How to Unlock Revenue with AI-Native GTM with Dave Boyce

Speaker A: Scott.

Speaker B: I'm Scott Stauffer, co founder and CEO of Scale Matters. Welcome to another episode in our webinar series that we call um, Bridging the Gap Between Finance and Go to Market where we're trying to provide thoughtful content, uh, that helps to create alignment, uh, between finance leaders and revenue leaders, hoping that that helps to accelerate growth. Uh, before we jump into today's topic, uh, and introduce my guest, let me just uh, give you a little bit about me and Scale Matters. Uh, I've been doing this for a long time. As you could probably tell just from the looks. Uh, I'm founder of Scale Matters. This is my fifth um, uh, CEO gig over the last 30 years. One which ended up in an IPO. A couple other good exits, quite, uh, a bit of experience uh, in the SaaS space and in particular sort of fashion my as a quant CEO so very, very um, much committed to data, uh, driven decision making, modeling, et cetera, to help uh, optimize companies. And that's sort of the um, genesis uh, of Scale Matters. Uh, revenue and finance leaders come to us uh, when they're basically struggling to answer important questions like I don't know, what is the ROI of our different channels that we use to source prospects or what's the root cause of our recently declining win rate or is my sales team properly sized right in relation to the top of funnel investments. In almost all these cases their company's uh, go to market data posture is not really set up very well to help them answer these questions. And that's really where we come in to help. And I'll give a little bit more on us towards the end of the uh, webinar. But um, let's get back into uh, today's topic which is um, how to unlock revenue with AI Native Go to Market. Um, uh, we're going to be joined with, we are joined by Dave Boyce, who's the executive chairperson and uh, EVP of product at Winning by Design. And they've done some really good um, research to understand kind of the growth dynamics of AI Native companies and how those learnings can basically be leveraged to some extent by any company, uh to reignite growth. Uh, and that's really what Dave's going to um, help uh us understand better today. So Dave, uh, welcome to the show. Uh, you want to give a few minutes on yourself?

Speaker A: I am the executive chairman of Winded by Design. Thank you. I um, have a book coming out next month on a lot of these topics. Um, AI and product LED growth, um, kind of the automation that we're driving into gtm. And a lot of that's what we're going to be talking about today. So I think it'll just come naturally in the conversation.

Speaker B: Cool. Uh, why don't I go ahead and uh, I'll stop sharing, Dave, and let you, uh, drive it from here. And just for the audience, we're going to let Dave kind of go, hopefully uninterrupted by me, uh, and to make sure that we get the narrative, uh, through. And then Dave and I'll go into some back and forth after that just to dig a little bit deeper.

Speaker A: That sounds perfect. And I think you gave me a budget of 10 to 15 minutes, is that right?

Speaker B: And I'll cut you off at 20 if we get too far.

