[00:00:00] Chris: this is how it works at most companies today, I. You have an idea, I wanna prove lifetime value, reduce churn, you then have a marketer go over to the data team. runs a bunch of SQL, passes it back to the marketing person. So, oh, too big, too small. Meanwhile, that takes days, weeks, months, [00:00:15] Do we solve that problem by basically making it self serve, investing in the underlying data infrastructure, the semantic layer [00:00:22] Phil: When you talk about like these swarms of agents and this idea of, constantly adjusting campaigns in flight, is that one of the use cases? [00:00:30] Chris: Agents can look at the data schema, the underlying business logic and say, aha, in the past, these are the sorts of things that worked well [00:00:37] So let's surface those proactively, right? [00:00:39] Then you get do stuff, right? You gotta figure out when you wanna talk to 'em, how, what sequence, And then you gotta run those experiments and then learn from them, [00:00:47] At each step of the way, there's agents to basically do those tasks with and alongside humans. [00:00:52] ​ [00:01:19] I this Episode --- [00:01:19] Phil: What's up everyone? Today we have the pleasure of sitting down with Chris O'Neill, CEO, at Growth Loop. Chris is former director PM at HSBC, president of Google Canada, CEO at Evernote, chief Business Officer at Glean and Chief Growth Officer at Zero. [00:01:34] In this episode, we explore findings from the 2025 AI and Marketing Performance Index Report. [00:01:39] How to use AgTech AI for marketing campaign execution, [00:01:43] how reinforcement learning optimizes gen AI content, [00:01:46] how to use red team drills to test AI data ownership, [00:01:49] and why modern marketing teams need internal AI regulation roles and skill ladders, [00:01:55] all that, and a bunch more stuff after a super quick word from one of our awesome [00:02:00] partners. [00:02:02] ​ [00:03:06] Phil: Chris, thank you so much for your time today. Really excited to chat. [00:03:10] Chris: Yeah, let's have a spicy conversation on MarTech. [00:03:14] Phil: Let's do it. The 2025 AI and Marketing Performance Index, that growth loop your team put together is excellent. there's a couple of timely questions that I think a lot of teams are wrestling with that it [00:03:25] answers. Uh, one of them for me was like, are top performers ahead of the AI curve or are they focusing on the foundations and are they kind of seeing a lot of the AI stuff as hype? And are top performers focused on speed and quantity, or does quality still win in this like sea of sameness? With, with Gen ai, [00:03:44] I've chatted with a lot of folks that are betting on patients and polish, but the report that your team put together actually shows the opposite with both of those takes. [00:03:52] So [00:03:53] top performing marketing teams are already scaling with AI and well ahead of the curve, and their focus is on [00:04:00] speed for driving growth. So. For some, maybe this is a bit of a wake up call for others. I'm sure it's like confirmation might seem a bit obvious that teams that are using AI are working fast and growing faster. We kind of get the why behind this. I'm interested to unpack the mystery of like the how with you. So let's [00:04:18] dig into like how teams. Can implement AI today, how they can grow faster with that, and maybe we can prepare marketers and marketing ops folks for the next crazy five years that, that are to come in ai. [00:04:29] So let's, uh, let's do it. [00:04:31] The first question I have for you, Chris, is [00:04:34] 1. How Marketing Leaders Should Actually Be Using AI to Impact Profit --- [00:04:34] Phil: where do you think AI can realistically improve? Things that are like strategic level, like company profitability, maybe even by like half a percent, uh, in the next 12 months. Like what are some of the key areas you think marketing strategy leaders should be focusing on first? [00:04:53] Chris: Yeah, it's about results always. It's always about results. So yeah, I'm a big fan of, of having teams articulate [00:05:00] very specifically how they're gonna drive top line growth or lifetime value, uh, and or cost, um, efficiency. So that's what this is all about, right? It doesn't surprise me at all that. Those, those that are winning or those that are, you know, we talk about speed. [00:05:15] Actually, I'm a better fan, bigger fan of saying velocity because speed, just for its own sake is kind of useless. It has to be speed combined with direction. You have to be clear on what your, what problems you're solving, and then use systems, AI fits in nicely to basically go accomplish, you know, get in that direction more quickly than others. [00:05:34] So that, that's really the top takeaway. So being able to articulate with precision how AI is going to drive and improve your, your, your business, your profit and loss statement, that's where it starts. And there's a whole bunch of nuance of course, that I'm looking forward to diving into with you today. [00:05:52] Phil: Yeah, so [00:05:52] 2. How to Use Agentic AI for Marketing Campaign Execution --- [00:05:52] Phil: the 2025 AI and marketing, uh, performance index highlight. I'm, I'm gonna call this out a few times 'cause we're gonna clip up, uh, parts of this episode [00:06:00] for this, but. Um, [00:06:02] many marketers that are using AI today, the first use case that comes to mind is like analyzing performance, taking a ton of data, coming up with insights from that, letting the machine kinda tell you what to make sense out, out of that data. [00:06:16] So, analyzing performance, but a lot fewer marketers are using it to act on insights, like to do something with that data. What do you think is the reason behind holding folks back from, from kind of closing that loop? [00:06:30] Chris: Yeah, there's, there's lots. I mean, I think synthesis, using AI to synthesize things is a powerful use case and work and in personal and that's great. It's a very powerful tool to do that. Um, so it can cut down the, the time it takes to understand and get real insights, and that's great. That's a good place to start, but it's so much more than that. [00:06:47] Right. Uh, anytime, you know, any insight sort of action is, is kind of useless to me. Right? So it's about actionable insights. So how do you take an insight, uh, or proactively use AI to surface [00:07:00] insights, um, like who you should actually be speaking to at any one point in time. So it's, it's applying it to specific steps of an integrated workflow to make any one of those steps more automated or shorter, um, in time. [00:07:14] And then combining or stitching them together. Sometimes we use the term agent swarm really to swarm together to kind of move through an end-to-end marketing loop. These are the things that are starting to separate the, uh, the teams that are pulling away the best from the rest, so to speak. Um, we're early in that journey. [00:07:32] Some of those tasks are better than others. But it really doesn't just stop at, Hey, I synthesize, what am I looking at? Here are some insights. You actually have to go do something about it and do it alongside humans, with agents, uh, as part of your team. [00:07:49] Phil: So gimme, gimme one example that comes to mind there because like a lot of. Of folks will chat about AI agents taking [00:07:55] over some of these things you mentioned like swarm of agents, like one of the [00:07:59] main [00:08:00] topics that. I think it's easiest to grasp for the listeners here, like this whole idea of manual campaign blasting. [00:08:07] There's a lot of marketers that are still doing this manually. They're coming up with a segment of audience. They're coming up with a message. They're deciding the time, and they're setting that out. And [00:08:16] there's some folks that have gravitated to