Kevin Kerner: 0:00Hey everyone, this is Kevin Kerner with Tech Marketing Rewired. Today's episode is a great one. I sat down with Chris O'Neill, CEO of GrowthLoop, someone who's led at places like Google, evernote and is now helping reshape how marketers actually get work done. We got into Chris's idea of compound marketing and what it really means to have AI agents as teammates, and why Chris thinks we're headed into a results-as-a-service world, not just software. We, chris, thinks we're headed into a results as a service world, not just software Talked about speed, smarter systems and, honestly, how to take most of the pain out of modern marketing while leaving time for all the creativity. And if you've ever been stuck in cross-channel chaos, you'll definitely want to hear how Growth Loop is solving for that. Let's get into it. This is Tech Marketing Rewired. All right, hello everyone, and welcome back to Tech Marketing Rewired.

‍Kevin Kerner: 0:49

I'm your host, kevin Kerner, and today I'm very excited to have Chris O'Neill, who's the CEO of Growth Loop. For those of you that don't know Chris, he has a really incredible career. Up to this point, he's been managing director at Google Canada and Google X. He served as the CEO and chairman of Evernote and Glean. I loved Evernote, by the way. I love it. Thank you. It was one of my first note-taking tools and is currently a board member at Gap. So, chris, I am super excited to have you here on the podcast. Yeah, it's great to be with you. Yeah, yeah, it's going to really be amazing to talk about this new category of eugenic marketing and what you're building at Growth Loop. One fun fact that I'm not sure everyone knows is that Mookie Betts is on your board, which is amazing to me.

Chris O'Neill: 1:33

He is he is? He's an amazing, obviously athlete and human, very curious and really wants to learn more about business, and we're fortunate to have.

Kevin Kerner: 1:48

Yeah, that's great, I think, having that different perspective. Now I'm a Guardians fan and the Dodgers just killed the Guardians in this life series, so tell him to take it easy on us this year, which I'm sure he won't.

Chris O'Neill: 1:57

He will not.

Kevin Kerner: 1:58

They are very competitive. Okay, so there are a lot of things I want to dig into, but let's start with just this amazing transformation that's going on right now and the impact for leaders. You've seen all this waves of innovation across some really impressive companies, and you have an amazing co-founder in David, and then Tamim, the CTO, who's doing some incredible work. I'm curious, from your perspective, what feels fundamentally different about building a company right now, from a CEO perspective, about building a company right now from a CEO perspective.

Chris O'Neill: 2:24

Yeah well, I've been around long enough to have been through lots of different pretty big disruptions or changes from the shift to mobile, obviously, the advent of the cloud and this one certainly has elements of disruption like that in terms of how it means the interaction between consumers or customers and how they consume information. That's all true. The big shift here is really on the supply side, in other words, it's how we create, in this case, marketing, but really how we go about the work of work. Ai is fundamentally changing that. So it's really, I think, the biggest thing that's happened in my lifetime. And then the pace with which it's happening is unprecedented. I mean, it literally feels like every week there's a new model, a new protocol and it's unlocking a whole host of use cases we could never even imagine. So it's just so incredible and I know there's a lot of hype and I do think that it is well-deserved and in many ways we'll see, the impact is probably greater than we can imagine right now.

Kevin Kerner: 3:29

Yeah, it really is incredible. Yeah, mcp, microsoft's new NL web announcement. It's just, it's just really happening so fast. The models are getting so much faster, quicker. Well, now clicking into the growth loop thing what was the catalyst that that led you to become interested in this more operational role inside of growth loop?

Chris O'Neill: 3:50

Yeah, I've been inside companies, I've been an investor, I've dealt with every tool over the years, and the sad reality is marketing is just too slow, too manual. It doesn't really allow to scale up to the opportunity potential to deliver delightful experiences consistently to consumers. That's always bothered me. It's bothered me for over two decades I mean going back to my time at Google. We were obviously solving a part of that, but the large and sad reality is most experiences that customers have are very impersonal, they're very suboptimal, and that doesn't need to be that way.

Chris O'Neill: 4:26

So this is about, really, how can you use technology to serve up fundamentally different loyalty experiences, lifecycle interactions with customers? And that's really what it's about is how do you apply agentic AI in this case, on top of what's called first party data, right To basically drive growth faster and more relevant for consumers? And we'll talk about this concept of compound marketing because, really, that the missing gap is not only that you're driving gains, but you're doing so faster, right? So this concept of compounding is one I'm personally obsessed with and I'm happy to dive into it a little bit more with you.