Speaker A: Got it. Well, I'm going to wind up and go fast, so hopefully, uh, you know, hopefully we people can lock in. We're just going to walk through kind of the, the theory, the research, as you said, Scott, kind of the core principles, and then we can just take it anywhere you want after that. But I'm going to go pretty fast, so, um, so buckle up. Um, so today's SaaS environment is tough. I think everybody knows that. I'm going to show you some data. Driving revenues harder than it ever has been. One of the key things for people of our age, our vintage Scott, is we thought that we'd be teaching our teams how to do it like we did. And instead how we did isn't going to get us there because the world is changing, um, right under our feet. And so it turns out if we want to reignite growth, I'll show you what I mean by a growth stall. We're going to have to actually do stuff differently, which means we got to crack stuff open. We got to re architect our GTM systems. We're not just teaching our teams to do it like we did, we're actually rediscovering how to do it in a way. And we're learning a lot from the AI natives, so. So once we have that figured out, we'll use AI as a force multiplier. Totally get it. But what are we actually going to do? Like which playbook, if these playbooks aren't working, which playbooks are working? And you know, a really sobering thing is we're. Why do. We've worked with over 1100 companies, Scott, and you know, and there is a clear disturbance in the force right now. Like we are seeing Companies get to 10 million, 20 million, 50 million, 100 million without employees with less than fewer than 10 employees, and then to 500 million, which is Cursor, I'm going to show you. Uh, actually, I don't even know if I'm going to show you because I cut that slide. We only had 10 minutes, but cursor just got to 500 million with fewer than a hundred employees. So. AI native companies are not hiring dozens of people to grow from one to a hundred million. They're not. They're doing something different. So what is it that they're doing and how can we learn? Now, here's, here's the vexing thing. The market doesn't care where the growth comes from. The market just wants growth. Like, um, you know, we, from 2022 we kind of got. Our worlds got rocked. The, uh, zero interest rate kind of policies, um, uh, went away. Growth at all costs became not an option. We used to hire one unit of quota capacity, get one unit of quota delivery, hire two units of quota capacity, get two units of quota delivery. That we couldn't do that anymore because it turns out it was, it was, um, it was unscalable. But look, the market wants it, um, and you can see it in, uh, um, valuation multiples across the X axis, left to right, less than 10% growth, 10 to 18% growth, 16 to 25, 18 to 25% growth. Um, and I get a higher and higher multiple depending on how I grow. And there are some hyper growers that are above 40%. And yeah, it keeps going. That's like the premium valuation is down. If you don't believe that, go to the public markets. This is, this is 90, um, companies. And the R squared correlation between growth, it's oriented the same way. X axis is growth rate, Y axis is um, is valuation multiple. You know, against growth alone, I get a 39R squared. I'm like, okay, but what about profitability? You got to give profitability some credit. Okay, great. Then we're at a dot 40 r squared. So it almost carries no water at all. Okay, well, what if I double weight the growth and then add in profitability? Okay, now we're a little better. Dot 5 1. But growth is doing all the work. Um, you can't hack it. We're not, we're not going to get evaluation based on profitability. And no growth rule of 40 is not evenly distributed. All the work goes on growth. And it turns. And as we're talking about, um, before Scott, you know, there was a time when we could go buy that growth. And if you look at kind of what we're paying for growth. You can see what was bought and what was earned. Um, so back in 2021, across all SAS companies, we're growing at 36%. Today we're half of that. Back in 2021, we only paid a $24 for an incremental dollar of ARR. Today we're paying 60% higher than that. Um, this just goes to the fact that kind of doing more of the same is not working. There was a m, there was a market research or sorry, reset in sas and, and doing more of the same is not working. Buying growth that may or may not stick around customers that m we may or may not want to have is not working. And we are stalling, um, as an industry. And by the way, this happens to every company at some point in their life. No matter what. We're always going to have a planned growth trajectory. We're always going to get to a point where we're going to hit diminishing returns. Our growth is going to deviate from that. But we're seeing it in the numbers right now. Here's a north, uh, of $1 billion company. You run the second derivative on growth and you see, you see it start tailing off. And these are real numbers right here. They're kind of stylized, but they're real numbers. And inside of those numbers are three things. This is the revenue stack. The gray is retention, the magenta is expansion, and the light blue is acquisition. Okay, cool. We understand that over time we're accumulating. That's what a recurring revenue business does. We carry our renewal base forward with us, we expand on top of it, and we go buy new customers. It turns out because of the dynamics back here where it's kind harder to get growth than it was, um, that just to get that little light blue line to show up consistently, which by the way, it's not really growing, right? Like we're not in this. This is real. These are, this is real data. And it might reflect the, the shape of your data too, or may not. But just get that little light blue line to show up in, in the current quantity, not really growing over time, we need more leads. So to get essentially the same number of new customers as we used to get in 2021, instead of needing 10,000 leads, we need 18,000 leads in this example. And yet, and here's