more advanced ways of doing that and using ML and having propensity models decide what is the right segment of folks for the right message. When you talk about like these swarms of agents and this idea of, you know, like constantly adjusting campaigns in flight, is that one of the use cases? Like what, what are your thoughts there? [00:08:37] Chris: Sure, sure. So, I, I mean, part of the reason I'm back, uh, in, in the marketing landscape in the, uh, this world is it's just, it's so in need of being refreshed. It's so stale. [00:08:48] Most companies are doing what you just described. They're basically segmenting and they're doing what I call symptoms called waterfall marketing. [00:08:53] It's like you break everyone into a segment and you blast the same day. It's like, it's so ridiculous. Right? It's, um, it's just like [00:09:00] no wonder brands have, um, you know, unfavorable scores and Right. It's just, it's just like it can be so much better. And if I go back to the founding of this company, it really resulted from a breakdown between marketing teams and data teams, right? [00:09:13] So typically, and this is how it works at most companies today, I. You have an idea, I wanna prove lifetime value, reduce churn, whatever you're looking to do, you then have a marketer go over to the data team. Uh, and sometimes I like to think of this as like a bread line, right? Like the marketing person goes over to the data team and says, please, can I have a little bit of bread, right? Uh, that marketing team has to wait there while the data person basically runs a bunch of SQL, passes it back to the marketing person. So, oh, too big, too small. Little Goldilocks action. Meanwhile, that takes days, weeks, months, just to basically figure out who you might want to speak to, who would be an interesting set of your customers or your prospects that would be receptive to something that would drive a business outcome. [00:09:58] Do we solve that problem [00:10:00] by basically making it self serve, investing in the underlying data infrastructure, the semantic layer to build confidence and trust and consistency in the answers in the business logic. Right. So that, that's, that's all taken care of. Now, agents do it even better, right? Agents can look at the data schema, the underlying business logic and say, aha, in the past, these are the sorts of things that worked well relative to that business objective you were trying to accomplish. [00:10:27] So let's surface those proactively, right? That's a very specific example. Uh, people are using our product precisely that way every day, so you can get hundreds or thousands of these different audiences. They're proactively suggested and more powerfully suggested than any one human could do. So that's, that's where it starts. [00:10:45] Uh, of course that's not the end. Then you get do stuff, right? You gotta figure out when you wanna talk to 'em, how, what sequence, call those journeys. And then you gotta run those experiments and then learn from them, right? Write the information back into your data cloud in one place. Not [00:11:00] five places, but one place. [00:11:01] Uh, and then, you know, you basically can learn it, lather, rinse, and repeat. Do it all over again. So. At each step of the way, there's agents that are, we're using to basically do those, those tasks with and alongside humans. And I dare say they're actually doing a better job in many, many of their tasks. Uh, yet humans are in the loop and will remain in the loop for the foreseeable future because of their creativity, because of their ability to understand consumers on a different level. [00:11:28] So that's just for starters, Phil, like that's the early part, but thinking of agent swarms in an end to end fashion, that's why we call our company Growth Loop. It's really about an end-to-end closed loop. And those that think that way holistically and then use AI at every step, I believe, will be the ones that will be able to go faster and ultimately move the needle on, um, on growth, um, or the objective that they care about most. [00:11:53] Yeah. [00:11:53] Phil: Very cool. I, I wanna break that, that use case apart in, in a couple different, um, segments there. Uh, [00:12:00] so there's like the, the segmentation part, like as the first segment here, like figuring out the right audience based on some of the past data. So maybe the marketer is surfacing up this one campaign that we want to do. [00:12:12] We think it's gonna improve LTV. Who should we send this to? [00:12:16] 3. How Reinforcement Learning Optimizes Message Timing and Personalization --- [00:12:16] Phil: There's also the part about like, okay, maybe we have the right audience for improving LTV. They're the right candidates for it. Then there's also the part about like what is the right message to send out to those folks, and that's where some of the stuff around generative AI comes up and I. [00:12:30] There's some risk with this like idea of algorithmic sameness, like in this future vision that some of your customers are, are working in and, and what you're kinda building. How do you avoid this idea of algorithmic sameness where every agent converges on the same optimizations, kinda killing differentiation when it comes to creating content and, and messages. [00:12:51] Chris: Yeah, I'll pull that apart even upstream if we, to get your question about algorithmic sameness. So it's, it's a good con, it's a valid concern. Uh, I'm, I'm more optimistic than [00:13:00] most people I think on that. But, uh, there is actually an interplay between artificial intelligence and machine learning and more specifically reinforcement learning. [00:13:08] So you talked earlier about propensity models, price elasticity models, you know, they have an important role to play. A lot of people conflate those two, but it's very specific. Like we're, we're experimenting all the time with reinforcement learning to basically help with the messaging and the timing because you are allowed to, you know, it'd be silly to not use the vast, um, uh, amounts of data from previous experiments. [00:13:31] Right? That's a machine learning, or more specifically, a reinforcement learning opportunity or problem. We then ingest those to help inform that journey, right? So it's not just starting with an, an audience in, in the, like that, that lives in as an island, you actually layer in the power of the data science teams at, at companies to basically better inform it. [00:13:53] Your question about algorithmic seamless is, is interesting. Had a really interesting chat with, uh, really leading lights at, at gap on the creative side. [00:14:00] I was so delighted to basically hear his perspective. Um, this is a person that grow has grown up designing, using like his hands and his belief is very enlightened that this is going to allow him a designer to be more creative. [00:14:14] And I believe that's true with marketing too, right? It's gonna remove the mundane to focus on the exquisite, right? So I'm not really worried about that. We're gonna be able to expand the amount of things we do, um, uh, untethered from human human capacity. And, you know, these agents can work 24 7. They can operate autonomously. [00:14:33] Provide them the context and they can go do things we can't even imagine today. But core to that is removing the mundane stuff so that humans can actually be more creative. So I don't, I don't subscribe to the, oh, it's all gonna be sea of sameness, right? If anything, that's what's happening today, right? [00:14:50] If [00:14:50] you look at B2B brands or most brands, they feel like a sea of sameness. So this is an opportunity. You know, if the humans are freed up to do what they're best [00:15:00] at, ingenuity, creativity, understanding the zeitgeist, right? Like what's happening in the world at any one point in time. Humans are really good at that. [00:15:08] And distilling that and having creative, fresh ideas that you then feed in, uh, and then the agents can help magnify and basically bring those to life. So I, I, I think that's the enlightened view, that this is gonna allow us to do more things, more creativity as opposed to less. [00:15:26] Phil: Very cool. So I think that you're kinda saying, and correct me if I'm wrong here, but like some of the parts that agents can automate the, the segmentation, the insights, the reporting, that's stuff that maybe was a bit more redundant for the humans. But when it comes to the messaging and the creativity and the strategy behind that, that's where. We're gonna be able to come up with fresh new ideas to avoid that sea of sameness. And if we don't have that human ingredient in there, that's where that like, uh, the sea of AI content kind of all looking the same. 