the same way that I am around adoption when you share with someone this supercomputer, I'm surprised by the number of companies saying yeah, we're not doing anything with that right now. And the reasons they give are often regulatory and future regulatory issues and stuff. And I really think with the EU AI act and some of the things around NIST and whatnot, I think we have enough clarity on what, what to do that you can't let that be an inhibitor to move forward. So table stakes for a normal organization, ubiquitous access to one or more AI capabilities in the form of, you know, some, some type of foundation model access. Something that is, you know, I would say a number of them beginning to move or look at the addition of co pilots or copilot like you know, personal augmentation and then the emergence of rag solutions for you know, some type of talk to your documents or now we call them knowledge assistants that are, that are serving a single purpose. That's what, that's, that's table stakes. What is, you know, the majority. What's beginning to happen now is. Well you saw like a number of folks beginning to experiment with agents or agentic capability is starting to get on the, you know, on the other end of that. But listen, we're so early in the table stakes part of it in terms like the amount of that, like is. Is there a sense that they've gotten to the end of it? No, it's like all very much the beginning. It's not ubiquitous across everywhere the. So that's my take on it. Nathaniel, I suspect that that's consistent with what I've heard you talking about as.

Well. Absolutely. Yeah, I think that's the case. Another way to frame it is I definitely think that there are starting to be leaders, average and sort of people who are a little bit behind. But I think we're very early such that that could shift very.

Quickly.

Yeah. Given. Given where the table stakes are, given how emergent things are. And I think that, you know, jumping ahead maybe to agents a little bit, which will probably be a big part of our conversation, agents are almost a great equalizing force on that again, because there's, you know, even the organizations that feel like they've done really hard work over the last two years to wrap their head around this, the copilot era, and build systems and start to think about their data, which, as you pointed out, I was thinking about, as you were talking, the, the survey that you guys put out last week, 85% of people said that data was their biggest issue. That's very clearly something that's on people's minds. But even those folks who are thinking at that level, they still, the vast majority of people have not even piloted an agent, to say nothing of.

Deploying.

Right. And so there's a moment now, and I think that this is a real opportunity for a lot of organization that have perhaps felt behind to jump in with both feet to, you know, really start figuring this.

Out. Before we move off this point, though, I just want to reflect something you say a lot, but that we believe as well is, you know, that idea of, of leaders and average and laggards. This is. We use that. That phrasing. There's a period of awareness and cultural change and training and training people to be different kinds of leaders and different kinds of, like that work you're gonna do at some point in your journey. And there's no. It's a no regrets move to get started on that asap. Right. I mean, that is clear. The other thing is, is this, get a mindset that this is transforming your business. You have to be thinking about your processes differently. And I think the last time we probably did work like that, maybe a lot of the leaders weren't even in the workforce. You know, it was like late 90s. We were last really thinking about rewiring the enterprise like this into a business small. It didn't exist. Most of the stuff after that, you know, built on that concept. But it took us, what, 10 years around that, you know, so. And we're trying to do that all much faster. So I just. There's some no regrets moves in here, improving data, getting the training and stuff rolling. Even if you believe agents, while agents are going to. And I do believe this agents will actually be adopted at a faster rate and what have you. For the employees that aren't agent, that aren't, you know, for the, for the work that they're doing where they're not working with agents, the. All this AI work will still have been this generative AI will still be an incredibly useful thought partner and incredibly useful part of that, that, that, that enablement of the workforce in the future as well. So no regrets moves.

There. Yeah, I mean I think that's a positive thing. Just to really double click on. There's very low downside to almost everything that organizations could be doing, trying, experimenting with right now relative to where the organization needs to be in five, 10.

Years.

Yep. Let's talk about agents. So is 2025. Let's start. Start broad. Is 2025 the year of agents? How, how do you think about.

This? Okay, if, if what we were talking about with generative AI was still early innings, we are starting the game right on Agents. Agents will be, I think it. Agents will be the dominant topic for the next 18 months. There'll probably be a hype cycle around it where folks go through like it's way overhyped right now for what it's going to deliver this year in 25 would be my guess. But it will begin to. And then it'll go through that period. It's probably underhyped. How big of a change is this really going to be in the enterprise? My suspicion, I think that this is going to be easier to adopt than other technologies in the, in the AI space. Like it's going to be easier to adopt in generative AI because it's going to materialize right in the workflows and or as a teammate that sits beside it. So anyway, yes, I think it's the year of the agent. I don't think it's the last year of the agent. And I think, you know, we'll definitely start to see those counterfactuals like reports, oh, agents were overblown. I suspect that'll be my prediction for 2025 is in the, in October time frame, someone's going to start writing the agents were overblown.

