Intro And Dallas Check-In

Michael Hartmann 0:01

Hello, everyone. Welcome to another episode of OpsCast, brought to you by MarketingOps.com, powered by all the MoPros out there. I'm your host, Michael Hartman. Today I am joined by David York, who is currently Chief AI and Innovation Officer at Helix CXM. It's a mouthful. David has spent years working at the intersection of marketing operations, RevOps automation, and now AI transformation. In our conversation today, we're going to get practical about what it actually takes for go-to-market organizations to move from talking about AI strategy to operationalizing it in the real world. A big part of this discussion is going to be focused on the uncomfortable reality that most teams are still overwhelmed by manual work, fragmented processes, shadow systems, and operational debt. And if teams are already drowning, adding, quote, figure out AI on top of everything else doesn't magically create transformation. So we'll talk about why operational excellence still matters, why AI adoption often creates more work before it reduces it, how organizations should prioritize those initiatives and why understanding how work actually gets done today is the foundation for building an AI-enabled go-to-market engine. I'll take a deep breath. David, welcome.

David York 1:12

Yeah, thanks for having me. I'm really excited to be here. Appreciate it.

Michael Hartmann 1:15

And so for our audience, like this is all Dallas, right? Well, DFW, call it.

David York 1:21

That's right. That's right. DFW squad in the house.

Michael Hartmann 1:24

That's right. So we got we got it covered. Yeah. Well, the sun has just come out of my part, my neck of the woods. I don't know about you, but it's been a rainy week.

David York 1:33

Yeah, it is wet, which is fine because it's about to be dry for like three months.

Michael Hartmann 1:37

Well, I just I just I just planted a tree and uh the wind I wasn't happy about. The rain, I I'll take. Exactly. So all right. Well, one of the things that you and I talked about when we talked before, and it's been kind of in my head, and and actually there's something else that came up today, this whole idea of like invest like throwing more on top of people. Um, I think it's gonna be interesting. I saw some on LinkedIn today, but um you said something like AI strategy quickly moves to execution. Um, and I

AI Pressure Shrinks Strategy Time

Michael Hartmann 2:10

think you know, in my experience, like a lot of people talk about like, oh, we need to have a strategy before we start moving. It feels like that that really has with AI, I don't know if if it's truly different, if it's if it's something else, but like how why is that gap shrinking or tough to kind of build into the plot process? So there's at least a little bit of strategy before jumping right into execution.

David York 2:34

Yeah, it's funny because so I am talking to big customers kind of on a continual basis, right? And we're talking about a lot of different things. Uh, you know, obviously we start off, I'm I'm a marketing ops guy, have been for before they called it marketing ops. So um I feel right at home in these conversations. Uh, but but I think I think what customers are challenged with is is kind of the obviously the the pressure that they're having put on them oftentimes by senior leadership and management is that you know we have to do something with AI right now, right? Like that's the thing that's that's driving a lot of this. And so, as you know, and everyone, I think everyone would agree, we all feel like we're all behind with AI, yep, collectively. I don't think there's anybody that's like, oh, we're ahead with AI. I think it's a collective problem right now. Um, so you you you layer the pressure from the top, kind of the conventional or the the broad feeling that we all have that we're behind to some degree. And that kind of gives us this feeling like we have to do something now and do it more quickly, yeah. Which gets us out of the thinking phase a little faster than we should, right? We we we are like, yeah, strategy's cool, but we need to build something right now to show somebody a return. Yeah, right. And I feel like what's interesting about that is if you think back through just like historical marketing ops challenges, right? There's always been a little bit of that there on the marketing ops space, which is like I think I even think it goes broader than that, right?

Michael Hartmann 4:36

You know, even if you just keep it to technology changes, yeah, like marketing is not the only place where that has had an impact.

David York 4:45

Agree, agree. Yeah, it's everywhere for sure. Um, so I think that's the challenge, right? Is like like I've I go in and I I try to encourage and talk about the strategy side of the equation to say, hey, let's let's get the roadmap figured out so that we know what to build, right? What do we know that we're building the right things that are produced the right amount of results, yada yada yada, right? But what ends up happening faster than I would prefer, and more often than not, is we need to execute and show somebody up above something of consequence so that we can showcase to the world where we're going with AI, what we've done with AI, right? The pressure is coming from somewhere. And so we're all kind of feeling that pressure, and marketing ops, being the more technical group within most marketing organizations, I think feels that pressure as strongly as anyone. So that's kind of where I think we're at with this, right? It's like there's like this tension that we're pulling behind. Yeah, we know we need to have strategy so that we can be working on the right things with AI, but we also know that we have to work on something now because we're kind of getting that pressure put on us.