'cause like you said, like we're seeing that today, like my inbox is [00:16:00] flooded with. AI agents that are doing outbound stuff and they all look the same, they're all the same template variables in there. [00:16:07] Some of [00:16:07] them maybe aren't AI agents, but we're seeing all that on LinkedIn, right? Like all the posts on LinkedIn, you can just spot the chat GPT written posts just because they have 17 M dashes in the first paragraph. Like that's, that's the part where I am excited also, like you where like if the humans are actually subscribing to this thing about focusing on the creativity and not just. Handing the whole a content and messaging generation part to ai. [00:16:33] Chris: Yeah. Yeah, I think that's right. I mean, I think when I talk to CIOs or Chief marketing Officer. In addition to some of the things we were talking about and some of the things that, um, came up, came through clearly in the report, you know, most of em are just drowning in the teams are drowning in complexity, right? [00:16:48] There's all this bloat, there's all this tool bloat, and there's just like high, higher expectations. Uh, and it just becomes really hard for people to execute. So, you know, my aspiration, my belief when we're starting to see the leading [00:17:00] teams do this is like you're gonna remove some of the complexity, right? [00:17:02] You're gonna allow agents to reduce the complexity, right? I think the overall job of leaders and, and I think we, you know, we kinda say this as succinctly as possible is like reduce the, the distance between I have an insight and I wanna deliver impact. Right? Right. Now that's a really long and twisty windy road idea to impact. [00:17:22] Uh, I'm really excited because, you know, that will compound over time, but if you reduce the time, why you do more iterative things. It requires creativity, but it requires relentless, like relentless pushing on like speed, right? And velocity and the tools like AI is an amazing lever to do just that. [00:17:44] 4. How to Use AI Agents With Compliance and Velocity in Enterprise Marketing --- [00:17:44] Phil: I think the road is long and windy, especially in some of the more regulated industries like when we talk. About enterprise or like health tech or FinTech. And I think one of the areas where a lot of folks are getting stuff is that risk in, in those more regulated [00:18:00] environments. How do you think about decisions that we should be comfortable delegating to these agents and, and what is the kinda like human in the loop fail safe for some of that [00:18:09] stuff? Like the, the AI and marketing performance Index showed 86% of respondents said AI is better. With human intervention in your view, like where should marketers never fully automate? What are your thoughts there? [00:18:22] Chris: Yeah, and so, so there's a lot in there. I mean, I'm sure there's self-preservation and the answer there, [00:18:26] 86 percent's an awfully high number. Um, but I, but I actually happen to believe that as we just discussed, humans do have a role to play in that loop. Um, this is a question that I ask often of teams of, uh, as a board member or as an executive or when we talk with other of my peers, you have to be really clear about governance. [00:18:44] You have to be clear about when are you going to let agents run autonomously and where are you not? So look, that just depends upon, yes. Is the regulatory risk, right? If you get this wrong, is there gonna be like, you know, is there gonna be a trust issue with your customers? Uh, where if you hallucinate it's gonna [00:19:00] be a problem, it's gonna be regulatory thing you need to adhere to. [00:19:02] So obviously there need to be guardrails here. Uh, and companies need to be really clear about, about who, like data strategy, who owns what. Um, a big fan of red team drills, right? So you actually simulate, uh, uh, hallucination, uh, IP leakage, like be really thoughtful about these things. Uh, but that's, that's a, I think it's actually mostly an excuse. [00:19:23] I think reality is AI going to be. Equally big lever, maybe even bigger lever for some of the slower moving regulated companies. Lemme give you an example, right? In, uh, financial services, you have to record a lot of the interactions between your customers, right? AI can do that, of course. Then you have to preserve those records, regulatory reasons, so you can now actually use AI to understand not just the sentiment, but actually are you adhering to regulatory requirements. [00:19:52] That's a really hard thing to do today. AI can actually help you with that. And there are many, many examples, right? Um, you know, oth other thing, these regulatory industries, [00:20:00] often the excuse is, oh, we've grown through acquisition, we've got all these different tools and systems that don't talk to one another. [00:20:05] Well, good news, things like MCP agent to agent allow, you know, there be protocols that allow exchange across disparate systems to exchange context to allow the agents to do their work. I mean, this is the stuff that is actually allowing, uh, to overcome the decades of excuses. They cannot do this stuff. So, look, I regulatory captures one thing. [00:20:28] That's fine. It has a role to play, but, uh, I, I don't think they're exempt from the velocity and from the innovation that's going to be unlocked. Um, so every industry has, has a role to play here. Ai, sorry I say it differently. AI has a role to play in all these industries, regardless of whether regulated or not. [00:20:45] Phil: Yeah. To your point about velocity though, like the, the marketing ops person on the ground in a startup, that's like, you know, I. 20 people to a hundred people. They have, they're supporting a team of like five, 10 marketers. The marketer sees [00:21:00] a new AI agent tool. They want to hook that up in the stack. We're talking about doing that really, really fast. [00:21:05] The marketing ops person can get that set up. They could do something really fun quickly. In the health tech center, we're talking about PII and PHI and that marketing ops person isn't enabled to do that. Like they could do it in a startup environment, but in enterprise, they literally cannot. There is. A data governance person that needs to okay, that there is an IT person that needs to okay it. [00:21:27] There's a legal counsel team that needs to decide whether we do this or not. So when you talk about velocity, it sounds really cool to say that enterprise teams for sure have a lot of use cases and there are tons of benefit there. But from like an actual practical standpoint. What are your thoughts on getting all of those teams together for that compliance and regulation piece so that we can enable velocity? Because today it's, it's, it's way easier said than done. [00:21:54] Chris: Of course. Yeah. And AI can have a role at every one of those things. Right, legal, [00:22:00] right. You can