Article. Yeah, it's interesting though. So one of the, one of the more interesting stats that I saw towards the end of last year came from this Menlo Enterprise report. And it was about the, the shift in buy versus build behavior. And so they found that in 23, the, the organizations that they surveyed, it was 80 by 20 build in terms of the gen AI solutions they deployed. Where last year it was 5347, so almost half and half. So still slightly more buying than building. And I think that part of what it reflected was increase in confidence plus a, a recognition as they dove in that there were certain types of applications, you know, maybe vertical applications, applications that use their specific data that were specific to their, you know, their industry that just weren't available yet. I think it's going to boomerang and now we're seeing all of those sort of vertical solutions start to start to come.

On.

Yeah. And the thing that I wonder as relates to that question of, of overhyped, I think it's almost inevitable. I think it's probably hard to deny, but at the same time I do think that for a, for an inning zero type of situation, the enterprises that are going into this are a lot more sophisticated than they have been in the past at inning zero in terms of, you know, the, where they get. And so it may be that the, just the deployments and the experiments are more modest and it's just that they find when they dig in that they actually can't be as hyped as they, as they had hoped.

For. Here's one of the reasons why I think I, here's, here's a reason why I entirely agree with that point. I know I just claimed the overhype, but, but like is so much hype that it's, it's kind of inevitable that someone write that article. Right. But what's different here is the emergence of agents inside the enterprise platforms. We were always talking about 2025 was going to be the year AI began to materialize in the enterprise software and they've been working diligently to do that and that's now taken the form of agents. Right. So that's like, that seems to be the dominant way that they're thinking about that that'll, that'll materialize. And because of the investments others have made in cloud or SaaS based systems and what have you, it makes it a lot easier to adopt these things. Right. And so I think that'll be very compelling to IT departments. I know a lot of the clients we're talking to that's like this is, this part of AI is not complex for them to think about and understand. They get it, they get why it's going to matter. I don't think that everybody's fully understood like I don't think there's as much sent, like, I don't think people, because they haven't really had experience them yet this idea that they're going to be teammates and how they're going to get invoked and they're going to work and that's going to require training about like how you work with them and work effectively with them. There's a whole management layer that I think is interesting. I don't think that's actually going to happen in it. It's going to happen somewhere else in the organization. But, but anyway, I think it'll be easier. The enterprise side will be easier. Those are the people that are, those are the capabilities that materialize in the platforms. And then there's the, you talked about like the enterprise wides or the ver vertical agents that are going to materialize that work on the platforms. Right, like that work across the platforms. I think those will be later. Like, I don't, I don't think those will be coming in scale and especially where autonomy comes in, you know. So we're really focused this year on the enterprise agents as part of it. But also you need to be making your experiments in orchestration and, and the outside the system agents as well because those would be really useful too. And there's a lot of third parties that are offering these. You know, you guys. And I think in one of the last podcasts I listened to you, you guys were talking about crai and there's a variety of those like that, that, that seem like they're providing pretty good insight into like where things are.

Headed. Yeah. What's interesting too, one of the, one of the things that is fascinating about this generation of agent companies is I think they have an appreciation for, you know, one of the things that you said, which I also very strongly agree with, is underestimating in the long run just how different the, how disruptive, how much they're going to change the shape of organizations. You see a little bit of a hint of this in that the way that this software is being deployed inside companies is these, these, these startups are all basically deploying engineers kind of palantir style from a decade ago where they're sitting with, you know, inside companies doing this stuff. It's so different than, you know, point and click kind of software that we've had in the past, which I think, you know, is reflection of just how, how big the change might.

Be. Yeah, it's interesting. Right. And so how systems integrators or business consultants interface with that as well and like come into that ecosystem because if, if it's only that they can deploy, which seems to be, which has been part of the model in the beginning, than the scale which is it can't scale fast enough. Right. And so that's why we're having those conversations and talking about. And then of course, you know, we develop those same sort of capabilities in our own platforms, like in our tax platforms and what have you. And so trying to learn that lesson too about how we bring that effectively into those departments has been really.