something where I know we share, we share religion here, Scott. And yet we put 75% of our attention on that light blue bar. We're forecasting and calling and trying to Hit what? A revenue number? No, a bookings number. Like, wait a second, so just that little blue sliver? Yeah, that little blue sliver. We put our best talent on it, we put our hiring plans on it, our board asks about it, we review the books, bookings number with our um, with our CRO and all the rest of that, um, is opportunity that we can and should pay attention to if we want to grow over time. The net result of all of this bringing customers in that we, that we have to work hard to defend, um, paying more to acquire customers than we want, putting all of our attention on, uh, upfront is that 82% of public SaaS companies are currently in unsustainable range. So the light blue line over There is the $1 mark, meaning I'm paying $1 to acquire $1 of net, new ARR. Net of all expansions and attritions and new acquisitions. $2, $4, $8. That's unsustainable. Investors, um, don't want to fund that kind of a losing, uh, business anymore. And the point is not that it's not working anymore. It's just not working like it used to be. Um, it's not working as well as it used to. And that's a permanent state of affairs. We know that AI is entering the market and we see AI Natives redefining. So what are AI Native companies doing? They're operating on a core set of first principles. I'm going to go really fast here. You're going to recognize these from your personal life because we're all customers as well as sellers. So even CFOs have a personal life and we buy things and we go out to market and we trust our uh, and so AI Native companies were built in the last five years. They've built these companies not assuming that they're going to hire dozens of people. And instead they're, they are agreeing, they're just agreeing with the reality that this is how the world works. Now number one, knowledge, the knowledge asymmetry is flipped. It used to be that we knew more about our product than our customer knew. Today our customer knows more about our product than our salespeople know. You literally go into ChatGPT right now, type the name of your company, say, give me three alternatives to that solution. Tell me all the pros and cons, tell me the pricing, tell me the reviews, and it will give you all of that. Uh, a customer can educate herself pretty much better than a, uh, than a well educated sales rep in very short order. Second, it used to be that all the information was coming if I'm a buyer coming from a seller, and maybe I'm being advised by an analyst. But now I'm getting information from, you know, networks of peers, review platforms. I graduated with somebody who is also using it. I'm pinging her, I'm getting influencer, uh, input. I have, uh, a lot of information available to me that's not coming directly from the seller. LLMs have kind of broken that logjam wide open if the Internet didn't already. And the third thing is, we used to think that we controlled the process, that we sellers thought we controlled the process. Oh, you want to see a demo? Well, let me first do some discovery. We'll have a discovery call. Oh, you want to see a price? Well, let me first get through my process, and then I'll be able to configure a solution, and then I'll be able to give you a price. Okay, you won't give me a demo? Someone else will give me a demo. Okay, you won't give me a price? Someone else will give me a price. Information wants to be free. It's all out there, and we're not in control of it anymore. So trust has shifted. Control has shifted. Um, the, uh, um. And then this is the big one, Scott. If we want to grow exponentially, which we do, we want to be in that premium valuation zone. We can't build any of your systems, Hire one unit of quota capacity, get one unit of quota delivery. That's this thing on the left that can only deliver me linear growth. If I want to get exponential growth, I got to figure something else out. And it turns out that system, Systematic systems. Systematic systems, but automated systems who don't get tired, who speak all languages, who operate 24 7, who don't take vacation, can do, actually have the potential to grow exponentially. It doesn't mean they will grow exponentially. We still need product market fit. We still need go to market fit. But if we can hand some pie pieces of this, uh, off to a system or a machine, I have the chance of growing exponentially. So what does that mean for us? You know, we, we all thought that we were going to be, you know, I'm in my 50s, Scott, you're probably in your late 40s. Um, uh, we all thought that we were, you know, we all, we came up through the ranks and we were all going to go run these businesses, um, you know, the way that we used to. Well, the archetype has changed over time. It used to be that in 2020 12, software is eating the world. Uh, Marc Andreessen, the technologist who's inventing products that really solve problems. The technologists really created value. This is Google and, um, this like Google and PayPal and eBay and like, solving real problems. Then, gosh, we got high on our own supply and we went out and tried to launch all sorts of stuff because VCs were throwing money at us. And we, and we figured out, oh, we can just raise money and we can hire reps and we can scale based on money and we can go put stuff together and we can even do, um, M and A. And, you know, the financier was able to go build unicorns. Uh, didn't even have to be a technologist. Then we have the SAS crash we talked about. We had to stabilize. We had to take cost out. We had, uh, you know, we did right size, and for just a couple of years, that was the right thing to do for a CEO. Market is tired of that. They don't want that. Like, I don't want to be stabilizing. I want to be growing. So we're back to growth, but we got to grow in a different way, which means I got to be an architect. I got to, I got to architect AI native GTM into my systems. So here's where we're going to speed up. What's my time check here?