sample, you can sample the creative, you can sample whatever you want, and have tools surface where there's risk. Right. So I I just don't buy it. I, I think that that's the, that's the excuse of many countries and, and, you know, regulated industries that this like, you know, sorry, we're different. [00:22:15] Um, you know, the reason this, this technology is as transformative as it is, it's just, it's, it's just going, it is going to cut across every industry, every country. It just is. Um, and again, like anything I. There'll be some, there'll be some downsides. There'll be some people using it foolishly or using it for nefarious purposes, and like, we can't let the bad outweigh the, the, the, the massive amount of good. [00:22:37] Uh, that's the same with any technology. This one's just different on so many dimensions. One dimension in particular is, is the work of the work itself. A lot of these technology shifts, if you think about mobile phone or just the advent of computers. It changed the interaction between information and consumers. [00:22:53] It changed how we consume information, essentially Google. Amazing. They're providing information, uh, you know, the [00:23:00] world's information at your fingertips. Amazing, right? That's great. Uh, this has all that and more. The more part is it changes how work gets done, right? It actually reduces the amount of time that it takes to do things. [00:23:14] And to do them well. So again, I, I think like a lot of the, the companies that are lagging around, like they're, they're just gonna die. Like if they don't lean into this stuff, I don't wanna be dramatic about it, but it is just true. Like this is fundamentally gonna change the work of the work and those who get it right, those who don't afraid, they're just not gonna be relevant. [00:23:34] Um, probably in, in a faster, uh, down downward cycle than, uh, any other innovation, uh, shift that we've seen [00:23:41] Phil: Yeah, yeah, totally agree. Uh, I, [00:23:43] 5. How to Use Red Team Drills to Test AI Data Ownership --- [00:23:43] Phil: I think like one of the greatest things that AI has done for marketing ops folks is this renewed understanding of the importance of data quality. I chat with marketing ops teams all the time who've had a couple of like epics or, or projects on the roadmap deprioritize quarter after quarter to improve data quality and now. We have CEOs and and boards saying like, we need to fix deduplication, we need to fix enrichment, we need to fix, uh, our bi-directional sync with Salesforce and all our other tools. What are your thoughts on this idea of data strategy and this importance of data quality and having, um, like you called it, a rights map and like a current ledger of who owns accesses and can train on our most valuable data sets? [00:24:25] Like who should own this? What are your thoughts there? [00:24:28] Chris: Yeah, so, so couple thoughts. I mean, you don't have an AI strategy if you don't have a really sharp and great data strategy. You just don't. Um, if we go back five, six plus years ago, people didn't understand this right? The rise of the data clouds has really made this really clear. So, so, so that we've, we've crossed that chasm like years ago. [00:24:46] So even the more laggard, late, late majority companies understand that they need to get their data together. You just look at the rise of data clouds and how, how prominent they are and how effective they are as a store for your data, not just your customer data, your marketing [00:25:00] performance data, your transaction data, loyalty, et cetera. [00:25:03] It's so obvious that that's where, that's where you, you should be putting your data. Right, and I'm not advocating any one particular data cloud, but just to say, just look at how effectively they've grown and they've grown for costs and security and AI reasons and more. So we're not, we're well since past that, that time, you know, that used to be the case. [00:25:21] So. Yeah, being, being really clear about governance of data, who has rights, who basically is responsible, basically makes sure you track and then understand what data is being used for, what like that needs to be treated. Think of like, uh, you know, asset management, like this laptop I have at a company. [00:25:39] Like we track, like assets like that, you know, historically haven't done that with data, which is arguably one of the most important assets any company has. So. Similar to the red team drill I mentioned before, you need to simulate to make sure that your practices hold up in an emergency or that if something goes wrong, you know who, who is responsible for what, when there's IP [00:26:00] leakage, you know, when there's hallucination in something core, um, I think you have to have a mindset. [00:26:06] That data really matters, and then you have to have a mindset that there's going to be stuff that goes wrong. This is frontier technology, and I think the teams that, uh, are succeeding have realistic expectations. They basically don't think it's gonna solve everything and not have hallucinations or have failures. [00:26:22] It's going to, and then just get over it and have processes in place to, so to, to know who's gonna be responsible for what, when this thing. It doesn't work the way you expect it to. That's just the nature of new technology. So maybe that's how I think about the data piece and, and how it relates to governance and red team drills and like really being thoughtful and, uh, really nimble, um, with, uh, with the underlying data itself. [00:26:46] ​ [00:28:36] Phil: Cool. Un unpack that red team drill, uh, idea a little bit more. Like is this something that, um, growth Loop has tested internally, uh, when you're rolling out new things, like gimme a practical example [00:28:46] there. [00:28:46] Chris: Yeah. Yeah. I mean, I more thinking, so the, the, the practice has, has taken hold in almost any company when you're simulating a cyber cyber event of some form, something happens. Um, and, and you, it's, it's impossible to be too clear [00:29:00] as to who's responsible for what I. When you're going through something like that, when there's like, you're, you're under a lot of pressure and your time pressure and there's consequences, right? [00:29:09] Erosion of trust with your customer is like, you know, is a really a big problem in companies. So this concept of red team is basically simulating something that is not ideal. Uh, so that when, if and when it does happen, you know who does what. So yeah, we, we do similar things here. Any great company I, I've ever been a part of does something equivalent for cyber. [00:29:29] And I'm just here to suggest that you need to do the same [00:29:32] at a much more micro level with your AI strategy so that people are really clear, uh, and, and can avoid any of the missteps that that inevitably will happen. So you, you can recover very quickly. [00:29:43] Phil: Very cool there. [00:29:44] 6. Modern Marketing Teams Need Internal AI Regulation Roles and Skill Ladders --- [00:29:44] Phil: There is like a lot of folks who do believe that one of the roles that's gonna come out of marketing in the next couple of years is similar to the governance title, but [00:29:53] it's almost like an AI regulator for marketing output, like marketing stuff that comes out of these [00:30:00] AI agents to prevent. PR disasters and, and like you mentioned, like the, the erosion of, of customer trust, like the, the best example I have, I, I know you're an avid golfer. [00:30:09] Um, I don't know if you're familiar with like the, the PGA's uh, disaster with, uh, generative AI images [00:30:16] couple of years back now. Um, but they, they had a bunch of different golfers and they were trying to use AI to generate finishing the headshot. [00:30:25] So the headshot was like top the, like stopping