Michael Hartmann 6:06

Yeah. It's so it's interesting. I mentioned this LinkedIn post as uh another former guest. Uh I hate to always do this to somebody who's not going to be a part of the conversation, but Kyle Lacey, who I admire, um posted something like today about how it's tough for these marketing teams who are being asked to do this and like somehow fit in like AI into what

Is AI Really Different This Time

Michael Hartmann 6:29

they're doing. But I mean, my stance was like this is it doesn't to some degree, yes, it's it's maybe it feels different, maybe because we're living in it in the moment. But I like as I as I've stepped back, I I kind of think it's actually not that much different, right? We're just having gone through the whole phase of change because I've gone through personal experience and I know others have too, where you know something comes in and you're expected to learn it while you're still keeping things running, and it's a challenge. I get it, right? And so prioritization of time and and all that. There it's or maybe it's a season where you have to invest more time. I mean, am I am I am I crazy here that this is like it feels more like the same, but or is it really fundamentally different?

David York 7:20

Well, no, I I don't I I agree with that because if even if you think about something that we've all a lot of the folks that are listening probably have gone through is like marketing automation, right? Like just just that transformation and change, again, campaigns didn't stop going out the door when you were deploying your marketing automation platform for the first time. Yeah, right. Like you were still doing this and adding this on top of that. I think the difference is the proliferation of AI, where marketing automation touched a certain set of activities within your org, right? AI has the potential to touch almost every activity within marketing, right? It's not just, hey, our email campaigns and our segmentation and kind of that motion, but now we're touching like content creation and project management and kind of every element. And and now we're doing it, yeah, and now we're doing it with a bigger org, right? Like a more mature organization that has more moving parts, etc. etc.

Michael Hartmann 8:28

I mean, so what well, I think maybe what one of the things that feels a little different now is that it's it's much more democratized, right? Like there's you know, unless you're in an organization that is, you know, on your IP range, right? Limiting what you can get to on your browser, which you most people can usually figure out a way around it. Like

Shadow AI And Needed Guardrails

Michael Hartmann 8:52

people can just go off and use these tools to do stuff. And then so I think maybe that's the the fundamental change is like it's so easy for people within the organization to do stuff independently, and that and then uh all of a sudden something breaks, right? An email doesn't get land in an inbox the right way, a lead doesn't flow to the right person, you know, we get the wrong uh information about a lead because we use some sort of automation to append data. Like all these things are real, and and it and maybe that's the big difference is like there needs to be some amount of strategy. Like I'm really struggled with this because I am one of my favorite books of all time is called Execution, the discipline of getting things done. And it's all about like strategy is important, but like to a point, and I think it feels like that balance has shifted from too much strategy to no strategy and just go do stuff. So it feels like we need to kind of bring it back to the middle. And what's your take on that? Is that I mean, is that really where it is? Like, is that the big difference that's so easy for anyone to do something?

David York 9:59

Well, it it is, and I have a customer uh that I've been talking to about this in particular because they are kind of like like, okay, let's go figure out how we adopt AI and this part of our business, right? Like from you know, content uh creation to campaign briefs to like all of this. And in one aspect, to your point, there was you know another person, another team that just kind of set off down this path, right? To say, oh, we're just gonna go out and and do this. Um and and and and and uh the person I've been talking to was like, whoa, whoa, whoa, whoa, whoa. Like, wait, we we gotta we gotta think through this a little bit more before we just go out and do it. But it's that's that tension, right? Like we're we're living in that we need something now, but like everybody needs to know, like, but is but but is that the right thing? Is that the thing is that the thing we need? Like we we want it, but is that the thing we need? And and the time spending the time there is is is a real it's a real thing, right? It's a real balancing act that that folks are struggling with and having to deal with.

Michael Hartmann 11:18

Yeah, it's uh it's it can't keep coming back to a word I use, right? There's trade-offs, right? And it's like deciding on it feels like there's a need for open dialogue within an organization to go like, okay, if we're like if you're gonna go do something great, like here's some guardrails, right? So uh, but let's make sure we're all on the same page. And is that is that the right thing to do now versus something else, right? Those are kind of discussions that feel like they're not happening on a consistent basis.

David York 11:50

Prioritization is is huge right now, um, when it comes to, I mean, it when it comes to everything, but AI in particular, right? Is like, are we working on the right things? And I know, and I would say I've not talked to that many people that have that figured out, like that can say, yes, we know what we should be working on right now as it relates to kind of our AI initiatives. Like most people are like, yeah, that's definitely a good problem that we need to solve, or that's a car, that's a conversation we should probably have more of.

Michael Hartmann 12:29

Yeah. I mean, I think the problem is people get down the path that they go, how much, how long should that conversation have go?

David York 12:38

Yeah, 100% sure.

Michael Hartmann 12:39

Or maybe they think it's just one conversation when it in my book, right? It should be an ongoing conversation. But well, so this takes us back to like another thing that you and I talked about is that really maybe even before the strategy, but before really trying to incorporate AI and take advantage of it, the there the organizations need to kind of understand where they are operationally, right? Where are they doing well, where are they not doing well, and and get to a

Operational Excellence Before AI Scale

Michael Hartmann 13:09

I think the term you use was getting better to a state of operational out excellence or close to it. So what what did that mean? And then like maybe go a little bit deeper on why that's so important.