Speaker B: Uh, oh, you're good. You're good because you get, you're getting to some good stuff. So I'm good.

Speaker A: Yeah, I'm getting to some good stuff. So, um, look, the first thing I want to do and you talked about in, in your opening, you know, instrumenting our, um, you know, our gtm, we, if we're gonna, if we're gonna go re. Architect something, we gotta know what's working and what's not working. We have to know what's working and what's not working. That gray bar of retention, that magenta bar of expansion, that light blue bar of acquisition, those are all independent processes that are running inside of our business. And we got to know which ones of them are working and which aren't. And by the way, we got to know them by go, uh, to Market Motion, too. SMB, Mid Market Enterprise. I might have a PLG motion. I got to know that, which means I got to have it instrumented into my business. These CR stand for conversion rate. Um, so from left to right, this is my customer journey. That's a traditional sales and marketing funnel. Left to right, turned on its side, I get to that middle that's where I, that's, that, that's what we've all really been focusing on in terms of acquiring a new customer. But then from there that's, that's actually where the recurring revenue begins and all of the compounding begins and the customer journey begins. Because then I got to onboard, retain, expand. Those are the, that's the, the gray and the um, magenta bars. And I need to understand how that piece of it works as well. Let's assume that we've put in a, uh, data model like that and we can see what's working and what's not working. And let's also assume that we're not AI native companies. We weren't built in the last five years, we weren't built without humans, and we actually have kind of human LED processes. Okay, cool. We can still measure them, we can still figure out what's working and what's not. Let me just talk about the um, the, uh, the gold, the blue and the, and the pink lines here. If we want to make this work better, how do we do that? Um, we all have aspirations. That's the pink line. That's top down. That came from our board, that came from our shareholders. That might have come out of our kind of fever dreams. That's what we want to do and we're deviating from it. As we said before, that's the bottoms up. That's what our capacity is capable of doing right now. That's the bold line. Okay, cool. So how do we do, um, how do we do better? Like get more out of our existing capacity and that could get us to the light blue and then how do we do smarter? Which is like, is there stuff that I can unlock that's not in my existing capacity that I could go, you know, that non linear growth, that exponential growth that would get me from, let's say the blue line up to the uh, uh, magenta line. All right. To do better, I can do, um, there's three ways I can do this. I can do more, I can do better, I can grow smarter. Um, and there's two things that we've seen, Scott. I'm, I'm fast forwarding through this, but if there's a takeaway, it's, it's right here. There's two things that we've seen that can get us from um, that blue line up to that magenta line. So it's not. So I'm not talking about do better. I'm not talking about train my people better. I'm not talking about you know, be tighter on discovery, have better processes. Yeah, that's going to get me from gold to blue. That's get more out of my current system and that's great. And if we're, if we don't have everything unlocked from our current system we should do that. But there's two things that AI natives are doing that would take us even beyond that and the first one is automate everything that's automatable. So let me look at that customer journey. This, this view of it is through a, ah, is through a kind of human people powered centric kind of sales centric lens. You can tell because it says lead gen, lead dev selling. Like these are activities that we are doing, prospects, leads, opportunities. But what if I said hang on a second, I would actually like my customer to get to Impact sooner and more seamlessly. It seems like that's what's happening with OpenAI and cursor and Lovable and Replit. These companies that are going to 100 and 500 million without any salespeople. It seems like customers are able to self serve their way into Impact. So what would that look like? First of all I got to get them impact way earlier. Like I got to accelerate that maybe even before they've committed to pay. We're probably, I don't know the platform that we're on Livestorm, but I bet there's a free version. I bet you're able to start free. We know that you can start free on ChatGPT. We know that you can start free on Gemini. We know that you can start free on Lovable and Replit and all these platforms. So can I get Impact into the customer's hands even before and then can I automate everything about that customer journey or anything about that customer journey, let's put it that way. And if I can't automate discovery or acquisition, I'm automating a lot of discovery uh right now with SEO and SEM. But maybe I'm not automating acquisition, maybe I'm not uh, automating activation. Well you say my product's really complicated. I can't do that. Okay, cool. What about renewal? Let's go to renewal. You already have a contract. You already have your product configured. You already have terms of service. Can you just say click yes, I'll take it for another year or do you have to have a human intervene right now? That's like a super simple thing to automate. Or what about expansion? There's I want to think about automating that customer journey because that gets the machine working for me instead of me working for the machine. If every single one of those things takes human effort and slows down and puts process in between my customer and impact, then I'm just putting grist in the gears. And that's what causes me to deviate from the growth trajectory that I wanted to be on. So anything that I could automate. Why wouldn't I? Um, I have a phrase for that that I'm going to drop, uh, on you in just a second, Scott. So that's the first thing. Automate everything that's automatable. Second thing is it, it is true that, you know, it's, it's like an accepted truth that the bigger you are, the harder it is to grow. You know, I'm a, I'm $10 million. Adding $2 million is easy. I'm, um, $100 million. Adding $20 million is a little bit harder. I'm $500 million adding $100 million. These are all 20% growth rates, by the way.

