Why revenue growth breaks down (and how great companies fix it) with Dan Bernoske

Speaker A: Welcome to if Prices Could Talk, a, uh, podcast where we explore how pricing, sales and negotiation drive growth. Brought to you by Holden Advisors. Let's dive in. Welcome to if Prices Could Talk. My name is Emily McCauley. I'm here with Brian Doyle, President and CEO of Holden Advisors. We are joined by Dan Bernoski, founder and CEO of the Cortado Group, a firm that helps deliver top line revenue improvements to investor backed growth companies. Today we're going to explore all things growth, revenue and AI in the realm of professional services. Dan, welcome to the show.

Speaker B: Thanks for having me.

Speaker A: Absolutely. Let's get rolling. We're super happy to have you tell us a little bit about yourself and what the Cortado Group does.

Speaker B: Yeah, well, it's hard to beat that intro you just gave me, so thank you for that. Um, but I started, I founded Cortado Group seven and a half years ago. Now. Time, time really flies with as uh, you mentioned, a focus on um, PE backed companies is our ideal customer profile. And one of the things that I'll talk about today on and off is just how much that market has changed both from a buyer point of view and then also from uh, the point of view of what, what we offer to that market. So it's, it's been real, real exciting journey.

Speaker A: Wonderful. All right, correct me if I'm wrong, you came out of product, not consulting. How does that change how you have built a firm?

Speaker B: Uh, so product for the first half of my career, um, just for the sake of reference, started at uh, General Electric, then went to Nextel Communications, which is now part of T Mobile and spent a lot of time in mobile, wireless, Boost mobile, nascar, et cetera. So a lot of really interesting places to cut my chops and product and then moved into go to market consulting. What puts me in this really, I think unique position in today's climate is the dawn of AI, where now I'm looking at Cortado Group as almost uh, like a tech hybrid firm. So we have to productize our offerings more than ever before and actually we can because of the AI capabilities. So it's putting me in a real unique position I believe, to be able to move into this new era of AI enabled consulting.

Speaker A: Absolutely, absolutely. All right, so we talked a little bit about how the Cortado Group kind of grew up in the age of AI. When a client calls you what is usually already broken.

Speaker B: So typically we'll, we'll start with um, obviously asking, asking the big question of our client. What is the quantifiable problem that they're trying to solve. And usually it, it goes back to one of two big, big drivers. Because let me back up a little bit. If you're, if you're a PE backed portfolio company, you're on a timeline, you just got bought. There is a deal thesis that you have to meet within a limited time frame and that could be three years, five years. That timeline's getting stretched, but it's limited. And at the end of that exit is going to be the expectation that uh, whatever you were bought for is going to sell for a much higher price. And those, those value levers are typically EBITDA margin. So what does that margin look like? How is that grown? And then revenue growth naturally. Um, so typically when we're called, something is broken in your go to market engine. It's your talent, it's your performance conditions, something's happening that's preventing the proper growth to happen. And so we're called in to figure out what is that problem and then how do we accelerate the um, resolution to those problems so we can get that exit at the end. So that's typically what we end up seeing and hearing.

Speaker A: Yeah, absolutely.

Speaker C: Uh, Dan, I just read an article, uh, about, they're calling them um, zombie funds. And these are um, they are PE funds who have bought these different portfolio companies and they can't sell them. They're not growing. The thesis is not proving out they can't sell them. And sometimes they think it's like, oh well, it's the market or it's this or it's that. But in my mind it's really sort of one of two things. It's either that go to market motion that you're describing, or it's pricing.

Speaker B: M. Yeah, yeah, yeah. I mean we find, I mean, I tell you what, pricing is one of the biggest value levers in that space, isn't it? It's pretty, pretty amazing. Yeah.

Speaker C: I mean like from our perspective it, it just, it flows to the bottom line like nothing else. Which is part of the reason that, that we're in that industry. But it's what I, I like about and for listeners, uh, Dan and I have known each other for a while and, and just love the synergies between our two organizations. So um, maybe Dan, I'll put it to you and then, then I'll sort of chime in. Like where do you see the overlap between pricing and revenue ops?

Speaker B: Um, it's kind of. So I. First of all, a lot of folks look at revenue operations. So when I think of revenue operations, I Want to make sure you and I are on the same page. I'm looking at Salesforce HubSpot, the ability to produce um, really accurate forecast data, uh, very uh, informative, uh, pipeline dashboards, things of that nature. Is that, is that the way you look at Revops as well?