David York 13:23

Yeah, I think there's there's there's a lot to unpack in that phrase, right? Um, because I because I kind of look at it in two ways, right? So so if you if you look and follow the kind of thread from marketing operations just as a whole and and and where we're at today, a lot of companies have been running and you know building and managing their stacks and all those types of things, have been uh adding manual processes in here and there to get the work done, right? To get out. And if you statistically speaking, right, that's about a third of our work in marketing ops ends up being kind of manual stuff. So what we what we what we see, and and this, you know, people say this phrase a lot, like AI is going to magnify everything that you already have because it really doesn't it doesn't necessarily fix things, it scales things, right? It it allows you to do more of what you're already doing faster, more efficiently at scale. Yeah. So the foundational side of this thing, right, the that bottom layer of what you're building on becomes really, really critical. And and so the the work that we've often pushed off that kind of not so fun, right, operational data cleanliness. That the proverbial can that gets kicked down the road all the time. Every time, everywhere, and all those manual processes that just get baked in over time because they have to be we had to get shit done, so we powered through it. Exactly. All of those things become kind of like this breaking point when you start thinking about AI and the scale that AI provides, right? Those are the things that become breakpoints in your success um down the line. And so, so when I talk to folks about it, I'm like, okay, operational excellence. Like the the first thing to do, and one of the most important things to do is to make sure that foundation is somewhat solid. It doesn't have to be perfect, right? It doesn't have to be amazing, it doesn't have to be rainbows and unicorns, uh, because there's always going to be some some mess somewhere.

Michael Hartmann 16:09

Yeah, for sure.

David York 16:10

But the idea that you that you feel and understand and have some some kind of a strong foundation to build on, right, is is critical because you need to make sure that you're scaling stuff that already works with AI.

Michael Hartmann 16:27

Right.

David York 16:27

If you if you start attacking attaching AI to all these other things that are kind of patchwork and duct tape together, you're gonna get that at scale and it's gonna create more problems potentially than than solutions.

Michael Hartmann 16:42

So I mean it feels like that uh there's a fundamental thing that needs to be there, which is like you need to understand what I even hate to say it, like process, like what your pro like how things work today, like documented well enough to be able to identify where those things are that might break, or there's bad data, or there's a manual step that we didn't, you know, is happening and we all kind of know it, but like at least on the team, but maybe not brought it more broadly. Like, is it really so it begged the question like should we be focusing on understanding that really well before we start taking big steps with AI?

David York 17:22

So the hill that I'm willing to die on is ringing the hill that I'm willing to die on is the unsexy process work in marketing that we need to do in order to succeed with AI. And and that's and to me, this is everything at the end of the day. It is if you don't understand how your business works today, how your marketing works today, as in as great a detail as you possibly can, your ability to succeed to reimagine that work, and that's just where we get into this, right? Like to reimagine something, you need to be able to imagine it first.

Michael Hartmann 18:12

Right. Yeah, yeah.

David York 18:13

You got to imagine it the first time of what it is today. So that process work for me is absolutely in my mind and from my perspective, numero uno on every list of things you should be doing before you set out to do this work, is to really dig down deep. And again, it is no fun, it is not sexy, it is not rewarding. You're not gonna like no one's gonna jump up and down and be like, oh, that is a beautiful process diagram that you've built.

Michael Hartmann 18:48

We went from uh 50% completeness on this piece of data to 90%, right? Like exactly. Um I I I was lucky enough that I actually talked about this say yeah, I use the same words, right? It's not sexy work talking about data, and I had a boss who's like, actually, that's really sexy. Like, we need to like carve out time, which we didn't get to do,

The Unsexy Work Of Process Mapping

Michael Hartmann 19:07

actually. Um I'm curious, I so this is a thought. I don't think we talked about this before. It's been in rattling in my head. This whole idea of like these fundamental, like understanding the current state, um, problems with data. If I think about this, like I've I've thought for a while, like, yes, I know the data is gonna be bad in almost any, especially B2B. It's just by its nature, right? I think anybody's expecting quote perfect data is fooling themselves, right? That's because it's just not gonna happen. Yeah. Um, too many human inventions, I mean weird stuff that companies do. But I was like, why can't we use AI to help solve that before we start trying to do more with it, right? So I think like to me, when I hear people like, oh, it's gonna, you know, putting AI on top of your bad data or your bad process is gonna give you, you know, make you faster at dealing with the bad data or bad process. I'm like, well, what if we shift it and said take a step back and say, how about we use AI to help us understand the current state and then help us to make improvements on it too, like before we start going even bigger. What do you think about that?

David York 20:19

Yeah, I I would definitely say that falls into the low-hanging fruit category uh to some degree. The challenge you get is all data across everything could can be big. It can be a big undertaking, right? So when I when I talk to customers about here's here's what you want to do from AI standpoint, is you want to be able to bite something off that is is doable first. And so what that may what that may mean is take a process or a part of your your marketing

Using AI To Fix Data First

David York 20:59

where it's a limited amount of data, and and you could start with the cleanup of that subset of data, yeah, versus trying to bite off the whole entire thing, because a large organization that can spiral really quickly into analysis paralysis and the ability to go, oh my god, this is gonna take us months of work, versus saying, okay, look, if we just are attacking this one problem, I think your point is is spot on. Like let's let's dial in the data that touches this one process and let's fix that data first and we can use AI to help speed that process that up. Yeah. Um, and I think there's I think there's definitely value there. Um yeah.

