Are These Invisible Business Obstacles Killing Your Growth? (CEO Guide to Eliminating Friction)

Speaker A: Okay, Dan, so, uh, here's the question I want to ask you first, and welcome to the, uh, first episode of the podcast, like, humanity. Awesome. Everyone that I've talked to over the past year or so who's trying to bring AI into their go to market strategy, their messaging, whatever, right? Uh, everyone, when I ask them, like, how did you learn it? Not one person has said, well, I started at the, like, topmost level, strategy wise, and I studied that, and then I selectively moved down incrementally into more tactical. No, every one of them says, uh, I was trying to solve a particular problem or I was trying to see if something was possible, and I started and solved that problem and then sort of moved out from there. Did that happen to you? And if so, what was the problem you were trying to solve?

Speaker B: Yeah, I would say that my observations are very similar to you, Dean, in the sense that, uh, um, as you think about AI applied to go to market, we're typically seeing it done what I've described as at the edge of our GTM tech stacks. And rather than thinking about solving these really large, hairy problems, uh, individuals are very much focused on how do we automate more of a. Maybe like a manual task, like an SDR outreach. Um, and so, um, your observation matches exactly what I've seen as well.

Speaker A: And what problem were you trying to

Speaker B: solve in terms of our business and all? Nacp or just you personally? Um, well, so.

Speaker A: Started somewhere.

Speaker B: Yeah. And so right now with AI and gtm, I would say we're. We're using it. Um, gosh, it's becoming, um, a huge, huge, huge important part of our business. And the way that we're applying it is around this idea of creating what I describe as more of a brand narrative, which is kind of the anchor for all of our messaging and positioning. And so, um, as we continue to work with clients, we continue to learn more about this space, we update our brand narrative, and then that information gets fed into the memory that we have, um, within. We're big users of, uh, Claude. And then that information is used to help us, um, improve our messaging and our positioning. So that's a big focus right now for us.

Speaker A: Okay, now pretend that I'm, uh, a CEO and I know a lot about my own business and I know a lot about my industry, and I don't know anything about AI agents. And explain to me in terms I'm going to understand without, like, the inside baseball kind of jargon, what an agent is and does and where does it even live.

Speaker B: Yeah, I would personally Think of an agent as a software application that is helping a member of your go to market team take a, uh, topic that tends to. I say topic meaning a task that tends to be very laborious and time consuming. And the agent will make that process, um, more efficient in the sense that it will automate some of that laborious work, um, so that the members on our team can be much more productive in terms of focusing on what I describe as more of tuning things versus starting from a blank piece of paper. And so, um, the way in which I view AI at this point is it's more of an efficiency productivity tool to help really smart people be even more productive than they already are today.

Speaker A: Okay, so, um, I've seen a lot of AI GTM kind of projects started. Uh, I've seen a much smaller number of them completed and a chunk of them fail. Why, uh, do you think so many of them fail?

Speaker B: My perspective on the topic, Dean, is that when we start these AI projects within the GTM space, we start with the assumption that the current workflow is correct in the first place.

Speaker A: Uh, I don't mean to interrupt you, but, uh, there's a, an investing book, like, you know, sort of like a Consumer Faith that came out a long, long time ago, like 30, 40 years ago. And uh, there's a chapter on understanding risk. And what you just said remind me of that. It says, uh, if you don't know what kind of person you are, investing in the stock market is an expensive way to learn. All right? And I used to say the same thing when I worked at a company called Kronos. I was a project manager there. Payroll.

Speaker B: I know him, Lucky.

Speaker A: I worked there for a while. And, uh, uh, one of the things we used to say is, like, when we come in and put in your payroll system, like, we're going to learn and you're gonna have to, like, show us all of your rules for paying people. If you don't know how, you pay everyone. Ah, this was an expensive way to figure it out, but it will be figured out. And so it sounds like it's the same thing. If you think your processes are already, like, ironed out and great, great. If they're not, but you think they are, you're going to hit some bumps.