Speaker C: Yeah, I mean for sure. And you're the expert. So you tell us if that's how it should be defined then. Son of a gun. That's how it's defined.

Speaker B: Yeah, well, I mean sometimes um, the real close, uh, kissing cousin if you will is going to be um, revenue intelligence. So uh, things like um, comp quota, territory, design, um, how you build your ICP and segment your accounts. So they're all pretty, pretty closely related. But um, analogy I love. Honestly it's like Revops is the Garmin Watch or Fitbit, um, and your organization is that high performance athlete. And so the way I look at Revops kind um, of through that lens is that if you're, if you're collecting the data correctly. So think about Revops job is starts at the front line of the sales reps. They should be getting you things like win loss data or hey, you know, we've been going after this market. Is it the right market, get that feedback. Um, if you look at the comp and quota, is it actually incentivizing the right behavior in your sales reps and understanding what's the willingness to pay, uh, from the buyer. So the relationship is that RevOps can give full visibility and information to the pricing strategy. And so I look at those two as so, so intertwined for just that reason. There are other reasons too, but that's a big one.

Speaker A: Yeah, I want to see this up for you for a sec, Brian. What I think I'm hearing is that Revops sees the leakage pricing explains it, capability stops it. And so none of them are self executing. A great price list plus a Salesforce that can't defend value still equals a great price list. It doesn't equal results. And so I just want to have Brian talk about that for a little bit.

Speaker C: Yeah, you encapsulated that great. Emily. Uh, uh, I agree that it's these parts that don't work without the other. And really, I mean that's where Holden Advisors started was a boutique pricing strategy, period. And what we found over time was that if you can't roll out that pricing strategy in a way that helps you retain the customers that you want to retain, then it's worthless. And um, and that's where we spend a lot of Our time right now is that, that implementation of how do you make this real? And the reason I love having Dan on is that there are multiple layers of how do you make this real? And Dan is, is a few of those different layers of how do you make a great pricing strategy real. It's, you know, it's, it's, it's overcoming objections, it's negotiation from our perspective. But, but like Dan, there's so much more in terms of what, you know, whatever you want to call it, whether it's revenue ops or you know, it's, it's the strategy and the execution of doing what you do.

Speaker B: Yeah. You know, glad you framed it up that way because I say this, uh, quite a bit when I'm talking to private equity clients. Um, the, their deal thesis, uh, their ability to get a good exit on their investment is at, at the mercy of the frontline sales reps. Think about that. Because it's, that, that's where the rubber meets the road. To your point, let's just take pricing. Um, well, one of the things we see all the time is the sales reps, they want to, they want to hit their quota. They want to, they're all kind of coin operated, so they want to hit their quota, they uh, want to exceed their commission structure. And so what happens is a tendency to want to discount super easy, sort of abandon all the pricing discipline that you've put in place. So without, without having that execution component, it gets, it gets pretty, pretty difficult. And I'm sure you see it all the time. You know, you get out the best designed pricing strategy and then it falls.

Speaker C: That's right. And we refer to it as capability building, but more so it's skill and its confidence. And if you don't have those two things coming together like skill, do you have the tools, do you have the ability, all of that and have the confidence to go have those hard conversations? It doesn't work. And I had not heard the Fitbit or the Garmin watch analogy before, but it is it. Sometimes the, the Garmin watch doesn't report accurate data back right when we're wearing the watch, we're like the heart rate is the heart rate. We're not really second guessing that. But when you've got a sales rep that you say, hey, let's talk about win loss. Why did you lose this? The most common response is well, our price was too high. And that uh, may or may not be accurate. So when you complete your part of the puzzle and you're working with that subjective garmin watch, so to speak. Um, how do you ascertain if you're getting accurate information or just sort of, you know, subjective? Makes me feel good information.