Speaker B: Whoa.

Speaker A: Adding $100 million, that's a lot. Um, but that's only because we think that we're working for the customers. What if the customers were working for us? And this is something that AI native companies and PLG companies understand. There are actually users that are using our product that are part of those trust networks, part of those referral networks that can pull new customers into our system. And if you can get this working, literally only at this scale, Scott, of one in a hundred, if one in a hundred, um, users of our system will refer one other, uh, customer into the front end of this funnel, we can start to tip into, um, super linear growth instead of sublinear growth. Um, so these two things, automating everything that's automatable and getting growth loops to work for you wherever possible, can actually unlock some of that exist, that capacity above what just your existing system could do. So just to bring that all home, I've got like three more slides. You know, in, in the old world, in the old view of SAS native, we, we think about hiring people. I'm going to hire a person with a good network, good skills, good experience. I'm going to hire another person with a good network, good skills, good experience. I'm going to give them a quota, they're going to do amazing things. I'm going to put things in place to support them. That actually doesn't scale. That, that depends what happens when you need to hire your 1000th superhero and your 1000 first superhero. You're going to run out of superheroes pretty soon. What we need to do is flip that. We need to define processes first. Like what is the process that's going to help my customer get to impact? What are the systems that are going to help me systematize that and then how do I plug people into that? The left hand side would be like running a basketball team on a superstar. The right hand side would be like running a basketball team on a game plan. And that's what we got to do. We got to program it into our, um, into our businesses. A lot of us, um, Arvind Krishna, the CEO of IBM, just said, um, the era of AI experimentation is over. And he doesn't mean don't experiment. He means you've had time to experiment, now you gotta, now you gotta put it in place. Um, and so, and the risk right now is super scary to step in the waters of automation, to step in the waters of AI. The waters are moving really fast. It's fat. It's scary to step into fast waters. It's even scared or missed the boat. So the mistake is not that we're gonna step in too soon, it's not ready. The mistake is that we're not gonna start getting experience with it. Um, and so the, the last thing that I want to say, and then I'll probably hand it back to you, um, Scott, is there's a, there's a book, um, by Will, and I'm forgetting his last name called Unreasonable Hospitality. And this is a book about hospital or sorry, about um, restaurants and about, um, hotels. Unreasonable Hospitality. So has nothing to do with sas, has nothing to do with technology. But, but he's got a quote in there that is really, really, um, germane to what we're talking about right here. And that is automate the predictable. Thank you, Will Guardia. Thank you, um, Ken. So that you can humanize the exceptional. So we're not removing humans from the system, but we're deploying them where we need exceptional experiences, where we need to go solve exceptionally hard problems, where we need to do stakeholder management, where we need to do, where we need to go, breath confidence into our customers, where we need to go break log jams. That's what we humans are really good at doing and machines can't do. But if we've got humans tied up processing orders for renewals or processing kind of expansion orders or, you know, doing. You're running demos the same way day in and day out where a machine could have done that. Why don't we just automate that stuff so that our humans can deliver the uniquely human experience.

Speaker B: Cool. So Dave, thanks. First of all I want to make sure that I took the uh, two key go to market things from AI native companies. One where you had it under the phrase automate the automatable or automate everything was really about um, I think getting faster time to value for the customers. I think the uh, point that is very poignant there is uh, these companies are giving prospects value out of there before they're even customers. Right. Uh, and that starts to accelerate the trust et cetera that is so important to get people to uh, write a check. And then the second part is kind of this feedback loop of users becoming um, becoming your best megaphone really. Um, whether it's in communities or just one on one or whatever but, but they're very uh, good at getting this sort of virality driven by users which will always uh, be substantially more cost efficient than the traditional pipeline, uh, gen or demand gen and pipeline management stuff that we do. So, so those are the key points for from AI native gtm. Is that fair?