Michael Hartmann 21:50

And I and I mean I'm thinking even of things like we've got, you know, 14 different ways that the United States of America is represented in our database. Like AI seems really well suited to normalize that.

David York 22:03

Yeah. Yeah, for sure. Agree.

Michael Hartmann 22:06

So yeah, it's it's I mean it's interesting. Like so I I keep running into this and hearing about the that, and I'm like, there's gotta be a way that this can help with that unsexy stuff, right? Because it's I still believe it need it ultimately your marketing's gonna be better because you're gonna get the right people, you're gonna target the right people with the right message, you can better understand your customers. I believe that truly, if you fix the data and fix the processes. But I think it feels like it's a thing that because it's not sexy, it's not there's not an obvious immediate um tangible output. Right, there's not email going out, there's not a landing page, right? Uh there's not an event. It's it it that's why it keeps getting kicked down the road. Like nobody thinks it's a bad idea. It's just yeah, we figure some stuff out how to make it work anyway, despite that.

David York 23:03

And inherently uh data quality doesn't feel like a revenue generating activity, yeah. No or a demand generating activity, right? So that's why it always gets pushed down on the list because of that, right? Marketing and marketing folks and sales folks, and you know, it if it's it needs to be revenue, some kind of revenue generating activity for us to really, really want to pay attention to it. Right. And and everybody's already doing so much as it is, right? You don't have all this free time to begin with. So someone's gonna come down. I when I was in marketing, I remember our CMO would would come through our offices in marketing, and we had this one section, which it was all marketing, and he would come through and walk through, and it was like you know, papers flying in the air type of it was like a scene from a movie, right? Papers are flying in the air, blah, blah, blah, blah. He's spouting stuff off, and the next thing you know, and then he leaves, and then everything that we were just working on completely got turned up. And I think that happens a lot still. We always we we would say internally, like, oh yeah, the best marketing campaigns are the ones that needed to be done yesterday. The best ideas are always the ones that needed to be done yesterday, right? In marketing, and that's how and that's how it is, right? I think that most people feel that in in a lot of organizations.

Michael Hartmann 24:36

Yeah, that's interesting. So, what do you think like one of the things that I can't another kind of AI-related thing I hear about is you know, marketing or AI is gonna replace big chunks of what marketers or marketing ops teams do? So you've been dealing and digging in more with AI and working with clients and what you do. So do you think that's realistic? Is there a disconnect there out there? Like what are what are are people's um people who say that and think that are they are they misunderstanding the current capabilities of AI or what's coming very soon?

David York 25:18

I think there's kind of two boats. Um, and I think companies look at it from two different lenses, and I and I um and I I I fall under the I fall into the category of AI is going to allow us to do more things that we couldn't do previously. And so I I look at it as huge opportunities. And and therefore, if we think about it through the lens of opportunity, uh there's tremendous amount of upside, and there's always gonna be more work to do because of AI. The other lens that folks are kind of looking through, and this is, and you see this in the market, right, is like the efficiency side, yeah, which is like, oh, we apply more AI, therefore we're just gonna be more efficient and we need more pe we need less people to do it.

Michael Hartmann 26:13

Right.

David York 26:13

And I feel like what's happening right now is a lot of companies are falling into like one camp or the other versus

Will AI Replace Marketers Or Expand Work

David York 26:21

uh kind of thinking about it in parallel, right? Because there obviously there is some efficiency, and and that's intended, and that's great, and it's important, but what you get from that is an unlock of more opportunity and to go pursue those opportunities. And there's there's some data, and I don't have the exact stat, but there's some some studies that have basically been done by you know large-scale consulting company, I forget which one, which basically said, like the companies that are winning with AI right now that are seeing actual return on investment are the ones that are really kind of dialing into that opportunity world and thinking about things from the opportunity standpoint. And the best story of this that I've ever heard, which a good friend of mine and I were talking, and he he shared this with me, is is IKEA. So IKEA basically built uh an AI customer service rep, quote unquote, like on their website. And what they found after they had deployed it, that it was able to basically handle, I think the numbers like 65% of all customer service inquiries that came through the website, through the website without any human involvement.

Michael Hartmann 27:42

Okay.

David York 27:42

So like over half, huge. Now the the the issue was, and it's not really the issue, but the the the thing was is they had like I don't like seven or eight thousand customer service people like worldwide.

Michael Hartmann 27:57

Yeah.

David York 27:58

That were trained in all their products, have been answering all these customer service requests. Well, now they have 65% more capacity than they had because all of this this AI, you know, chatbot basically took that over. So IKEA, what they did, and this is where we kind of think about opportunity, IKEA said basically, hey, you know what we've been wanting to do for a long time that we've never been able to do is build these uh design comes a design consultancy in-house to help our customers design their spaces and and and do really you know cool things with our products in those spaces. And by the way, we have thousands of people that are already trained on all of our products and everything that they everything that we sell. And so they basically took all of those those thousands of people. I don't know what the exact number was, but you know, let's just say it was 65%

IKEA Shows The Opportunity Play

David York 28:59

of them, right? Uh, and they reskilled them and and upskilled them to be these design consultants. So here's the here's the punchline. IKEA generated a billion extra dollars in revenue from that design consultant practice that they built. And I think that is the most like telling example of how companies should be looking at this. And again, I don't think they all they always are because we're we're seeing this week after week after week, but that is so telling of an example, and it shows real revenue, real growth, and they were able to realize this new opportunity because of AI that they didn't have previously.