Useful as well on the idea that of just the magnitude of disruption. I don't know if you caught this, but in his CES keynote, Nvidia CEO Jensen, he, he said that it is, it is the new HR department for agents. So that was a fascinating way of, obviously a little bit provocative just to get people talking, but a really fascinating way to think about agents as a new, a new portion of your workforce are these. You have digital workers now and you have human workers and you have a combination where they'll meet. And that just, you know, it's a totally new management structure on top of just a totally new technology.

Structure. Yeah. And I. Far be it for me to disagree with Jensen. All I would say is that it's not entirely obvious to me that it is the right landing zone for that. Right. I mean, in fact, we've been having a lot of conversations with HR departments that just like you manage contractors, employees and other labor. Why wouldn't the HR systems and HR processes actually be really well tuned to the agent space? Because I need to onboard them, I need to train them, I need to think about their performance, I need to give them performance reviews. I probably need some type of management technique over top of them that may be different. So you could argue that actually the HR systems themselves are well constructed to think about all the different things that I need to go do with agents. But I think it's going to be fascinating to watch how this works.

Right? Yeah. I think about this a lot and I do think one of the sneaky second order effects of AI and will be extended with agents are certain parts of the organization having even more significance attached to them because of this new set of skills they need to develop. So HR actually evolving the capability to manage agents and help individuals manage agents becomes a very different proposition. That's, that's really valuable. I think about this with L and D as well. You know, learning and development for organizations are all different, but some organizations kind of treat it as this. It's basically a.

Perk.

Right. It's a thing where they're investing in their people being, you know, better in the future and so go off and do your thing. I think L and D is up, you know, increasing in importance as these skills are now really mission critical to the organization in an immediate timescale. And I think that it's reflective just of the sort of broad management changes that, that these technologies are bringing with.

Them. So one of the things I was thinking when I was listening to that speech was if you extend that, it has the potential to be like everything in the whole organization becomes it. And at that point you've still got to organize it. Right. Like so, So I, so I think that, that, that if it becomes everything, then it says, well, but what does the. What is, what do we actually fundamentally do in H R? What do we fundamentally do in these operational. Anyway, so that's kind of how I was thinking I was receiving that.

Anyway. Yeah, absolutely. The other thing, one thing that we have noticed sort of along these lines is the analogy of agent as an employee, even if it ends up being the wrong heuristic in the long run, is very useful for helping people think about it in, in the, in a way that it's not just another piece of software. Right. So the idea of agent hiring, it's like, okay, well, what are the tasks that I would hire someone for? What are the, the, the, you know, what are the qualifications that I'm looking for in that person? What makes a good candidate versus a bad candidate? What is the infrastructure I need to put around them to help them be successful? These are all the questions that people need to be asking as it relates to agents. They're just, you know, in a totally different light than perhaps they've thought about them in the.

Past. Yeah. And in fact, I think I mentioned before that to you that, you know, we took a minority investment in a company called Emma and their CEO Surajit is really, he's. Before people were using the phrase agent in the way they are now. He was talking about synthetic employees. It's what we were really. It's what we were really interested in. Because that phase, when we get to that phase, the idea of a synthetic set of capabilities that can do a whole set of functions that look more like a job. That's a really interesting period we're getting to. There's a whole set of things need to be think. That's a real mind shift change. And I do think it's quite elucidating to think about it that way. And you know, there's another, another company we work with called Auditoria. They've been talking about teammates for a while and actually really changes your thinking between that and what a co pilot is intended to do, which is around an individual. Right. And so anyway, I just really, I agree with you. I suppose I get. I agree with the point you're making that that heuristic is, is useful as you start to think about like how am I going to make progress.