Speaker A: I think that is fair. Yeah. Thank you. I think that's a great summary.

Speaker B: So um, let's a couple questions. Uh, oh, first of all just a little bit of fear, uh mongering if you will. So you had that interesting slide about the efficiency crisis in the public companies and the whole left, 2/3 of it or right, 2/3 of it were basically you suggested they're unsustainable because investors aren't going to keep paying for that type of uh, economics. Um, same thing in the private world. Because, because I mean we've got a ton of these companies out there that are just have crappy economics right now. Um, any guesses what's going to happen if they don't apply some of these learnings and get to a better place?

Speaker A: Yeah, same thing in the private world. You know the most demanding shareholders in the world in my opinion are the private equity guys. Um, even more demanding than venture, definitely more demanding than public. Um, and they are demanding rule of 40 and they're tired of um, a profit only rule of 40. It's fine if you're profitable, you can go forever. That's fine. It's not like you're at risk of extinction. But none of the PE guys like having their highly profitable low growth companies. So they're pushing really really hard right now. Those companies won't go extinct but the companies who, who are in that kind of two, three, four dollars um, acquisition cost or not acquisition cost but um, cost of a net new dollar of ARR. They actually are going to go extinct. Um, like they weren't. They're not even invited to the PE party. Like they don't get purchased, they don't get rescued, they don't get bailed out, they get restructured and then they get, and they get eaten. Um, and there's a lot of walking dead companies out there that have to figure that out.

Speaker B: Yeah, well, that's part of why we're doing this particular session is to try to help these companies that might be in that situation.

Speaker A: Sure.

Speaker B: Take some of these learnings and find a way to get more efficient.

Speaker A: I'd never been in that situation, Scott, but I have. It's very uncomfortable. You know, you have a lot of uncomfortable insights about your business and yet you're scared to go work on them because you know you got it because you have a bit, let's say you have $100 million business. You don't want to go destroy value by destroying pieces of your business. But the fact is you don't have any value. Like if you have a, if you have a company that's in program shutdown, you don't have any value.

Speaker B: So yeah, you may have revenue, but that doesn't mean value. Right? Yeah, well, you showed it growth is, uh, uh, 50% R squared. So, um, Couple of questions. So assume a company's products, assuming that a company's products are suitable for kind of a PLG and, or sort of an AI native go to market motion. Where do you see most companies get hung up either, you know, transitioning to these motions or augmenting their traditional SaaS motions with these motions. Like, is it around worrying about price cannibalization? Is it sales compensation? What, what do you see people struggle to, to kind of move into this new generation of go to market.

Speaker A: Those are definitely two things on the list for sure. There's all sorts of reasons why I don't want to change. Like, humans don't like to change. We, you know, we just don't. Like, we have lots of things to say about the proposed change that's going to come mess up my world. Like, if I make money based on quota, don't mess with my quota. If I make money, you know, based on, you know, the way that I'm generating leads right now. Don't mess with that. But those are, those are like human kind of resistance things. And by the way, they'll kill your company. Like if, if somebody says, I have a friend who's an attorney, he says, AI can't do my job. I'm like, really?

Speaker B: Yeah. Seems like one of the easier ones.