at the chest and then they're like, oh, we use generative AI to finish the body. And all of the white golfers had a nice professional business outfits, clean background. And the couple of golfers that were colored had like work outfits and they were like constructions and working in the fields. [00:30:46] And so it was like a bunch of folks who were just like, how, how did the marketer allow this to get posted on social? Like no one thought of the bias involved in the models. Like, what, what message are we sending? So. Anyways, it's, [00:31:00] it's still live on their, on their social media page. You can still find it today. [00:31:03] I referenced it a couple of times, but it's the perfect example of like human in the loop in, in this certain situation here. It's a cool idea, like use generative AI to finish headshots and like, oh, cool. But [00:31:14] you know, there's a ton of bias in these things and hallucinations. And I think the, the red [00:31:18] team drill would've come in handy here. [00:31:20] Like, what if a PR disaster happens because of something that just came out of our model, right? [00:31:25] Chris: yeah, yeah. That's just unacceptable. And, and like, that's true, that's true of any process, right? Like you need to build [00:31:31] in a attack. I, I, I just don't understand how companies can, um, can do that, especially when there's, you know, that there's trust at, at at stake, and. It's how, that's how brands get built right? [00:31:40] By, by being in tune with customers and like, it's an obvious violation of like any, any sensibility, uh, in any brand, uh, in the world. So it's a [00:31:49] failure in a process more than, more than the tech itself. [00:31:51] Phil: Yeah. Yeah, totally fair. Um, let's talk about some of the other roles here. So like the, the role of a marketer was [00:31:57] already really misunderstood, um, in the [00:32:00] last couple years. Now with like AI and GTM being one of the most obvious use cases for ai, marketers are feeling a ton of pressure. It comes out in, in the report in a [00:32:09] bunch of cases. Marketers are expected to do more with less and, and [00:32:13] work a lot faster. What skills do you think are top 100, top 50 marketing leaders need to have that maybe they don't have today in the next like five years? [00:32:26] Chris: Yeah, I, I mean, it's, it comes back to, Hey, how can you move with agility? Um, how do you en enhance velocity? That requires a mindset that says, Hey, we're going to move quickly. We're going to move quickly in a certain direction. So you need to be clear about what direction you're going to. So being able to sort through. [00:32:43] A lot of different disparate information to set a strategy and then be clear about how you're gonna get there more, more quickly. That requires comfort with failure. 'cause you're going to actually have missteps along the way. Uh, if you're gonna move quickly, you don't sit around and, and like try to, you know, produce one, one [00:33:00] campaign, wherever that means, uh, and, and then study it like four weeks and weeks and weeks. [00:33:05] You need to get multiple, uh, campaign ideas or things out, experiments into the world. Be okay knowing that like you're going to not get perfect, perfect statistical significance at every step of the way. You're going to have to try to break a few eggs to make an omelet. Like that's, that's a mindset. I think. [00:33:21] Um, working, working cross-functionally is always something that's important, but the, the interplay between data teams, data science teams, marketers, and the tech, the underlying tech that the infrastructure. Building out these models and basically flexing them, evaluating the models. Right? The way you get ahead of that stuff is actually evaluate the effect efficacy of any model. [00:33:40] So we have teams that are looking at all the different models each week to basically see which tasks are better or worse than others. So evaluation is another skill as well that is not necessarily, um, something that historically has been around. But if I zoom out for just a sec, to summarize, you know, basically any big innovation in artificial [00:34:00] intelligence, whether that's. [00:34:01] Artificial, you know, uh, whether it's self-driving cars, whether it's advertising with Google in the first place, whether it's Billy Bean with Moneyball, right? There's three ingredients, maybe four if you include what I just said about eval. So like you have a step function improvement in the data, the underlying data, right? [00:34:15] We've got lots of different data. We've had a step function improvement in the real-time nature of data. That's one. Secondly, you need task specific algorithmic, uh, improvements. So we think with these agent swarms, right, it's not only the steps individually, right? Oh, who am I gonna talk to? What am I gonna talk to 'em about? [00:34:31] In what sequence? Each of those are steps in a, in a end-to-end marketing flow. Um, but then you have task specific algorithms that govern how the things fit together, right? And when you get improvements in those algorithms and you get unlock, and then the last is maybe, maybe sounds hand wavy. You need courage. [00:34:49] It's like you need the courage, the chutzpah, to basically say, we're gonna try something that isn't perfectly figured out, but we're gonna do it anyways. We're gonna do it because it's a better [00:35:00] way. And each of those different iterations are like step function improvements in the past. Those are the common ingredients, and that's what you need to, to inhibit, uh, sorry, inculcate in your teams, really. [00:35:11] So, and overcome the nonsense, overcome the timidity, right? People just need to lean into this stuff. [00:35:16] Phil: Very cool. A lot of folks say curiosity is kind of the first thing. Like, oh yeah, people need to, you know. Research go down rabbit holes, like fill, figure some of this stuff [00:35:25] out. But, uh, you're the first one to mention courage. And I think [00:35:29] that's a really powerful idea here because there, there is a lot of hesitancy with this stuff. [00:35:34] But the, the 2025 AI and Marketing Performance Index did show that, you know, the top marketers, the ones that work at teams that are growing the fastest. Are taking this stuff very seriously. They're taking AI seriously. They're scaling it, if not like, thinking about it mindfully, and they're upskilling their skills. These marketers, like they, they own their own careers, right? So they're like helping the companies by doing this stuff, but they're also investing in their own careers by taking this stuff seriously. [00:36:00] Not every company can afford to hire like a top 100 marketer that has that courage and, and that curiosity. How do you see your responsibility as the CEO or like, uh, the responsibility of the boards to plan, to upskill or to hire folks that, or to like acquire, um, these skills for, for the marketers on the team right now? What are your thoughts [00:36:20] there? [00:36:21] Chris: I don't subscribe to the premise, by the way. [00:36:23] Like I don't think it's about affording, you know, like these tools subscribe to these tools are $20 a month, like. I mean, like, it's, it's cheap, as cheap as chips as they say in Australia, right? It's like, like these tools are amazing. Uh, there's a reason the underlying companies that are, are offering them, at least the ones that are effective are, are growing at the, the rate they are. [00:36:42] So I don't buy that. I just, like, everyone has access to these tools now. Right? That's the amazing thing. That's the chatt moment. It got caught. The creativity of everyone, the imagination. [00:36:53] So, so I just don't subscribe to it. I, I think you need to basically think of these as like teammates, you know, literally, I. [00:36:59] [00:37:00] At, at