Kevin Kerner: 5:03

Yeah, I would love to. Yeah, it's one of the reasons I have not as much hair anymore is because how hard marketing and marketing has been for the last 30 years yeah.

Kevin Kerner: 5:12

So I'm, I'm, I'm very excited about where you're headed, because it won't grow back, but maybe it'll make things easier. Um, let's hope, yeah, yeah. So, um, you've been using you mentioned this term, compound marketing, and I've seen the demo and I can't seem to sort of shake how impactful this could be. Let's click into that a bit and explain from your perspective what this new category of compound marketing is and how it's different than today's marketing.

Chris O'Neill: 5:39

Let me zoom out for just a second. I just have a fundamental belief that all great companies create categories. Right. It's not just about saying, hey, we're like this person or this company, but we're better. It has to be about difference. It's to say like we're fundamentally approaching the world in a different way. Think of all great companies, companies like Qualtrics or Google. Evernote, in its day right, set out to create a new category. It's just fundamentally different. We're solving a different problem in a different way. So that's the framing abroad and that really has to do with how you drive growth and how you talk about benefits.

Chris O'Neill: 6:13

When we looked across the market and it's super crowded, as we know tens of thousands of different tools and companies chasing this in this broader, you know, marketing technology landscape. So you have to cut, cut through the noise. So it starts with that. The other thing I'll add is I've been obsessed with compound interest in investing since a very young age. I remember parents I told this story recently and I grew up in a very small town in the snow belt of Canada and I was given this book called the Wealthy Barber and it talks about this metaphorical wealthy barber named Sam who basically lives a very simple life, but followed a formula that really involved compound interest and doing small things over time to build some substantial wealth, not just in a quick way, but overnight, slowly and surely. And that's the power of compounding the eighth wonder of the world overnight, slowly and surely, and that's the power of compounding, you know, the eighth wonder of the world. So it got us to thinking how do you apply that concept that's very familiar in investing to marketing? So the difference between, you know, a 1% gain every month versus a 2% gain every week isn't just a little bit, it's actually 13, 14, 15x, right. So we're here to basically help marketers apply AI to their data to move quickly, more quickly, through the entire cycle.

Chris O'Neill: 7:31

Right From figuring out, I have an idea that's going to move churn or reduce churn or perhaps boost lifetime value. How do you take it from that idea to actual impact? We're about compressing that impact. So, idea to impact cycle through marketing who do we talk to? What do we talk to them about? When do we talk to them? And then how do we measure it all? And then lather, rinse and repeat by basically figuring out what worked against our objective function of lifetime value or reducing churn or whatever it happens to be and just continuously doing that, and that's the name of our company, growth Loop, but it really is leveraging the power of compounding which, again I mentioned, is considered the eighth wonder of the world. So we're thinking and borrowing phrases and concepts from other parts of the world and applying them to marketing.

Kevin Kerner: 8:20

Yeah, fascinating. Yeah, we talk a lot about speed to outcome and so when I heard the compound term, I was like I linked directly to that. It's like how do you get to that outcome faster? It's like moving that outcome from here down to here and there's a bunch of little steps to get to that outcome and so if you can compress that, you just get there faster. What were the act like? Like seems like solving this, solving this compound marketing thing must have had several activators. Like what did you work on to make sure that that you could begin to realize this vision of compound marketing? There's a bunch of different parts of it. Like what are they?

Chris O'Neill: 8:58

Yeah, yeah, it really all starts with data. It really all starts with data and one of the things that is also happening in the background is not by accident or coincidence is the improvement of the quality of data and the semantic layer of data. So the rise of data clouds is really a big part of what's going on. So for too long data has been siloed off so it's very difficult to kind of stitch it together and then access it. So the democratization of data through the rise of things like Snowflake and Databricks and Google BigQuery and AWS, et cetera, that's a really big part of it. So it really starts there. It has to be the quality of your data and your data strategy and the guardrails you put around. It is where it has to start.

Chris O'Neill: 9:40

Ai is not very useful, machine learning not very useful if you're doing so with poor data. So it starts there and then it really we started to experiment with not just removing friction from very manual processes, we're sort of automating the end-to-end marketing cycle. So that has to do with self-serving for marketers, right? Historically it means I have an idea I got to go talk to a data team to write some SQL and pull a query and then you bounce back and forth to see is this too big, too small? Do we have the right audience as a starting point to at least have the conversation, make an offer that would help boost loyalty, or whatever it happens to be?