Michael Hartmann 29:46

It's so interesting. So as you were telling the story, I was waiting to respond, right? I was listening, but I was already like because I have a story from my own experience where it was a customer service woman. Um, this was when I was at Texas Instruments, and I was asked to go try to help figure out like typically support costs had gone up at the same rate as revenue, right? Because you're hiring more people. And so the goal was like, how can we it wasn't couched and I avoided like cost savings because the implication there, because I didn't want the support teams who I had no, you know, I didn't have any real power over them at all. So I needed them to be supportive. But I was like, what I want to do is see if we can figure out a way that we can call I call it bend the cost curve, right? So as revenue goes up, right, we want to recapture more of that revenue as profit, which means what we did is we we were able to find a way similar to this, like this is 20 years ago, right? Kind of using knowledge, a knowledge base model that was different than what most companies did, including what we were doing. And the the sell I made was what's this gonna do? Is it gonna make it so the teams that's still there? They're already have their own versions of a knowledge base, right? For all this repeatable stuff, right? So let's push that out, make it available for self-service, right? It was what AKEA did. Right now, the things that they get to work on are the fucking crazy, complicated things that really you know make them work their brains, which they like. So like and and and then the company captures more revenue to the bottom line. And so I love the story about IKEA because it goes another step further, which is oh, not only that, we're gonna generate more revenue through another way.

David York 31:31

Yeah, yeah. And and again, so I so I feel like this story of AI, right? There's there's very much two sides to it. And one of the things I try to talk to people out pri primarily uh, you know, kind of early on is like one of one of the elements here is getting your strategy straight as it comes to people, yeah, right within an organization. Because there's, as you know, as all of us in marketing ops know, it's always people, it's always process and technology. It's all three. It's never one, it's never just two, yeah, it's always all three. And so the people side of this whole transformation and this whole equation is critical. And again, the companies that are going to win are the ones that get that part right at the end of the day. Yeah, and and and look at this as opportunity for expansion and growth and new ideas and creativity and all of those things versus just doubling down on this whole cost savings thing, right?

Michael Hartmann 32:35

Yeah, I mean, I think I think um when I think about it, the value in it is freeing up the human capital for creativity, right? Because I it feels like the sense I have, and I think this is still true, is that AI, especially the NLMs out there, like they're not real, they eventually re come back to the mean, right? They generate average stuff. So if you're a lower-than-average performer, right, it's good for you. If you're a higher-than-average performer, it actually is sucks, it's not good for you, right? You're producing lower quality work faster. But I think like what I I was talking to uh uh someone who's a writer and I was asking, like, what do you think about all this? It's like they happen to be in the business of breaking news, like it can't do that, right? That can help make our writing better and clearer and that kind of stuff, but it's not breaking new news because it doesn't have anything to build it on. Yeah, and I think that's also true of just generally new creative ideas, which they're so far I haven't seen them be really good at generating.

David York 33:34

No, it's it's the it's the ultimate averager. Yeah, if that's a word.

Michael Hartmann 33:39

Yeah. Yeah. I mean, that's it's kind of what it is, and I that's as much as I personally have seen value from it for certain things, I've started probably leaning on it more as a tool to help me think and do research and less about producing ideas or a lot of writing that I I think is important. I mean, I'll use it for some stuff. You've talked about like we had the document we we I shared with you before we got on here, right? I use that uh I use an AI tool to help me prepare that, but I don't I do read it

Creativity, Notes, And Better Thinking

Michael Hartmann 34:19

and I change them.

David York 34:22

But well, it's just summarizing something that already exists, a human-to-human interaction. You're just summarizing that, and that's what it's great at. I think it's fantastic.

Michael Hartmann 34:31

It's so good at that. Yeah. I mean, I do I do worry about my own stuff. I I've gotten back in the habit of trying to take my own notes. I was letting AI stuff do it all the time, but I find that I don't recall as well. I actually need like I'm sitting here looking at a stack of notebooks. Most of that's been typing. I'm like, I also think based on everything I know about brain research, like writing actually has a different cognitive effect, uh retention of information. And like I need to do, I need to start writing jotting notes down, even a little bit.

David York 35:04

So yeah, I I'm I look over, I have the same. I have one, two, three, four notebooks right here sitting next to me.

Michael Hartmann 35:12

I've got a stack over here.

David York 35:14

I always I always take physical notes. In fact, I'm on calls and people see me turning my head to the side, and I'm like, because I'm like writing over my notebook while they're doing it. Because I I am too. I've like that's one thing that's analog that I absolutely love because number one, recall is great. And number two, I I flip back through them, you know, on on the regular when I'm thinking about something.