Growth Loop, we're thinking we're, we are always thinking about, uh, what most people are familiar with, job ladders or career ladders, you know, what's expected in a different job, family at different stages of one's career, and what is, what does the basics look like and what does mastery look like? [00:37:13] Uh, we're doing the same with ai, your AI competency, right? So to say, Hey, what's expected by function and a different seniority in terms of the use of ai. Uh, so, so being explicit and intentional about that. Not just saying a, were an AI first company, and like, you know, thinking that, so osmosis will kick in and it will magically happen. [00:37:32] Being intentional just as you are with a career ladder. Uh, so that's one specific example, but I, I think it has to run through centrally through a talent strategy, right? Whether any size company, you know, basically you think about developing, uh, acquiring or attracting, developing and then retaining people if your value proposition doesn't have. [00:37:51] AI fluency or the ability to basically use these, these, these powerful technologies to improve and change the work, then like, I think you're [00:38:00] just gonna get left behind. So you need to be intentional about it with your talent strategy. So like, you know, and, and I think like I, the top, like a hundred or whatever, depending on the size of the company, you need to be really clear about it and you need to, you know, map it out just like you would, um, sort of a, a succession planning strategy, right? [00:38:17] You need to think about how do we enable each one of these top end. Hundred people in a company to make sure that this isn't like optional or you just leave it to leave it to hope. Hope's not a strategy here. [00:38:30] Phil: I love it. Love, love your thoughts there. So we, we kind of talked about like human in the loop, uh, a a bunch of times here. [00:38:36] I love that. Growth loops philosophy around this isn't just like we're replacing humans for a lot of this stuff and you can just like buy our agents to, to replace a lot of that stuff. [00:38:47] There is still a core human in the loop component to this. [00:38:51] What do you think happens to the role of the marketer and the next few years when most of these companies finally do roll out AI agents within their, [00:39:00] their current strategies is, is the future of marketers becoming. Way less of these like ex execution folks, and that means like marketers themselves become more like editors of the process. The, the journeys that as you guys kind of call them or we become like product managers. What are your thoughts on like this evolution? I. [00:39:20] Chris: Yeah. I mean, I think mediocrity goes away. I think any of the 80% of the execution focus only goes away. Uh, and that you have repurposed elsewhere. Um, that's not to say that like certain functions and jobs won't go away. They, they will fundamentally change and probably go away, but I'm, I'm of the belief that yeah, your skills will be retooled. [00:39:42] Yeah. Editing you into curiosity. Yeah. Curiosity. Coming up with ideas, creativity, working across the different parts of the organization, the facility with technology, right. I, I think the, uh, the folks who sit in one channel with one tool and, you know, just kind [00:40:00] of put their head down and do their job like that, that's not, that's not the way this is gonna work. [00:40:03] They're gonna be working more like in technology. Agile pods. If you think of like. How people develop, develop software. It'll be similar in marketing, right? You have a very small self-contained team that's gonna move end to end and move very quickly. So it'll look different. Fundamentally, again, the hallmarks will be speed and curiosity and, and courage to basically try massive amount of, uh, experimentation and moving with, with pace. [00:40:27] Um, so that's, that's sort of how I see it. Um, I think there'll be a, you know, uh, blurring between data science. In marketing, um, like we, we've seen in the past, but it will be for reels and then a technologist, right? So that's, that's how I roughly see the marketing teams evolving, smaller, more nimble, uh, with a, with a real stripe of technology and AI running right through it with data scientists, uh, doing machine learning, reinforcement learning alongside swarms of these agents to get things done. [00:40:58] Phil: Very cool. [00:40:59] 7. How AI Agents Affect Entry-Level Marketing Roles --- [00:40:59] Phil: One of the questions that I wanted to ask you around this topic of. Replacements of jobs are changing the way that roles are kind of like structured within the companies. Um, a lot of folks are wondering like, what happens to the entry level marketers? Like everyone says, treat agents like you're, they're they're [00:41:21] really good, efficient intern that [00:41:23] operates at [00:41:23] 24 7 and you're the marketer. [00:41:25] You've got 10 years of experience. You understand the strategy, you have experience. You can guide it. What about those like fresh grads? Like I know you have, um, uh, kids that are in school right now and they're gonna hit the workplace, and like, what advice do you have for companies thinking about investing in the next like workforce as these AI agents are able to replicate a lot of these, like entry level roles? [00:41:49] Like what are your thoughts with that, that aspect? [00:41:51] Chris: Yeah. Well, I. Who knows is a short answer. I [00:41:54] Phil: Yeah. [00:41:54] Chris: a concern, right? I I think you couple that with, with, um, people tending to be not in the [00:42:00] office with, with peers as much. Uh, and it's, it's a challenge, right? Um, so it's, I'm not gonna sit here and suggest it's not my advice to my children. Uh, my, my son in particular, they're, they're, they're, they're AI natives, right? [00:42:11] They, they're like, they're doing stuff with ai. That like blows my mind in terms of how they study, how they do research. I mean, just how they do pretty much anything. I guess therein lies a superpower, right? Like. Like every company is trying to figure this stuff out. So how do you, you know, enable like vast swaths of your company, who's not the entire company to have these superpowers that AI unlocks? [00:42:34] So my advice I said is be, become like a super user, a power user of ai. Um, you will be viewed as a magician, right? And this has been of my advice going back for decades now, right? Whether I talk to a board, whatever, usually, usually not always. People earlier in their life have, uh, you know, curiosity and they have more facility with, with tools. [00:42:57] So find like a reverse mentor, [00:42:59] [00:43:00] right? We talk about mentorship usually in the, like the gray-haired guys, gals, like basically mentoring down. I'm a big fan of the other direction. Have a reverse mentor, someone that shows you how they use technology. Right. Um, I have a good friend who, uh, runs a venture capital, um, shop. [00:43:15] I'll never forget, like many years ago he had like. A bunch of like middle school and teenagers come, they had to focus on gaming, uh, and they basically start talk to this, these kids as teenagers about how they thought about games. And it was mind blowing, right? [00:43:30] Like the future is here, it's just not evenly distributed. [00:43:33] It is a very famous quote. And [00:43:34] well, how do you get to that future? Well, you talk to the younger generations and you actually have the curiosity, basically sit beside them and actually watch how they think about stuff and then ask them questions. Be curious. Right. You, you talk about cur, I'm a big fan of curiosity. [00:43:49] That's how like, it's not, it's, it's not just gonna be like, okay, well there were used to be these entry level roles and they're gonna be