Speaker B: That's a perfect metaphor. And I couldn't agree more with you. Dean is, um. And this is a topic I'm super, um, passionate about for a lot of different reasons. But I would argue that the way that, um, we've structured our organizations within the go to Market space, um, has drifted to a point where this is the, the organization's design is broken. And so as a result, um, when we approach AI projects we tend to automate and scale things that are already foundationally broken. Um, and so, so that's a big driver of why more AI projects are not successful. I also think there's another piece to this as well, which is software companies um, are, have always been focused on creating software and along with software and the beauty of the licensing model is that it's a business that has very high gross margins. And so software companies um, are focused on selling software, not necessarily um, providing consulting services to help people reimagine their workflows. And, and my observation, having spent the last 20 years working at software companies is that um, because of the fact that you know, the gross margin on consulting is much lower than you know like a 90% gross margin on software, the software vendors intentionally don't try to help out there because they're worried that it's going to lower their company valuations. So I think there's a double edged

Speaker A: sword there actually that like leads right into my next question which is if I'm a CEO and I'm starting to talk to my own people and maybe I know some people uh, who could have been vendors or could be vendors or other experts, what are they not likely to tell me?

Speaker B: Well, um, you know, I would say in general there, I think a lot of this goes in, into um, their business model but also their um, you know, their fund, their funding strategy. And so you know most software companies today are venture backed. And a big part of being a venture backed company is this idea that you're going to you know, very quickly scale and that puts a huge amount of pressure on the leaders within the organization to drive growth. And I think that with that pressure of know, um, compounding annual growth rates of 40, 80, 60%, I think there's um, times in which we ah, lose track of the customer and why organizations even exist in the first place. And so rather than being an organization that's there to actually serve your customers and make them more successful, um, what we end up with is um, focusing on growth at all cost. Um, and because we need to keep our investors happy and make sure that you know, we're um, meeting all the commitments that we made during that fundraising process. And so um, obviously there's, there's nuances and variances of the story but um, having spent the last 20 years in venture backed companies and owning the customer base, what I can See time and time again is that the revenue leaders are um, across the entire organization, they don't have a great way of really understanding what percentage of the customers are happy and getting value. Like there is no metric that um, you know, that doesn't live necessarily in a dashboard. And I'd say the closest you can get to it is something like nps, um, which a lot of people kind of discount to, you know, to even start with. So um, this idea of value realization is something that we all talk about, but I promise you, uh, that construct is often not showing up in board decks.

Speaker A: Um, okay, I have a question that I hadn't thought about before. But uh, in your experience, like uh, really it might just come down to opinion and not even experience. Uh, should a company have already finished its go to market initiative? Let's say it didn't really have like an organized go to market initiative with you know, like messaging set up and like handoffs from marketing to sales to operations to customer success. Should they already have that? And obviously, you know, there's no such thing as uh, a manual process companies at a large scale anymore. Right. There's always some automation. But should they have, have the manual stuff done first and then come back to see where they can implement AI, or should they start with like AI as part of the mix if they're going to do a, uh, go to market initiative and finally formalize their go to market plan?

Speaker B: Yeah, I mean that's a, that's a really interesting uh, question because there's so many different dimensions of the problem statement and how the approaches.

Speaker A: I mean I can foresee it where you're talking to a CEO and she says, look, like, I get that, you know, there's possible efficiencies and risks as well, but let's focus on the efficiencies, like possible efficiencies for like introducing AI at uh, certain stages of our go to market process. But we don't even know how to do go to market yet. We've never even formally done it. Right. We're out of friends and family stage, we're doing a lot of advertising. Right. Our churn's really high because we haven't really like dialed in our ICP yet. And so, uh, is the AI going to help us speed up getting an A to Z process in place or should we do the A to Z first? Right. Just practically tell me what to do so I'm not going to waste a bunch of money and time.

Speaker B: Sure. So I'll just share our experience on the journey so for us, um, we were founded uh, in 2022. So before I became what it is today.

Speaker A: You're ancient companies.

Speaker B: And so um, what has happened over the last um, six months is we've completely re rewired our entire business. And what we now are much more focused on is creating uh, systems ones with feedback loops. And what I can tell you with a huge amount of confidence, Dean, is um, the productivity improvements have not showed up yet. And if anything we become less uh, productive. And so we have um, engineers who um, are frustrated because we are asking them to foundationally do things different. And then on the go to market side it's the same ideas where we're building this system and the system has an anchor. And so we're constantly having to go back to that anchor and readjust things. And so um, there's this promise that AI is going to pay dividends. Um, and I think it will, but I think uh, our expectations should be such that there's going to be, it's will be one step backwards before you get to two step forward.