Michael Hartmann 35:37

So a little, a little another nugget that I found is useful, um, and it just happens naturally if you're taking handwritten notes, is that there's pauses in silence. So if you're having a conversation, especially if you're doing one where you're trying to learn from that person, whether it's for a client thing or whatever, people don't like silence and they will fill in that silence. And that's generally, I find like really, really good information that they wouldn't already plan on sharing happens in those moments.

David York 36:07

Yeah, I I I believe it.

Michael Hartmann 36:09

So when I was in my consulting days, we used to intentionally do that when we were doing client interviews. So I like it. Yeah. Um, so okay. So I think one of the things you said you're you're also working on a book nowadays on and how to kind of overcome some of these challenges we've talked about. Um do you have a like a I think you mentioned you have a framework that you're kind of building out for it? How's that what does that look like? And what can you share with us?

David York 36:42

Yeah, so um by the time this posts, it should be live. So it's it's it's coming out, it's it's it's on the it's already got a release date and everything. And so it's it'll be uh it should be live, it'll be live here at the end of May.

Michael Hartmann 36:57

End of May of 2026. Got it. Okay, yeah. Be very close. Be very close.

David York 37:02

Yeah. So um, yeah, so so it's called AI Powered Growth, a seven-step system, AI adoption system for go-to-market transformation. That's the headline. The idea there is basically, and a lot of the principles, even that I'm talking about here fall into that, but it again, it's it's basically seven steps, and they're all built around people, process, technology. And fundamentals are changed, do they? Uh no, no, it really doesn't. And and I and I've been doing this like for so long. The more I do this, the more it just always comes back to that to some for some reason. It never changes. Yeah. Um, but yeah, so we talk about you know, the shift that marketers are making in their in their business and kind of how they need to shift their thinking just in terms of how work gets done. We get into how how are we investing from the human capital side of things. And then really the the bulk of kind of the process work

David’s Seven-Step AI Adoption System

David York 38:09

uh is all about process definition and classification, documentation, and then basically going through a series of steps to kind of reimagine your processes with AI, see see what you're doing today, figure out where you can go with that. Um, we talk about taking inventory of all of the capabilities. So, this is one thing that I that that a lot of people don't think about, but if you think about all of the products that you have in your tech stack today, sure, every one of those products is adding AI capabilities and features into those product stacks. Right. And so when you think about all the things that you want to do with AI, right? You you let's say you document all your processes, you think about like how can we rethink these and reimagine the way we do work with AI. What you also need to do is is kind of bounce that up against all the stuff that you're already buying and paying for, yes, to figure out what do I where are the gaps, right? Can I can I do all these things with this existing stuff? Is it a 100% match? Is it a 70% match? Eight, whatever, right? Like it's it's not gonna match, most likely. So you're trying to you've got to think through that. So that that layer is in there. And then of course, once we figure out where there's gaps and where there's missing parts, or where, hey, this feature kind of does what we need it to do, but we need to build out something on top of it, right? Or whatever that is, yeah. Then there's this idea idea that, hey, we we can get to to a roadmap state where we could say, okay, here's the processes, here's the things we want to build with AI, here's how we're gonna use all of our existing technology. Here's where we need new technology, and what does that look like? Do we need to buy something? Do we need to build something? Kind of all that stuff, right? And then building out a roadmap to say, okay, this quarter we can do this, this, and this. Next quarter we're gonna do this, this, and this. So kind of going through that whole thing to getting you to a place where you have an uh an AI roadmap that you can actually execute on. And then the last piece of it is around metrics and adoption metrics. So one of the things that again, I've been sitting around thinking about for far too long is how folks measure this stuff. Yeah, and there's not really any hard, fast thought, any anything out there around like, hey, this is how you can measure it. There's a whole lot of data that says people can't measure return. Yeah, no one's getting a return. We're like, we're not getting a return. So so I set out to say, well, there is things that you can measure about the all of this stuff that you could do. So we put in a framework for measurement and said, Hey, here's here's five things, dimensions that you can kind of measure to actually produce some of that. So, so that's kind of the framework the book is based on. There's a a lot of stuff about growth in there, just kind of principles of growth and how process is is key to growth. That was definitely something I hammered home in there. Um, and then the cool thing is we actually built a bunch of tools, like applications and software that actually help physically walk people through all of the steps. So it's not just, hey, go do this stuff. We have like, you know, worksheets and analog and examples and spreadsheets and stuff you can download. But then alongside of this, we actually built out an application that you can go log into and it walks you through the processes. You can, you know, upload a process map and it'll parse everything out and give you ideas around what you could do, how you could use AI to reimagine it, like kind of all those things. We have like a dashboard that you could actually use to track those adoption metrics. And so we it's not just hey, here's the ideas and here's the the frameworks to do this, but here's also tooling technology to help help get you further down the road.

Michael Hartmann 42:36

Interesting. Um couple questions as a follow-up. So on the um the roadmap piece, I mean, I think everybody would want a roadmap. Um with AI stuff, do you find that the roadmap structure, how frequently it gets updated, reviewed, changed? Um is it is it is the frequency um shorter, right? In their terms of are you doing it more often? Or you is it like how far out are you looking? How does it feel? Is it any different than you would have would say a marketing automation platform or some other sort of tradition, I even hate to say traditional, you know, Martech product.

