What Companies Should Be Doing With AI and Agents Right Now
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Thank you.

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 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. I'm sure we're going to run up against time very quickly. But for background context, for people who aren't familiar, I gave a little bit of an intro to you before. But just tell us a little bit about your role at KPMG, the landscape or 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 Innovations.

And I've had a lot of different jobs at KPMG. I 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 is 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, uh,

have a lot of opportunity with this technology. 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 we are, as we say, we're heavily impacted by the opportunity, both potentially positively and potentially negatively. 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, this is great. So I think most of our conversation is going to 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 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

And 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 whatnot. And that just really wasn't something that we had seen on a like a little bit more sort of like something I experienced. You know, I always say we were giving access to supercomputers on people's desktops.

And I've been surprised, even though it's something that we're talking about right in the beginning, about how hard people change is. I've been surprised by the resistance to people picking that up and just immediately gravitating to using it. And so it seems so self-evident, and yet there's a resistance in there. And so it kind of leads you to that narrative around enterprise AI is adoption is really challenging, right? It's a 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 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

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 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 process.

and what have you and to work on things where we haven't built systems. So it's funny, there's a funny thing about like, well, you know, emerging in my workflow.

Yeah. So this actually, you segued 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 some of the biggest and most common challenges that you're made? Obviously, we're talking about this, you know, just the adoption itself, utilization. But is there anything beyond that, that you guys see or encounter frequently that, you know, is holding things back or just, you know, is a challenge that, you know, a lot of organizations are facing? Yeah.

Well, there's a long list of those things, right? I mean, I suppose you'd expect a consultant to say that anyway. But here's a couple that jump to the top, right? Is the technology moves so much faster than people are moving that anything that somebody believed about it, especially if 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 don't move as fast as the tech does. So there's one, that's one. Two,

So you inherit everything that's great and everything that was on your to-do list in these programs. This is enterprise transformation. It affects every part of the organization from the front office where you support customers all the way through 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 and 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 really valuable technology.

um, out of the box, but it gets a lot more valuable as you start to get your data into it and what have you. And, you know, how many, the understanding the data, data use, the availability access are most people are managing their data, managing, they have it, they have it, uh, reasonably well managed, but not available to the AI. Um, and then, you know, I think that, uh, um,

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. So there's just a mind shift change, Nathaniel, when you think about 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. Because one of the – I mean I was 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. There's super important issues that 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 a huge portion of people that were using AI weren't telling their companies about it. Yeah, right. And that's,

To me, my speculation, and they had some data analysis around this, 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 a big issue. I know you quoted our 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 is.

um, for our poll survey said that they, they know that the executives were the main users of AI in their organizations. And that's not, um, our take on it. Our take on it is, is, and all, you know, when we instrument the systems, the, the demographically, the more recent hires, the younger organ, you know, younger folks in the organization tend to be the ones that are actually, um,

the bigger users. So anyway, that's but maybe not talking about it, right? Even in organizations where you've said, I want you to do this, you have to keep hammering the message and hammering the message because schools are really struggling with this. AI is cheating and I know you quote Professor Ethan Mollick a lot talking about school can be better with AI in it, but

And so we but yet there's like kids are petrified to use AI in 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 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.

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.

Um, and where there were very few in the leader category, you know, ones who were moved past and maybe put a little definition around that. Like, so ones that have an actual strategy, they've, they've put leadership in place. They've, um, defined the program and its set of goals and outcomes that they're looking for. You know, I, I,

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 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 some type of foundation model access. Yeah.

Something that is, I would say a number of them beginning to move or look at the addition of co-pilots or co-pilot-like personal augmentation. And then the emergence of RAG solutions for some type of talk to your documents or we call them knowledge assistants that are serving a single purpose. That's table stakes.

What is, you know, the majority, what's beginning to happen now is what you saw, like a number of folks being to experiment with agents or agentic capability, um,

um 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 uh 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, 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 even the organizations that feel like they've done really hard work over the last two years to wrap their head around this, the co-pilot 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 survey that you guys put out last week. Yeah.