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Fast? Yeah, it's a really good question and probably takes a little while to unwind. But start with, I think agents benefit mentally from thinking about it a bit from like the way we have digital transformation, at least in this first iteration. So we talked about the emergence of AI inside enterprise platforms. I think that's enterprise platforms cover everything from the front to the middle to the back office. So I think those tend to be a little, I'm not going to say they're lower risk, but they seem more straightforward in terms of how that's going to happen, the deployment mechanism, because it's inside your plat, your SaaS platform or what have you, I feel like it's a little more straightforward. And then so, so that's a good place to be evaluating opportunity against technology decisions you've already made and business decisions you've already made. You're already using those things. So it's, it's, it's clear. You know, if you go back to our adoption conversation, it's going to materialize where the people already are. So I think that's, that's part of it. And then as you think into the future, it's going to completely change the interaction layer. And so I think starting to get your head around like the employee experience and what the interaction layer is going to look like. That's a really important piece of it as well. So those two points. And then when you get into operations, it's the biggest front office and operations are the biggest opportunity area. This is where you can really change the dimensionality of your business, right, in terms of how you serve your customers, how you build your product, what have you there. My take on it is the no regret move is starting to think through work groups. Like what work groups? Where is work done? And to the extent you've got a work group that does something or consistent set of individuals doing things regularly, then I think that's a really good place to be thinking about one of those works on the system, not in this doesn't emerge in the system necessarily. An example of that might be almost like a QA agent that's sitting on top of drug prescription in the operating room or what have you and sees like oh, I'm asking for this drug and the amount and actually is like it's not easy but it's already done where that would be looking to say hey, is that the right amount? But actually you know, doing the research on the drug with the, you know, in the follow ons and other things like that, just an example of something or you know, on a more mundane level, I've got a retail, I got, I've got a retail brand that's looking at like store performance and they've got a store performance workout thing that they can do. There's a variety of different capabilities that's kind of a good thing to build an agent, potentially build an agent around if it's value added. So I guess I'm, I'm suggesting to you early is going to be in these enterprise work groups a lot of pilots in the operations especially where there's a concentration of similar workers doing.

Something. Yeah, I think this reflects sort of how we're thinking about it and what we're seeing as well the, the big opportunity that I think has people so excited is agents actually being able to do complex, you know, multi tiered workflows and you know, big, big sets of, of projects all on their own at this stage. It's sort of, it's fairly close to an automation process around specific tasks where I think a lot of the value is going to be realized in the short term and it still gives you that sense of, or that the ability to start testing it. So we think there's going to be a lot of pilots around financial analysis because so much of the, that analysis is like the same type of analysis just with different data sets inputted, you know, over and over again. Similar with, with build.

Processes.

Right. You can, you know, segment very specific parts of the developer process that get repeated over and over again. You know, there will be again, if you, if you kind of look at how, where entrepreneurs are with what they're trying to build with agents, it's very clear that they're still trying to solve a lot of the problems that are going to be required before we get to things like multi agent workflows and you know, more, more complex.

Endeavors. But if you have that mindset those, those task oriented agents will eventually be scale, those will be the ones that scale up to be more like sort of enterprise automation. Things and eventually will be knitted together with orchestration to construct those synthetic in place. Right. Like the synthetic, those synthetic employees will actually be made up of those other investments likely. Right. Orchestrating across them with goals and objectives that are much more complex. I think there's a famous saying that all complex systems started. All effective complex systems started out as simple systems. Right. That that's the way that they. And then as they begin to be put together, that's how you construct a complex system. It's really difficult to build a complex system out of the box. I think that's the right way to think about.

Agents. Do you think that the excitement slash hype around agents runs the risk of sucking the oxygen out of the room for high value use cases or opportunities for gen AI that could get kind of shunted to the side as people go after the shiny new.

Thing? I 100%, I believe that that is not only a risk. I think it's happening because. Because there's a burgeoning question around roi. Right? What is the roi? What's the value? I've gotten this, this is a pretty direct path to near term. It's a more direct path towards near term roi, I think in it. And so I worry that, you know, everybody's got limited budgets. How much of my budget am I giving to different things. I've made exactly the same, the same point both internally and with clients is agents will be absolutely something that we're going to be great at both in the use, deployment, what have you of them and integrating them into the work that we do everywhere we do it and for the things that we don't do agents with. So the augmentation, the creative partner, the individual AI that you need to be great at and I need to be great at using that. We're going to continue to invest in that too. So. But I do think there's a real risk that people would say well we'll get to that later and actually that that is there. We, you know, if a process isn't digitized, it's going to, it's. It's hard to think about the, you know, then, then you've got work to do and it's going to take longer to get agents in there. And yet I can use generative AI today on that immediately if I'm good, you know, if I think if I have that mindset. But we need to have people trained to have that.