Speaker A: All right, so if I'm uh, generally in a go to market initiative, it's, it tends to be either the, it tends to be the cmo. You could argue it should always be the CEO, but it's either the CMO or the CEO that are the sort of titular head of the initiative. Right. Uh, what's the first thing you think they should be asking companies that want to help them or uh, an internal uh, champion that wants to ah, switch to AI, go to market orchestration instead of just regular go to market.

Speaker B: Yeah. And so my first um, question I'd be asking about is the foundational data sets. And this is an area where I feel like the, the opportunity to make an incremental gain leveraging AI, it definitely exists, but the upside potential of it's going to be much smaller. And so what, especially within more of a marketing uh, domain, you're going to see it in things around like messaging, positioning. You're going to see it um, in things like you know, bdr, SDR outreach, um, but the, the big, the big force multipliers that are really going to move your business are not going to be in those domains. The force multipliers are going to come from solid data foundations that largely do not exist today due to the CRM, um, and being the, I'll call source of truth for all of your go to market data which um, most organizations really struggle with today.

Speaker A: M. All right, uh, it's hard enough already to get Sales teams to trust the leads that are coming out of marketing. Uh, how do you get them to trust AI generated and qualified leads?

Speaker B: Yeah. And so now, now you're getting really close to our domain.

Speaker A: It's called the CMO M, the CEO's Guide to Market Friction. And this is definitely a point of friction.

Speaker B: Yeah. No, and so I, I love this topic. So, a couple different things that, um, we've, we've seen firsthand. And, and it works, I can say this with a massive amount of confidence is, um, so today, if you look at the process that we use to generate leads, um, sales has, they typically have very little involvement in that process. And as a result, the marketing team, you know, they'll generate their MQLs and then they hope that they eventually turn into sales accepted leads or opportunities. And so what we've done with our clients is we foundationally changed the process where, um, everyone needs to agree on what good looks like before the campaign is running. And what I mean by that is if you want sales or any other organization to get on board with, um, you know, trusting leads, you have to demonstrate to them why these are good leads in the first place. And so the way to do that is to show them the metrics. And the metrics, what I mean by is, hey, these leads have higher win rates, they have larger ASPs, they have shorter velocity. Um, um, and then once, not only do they convert at a higher rate, but once they do, they're gonna stick with this and grow over time. And if you share that information upfront and you ask them for buy in and commitment and you get that documented, then what happens is, um, there is no subjectivity about what a good lead is or what a bad lead is because everyone is agreed up front. Uh, and so that's how we're solving that problem.

Speaker A: Okay. Now, um, you didn't know this because, uh, this is the first episode of this podcast, but this is a jargon free zone. So. Asp.

Speaker B: I'm sorry. Yeah, average.

Speaker A: No, it's okay.

Speaker B: Yeah, and so I, I get into my, uh.

Speaker A: Yeah, we all do. It's natural. I do too.

Speaker B: Yeah. And, and so I can get, I can give this another shot.

Speaker A: So what's ASP stand for?

Speaker B: Average selling price.

Speaker A: Okay. Okay. So, um, uh, I'm usually brought in either before a project gets started or when a project runs into trouble.

Speaker B: Okay.

Speaker A: That's usually the second one. And I'm, uh, not going to say the situation, the typical situation when I'm brought in. I'm going to ask you, uh, when someone wants to start this kind of project, should they be starting on something that could be an easy win or should they start on something that's like, ah, a hard pain point?

Speaker B: You know, I would frame that question with what's happening within the business. So if there's a situation where a business is, uh, you know, under a tremendous amount of turmoil or they're seeing, you know, huge, huge market headwinds, I would be more apt to make making an investment and doing something that's going to change the trajectory of the business. If the business is, uh, you know, let's say the company is overperforming in terms of their sort of chugging along at least. Yeah, yeah. If they're doing well, then, um, there's an existential threat to the business. Then I would start to experiment with kind of low risk and probably low upside types of projects.