Chris O'Neill: 10:17

Now, first step was to remove and automate the entire end-to-end process, so that we've done that and that's really well-received, and that's where we got our start. Now it's about infusing AI at every one of those steps and having a supervising agent to basically make sure that these swarms of agents are actually working together in unison, consistent with the guardrails, consistent with governance practices, so it doesn't return garbage. So, essentially, we have this mindset of an end to end process. We're trying to automate. Now AI allows us to do each step of the way better in some meaningful way, and then, in the fullness of time, we see, with humans in the loop, an end to end process. So that's really what this is about, and we're really delighted with the early results.

Chris O'Neill: 11:02

I want to be equally clear, though like we have a long way to go. These models are incredible and they're advancing at an unprecedented rate. The slope of the improvements is astoundingly great and there's way more work to do to basically boost the quality of each one of the steps, whether it's the audience agent we have, or our journeys agent, or, uh what. There's literally a dozen plus of them that we're experimenting with right now. Uh, and we're, we're never gonna, we're not gonna rest until the quality continues to to exceed anything even imaginable. Um, with humans doing the same task.

Kevin Kerner: 11:37

Yeah it's the real unlock for me is the fact that you know the agents talking to working together and not being singular agents that are working. They are working on their point functions, but the fact that they're able to talk together, work together, is really a lot for me. Getting siloed marketing data between channels and analyzing it is one of the biggest pains. It's the biggest pain in the butt ever. You're in Google, you're in social, you're in some other channels. You get those, those data, typically separately and then you have to analyze it yourself. Are you? If you're, you know, maybe you're smart enough to put it into some sort of llm and have it combine it, but you're doing that pretty much manually and you're saying what you figured out as a way to, um, have those uh channels analyzed in a consolidated fashion using these agents.

Chris O'Neill: 12:27

Yeah, that's right, and it's actually upstream of that.

Chris O'Neill: 12:30

It's actually activating across all the channels, so once you have ideas and you have specific offers and specific things you want to do across your channels right now. Not only do you have to stitch together all that stuff upstream, to go and activate it requires separate discrete efforts and manual efforts, separate discrete efforts and manual efforts. We have abstracted that away where you have a single place to activate and then a single place to pull back and read back into the data cloud the results. These are the little things that get in the way, right. So it's about automating the workflow and this is the work of the work and why I think this is so powerful. So this application layer, ai, application layer things are so powerful because the real world is messy, right, and the insight and the task-specific algorithms you know, the step-by-step algorithms that stitch together, are going to create and are creating real value right now. So it's not just like any one of the steps require manual stitching together, it's kind of all of them. So that's where these agents come into play and really I think of them as like the glue, the glue guy or gal on a team. They're really, they're really stitching, doing the work to stitch and pull things together. So it's like having a thousand different interns going off and doing things you know again with, with supervision and bringing it back in the loop at the right time.

Chris O'Neill: 13:51

But really, that's what it's about is abstracting away modern day TPS reports. For those office space fans, right, it's like there's so much mundane, soul sucking work that really can go away. That's where agents will shine and that will allow humans to unleash their creativity, their potential, by basically focusing on the things that that humans can uniquely do. So, yes, jobs will be fundamentally transformed. Some jobs will go away, but many others will be recreated in a much more hopefully satisfying and way that's simpatico with the human condition and what humans are great at. So that's how I see this unfolding and, of course, no one fully knows, but I do see the opportunity for humans to interact with literally thousands of these agents in a very simpatico way, uh, in a symbiotic way, um, to really deliver real results that you can quantify yeah, I totally agree.

Kevin Kerner: 14:46

It's the 80 is 80, 20 rule. Like% of the work is just mundane work and, as someone that's been doing this for, you know, 30 years, there's just a lot of work that seems like it should be able to be done by the agents. It'd be interesting to see if there's a new version of office space out there that will be for the AI.

Chris O'Neill: 15:05

Some other DPS report. Well the agents are hard at work at it already, kevin, that's right.

Kevin Kerner: 15:11

Did you see VO3 last week? Yes, I did.

Chris O'Neill: 15:14

How mind-blowingly cool is that. I mean holy cow 8Z.