Mindset. Yeah, I completely agree. I think one of the big questions over the next half decade to decade is going to be what, what is human AI enabled work? What is agent work? And where do they blend and hybridize? And it's going to take a lot of experiments to figure that out. I also think that the ROI is, is the ease of ROI with agents is exactly why this is sort of going to be such a, such a challenge. You know, if you think about roi, this is obviously radically reductive, but it's either cheaper work, more work or better work. Well, agents, the promise is cheaper work, right? If they, if they work at a task, they do it, you know, for, for less than the equivalent human labor. And that's just a very clear starting point. Now of course that doesn't answer the question of how you reinvest that time and that's, that's a whole separate thing. But then you get to, you know, more work. It's like, well, many organizations, more work would be great, but they're not even sure how to handle more work yet because that involves thinking about reorganization. And so that's sort of a more complicated, you know, type of ROI to try to figure out because it involves some amount of, you know, replanning. Better work is the hardest because it's the most wishy, is the hardest to kind of wrap your heads around. But so much of generative AI right now, the output is better work, right? We find this all the time. The amount of the use case of brainstorming where, you know, every time you're thinking through a new marketing campaign, every time you're thinking through a new slogan, every time you're thinking through, you know, how you would design a product, you chat with, chat GPT about it because it's, it's as a thought partner, it's just going to make your work better. But how do you turn that into roi? That's clear. You know it if you experience it. But it's on an organization scale that has to think on an organization scale, it's a lot.

Harder. I just say this on the better work side. I think that's one of the things like in our survey, you know, a lot of the survey respondents were talking about roi, but they weren't necessarily measuring or achieving that, that sort of roi. And I think they were talking about productivity enhancements that drive to the bottom line. I think in a lot of the work that we're doing, we're seeing, we were absolutely seeing in better work quality improvements, materially measurable quality improvements. Quality has an incredible midterm effect on businesses obviously. And then pace speed, right? The ability to turn around things more quickly. And also we talked about this before, but the capacity of your best workers goes up. One thing we didn't talk about on agents, I just wanted to reflect on because it's a no regrets move. It's a really important topic topic and I don't hear as much discussion of it, although I do think it's holding up. Some of the, some of the move is people really understanding what the management framework. Not of that HR thing we were talking about. Like how am I. But, but actually like what is my ethical and responsible use guideline around it? I do think that's going to be a bigger issue as we get to more autonomy. Right. And more orchestrate. But like, like and synthetic employee. But, but that is a investment that should be made right now. Right. Like that is like you cannot, I cannot overemphasize that point. That is, that is, that is. And too, too often that topic is sort of slid to the back end backside right now.

Yeah. I think an extension of that that I think about a lot is there are at some point management is going to have to in every organization have some working approach to how they think about the replacement of human labor with, with agents and how it relates to augmentation and how they plan to reinvest the gains that they get out of, out of AI. And I think that there are, there are lots of organizations out there who right now, if they got their, their organization together, know that that's something that they want to, they want to reinvest in because they want to go from the fourth biggest in their category to the first biggest and that's what they're going for. They're not just trying to do things cheaper, you know, or faster. They want to grow. Boy, does it benefit them by articulating that to employees who are very nervous about what this is going to mean for them. You know, part of the, if we think the adoption issue with assistance is hard, getting people to sort of sit next to, you know, workers who could do a bunch of stuff that they did before is going to be even harder. Unless, you know, there's, there's a really strong clear communication around what the, what the long term looks like for.

Them. Now I've heard you talk about this before. I didn't mean to interrupt you, but I've heard you talk about this before and I'm on record as having this opinion. It's going to be dislocating in the short run. Like. And there's a lot of change management work and other things that need to be done. But every technological revolution of this type has led to an explosion of job growth. Right. So companies are bigger, there's more employees, there's more employed. That's our narrative is this is going to open up and spark growth both in the organizations and also the emergence of new companies and new new types, many of which we, you know, we haven't scratched the surface on in terms of not even knowing what that, what those things will be or what they will look like. And, and so, so anyways, that's, that's, that's our narrative around it. And I think it's kind of consistent with your point about you want to take share, you want to grow. We all have, we all had pre AI a list of to dos, a set of, of enterprise imperatives that were way too long to get to. If we can begin to get through those. What's the reinvest? This is what I tell senior executive teams and they reinvest is going to be into customers, sales support, product.