David York 43:14

So yeah, 100%. Um we actually call that step the we call it dynamic roadmap.

Michael Hartmann 43:25

Yeah.

David York 43:25

Okay.

Michael Hartmann 43:26

I mean, I I like this. Inherently, yeah.

David York 43:30

Yeah, you're right. Inherently, this is something that you're gonna revisit probably more frequently than than you revisit a regular non AI roadmap. Yeah, right, because there is so much. I the the chat the balance there is like you don't want to kind of knee jerk react to every change in the market, but you also need to know like, for instance, Adobe's really Releasing all kinds of new capabilities for Marketo.

Michael Hartmann 44:03

Right.

David York 44:03

Right. Right now. They're going through a bunch of stuff that they're putting out. So, so if you had things there, you you definitely need to re-evaluate and look at what you've got, you know, designed and kind of upcoming on that roadmap

Dynamic Roadmaps In A Fast Market

David York 44:20

to say, like, oh, this is a big enough change. We need to revisit some of this stuff and see how this plays into what we've had kind of already thought.

Michael Hartmann 44:29

Yeah.

David York 44:30

So I don't, I don't know that there's I don't really provide any hard fast rules about it necessarily on like, hey, this is how frequently you should do it. It's more like be conscious of the the pace of change when it with regards to this technology and the pace of change that your vendors and the platforms you're using are adding this stuff. And you just there's going to be some inflection points where you probably want to revisit this whenever something happens.

Michael Hartmann 44:59

And I think there's probably some organizational dynamics too that go like what's the tolerance for um ambiguity at some point in the future. At some point in the future, it's going to be ambiguous, right? What you're gonna do. Yeah, so like how far out does that go? If it's three months, right, you should be reviewing this pretty regularly. If it's you know, if they want 12 months, probably also need to be reviewing it regularly because you might like it's gonna be changing. So um and it feels like that's should be a part of it. Which also gets me now into so inherently, right, understand the current state, got a roadmap, start doing stuff, right? There's gonna be inherent risk and change, right? Whether it's whatever people are uh don't don't support it, like there's change management flaws, or actually something's broken, say lead routing gets broken. So always like to me is always a big one. How is risk management and kind of balancing the trade-off of the benefit versus the risk and the cost? How are you thinking about that in your in your framework?

David York 46:12

Yeah, I mean, the first thing I tell people, because this is just something that's inherent to where we're all at with AI right now, is at some point we all kind of have to put a foot in the ground and take a step forward first. That's kind of my overarching principle, right? Like a lot of folks are kind of sitting here just the risk of not changing too, right? Yeah, they're they're you're feeding, you're just you're sitting there kind of like being passive in this experience where you're just being bombarded with new models, new features, new capabilities, and and sometimes you're just kind of sitting, it's almost like you're laying there in the ocean, letting the waves just wash over you and and not going anywhere by it. So so so I I I feel that that's that's the first piece of the puzzle, right? Is like at some point you just got to put your foot in the ground and not look back. And and and I ironically, I've I felt that way as I was writing this this stuff

Risk, Two-Way Doors, And Tradeoffs

David York 47:17

because I was like, dear God, I at some like something new is gonna something's gonna change. And there is things that change like through the process, because I spent like a year kind of documenting, writing, and researching and all that type of stuff. Yeah, and I was like, stuff was changing as I go. So there was things that were going, but at some point I was like, okay, I can't just change with every every model change or release change or every update that came out of here. So there is this this thing where it's like, hey, we all have to put a foot in the ground and just start going from here, and then we'll adjust as we go, right? So to your point, I think there's a there's a risk of not doing anything that I I feel like is higher than the risk of doing something to someone.

Michael Hartmann 48:02

I was gonna say, I think people underestimate the the risk of keeping current state, yeah, right. A lot of times, because it's it's it's the one they know the best, even if they know it's flawed. And it's the you know, pick your version, right? It's the devil you know, or whatever. Um, but yeah, I think other people underestimate that. I I it's and it it's interesting to me. Like, I don't think as many people as I would like think about things in terms of trade-offs and risk tolerance. And um one of the and it feels like this is a great scenario where the concept of, you know, what can I do if I do this thing, right? Try something new with AI, um, is it something that I'm locked into, or is it something I can completely come back out of? Like it's the like I know there's a term that Amazon uses, but I think Bezos came up with this. Like, is it a door? Are we going through a door that closes behind us and we can't go back through, or is it one that we can go back through, right? And I think that's another part of the evaluation, right? Of the like, is it a risk worth taking because we can also kind of return to what we were doing if we find that it wasn't the right door to go through?