Kevin Kerner: 15:22

Yeah, I was playing it with my kids and they were going nuts and I have one kid that's in more art school and she did not like it at all. She was like no, this is not good. Although I agree with you, I think I do think that the like I do think that I mean I've been through these technology transformations before and there's more work to be done. As a marketer, there's it, and the work that will you'll be able to do, that you'll need to do, will be a different category of work. It'll give you more time to be thinking about the things that need to be done and sort of running at a whole different level with these assistants.

Chris O'Neill: 15:52

Yeah, I think it's going to unlock more creativity. I might be in the minority here, but I actually think if you have more tools in the hands of creators, designers, I think it's going to unlock creativity. So the mundane goes away and then it unlocks more time and energy. It's going to be massive, massive change around the change for people to absorb, let's be clear. But I'm of the conviction and the belief that it's going to unlock creativity and we're just, you know, on the verge of seeing that. But it will be bumpy for sure.

Kevin Kerner: 16:23

Yes, I agree. I have one quick question I'm just curious about from the CDP side of things Is your, do you see an opportunity for the agents to create data new fields inside of the CDP or begin their own? Are you doing that now?

Chris O'Neill: 16:40

We are. We're experimenting. The way we're experimenting with that is like synthetic data, right? So what we'll do is we'll create a synthetic like a synthetic profile human and it's remarkable how well and robust you can make a synthetic profile and then you simulate the interactions with that. So why does that matter to a marketing Like? Is it the same as a human? No, it's not, but it's really. It actually allows you to draw conclusions with a simulated behavior, which is generally we're finding remarkably powerful and accurate. You don't have to go spend a bunch of money on marketing to test a hypothesis using A-B testing in this case, right, you can do a lot of that in a laboratory. We're leaning into doing that. It's one of the most exciting things we're doing, frankly, is just like this simulation of human behavior. Uh, without having to go through all the steps and spend lots of time, energy and money to figure out what the response was, you can simulate it beforehand, which is super exciting that is amazing, absolutely amazing.

Kevin Kerner: 17:42

I talked to a company yesterday that was a research. They had a long history in consumer research and they were doing they were talking about doing that same thing, like they were using their research data history you know, decades of research data and putting it into an LLM and then creating these personas. It was data that they had. I thought it was super interesting. So the fact that you could talk to your persona or at least learn from your persona using synthetic data is pretty wild.

Chris O'Neill: 18:09

Well, let me tell you a fun story about how we created our category. Kevin, we did a version of this. It wasn't quite as extensive as creating thousands and thousands and thousands of different consumer personas, but what we did, we said who are our target personas? We're talking to CMOs, we're talking to CTOs or CTDOs, depending on the thing and we basically said, ok, great, we fed it a bunch of information using the same technology underlying Google's notebook LLM and we basically simulated conversations back and forth to say, hey, this is our positioning, this is what we do and does this resonate with? Like? We simulated a podcast, so what we're doing and it helped us refine it. I'll tell you.

Chris O'Neill: 18:49

We actually narrowed our category names down to two things. We said it was agentic marketing or compound marketing. We made the decision to do compound marketing for many of the reasons I've already discussed, so I won't repeat them, and also because it resonated with people, these simulated personas that we went into LinkedIn Navigator and said here are some actual personas, here is an actual content that we're thinking of talking, and we simulated these conversations. So it's not just a hand wavy thing. We're using it to help inform how we position our company by using these powerful tools and, we think, pretty innovative ways. So this is stuff we're actually doing today.

Kevin Kerner: 19:27

Amazing. That's a great use case. So I want to dig into a bit of the skills, the new stack and the new skill sets that are needed now. I love the idea. I hadn't thought of it before until I watched one of your keynotes about agents as team members. Like actually seeing the agents as a worker on behalf of you doing things. It's kind of a really cool way to sort of get your mindset around it. What kind of skillset shift do you think the next generation of marketers will need to adopt to harness these news agent call them agentic teammates.

Chris O'Neill: 19:58

Yeah, many, many of the skills are actually the same. To be clear, I'll start there and then I'll talk about what's different. The same is like look, I see a world where individual contributors are are going to need the managerial skills that great managers have today. What does that look like? Well, you need to be able to set really good goals, right. You need to give really effective feedback and you need to figure out how and when to delegate. Those are classic managerial skills.