Right. I mean I think that that's a pretty positive note to start to wrap up on. Is there any anything else that we should make sure, make sure to hit before we get out of here, Things that you're thinking about heading into next.

Year? Well, I mean you and I talk a lot about, talked a lot about adoption here. One of the things that I have seen, I don't know whether you've seen it is even for some of the companies that would say that this is absolutely going to be transformative and it's going to be something that they're going to be making large investments in. Often have not articulated this as a business transformation, but they're, they're articulated like a technology program. And so As a consequence CIOs have a tendency to be leading a lot of those programs. And CIOs are really busy people and they have incredible skill sets and they're some of the best business leaders in the business. But one of the things that I would suggest is a person, regardless of where they come from, who can move the organization, be identified to really run this program because this is touching every part. Enterprise transformation tends to be best when there's a clear guidance and federated out through the organization, but coalesced too many places we're seeing the POCs aren't moving forward but they're stuck inside a bunch of different places. They've sort of limited impact in that place but who's drawing it forward and, and being able to pull all those pieces together. I think agents will Actually make it like it will drive us more towards, oh, this is a technology thing. And I don't, I think we've well covered the fact that that's actually not what we're talking about. We're talking about a rewrite of how business occurs, a rethink of your ecosystem to deliver the services both in the, you know, like where the digital labor is going to come from, how you build it yourself, how you work. And, and I think those are business topics that need to be be raised up. But it's a movement, a transformation movement that needs to be led. That's, that's when we didn't really quite hit that. But I do think there's something about the construction of the program that could be looked.

At. Sure. Kind of building on that. So one of the things I get asked by investors is, you know, it's a very standard question for young startups. What's your ideal customer profile? Right. And usually that question is asked in terms of either one, what's the business unit? Or two, is there a particular industry that's, that's the right customer? But what we find ourselves kind of identifying, given that this transformation is so wide, it covers every industry, every type of business unit is for us what is ideal is a certain composition or makeup in the leadership around these questions. And there's two characteristics that we find pretty consistently of organizations that are a good fit for, for just, you know, who we think are ahead right. In that leaders category that's emerging when it comes to AI transformation. The two things are, one, they have some body like set of bodies, a group that is focused on this transformation who is explicitly mandated to know how it relates to.

Everyone.

Right. They don't necessarily have to have huge budget, but they have to have connection to every single. They need to know what legal thinks of it. They need to know what all the business unit heads think of it. They need to be that sort of that coordination layer across everyone. And the second piece is that that organization has to have direct senior C level leadership, you know, that goes right to the top of that so that they are, you know, whatever they don't have in terms of ability to move themselves. If they're just more of a coordination body, there's a direct path to actual kind of business level change and that we, we see versions of that over and over and over again. Even if it's composed a little bit differently organization. But the groups that have, that are thriving, the groups where we talk to where it's, you know, balkanized across, you know, there's a little bit of an innovation person over here and this one unit and a little bit over here. It just tends to be.

Slower. Yeah, absolutely. I mean we keep saying is is the persons that are going to do this are already really busy and you need you if you assess this is disruptive, transformative. We need to take enough off their plate so they can focus a lot of mind share on one of the most interesting, biggest problems that companies are going to face. We're again, we're early in this, so it's not like it's a temporary program. It's going to be going on for a while. It needs to be well franchised. You need to build this muscle memory. And you know, my version of your first part of that group is you need someone. You need some someones who are going to lead a movement in the organization. Right. Lead a movement because everyone needs to play a part and you need a group that is orchestrating.

It. Mindset shifts the historically the easiest thing for big companies to deal.

With. Yeah.

Right. Steve, awesome to have you on the show. Great, great conversation. I think a lot of, I mean somehow 2025 is poised to be even more exciting than 2024 and 2023 were with AI so great to have you here and looking forward to talking.

More. We're blessed to be doing this right now. It's just such an interesting time in, in business. So thanks for having me on as well.