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 one. And so there's a moment now, and I think that this is a real opportunity for a lot of organizations that have perhaps felt behind to jump in with both feet to really start figuring this out. Before we move off this point, though, I just want to reflect something you say a lot, but we believe as well is,

You know, that idea of leaders and average and laggards, let's just use 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 –

Like that work you're going to 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 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 reestablished.

really thinking about rewiring the enterprise like this into a business model that 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? Um, 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, well, agents are going to, and I do believe this agents will actually be adopted at a faster rate and what have you. Um,

For the employees that aren't agent, that aren't, you know, for the work that they're doing or they're not working with agents, all this AI work will still have been, this generative AI will still be an incredibly useful thought partner and an incredibly useful part of 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 broad. Is 2025 the year of agents? How do you think about this? Okay. Yeah.

If what we were talking about with generative AI was still early innings, we're starting the game, right, on agents. Agents will be, I think agents will be the dominant topic for the next decade.

18 months there'll probably be a hype cycle around it where folks you know go through like it's way overhyped right now uh for what it's going to deliver this year in 25 would be my guess but it um will begin to be you know and then it'll go through that period um it's probably underhyped how big of a change is this really going to be in the enterprise would be my suspicion um 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. Um, 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. Um,

And I think 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 October timeframe, someone's going to start writing the agents were overblown article. Yeah, it's interesting, though. So 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 shift in buy versus build behavior. And so they found that in 23, 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 53, 47. 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 or 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, 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, the where, where they get. And so it may be that,

Just the deployments and the experiments are more modest. And it's just what they find when they dig in that they actually can't be as hyped as they had hoped for. Here's one of the reasons why I think I... Here's a reason why I entirely agree with that point. I know I just claimed the override, but there's so much hype that it's kind of inevitable that someone would 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 seems to be the dominant way that they're thinking about that 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 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.

I don't think people – because they haven't really had experience then 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 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 anyway, I think it'll be easier. The enterprise side will be easier. Yeah.

Those are the people that are those are the capabilities and materialize in the platforms. And then there's the you talked about, like the enterprise wide or the vertical agents that are going to materialize that 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 becoming in scale and especially where autonomy comes in. You know, so we're really focused this year on on.

on the enterprise agents as part of it, but also you need to be making your experiments in orchestration and the outside the system agents as well, because those will 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 guys were talking about Sierra AI, and there's a variety of those like that, that seem like they're providing pretty good insight into like where things are headed.

Yeah. Well, it'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, 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, is,

These startups are all basically deploying engineers, kind of Palantir style from a decade ago, where they're sitting inside companies doing this stuff. It's so different than point-and-click kind of software that we've had in the past, which I think is a reflection of just 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 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 we can't, 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 of just the magnitude of disruption, I don't know if you caught this, but in his CES keynote, NVIDIA CEO Jensen, he said that IT is the new HR department for agents. Which I thought was a fascinating way of, obviously a little bit provocative just to get people talking, but a really fascinating way to think about it.

Yeah.

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, you know, 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, which –

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.

Yeah, no, 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 because a very different proposition that's really valuable. I think about this with L&D as well. 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 better in the future. And so go off and do your thing. I think L&D is 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 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,

Um, it has the potential to be like everything in the organization becomes it. And at that point you've still got to organize it. Right? Like, so, so, um, I, so I think that, that, that if it becomes everything, then it says, well, but what does the, what is, what did we actually fundamentally do in HR? What do we fundamentally do in these operational tech anyway? So that's kind of how I was thinking. I was receiving that anyway.

Yeah, absolutely. So the other thing, one thing that we have noticed sort of along these lines is the analogy of agent as an employee is

even if it ends up being the wrong heuristic in the long run, is very useful for helping people think about it in a way that'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 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 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 we took a minority investment in a company called Emma.

And their CEO, Surajit, is really – 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 agent.

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 that need to be changed. 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 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 agree with the point you're making that that heuristic is useful as you start to think about like, how am I going to make progress here?

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For a limited time, this audience gets $1,000 off Vanta at vanta.com slash nlw. That's v-a-n-t-a dot com slash nlw for $1,000 off. If there is one thing that's clear about AI in 2025, it's that the agents are coming. Vertical agents by industry, horizontal agent platforms.

Agents per function. If you are running a large enterprise, you will be experimenting with agents next year. And given how new this is, all of us are going to be back in pilot mode.

That's why Superintelligent is offering a new product for the beginning of this year. It's an agent readiness and opportunity audit. Over the course of a couple quick weeks, we dig in with your team to understand what type of agents make sense for you to test, what type of infrastructure support you need to be ready, and to ultimately come away with a set of actionable recommendations that get you prepared to figure out how agents can transform your business.