Chris O'Neill: 20:22

In this case, it's just going to be using an agent, right? So agents are remarkably good at following instructions, so you've got to set a really clear goal. Are remarkably good at following instructions, so you got to set a really clear goal. They're really good at responding to sub prompts or subsequent feedback. And then the last part is like figuring out when the human should uniquely be in the loop or or when the agent can be set free. Like that. That is a classic management that even individual contributors are going to need, because I believe everyone in the company is going to interact with agents. So that's where I'd say there's a lot more in common than not and this concept of as a team member. I'm a big believer. I love this term glue guy or gal right. These are the people that just elevate everyone's performance, and that's what I believe agents are right. They're basically going to allow you to focus on the things where you uniquely add value, and it's going to rise the overall performance and elevate the performance of everyone. What does that mean? Well, it means a different mindset. These are not here to take your job. It's here to elevate and augment.

Chris O'Neill: 21:20

So I'd say that the other thing I would say is you've got to think about velocity right. The real advantage here is the ability to compound and go faster, as we described. So really not trying to find perfection off the bat. It's about gosh. You can launch so many more experiments, so it doesn't mean you just do it for its own sake, but you basically have the ability to not let perfection be the enemy of done or good enough, because with these iterative loops, with agents doing most of the work for you, you can get through it quicker. So you have to have this mindset that velocity matters more, and velocity is a function of both speed and direction. It's speed times direction. If you think about physics, it's not just doing stuff for its own sake. It's about being intentional about the direction you're taking and then really using these agents to think more faster. And then I guess, more generally, I'm a big fan of slope over intercept in terms of people's capabilities. What I mean by that is the ability to learn and understand and apply insights to the next task you're going to go after or the next set of challenges you take on.

Chris O'Neill: 22:26

And I have a high school-aged daughter and I just just started college age son and what I'm really encouraging them to do and it's remarkable to see how they use these tools. It's amazing to me. Yes, he's really become. Become a ninja at using these tools. Be so good that people can't ignore you like this is like a superpower. The people, the younger generations, that are using this stuff natively. I mean, I watch how he studies, I watch how he basically creates these flashcards instantly, compresses hours of lectures into minutes of super summary notes. It's amazing and we're just scratching the surface. So I would say lean into these tools, become a master at using them, because we have only just started to see what an operating system that's rooted in these llm models uh, with deep personalization and deep context, will have. It's just, it's mind-blowing to me at this stage and we're just early in it.

Kevin Kerner: 23:20

Yeah, I have four college-aged kids right now and um it's, I think they're moving faster into this experimentation, to this stuff, than the colleges are certainly. I mean, they are getting a lot of pushback from the colleges and I am, of course, I'm on the.

Kevin Kerner: 23:33

I'm on the outer and bleeding edge of this stuff and I'm trying to talk to the professors and deans to say, look, you got to get on board here this stuff. You should be, you should be taking growth loop into universities and showing the some of the professors some of the stuff that's going on here, because you would. There's a uh, there can be a problem with people not being told not to use it at this point, which is really important.

Chris O'Neill: 23:56

We are doing a version of that. Actually, a former colleague of mine, Jim Lissinsky, is a, is an amazing professor at Kellogg and uh, he, he and I, uh, he's, he's, he's fantastic. He is fantastic, yeah, so so we're we're working with Jim on a couple of different fronts.

Kevin Kerner: 24:11

That's great.

Chris O'Neill: 24:12

Yeah, like, like students need to to, to feel comfortable with these things Like it really is. I think it's going to be, you know, again, as I said, it's going to be like a superpower for them.

Kevin Kerner: 24:21

So it just their world, is going to be so much different than I think what we, what we can even imagine right now and I, I'm a very optimistic person on all this yeah, I think though, that there'll be one of the things you, one of the things you've I'm sorry, chris, um, one of the things that, um, I think you guys have solved, for that is really interesting from a skill set perspective is you've tried to make the ui very easy and intuitive, like I like. When I saw the demo, I was like, okay, the problem with getting and I have a team that is really good at using these tools, but they're, if they use a tool and they can't, it doesn't work. When they first use it, they'll just drop it. I'm curious, just from a user experience perspective and intentionality behind the tool building the tool. Did you take that into account as you were, as you were building the thing, trying to make it as easy as possible, take the technical part away from the marketers so they could focus on the goal?