Speaker A: And um, uh, you'll often hear about, um, CRMs, the quality of data in CRMs. Um, uh, there's a level of data quality that's good enough so that sales can do its work and then talk about the level of data quality needed for AI to do its work.

Speaker B: Yeah. And so having spent three years, uh, tapping into CRMs and uh, unwinding them, what I can tell you with conviction is that the way that we think about, I want to say the way we, like we as an industry and as operators, we aren't thinking about.

Speaker A: That's, that's another way for, that's another way for Dan to say, those of us that have to clean up this mess, go ahead.

Speaker B: Um, we're, we don't think about the data in the CRM correctly today. And to double click into that statement, we tend to reference the CRM as this binary thing. It's good or bad. And the reality, once you do this work, what you'll learn is that there's probably 50 different shades of gray when it comes to data in the CRM. And if you were to continue to dig into that, there's data in the CRM that's actually pristine and incredibly high value. There's other data that's complete garbage. And a lot of it is driven by the individual that is inputting the data and what their role is in relationship with that data. And so if you were to continue to dig into that, what you'll see is that every single company in the world that has any real size and scale to it is using the CRM to do some foundational things that are required to operate a business. And so, for example, the CRM typically is the source of truth for bookings and it's typically um, and so understanding like when a deal is closed one, the actual dollar amount of the contract, the breakout between recurring non recurring revenue, the SKUs associated with the subscription, the start, the end date, those things have to be in place in order to pay commissions. Those things have to be in place in order to uh, activate licenses and um, turn on the entitlements. And those things have to be in place to send a renewal. Those things have to be in place in order to invoice. And there's obviously a lot of different flavors to that depending on if you're a consumption based licensing model or more traditional, um, just annual subscription. But point being is, um, um, there is some data in the CRM that's incredibly sound and if you have the right frameworks, I would argue that most uh, there is a path for most companies to do some really interesting things with CRM data. And so it's not this kind of binary like it's all good or all bad. It, it really kind of depends on the data sets that we're talking about.

Speaker A: But do you need like cleaner data overall for AI or not?

Speaker B: Um, I would say it really depends on what the, the jobs to be done are. And so um, to give you a real life example, um, so when you look at, go to market today and you look at how do we define our segments today, we typically will just randomly pick some filters and we'll say, oh, it's in this industry or in this revenue band. There's never been any data analysis that tracks like lifetime value, net revenue retention, all the key metrics that drive a company's valuation. None of that is even being factored today. And so if you were to ask yourself, hey, what if we were to take that, uh, the bookings data that we have that is in fact uh, reasonably accurate, turn that into a revenue model that's designed for segmentation. I'd argue you can use AI to identify your most profitable customer segments. It doesn't need to be at the same quality that the finance team gives to the board.

Speaker A: Right? Uh, fair enough, yeah, yeah.

Speaker B: So there's some really amazing things you can unlock. I think what most organizations are missing is the proper frameworks and taxonomies to even approach the problem in the first place.

Speaker A: All right, all right, um, we need to wrap up, but I have a two part question for you. Same question, two part. What's the most expensive mistake you tend to see companies making as they implement AI for their Go to market orchestration and what's the cheap, uh, hey guys, don't sweat this. So much mistake that they all tend to make.

Speaker B: So Dean, I'm seeing more of option number one than I am.

Speaker A: Yeah, I bet.

Speaker B: And so let's talk about two real life examples. So um, the first one is related to real life examples. So more and more AI is telling us who to target. And what we've seen is organizations rely on that, on that uh, technology and having correctly spent a year investing, you know, something like 10% of revenues chasing a segment that will never convert, nor is there any history of them, you know, converting at any kind of a, ah, high rate. So we've seen companies lose a year on their go to market and we've seen entire um, sales and marketing teams get replaced because of it. Um, the other thing that I think um, it's a very common thing right now that people don't understand the damage they're doing to their companies and their brands. It's the um, AI SDRs. And so um, there's one in particular, there's actually a very well known company that recently announced that their uh, AI SDR sent like 18,000 emails in a given month. And I look at that as um, a huge, huge uh, risk to the brand. And I see it as a great way to kind of burn out your market and damage your brand. And those individuals who are celebrating the volume are not realizing the damage that they're doing to their company, future quarters and years. And so um, so when I think about like what's a low risk investment, I just get really, really concerned, um, as we start to automate things like content creation, um, without a human in the loop and one that is very, very knowledgeable because I, I just see there being uh, there's this time delay problem that makes the impact hard to really measure and understand. So I think there'll be more harm done than good in the short term.