If you are interested in the agent readiness and opportunity audit, reach out directly to me, nlw at bsuper.ai. Put the word agent in the subject line so I know what you're talking about. And let's have you be a leader in the most dynamic part of the AI market. Yeah, in terms of trying to help people maybe navigate so that they don't find at the end of this year that they're frustrated with how overhyped agents were.

What do you, what do you, do you have a perspective on either a, what type or category of agents are more ready for more or less ready for prime time and B what,

what, how people should be thinking about piloting or experimentation or testing? Is there a proper scope? Is there a, you know, a framework that you think about? Anything that would help people guide to, you know, doing the right, taking the right steps without trying to go too far too fast? Yeah, it's a really good question and probably takes a little while to unwind. But start with, I think agents benefit from

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, you know, enterprise platforms cover everything from the front to the middle to the back office. So I think those tend to be a little bit, 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 SaaS platform or what have you. I feel like it's a little more straightforward.

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 if you go back to our adoption conversation, it's going to materialize where the people already are. So I think that's part. 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 um

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 a like almost like a QA program.

agent that's sitting on top of drug prescription, you know, in the in the operating room or what have you and sees like, oh, I'm asking for this drug and like the amount and actually is like, you know, it's easy. 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 on some other things like that.

Just an example of something, or, you know, on a more mundane level, 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 potentially build an agent around if it's value-added. So I guess 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 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 sets of projects all on their own. At this stage...

And 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 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 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 more complex endeavors. But if you have that mindset, those task-oriented...

Um, uh, agents will eventually be, uh, scale. Those will be the ones that scale up to be more like sort of, um, enterprise automation things. And eventually we'll be, uh, needed together with orchestration to construct those synthetic in place, right? Like the synthetic, uh,

um, um, the 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, there's a famous saying, um,

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 mean, we, we,

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 yeah,

So I worry that everybody's got limited budgets. How much of my budget am I giving to different things? I've made exactly 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, I think 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 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...

The ease of ROI with agents is exactly why this is going to be such a challenge. 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 work at a task, they do it 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 a whole separate thing.

But then you get to more work. It's like, well, in 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 type of ROI to try to figure out because it involves some amount of replanning. Better work is the hardest because it's the most...

wish you... It's 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 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 how you would design a product, you chat with ChatGPT about it because as a thought partner, it's just going to make your work better. But how do you

I 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, uh, on the better work side, um,

I think that's one of the things, like in our survey, a lot of the survey respondents were talking about ROI, but they weren't necessarily measuring or achieving 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, 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, and I don't hear as much discussion of it, although I do think it's holding up some of the move, is people really understanding what the management framework – not of that HR thing we were talking about, like how do I manage it, but actually 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 orchestrated, but 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, um,

overemphasize that point. That is, that is, that is, and, and too, too often that topic is sort of slid to the back end backside right now. Yep. I think an extension of that, that I think about a lot is, is,

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 agents and how it relates to augmentation and how they plan to reinvest the gains that they get out of AI.

And I think that there are lots of organizations out there who right now, they got their organization together, know that that's something that 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 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 don't mean to interrupt you, but I've heard you talk about this before. Yeah.

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, so companies are bigger. There's more employees. There's more employed. I,

That's our narrative is this is going to open up and spark growth in both in the organizations and also the emergence of new companies and new types, many of which we haven't scratched the surface on in terms of even knowing what those things will be or what they will look like. And so 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, right?

We all had pre-AI a list of to-dos, a set 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. The 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 anything else that we should 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 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 articulating it 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 like, 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...

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, where you,

And, and, and I think those are business topics that need to be, uh, be raised up, but it's a movement, a transformation movement that needs to be led. That's, that's, we hadn't, we didn't really quite get that, but I do think there's something about the construction of the program, uh, that could be looked at. Sure. I 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 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 that

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 just 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 that

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

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 we see versions of that over and over and over again, even if it's composed a little bit differently in every 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 in this one unit and a little bit over here. It just tends to be slower. Yeah, absolutely. I mean, we keep saying is the persons that are going to do this are

are already really busy. And if you assess this as disruptive, transformative, we need to take enough off their plate so they can focus a lot of mindshare 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 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. 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. And it's just such an interesting time in business. So thanks for having me on as well. Cheers.

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