Chris O'Neill: 25:15

Yeah, it's very intentional, to be clear, and I'll comment more generally in terms of the importance of design, because I do think it becomes more important. But, yeah, absolutely, this was designed for marketers. We say it's designed for marketers, loved by data teams. The data teams love it because again, it removes a lot of the low value added work of going back and forth with SQL. So the first iteration of this was very intentional to design a beautiful UI that marketers could just intuitively understand from the beginning, and that is how actually we've won businesses. Like literally, when we're doing demos and proof of concept with people, you can almost see the marketers. It's so fun. They're like wait, is that? Like what? Where's am I being punked right?

Chris O'Neill: 25:57

now, I just do that, did I, did, I just did I just literally create an audience in like three seconds with ai? Like they're like there's this big smile comes across their face so that's awesome so, absolutely it is.

Chris O'Neill: 26:08

And and I just on the on the comment of design, I actually think chats of not a great. It's a suboptimal interface. It's the best we've got, so we're using it, but it's it requires too much cognitive load on the user itself. You have to prompt and it's like you know. So I see a world with these agents sort of these thousands of agents, like these thousands of interns you have will fade into the background and you know, I think agentic AI is the spark. But it will be design that really brings the true power in the form that we haven't even imagined yet. So I think that that is an elevating again back to the point. Like design becomes more important, creativity becomes more important to really figure out how we harness the technology and allow it to fade into the background so we can really focus on the business outcomes and delivering creativity in service of customers. That's really how I see it happening. But, yes, that is a differentiator for us that people really do enjoy using our platform.

Kevin Kerner: 27:06

Yeah, that's an incredible vision. I mean, if you can pull that off, it's pretty amazing. A couple other thoughts here as we're wrapping things up. I've heard you mention the term results as a service, and I love the idea that there's a chain. There's not only you're building a category here, which is hard to do, but you're kind of building a different go to market. Talk to me a little about your idea of results as a service versus software as a service and how that plays out for you guys.

Chris O'Neill: 27:34

Yeah, a couple of things. So software as a service is really rooted in the concept of a seat. Right, it's linked to a human. There's this X, many humans using the software product, right, and so what agents allow you to do is to couple that right, agents are working continuously, they work autonomously with supervision, so it breaks down that connectivity, okay, well, why does that matter? Well, it allows amazing things to be possible. It allows for almost unlimited scalability, right, there's like all these different things which humans just can't get to. That you know, working 24, seven and having like the ability to span, like the capabilities, is really limited by the compute, which is not human limited.

Chris O'Neill: 28:18

Okay, and then that also allows you to think differently about the pricing model, the business model more generally. It's not we're charging you per seat. You can start to do things like tasks or you can, even better, do the actual outcomes. You're selling work. You can basically say look, we can prove, quantifiably, prove that there's a different outcome here. Right, maybe we moved churn or maybe we improved lifetime value. So it's going to create new ways, fundamental new ways for business value to be exchanged and charged for.

Chris O'Neill: 28:50

You're seeing it with Sierra Brett Taylor's company, which is doing customer service interactions right, they're not charging per seat. They're basically investing heavily upfront and they're saying look, we'll charge for a resolved customer service case. Right, that's their pricing model. So I think that that's one of the more exciting things. With agents, right, we have these agent swarms and the protocols that allow them. We have the 80-20 you're talking about before we're going to, basically, humans are going to work on the 20% that actually is unique to them and 80% of the other work will go to agents. And then, above it all is, basically we're going to start to see oh my gosh, wouldn't it be amazing if we could actually align value here, where there's actually a business outcome based pricing, as opposed to just artificial proxy called seats, which has no bearing going forward? And, if I may.

Chris O'Neill: 29:35

I think this is what's going on. This is why Salesforce is going out having to buy growth. Like, if I may, I think this is what's going on. This is why Salesforce is going on having to buy growth. The systems of record are really in trouble. You have the rise of these data clouds. You have agentic AI on top Systems of record have seen their high watermark. I really believe that. So it's really going to be a fascinating time in business to see how this all unfolds.

Kevin Kerner: 29:55

As a service provider, an agency. We're seeing the same thing. It's like there should be some payoff for can't be the old model and I think the a lot of the unfortunately a lot of the older, um larger holding companies are going to have a really hard time making that shift, that cost model shift to something that's more results oriented. It's really interesting times in this category I do think you're right.