Speaker A: Yeah, I think I know you're really smart and I know there's stuff that's exceptional about what you do, but how about automating everything else? And he's just not there. Like, those are the things that are going to kill us. So when I talk to teams, and I'm lucky, Scott, because often I get to work with teams who've already broken through these three things, but these are three things I got to get in my head. One is mindset. That generosity mindset that you talked about delivering value even before the contract, that's a mindset thing. You know, a commercially driven person who came up through the same kind of ranks that I did might be thinking all about dollars and cents. If you can shift your mindset to, hey, how about thinking about making an impact for the customer and then let the revenue follow. Let's, let's say that recurring revenue is a function of recurring impact, not the other way around. I literally see people and, and I've been guilty of this, say, hey, what if we make a two year contract or a three year contract, then I got them locked in, then I don't have to protect that revenue. Right. Uh, I'm sorry. Let's start with delivering impact and then the rest is going to follow. Let's not start by locking in our customer. That's just deferring the inevitable. That makes you walking dead for longer, but it doesn't make you not walking dead. So a mindset thing, I'm going to get in an empathy and generosity mindset, then I'm going to have to think about talent. I actually have to. If I want to run these experiments, I got to put my very best talent on it. Wait a minute. I need my very best talent in my core business. Do you? Maybe. But your core business is already built. This thing has to be built. Now I got to go figure out how AI is going to help me now. I got to go figure out how to automate these workflows now. And I can't use a second rate engineer, a second rate product manager, a second rate, um, systems thinker, analyst, marketer. I actually have to put together. It's a small team, it's five to seven people, but I got to put together a, uh, best and brightest team to go figure out the new horizon because that's what your competitors are doing. In an AI native world. That's who's attacking you right now with an AI native Perspective, best and brightest. If you're going to compete against them, you're going to have to have best and brightest. It's against a small piece of the business. I get it. Clayton Christensen would say this is a disruptive innovation play, and you're going to carve these out into, you know, into a distinct unit. Uh, they're called growth teams in Silicon Valley. But that's the second thing. Talent. I would call that talent. So one is mindset, two is talent, and the third thing is timeline. We really want to solve all of this, you know, by next quarter or for this quarter or to land our fiscal year. Probably not going to happen that fast. Like, I actually want to automate something takes some time. And to figure out what should be automated. Take some time. And to go run five experiments so that one's going to work. Takes some time. That's what all of our competitors are doing. They're launching 10 things, they find one that works, they promote it into the core. They go launch 10 more things, they find one that works, they promote it into the core. That becomes a flywheel over time. Um, let's just learn from them and let's go fix our business over time with the best people and the best mindset.

Speaker B: Interesting. Makes, uh, a lot of sense. So, as you know, Scale Matters is all about kind of helping companies get instrumented and then pull relevant data and curate it into insights and actually help leadership teams make better decisions. Right. So let's talk about instrumentation a little bit. You, you talked about it when you had your, um, the, the, uh, bow tie model up, uh, with the various, uh, uh, crs. What do you think? How does this whole sort of earlier impact generosity mindset that you said, uh, as well as the user LED growth, how does that impact the instrumentation? Have you thought about that at all?

Speaker A: Oh, for sure, we've thought a lot about that. And it is, it's, you know, it's tough. If I'm not, let's say I'm a human LED business, um, with, you know, sales LED business. If I'm not instrumented today to the bow tie, both halves of the bow tie, by the way, how do I get a customer into the system? How do I deliver, uh, impact, retain and expand that customer. If I don't have that instrument for my people at business, you know, forget about, um, bringing robots in, they'll have no idea what to do. Like, and forget about running experiments. You'll never know if your experiment was, you know, worked or not because you can't actually see it. Um, so I'm going to have to be instrument. I think it actually raises the bar and raises the urgency on exactly what you do at scale matters, Scott. Like, we have to be able to see how customers move through the customer journey. And if you want to get even more complex, we got to be able to see it by business unit or go to market motion. Like I said, SMB, mid market enterprise. And if you want to get even more complex, which we do, if we're running experiments, you got to be able to see it by cohort. Because now I'm going to run my AI AI cohort as a carved out a subsection of new customers. And I want to see how it performs against the normal cohort. That's like an a B test. All right? So I got some instrumentation to do if I'm going to see my business at that level of detail. And a lot of us haven't done that.

Speaker B: Um, by the way, we had one question from Ran. What does instrumentation mean? I think we're using the term to mean effectively, um, configuring tech and processes so it generates data that you need. If you think about a car, right, you got all these sensors in your car, that's instrumentation, uh, that ultimately, uh, produces data that some computer analyzes. And then it says, hey, your left, uh, tire is low on air pressure, right? That's an insight driven by the instrument. So that's what we mean is, uh, putting the sensors into your motion so it produces the data that you need. Uh, you know, Dave, in my opinion, most, uh, growth stage and mid market SaaS businesses have done a terrible job at instrumenting their go to market. Um, you know, you still have like the, the go to market people are just largely thinking about the left side of the bow tie. Finance is thinking about the right side. It's part of the reason we're trying to bring these things together. But one of the reasons is it's hard, huh? Because you need to think through, all right, if this is our strategy, what does our data strategy need to be to support that? Right? And then we've often expected the revenue leaders to be the ones that could actually implement this instrumentation, which I think is asked backwards, pardon me my French, because they're supposed to be the beneficiary of it. We would never expect, um, you know, the finance people to implement the erp. You're going to have, you know, people who are specialists at that. So my question is, do you expect the instrumentation in these PLG and AI native, uh, motions to be any better? I'm thinking you're saying it has to be.