David York 49:12

Yeah, uh that's a I I like that analogy uh because I feel like with AI, there's very few doors that are closing behind you. Yeah. Because of the flexibility that we now have, where and I'll give you an example. So my last agency I was running, we were doing a lot of build-outs, building apps and things like that, and um like for Marketo and Eliqua and other tools and stuff, and and after you know, kind of all the AI coding tools came out, like I started playing around with those and and I talked to one of my developers that was with me at the last company, and it came to me this one, and I basically said, I was like, dude, I just built something that would have cost me, would have taken like four months and probably would have cost like $80,000 two years ago. And so I I feel like it's like the benefit and the curse, right? Like it's it's like the benefit is I don't think that many doors are ever gonna close behind us now because the the uh the investment time and effort is significantly less than it used to be.

Michael Hartmann 50:37

Right.

David York 50:38

So we like it gives us the ability to pivot more easily than we could before. We we won't go like you're not gonna probably spend four to six months building out a process or putting things, you know, going through this type of doing this type of work because of just the the capabilities that we have in front of us from a technical perspective. Like the tooling is so much better and faster and just insane at some level.

Michael Hartmann 51:08

Yeah.

David York 51:09

But the curse of that, right, is we can build anything we want and do it really quickly. And and so what ends up being the critical path here, and this is again kind of a jump back to the framework in the system, is the prioritization of what to build becomes so much more important because you can build anything and you can build it quickly. What you're building, how to decide what you're building, how to like think about what what that prioritization becomes like one of the most critical pieces of data that you it almost it's like the holy grail of like the prioritization pieces is is like the thing you have to be able to to know and get right. Otherwise, you're off building a bunch of shit that nobody wants to use or nobody needs or doesn't give you any ROI.

Michael Hartmann 52:02

Yeah. Well, I think the other the other part, like the when I think about the like one of the challenges a lot of companies make is they get stuck in this sunk cost fallacy, right? We spent $100,000, $100 million on something, we've got to keep piling forward. Whereas if you're doing $10,000, right, um, and it's not the right thing, and you abandon it, it's a feels a lot different.

David York 52:26

Yeah.

Michael Hartmann 52:26

Even though the decision probably should have been the same in both cases, it's just the scale is different and it's harder to just emotionally let go of the huge investment. Um, okay, so while we wrap up, because I think we're kind of tied tight on time here. For those people who are stuck with this and are listening and uh are feeling FOMO or pressure and overwhelmed, what's like first couple of things they should be doing if they're in an ops leadership role or an ops role in general?

David York 52:59

Yeah, as I think I'll go back to some of the things I said earlier. So um my my first piece of advice is always like you gotta stop your head from kind of spinning and and just get grounded, so to speak. Yeah, right. You have to really just get grounded in all of this and then and and and start to approach it from a bit more of a pragmatic place, right? Um and and kind of start to take all the hype and the fear out of the equation, which is hard to do, yeah. Don't get me wrong, like easier said than done. That gets us to a place where we're thinking more systematically without all that, right? That's that's always number one. It's like we have to ground ourselves. We have to ground ourselves in the idea of like, okay, how do companies grow?

Michael Hartmann 53:57

Say some problems, you know.

David York 53:59

Yeah, exactly. Companies grow by developing systems and processes that support that growth over time, right? So we have to get back to like first principles type of thinking here, uh, for sure. That's the number one thing. And then the second thing I would say is you got to put your foot in the ground, right? And I said that earlier,

How Ops Leaders Start Without Panic

David York 54:21

and just and not and kind of like wall out some of all the AI noise for a certain amount of time to be able to progress forward, right? And those are those are probably the two biggest things. Obviously, you know, I've got a system to help walk through some of these processes when you say, like, yeah, we want to do this. Like it's very, it's it's very kind of first principles type of system. It's very grounded and pragmatic and you know, systems thinking and process oriented and all that fun stuff. But uh those are those are the big ones, right? Is like get grounded and put a foot putting the put a foot in the ground and just start start taking steps forward. And uh, and and yeah, and you'll and you'll get there, you'll succeed. It's just it's gonna take some time and don't don't chase the signing objects, right? Don't chase the signing up shiny objects, please.

Michael Hartmann 55:13

Yeah, well, and I think I'm going back to the original earlier in our discussion, right? It may be one where you're gonna have to uh prioritize your time and maybe invest time that's on top of your normal stuff, your normal job and other expectations to be able to do that first part so that you can be smart about what you're doing when you put your foot in the ground and take a step forward.

David York 55:36

Exactly.

Michael Hartmann 55:37

So, David, ton of fun. Always enjoy talking to you. I appreciate it. Thank you so much. If people want to, you know, we'll see on the timing of when the book comes out. We'll be really close, I think, depending on when your release date is. But if folks are interested in the book or want to talk more about this with you, what's the best way for them to do that?

David York 55:54

Yeah, so you can hit me up on LinkedIn. I'm easy to find. Um aipoweredgrowth.com is the website for the book, and then uh helixcsm.com is the agency that I work work for. So multiple multiple avenues. But um fantastic.

Michael Hartmann 56:12

So appreciate it. Again, David, thank you. And uh as always, thank you to our our audience out there for watching and listening to us. Uh, we always appreciate that. Give us some some love with if you feel like it. If you are interested in uh a topic or uh have an idea for a guest or you want to be a guest, you can always reach out to Naomi, Mike, or me, and we'd be happy to get the ball rolling. Until next time. Bye, everybody.