Chris O'Neill: 30:37

You saw it with programmatic, right, programmatic was a pretty, pretty tough change. This is even bigger than that, of course. It really is. I have lots of stories beyond the scope of today's conversation just about how you think about all the different agency jobs that they do and how they're being changed and how that value gets exchanged with their actual clients. But this is not just advertising. This is an audit, this is an accounting. It's again back to our earlier conversation. It's every corner of companies, you know, from the supply chain all the way through to customer facing, pre and post sales. So it's really a big deal.

Chris O'Neill: 31:16

And now that's going to be the biggest and longest poll, I believe, is change management. How do you get people to embrace these things and change their behavior? As always, right it becomes. I had an operations professor in business school who used to end every class we talk about gnarly supply chain problems and like really the operations of a company, and he'd always I'll never forget this he said you know, in the end it's an OB problem, right, Organizational behavior or a human problem, right, Like it's an opportunity or a problem that really traces back to how do you get humans to change their mindset and their behaviors, and that's really being laid bare.

Kevin Kerner: 31:51

So true, it's a revolution.

Chris O'Neill: 31:54

I'm excited about it, but it's a real human problem.

Kevin Kerner: 31:59

Well, chris, I have one thing I do at the end of these podcasts where I do an AI roulette question, and so this has been great. I could keep going with all kinds of stuff. So I load a question into perplexity and it says the prompt is I am doing a podcast with Chris O'Neill. Here's his LinkedIn profile and latest posts, along with his company website. At the end of the podcast, I want to ask him an AI roulette question, where you will create an unexpected and spicy question that I can ask. Chris, give me a good one. So I just hit go here and here's a spicy, unexpected AI roulette question.

Kevin Kerner: 32:31

This is unedited. Chris, you've led teams at Google, evernote and now GrowthLoop, all at the cutting edge of data and AI. Imagine it's 2027. Growthloop's AI has become so good at predicting customer behavior that it starts recommending not just marketing actions but actual business strategies, sometimes contradicting what your executive team thinks. If your AI chief strategy officer and your human board are deadlocked, who gets the final say? And would you ever let the AI overrule your own judgment if the data was compelling enough?

Chris O'Neill: 33:04

I love this question, but I do believe that, for the foreseeable future, humans will remain in the loop and ultimately be making final decisions. There's nuance and judgment that is hard won over the years, so this one's a relatively easy question for me to answer Is the humans have the final say? Ai and data in general. It should inform decisions and strategy, but not make it right. I think that that is a unique skill that humans and effective leaders have. They can hold lots of context, and it's about having consistency across a set of interlocking questions which form your strategy. So you know, I think that AI can help and challenge and help and maybe pave the way to good decisions, but no, I don't see this being something that the AI overlords way to good decisions. But no, I I don't see this being being something, uh, that the ai overlords just um, just to make decisions, so to speak, and I do think that's a government's question too for companies.

Chris O'Neill: 34:07

They need to figure out where humans oh yeah gonna remain in the loop and, where agents can be, can be working autonomously on their own yeah, I totally agree.

Kevin Kerner: 34:15

Um, I've done this since I've started the podcast and every time the AI question is so much better than my questions Every time.

Chris O'Neill: 34:25

It's embarrassing. I'm not so sure, kevin, you've asked some really great questions, but yeah, it is a good use case, right? It's really fun. It thinks much more holistically than maybe we do sometimes.

Kevin Kerner: 34:37

Well, chris, I've had such a great time talking to you. I could keep going, but I know you're a busy guy. I've watched the I will put a plug in if you haven't seen the Scott Brinker MarTech Day presentation. It's a demo so you really get a good feel for, like the actual product and the UI which is. I think that's why I asked the UI question. It really struck me how YouTube studio slash Google. It is. It's so easy to go through. But if people want to get started with this or want to get a little information, more information, or just want to connect with you, what's the best way to to get started?

Chris O'Neill: 35:10

You can. You can check out our website or you can find me in the socials. I try to maintain a good presence on LinkedIn is where I spend most of my time interacting with folks, so if people want to check out there, I'd be happy to interact with folks there. And, Kevin, thank you for this conversation and for what you're doing for the community. It's an important thing you're doing at a really obviously a really interesting time in the world of business and marketing, so thank you.

Kevin Kerner: 35:36

No doubt. Well, thank you so much, chris, and best of luck to you, and hopefully we'll talk again soon.

Chris O'Neill: 35:41

And same to you.