Speaker B: Well you are, you just asked the multi billion dollar question. Um, because it, it really does boil down to a rep's incentive to provide good data hygiene to, to their CRM. Um, so one way though that uh, that we're able to do that in, in the age of AI, I mean this is, this is a great topic is that, and we're starting to do this for a lot of clients is this tremendous amount of proprietary data that is being collected in call recordings. So if you, if you happen to be in an industry where it's just a lot of zoom calls, for example, it's easy to do obviously if it's a door to door kind of industry and you're rolling into on premise places that you can't really achieve this in an easy way. But um, what we're seeing though is in the call recordings you get this information and you can start to really coach your uh, team on what you're hearing in those calls. So if you use some sort of qualification criteria like medic or bant or something, you can uh, plug in that rubric into your AI model, whatever agent you've built, um, ingest those call transcripts from say a particular rep and start to really hone in on what are the strengths and weaknesses of that rep and give them some coaching. So from a qualitative point of view you can uh, quantify it with pllm, which is really good. So that's number one. But number two, one of the things you're alluding to I think is that um, in spite of all these AI advancements, it really does just boil down to the fundamentals. The fundamental truth of aligning with the buyer's journey, uh, making sure you don't rush the sales process, um, and then uh, being just holding those reps accountable, uh, that that's really the only way that you can start to get any sort of accurate read on performance.

Speaker C: Mhm.

Speaker A: Right.

Speaker C: Yeah. You know what, one of the, pardon me Emily. One of the things that um, that we do too is, and it's not the sales, I'm sort of expanding the conversation a little bit. It's not just the salespeople but we um, really push to speak with our clients, customers and have those in depth interviews because then you get right from the horse's mouth so to speak of like what do they truly value, what's on their mind, what bothers Them. And Emily's on a lot of these calls, so she can attest to this. It is not unlike these customers laying down on therapist couch. And they will open up and they will tell stories of like, this is what I love. This is what I don't love, man. I'd like this to be better. And you can marry that with the feedback you're getting from the sales reps. You can marry it with the data that's in the CRM. And then that gives you a, uh, a lens through which to look and say aha. Uh, this sample of CRM feels pretty good. I think this is accurate data. Or, um, we have a disconnect here. I wonder why.

Speaker B: Yep, yep. Yeah. Interesting. Yeah. I mean the uh, voice of customer. I love that. Um, we, I could see. Do you do that in the course of uh, like a willingness to pay, study or something? Like when do you make those interviews?

Speaker C: So it would be uh, oftentimes early in an engagement. If we're doing um, pricing strategy implementation both with our clients, we're going to be speaking with their customers. And it's less so about willingness to pay. Because if I say, you know, hey, Dan, how much would you pay for this? You're like, whoa, defenses are coming up. You're like, I'm not telling you.

Speaker B: I'll pay nothing and you'll like it.

Speaker C: Yes. Yeah, that's uh, paraphrasing Caddyshack, I think you'll get nothing to like it.

Speaker B: Yeah.

Speaker C: Uh, no, uh, Mr. Scholarship winner. No, what I'm, uh, what I'm referring to is, uh, sorry, um, is that we'll, we'll talk to customers about what they value, how they use the product or service, what it means to them, what they're able to do because of it, what they wish would be better. And that feeds back into um, a value perspective, a value calculation that then in turn helps us to set price versus a, um, survey. Um, going the other end of the spectrum, a survey about willingness to pay. Um, because you can people. When you're talking to people, you will uncover things that you might not have thought of to ask in a survey.

Speaker B: Yeah, it feels like a lot of the follow up questions are where, where the gold is as well, isn't it?

Speaker C: Well, for sure. And that's why it, it takes a special person to be quite honest. It's, it's not. And, and I'm not that person. It takes a, A uh, skill set to peel the layers and really get to the root cause of why people feel the way they Feel, Yeah. Um, it's unique and it's wonderful to work with people who can do that. You listen to them, um, on a call and you're like, m. Wow, okay, nice job.