the same. They're not. Let's not, let's stop pretending that let's actually start [00:44:00] to think about new ways, new things that we can do that take advantage of the synergy, like the, the combination of humans plus, plus, um, plus these, these magical uh, agents. [00:44:12] You know, and I, I also have this belief too, that like even individual contributors, at any stage of their careers, they're gonna be managers, right? Oh, you be managers of agents, but the same things apply. Think about it. What's a great manager? Do they do many things, but like, they set really clear expectations and goals, right? [00:44:28] So a manager and their their team, their team member, they know what to expect. Oh, guess what? You provide really good feedback. You set context, you provide feedback. Then you delegate effectively, right? So that's how, that's how everyone's gonna need to be. So I just don't subscribe to this notion that, oh, we're gonna have a real, like, people are gonna hit a wall. [00:44:49] Let's think more creatively about it, right? Let's actually use these entry-level folks to do, you know, the things that they can uniquely do because they're more facile and more comfortable with this technology, more so than anyone [00:45:00] else, almost in the company. So that's kind of my, my aspiration, my hope, and my advice to my, my, um, my 19-year-old son. [00:45:07] Phil: I love it. Uh, I think that's a really practical advice there. And, uh, yeah, I think everyone can learn a bit more from having a reverse mentor, especially when it comes with, with new tech and, uh, also like new jargon and, and new sayings and, and keeping up with like. Culture there, like there's a lot we can learn from, from younger folks. Um, I, [00:45:25] 8. From Composable Customer Data Platforms to Compound Marketing Engines --- [00:45:25] Phil: I'd be remiss Chris, if, if we didn't at least touch on CDPs a little bit, um, if you looked at Growth Loop's website a couple of months ago, or I don't know exactly the timing for the switch, but composable customer data platform was the kinda category that you guys were calling yourselves. Now you look at the website and you're seeing things that are more like compounded marketing engine. CDPs Promise. Things like Customer 360, uh, data activation, marketing, autonomy, omnichannel orchestration, all these buzzwords. They all sound familiar to marketers. How do you ensure [00:46:00] growth Loop doesn't fall into the same trap with that compounded marketing engine? It doesn't become another buzzword in in that category. [00:46:07] What are your thoughts there? [00:46:09] Chris: Yeah. Uh, I mean, ultimately it's about delivering results. I mean, uh, you know, I won't, won't comment it too unless you're interested in, in terms of the CDP. We're all like, uh, others say, look, by the way, growth Loop created the category of composable CDP. We didn't set out and call name it that ourselves. [00:46:24] They created it like Chris and David. Coming outta Google built software on top of BigQuery as an intelligent layer. So we didn't have those buzzwords back then. And, and, uh, for better or for worse, that wasn't something that they chose to, to, to do. Um, but ultimately we're setting out to bravely name and claim a category. [00:46:46] We're calling it compound marketing, right? Just like compound interest in finance, like replied to, to marketing, right? It's about how do you get little gains compounded more rapidly. To get, get the kind of outcomes or results that you need. So [00:47:00] ultimately, like I, I don't care what you do. If you, if you're not delivering results, then, then you're not relevant. [00:47:06] And, you know, without, like the, those, those CDPs had, had caught the world by the tail and caught the world's imagination many years ago, but ultimately didn't have the type of breakthrough results that they were promised, those promises were left unfulfilled, right? So, okay. How do you deliver results? [00:47:24] When you think about a category, you can talk about features and efficiency and that's important, but you can talk about growth and the benefits, and that's what we're doing with compound marketing, right? It's very clear what it is we're setting out to do. We're here to drive lifetime value or top line growth, right? [00:47:42] In their clear benefits for companies that, that embrace this, this philosophy. So that's what we're doing. Uh, ultimately requires a product that's delightful too, right? A lot of these products were just hard to use. You really look at them. Like some of our customers have told us as we've swapped some of these out, and I, I won't [00:48:00] mention the, I won't, I won't name the guilty, but, uh, a 2% of their campaigns were being used by some of these, these, uh, these CDPs. [00:48:07] And it's like, [00:48:07] you know, within weeks we're actually using for all of the campaigns, like this is a better approach. So ultimately people want to, if they don't want to use your product, if they find it painful or tedious to use. Uh, the whole point is to enable self-service. If your product stinks, if it's designed poorly, then no, guess what? [00:48:24] People aren't gonna use it. So now there's that part. And the last, I guess, is just being a, a really good partner, right? Do people view you as an extension of your team? Right. We've been really fortunate as a company to lean in and have that to be the case, right? People do view us as an extension of their team. [00:48:40] Are we perfect? Hardly, you know, we have setbacks and you know, it's like any relationship, you gotta work your way through it. But if you go look at G two reviews as one data point is one, we seem to be on the right track, right? People do enjoy partnering with us. They find our product easy to use to get up and running quickly, partially because of the architecture and the design. [00:48:59] Um, [00:49:00] so, you know, we're off to a good start. You know, we have a lot of work to do, let's be clear. But I think it's, uh, it's a brave and bold new chapter for the industry, and we like what we see in the early, early innings of the, of the game here. [00:49:12] Phil: Super cool. You, you mentioned being a good partner and like an extension of marketing teams and something else you, you kind of teased out earlier was like a lot of teams are dealing with a ton of technical debt and like a bloated MarTech stack and I think. [00:49:26] 9. How to Decide Which Martech AI Agent Gets to Act --- [00:49:26] Phil: What's harder than ever today for a marketing ops for a center or person that works in MarTech is understanding the overlap of functionality between different tools, like you're kind of evolu, uh, evolving from, uh, the CDP category and. IPA tools are kind of going into AI agents and customer engagement tools are now warehouse native and going into AI agents, like at some point in the next couple of years, the marketing ops person who's managing all these tools in their stack. All these tools are gonna have AI agents and we're almost gonna be like wearing like a referee hat, deciding we're gonna turn it on in this tool or not in that [00:50:05] tool like. What advice do you have for marketing ops folks that are in this midst of like having even more confusion with like MarTech overlap there and becoming like AI referees? [00:50:17] Chris: Well, I, I, I mean, reference a comment earlier about complexity, right? People are drowning in this complexity already and it's gonna become more complex. So I think it's about being clear about what you're trying to accomplish, being really ruthless about the tools that actually are, and the teams that actually have a built a mindset to iterate quickly, right? [00:50:34] Like. The sad reality is some of the larger players, the Adobe's and Salesforce is just iterating at such a glacial