Speaker A: They built a different way.

Speaker B: It's built into the product offering itself.

Speaker A: Yeah, but so it's not built in the system. When I say we, I'm going to say, um, uh, like SAS native. And when I say they, I'm going to say like PLD and AI native. That's not how we all grew up. I get it. But it just helps us. So we built a system where kind of our system of record, as you said, it's in CRM and it's in finance. Um, they built a system of record that's in the product and it's in tools like amplitude and heat. So they can see the whole funnel. They can cohort the whole thing. They had to fine tune this thing and cohort the entire customer journey. It may not tie back to their finance system. Or maybe that kind of wiring is a little weird. And then when you try to add, um, salespeople in and add CRM and you know, CRM speaks a totally different language and so you have to kind of reconcile that. But the base level of instrumentation for a PLG company is way better. One of our customers, canva, you know, they come in, they, they can track everything about the user experience. Everything about the user experience. Um, awareness, activation, uh, acquisition activation, monetization, everything. They could see every little thing, like running B tests. But when they bring salespeople in, like, how do I run a sales process? Like, what, what does discovery look like? What does, um, uh, what does qualification look like? Where does that information go? Does it go in the product system? No, it goes on a CRM. Oh, well, how do those two things talk to each other? So both have advantages. We're going to have to get them to talk to each other. But as Marc Benioff says, and he has a horse in the race, so, you know, discount it for what it's worth. But I believe he's right about this. We are the last generation to manage human only teams.

Speaker B: Interesting.

Speaker A: If you're going to run a human robot team, they both need to have a game plan. It's got to be written down. Like the robot's got to know what she needs to do and we got to know what we need to do and we got to know where the handoffs are. That means it's got to be instrumented.

Speaker B: So, so 100%, um, a lot of these companies, probably a lot of people on the audience today, their products don't lend themselves nicely to, um, uh, PLG type motions.

Speaker A: Right, sure.

Speaker B: Or, or they don't easily allow for self service and get earlier impact. What, what do you suggest to them? Um, suck it up. You can still have generosity mindset even though you're gonna, uh, uh, have to absorb more cost there. Uh, or what?

Speaker A: I've tried that. It doesn't work. I've tried. I've squared off with CEOs and heads of revenue with scaled companies who don't, who aren't predisposed to already believe that that's a hard sell, Scott. And then I said, well, why don't we go build it over there then? Like, you got your business over here, we'll go build it over there and then there won't be any channel conflict. And you know, we won't have that. It just doesn't work. You don't, you don't have the long term executive commitment. So what I think you want to do is go figure something out adjacent to what you're currently doing. Let's just assume that you've got a highly configurable product that is, that does not, uh, lend itself to self service activation. Okay, cool. Well, what could be automated? Um, SAP is literally launching SAP, by the way. SAP, like the most behemoth and clunky and um, kind of software in the world, is launching a sales agent right now. But it's not to sell. It's to configure pricing. M. And we've seen these kind of CPQ things, configurators, uh, so they're automating that and they're putting it into a generative AI model and they're putting an agent against it and it's, and it's writing shotgun with the salesperson. Salesperson go quicker and doesn't have to do that stuff on her own. Amazing. I can automate that.

Speaker B: Uh, there you go. Get David's book Aug. 26, called Freemium. There's good stuff in it. I've seen, uh, uh, bits and pieces of the content. I'm excited to see it myself. Thank you all for your time. Dave, thank you. Uh, I always enjoy, uh, chatting with you. There you go. Prompts my brain to go a little bit harder than it otherwise would.

Speaker A: So amazing. Thank you, Scott.