Speaker A: Celebrating the volume of emails sent as like there's um. What is that called? It's not Godwin's Law. Godwin's Law is the one where um, given enough time, any online discussion eventually starts talking about Hitler. Right? Uh, like someone will like accuse someone else of being uh. It's um, I think it's called Goodhart's Law. Do you know about this one?

Speaker B: I do not.

Speaker A: Goodhart's Law says that if you have a goal and a uh, metric eventually people will focus on the metric at the expense of the goal. And like the classic example is you want to get healthy. So you decide that you're going to track weight and you can of course, like, take that way too far and become unhealthy again. Start unhealthy, become healthy, and then become unhealthy by getting your weight too low. Right. And so Goodhart's law says that if you have a goal and a way to measure it, you, uh, will eventually focus on the measurement instead of the actual goal. And then pretty soon you're focusing and celebrating and obsessing about the wrong thing and you'll actually like, not achieve or like foul the goal.

Speaker B: Yeah.

Speaker A: So celebrating that, like AI agents are sending bazillions of emails to, uh, any B2B company operates in a pretty limited domain. Right. Like one of the main differences between, in marketing anyway, between B2C and B2B. In B2C you have like tons of potential customers but tons of competitors. And in B2B you have far fewer competitors because there are very few customers, relatively speaking. Right. I worked for one company, we had 120 potential customers in the world. Right. It was just tier one wireless operators. There aren't that many of them. Right. And so like, you can't like run an ad campaign or a sales initiative that might potentially piss off 10% of them. That's tens of millions of dollars in the next decade you're never going to get because you don't have enough of a market to burn any of it.

Speaker B: Could not agree with you more. And yeah, and so dm, look, real life prospecting emails that I receive, there's one, uh, and it shows up every, maybe a few months, a different flavor of the same message, which is, you know, know, reach your entire TAM in one month. And I, I was so kind of blown away with it. I actually put it on LinkedIn. Like I, I obviously blurred out the, um, the names of the, the people in the organization. But I mean, that is the last thing I want to do. Like when I, I want to, uh, who, you know, who's feeling the pain, where they are on their journey and then figure out how we help them be more successful with solving that problem. And so, um, yeah, this is in

Speaker A: fairness, from a marketing and messaging point of view, that is a great hook because it's definitely going to get your attention. You should just never, ever, ever try to actually do it. Right. It reminds me of that, uh, some lady years and years and years ago had a book and it was called how to lose £200 in a weekend. And it was about how to leave your husband. Right. Very different, like, one and the other very different sort of concepts. How to reach your entire TAM in a month is a fantastic hook, but, like, never try to do that. Never try to do that. Okay, fair enough. All right. Is there anything that I forgot that I didn't ask you? Anything that, like you. Geez, Dean, reminded me of something. I wish you'd said it.

Speaker B: No, no. I mean, overall, I really enjoyed the discussion.

Speaker A: Otherwise, I'll let you go. And I really appreciate you being here. This is great.

Speaker B: Yeah, no, likewise. Honored to participate.

Speaker A: All right, Dan, uh, uh, before we go, tell, uh, people, like, how to reach you and, like, where you are.

Speaker B: Sure. My, uh, the company that, uh, we're building is called Align icp, and we exist to empower, um, go to market leaders, primarily CMOs and CROs, um, help their organizations reach that next level, uh, of growth. And we do that by, um, applying some very specific taxonomies to their customer data to really understand who are those customers getting, which customers are getting the most value, and then how do we focus, uh, our GTM activities on, um, those customers that, uh, will help us, uh, grow and scale as fast as possible and so reach, um, ads on LinkedIn. My name is Dan Sparing, and then my email address is just dan lineacp.com.

Speaker A: all right. All right, thanks. I really appreciate this. This is, like, great way to kick off. Loved it.

Speaker B: Awesome.

Speaker A: All right, see you. Thanks, everyone. Bye.

Speaker B: Thank you. Bye.