pace. You know, they're, they're increasing their prices, their, their close ecosystems. I mean, Salesforce has moved last week to cut off the Slack APIs. Uh, I just don't even know where to start on that. [00:50:49] It's just ridiculous. So they're not, they're not innovating at any pace. So, you know, find partners that basically are going to innovate with you. That is, that is essential, [00:51:00] right? So try to reduce complexity. Look, it's kind of simple. The data bet on the data cloud and agentic ai, right? Systems of record as we con currently know them today, have hit their high water mark in my view. [00:51:12] And, uh, the teams that win will basically be able to be clear about what they're trying to accomplish. As I said, reduce the, the travel and the distance between. I have an idea that's gonna move it, uh, informed by AI and then delivering the impact, uh, with, with as much, um, pace as, as humanly possible. So I, I think it is gonna be lean into the teams that could iterate quickly, that don't set false expectations, right? [00:51:36] That say, look, here's where we are with our vision. There's our vision. We have a very clear vision about ai. It's probabilistic. No one really knows. And those who pretend otherwise are full of baloney. Uh, but then say, look, these are the things that are working really well right now. Let's embrace those. [00:51:51] That's also gone. An expedition. We call our team, the expedition pod that goes out like on an expedition to work with partners to say, Hey, look, this is the [00:52:00] frontier of this. This is like we're creating creative briefs with ai. Right now we're actually generating images in VO three's breakthrough a couple months ago, weeks ago now, breathtakingly crazy cool. [00:52:11] I wouldn't have thought we'd be doing video creation from agents like today. If you'd asked me even a few months ago. That's the work of the work here. It is gonna be chaos. Embrace the chaos. Find partners who can iterate quickly and well, who are clear about a vision for the future. And maybe a little softer on the path to get there, but do so together. [00:52:32] Right? So, you know, that's, that's really, uh, the, the nature of it. Like I, I, I think that we're starting to see a separation in the industry already. Those who who can deliver results, um, and utility are gonna win. And those who deliver fancy demos and vaporware. They're gonna just not survive. [00:52:51] Phil: Great advice. Chris, I had two last questions for you. [00:52:53] We're we're coming up on time here, but, uh, [00:52:56] 10. How AI Acceleration Ties to Data Governance and Marketing Pressure --- [00:52:56] Phil: the 2025 AI and Marketing Performance Index dropped, uh, but three weeks ago we got a, an upcoming webinar. Uh, you mean, and Rebecca kind of dissecting that even further. I. When you got your hands on it and you read through some of the insights, like what were some of the things that popped out that were really surprising to you that like you read through the data and you're like, oh shit. [00:53:17] Like this is, this is a really interesting component to, to this angle here. Like what are your thoughts there? I. [00:53:23] Chris: Yeah, I was delighted to see that like the concept of speed and agility and velocity came through, right? That those that are actually, um, you know, are, are using ai are sorry, are seeing that they can move faster. That was, that was one I. Uh, the pressure, I, I actually wasn't surprised totally, but the magnitude of the pressure and, and the fact that people felt it stepped up materially. [00:53:44] Perhaps it's a macroeconomic thing, I don't know, but that sort of surprised me that people have a lot of pressure, even more pressure than normal to perform. [00:53:50] Right? We have short tenures in certain functions like CMOs because they historically not delivered on a very short period of time. Uh, so I, I'm not totally there, [00:54:00] but. [00:54:01] The ability to use and view AI as part of the solution to that problem was something that was a pleasant surprise in my, in my estimation. Um, I think the fact that people are starting to get and connect the dots between the data quality and the need for oversight and governance as risks, not as excuses [00:54:16] to, uh, not, not move quickly or use this technology at all. [00:54:20] Really that they, they, they're thinking about that as something that needs to be put in place. I thought that was a pleasant, pleasant surprise as well. But like, look, we're gonna continue to take pulse in terms of what's happening with marketers. Like, we wanna be able to be a partner to cut through the noise, uh, not just hand wavy, uh, nonsense, but to ba basically be a partner. [00:54:37] Just help them make sense of what the best, uh, are doing, uh, versus the rest. And then help them guide them confidently on the journey. Because this is a journey, right? We're, we're very early in this journey. Let's be clear. So we're, uh, we're doing things like that, uh, with that purpose in mind. [00:54:53] Phil: Super cool. Yeah. So folks listening, if you're curious to get your hands on that report, uh, I got a vanity link that you can check out. It's [00:55:00] go dot growth loop.com/report. And, um, I think there's some really cool insights that, that came outta that. Chris, I got one last question [00:55:07] for you. [00:55:07] 12 --- [00:55:07] Phil: You're, uh, obviously a CEO board member, investor advisor. You're also a father and a huge Toronto Maple Leaf fan, big golfer, avid cyclist, and [00:55:16] hobbyist gardener. You got a ton of stuff going on in your life. What one, gosh, when we ask everyone on the show is how do you remain happy and successful in your career, and how do you find balance between all the stuff you're working on while staying happy? [00:55:28] Chris: Well, I, I try, I don't always succeed. I, I might just have to point that out right off the top, but, you know, it's all about people and problems. Uh, to me, like, uh, I'm very fortunate to work with incredible humans. I'll be surrounded by a wonderful, loving family, both here and, and up in my homeland of Canada. [00:55:43] I. Um, it, that's really as simple as it is. Like, am I, am I working with people that I enjoy, uh, in a, am I wrestling with problems that when solved will make a difference? Uh, is it a mission that matters in the world? And that's really the root of it in terms of my professional life. Yeah. And I, I, I [00:56:00] experiment. [00:56:00] Like I, at different times in my career, I burnt out a lot. Uh, so I've tried to figure out ways to, you know, be able to surge my capacity, uh, and realize that you're gonna be out of balance almost all the times, but you know, whether, yeah, it's, it's getting out. To tackle a hill on my bike with, uh, with some dear friends and grab a beer afterwards, or, you know, I, I do, I golf, but usually golf three or four holes at a time. [00:56:21] Like, it's just something to get outside. Um, but then ultimately just, just really be reflective and intentional about paying, paying it forward, whether that's in the form of investing in upcoming entrepreneurs, typically Canadians actually almost always, uh, or, you know, really, you know, paying, paying it forward in terms of people that, uh, who's a careerist, I wanna help shape. [00:56:42] That's what really is meaningful to me at this point. Right. I've been really fortunate to have incredible, incredible leaders that helped shape my career and give me, uh, chances when I didn't deserve them. And, uh, I'm trying to do the same for many people in my, in my professional and personal life too. [00:56:57] Phil: Awesome, Chris. Appreciate that answer. Appreciate [00:57:00] your time today. This is super fun. Thank you so much for joining us. [00:57:03] Chris: Yeah. What a pleasure. Thanks.