Speaker B: Talk about the therapist couch. One of the things that we've found is that, um, those conversations you're talking about are really, uh, uh, effectively repurposed for messaging positioning in the market, um, creating, um, uh, like sales plays that we could roll out to the. To the salesforce to help them preserve that value. Um, so I feel like the work that you do in Voice of Customer is yet another amazing piece of, uh, or body of work of um, proprietary data that can be used in so many different ways. So, yeah, huge fan of Voice of Customer. In fact, every. I think everything has to start there. Um, you know, when we just kind of riff on that a little bit. Back to kind of your comment around preservation of Value. Kind of early on in this discussion, um, we found with the fundamentals that are going to be true regardless of AI, everyone, you know, I'll make this comment. What we're hearing in AI is everybody thinks is silver bullshit bullet. And I can just turn every sales rep into an AI bot. And, and there's. That is, um, that's not true. Because the job of the sales rep is. Is empathy and the ability to. Like you're talking about. Those questions during the discovery process are so critical to know. How do I truly understand the problem that. The unique problem that this customer is facing. What are the root causes? And uh, during that process, it seems like a lot of trust is built. And then the buyer says, well, why don't you show me what the solution is if they're asking for it at that point. So I feel like the willingness to pay studies, um, or not willingness to pay. I'm sorry. Those Voice of Customer interviews that you do are such a great head start for the frontline sales reps, um, that we see. So it's just really so valuable.

Speaker A: Well, that's closely tied to what your product or service does to the bottom line of that customer, which creates power in the negotiation and leverage.

Speaker C: Yeah, right. Because sometimes, Emily, to your point, you have built this relationship that uh, Dan and I are talking about. And then you're like, okay, great. I think we're. I think we're on the same page. Let's go do something together. And then your customer says, uh, yeah, hey, really sorry, but here's my sourcing representative and you'll need to finish the conversation with them. And now it's like, well, and then that person says, you're, you're priced too high. And you're like, where, where I was, I understood the problem. There's this empathy. There's this, you know, like, now I'm stuck with the sourcing person. And if, if you haven't started with empathy, if you haven't started with that real decision maker, you are completely in trouble because now you're only talking about price. It's hard enough when you have to switch to price a little bit, but then there's that person you can potentially go back to and say, hey, I just want to be clear. I'm taking, you know, I'm taking my price down, I'm taking my value down. That's not what we talked about. Are you okay with that? And then, um, you know, oftentimes it gets reversed to the sourcing person's trying to do their job, but they're, they're not in the know of the, the several conversations that you've already had with the uh, actual buyer.

Speaker B: Yeah, it's, it's interesting, um, when we, we ah, very often analyze the CRM data from our clients and if, if they have the good data, which is kind of hard, but really good, clean CRM data, uh, captures a couple of obvious three, three big KPIs, and one is going to be average sales price. So is the ASP going up? How's it trending? Is it going up, down? Um, the other one is win rate, obviously. So are we winning more deals or not? What is the benchmark against our competitors? Uh, or more importantly, are we improving over last, uh, quarter? And then the third one is going to be sales cycle length, which is a huge indicator M. So when we look at the CRM data, uh, and if clients are really good, they're getting um, the average time in the phase of the buyer's journey or the sales process. And so understand my problem, it goes to uh, um, you know, uh, define, define the solution and then resolve my concerns. Like those are the kind of three, three big common stages and there's typically this bell curve where the reps, the good ones, are spending most of their time in the understand my problem phase.

Speaker C: Yeah.

Speaker B: Then it just kind of gets quicker after that. Um, and so then, then in that, understand, like those early stages, you're

Speaker A: putting

Speaker B: uh, a quantity quantify, you're quantifying the problem. Um, and then you're able to say, hey, look, here's the expected lift that outweighs the investment. Um, and it feels like that's a great place to preserve the price that we're seeing on the front line. Curious if you kind of look at it the same way or how you look at it from your perspective.

Speaker C: Um, I think that that is a. You're making a very important statement there. And what I'm translating it to is you are investing up front as a salesperson to understand what the real problem is of their customer. And when you do that, you might spend a little bit more time there, but the rest from there accelerates. Yeah, yeah. And it's. So I, uh, was recently asked to speak at a global sales meeting of a very large client of ours. And one of the exercises, um, the facilitator was taking the group through was, okay, I want you to write down the problems that your customers face. And it could be regulatory problems, it could be environmental problems, it could be whatever. What are these problems? And then, you know, okay, share. You know, share with the group. And so what we heard back was, um, we have a great solution for this problem. And it's like, well, no, the exercise was to, what is their problem and why is it their problem? And what does it do to them? Um, and what you did was you went from, I solve this. They probably have this problem. It almost doesn't matter. I solve that problem. And let's start talking about me and the product and service that I provide.

Speaker B: Yeah.

Speaker C: And it's like, well, yeah, like, to your point, Dan, you just got through that first step really fast. Hooray for you. But that next step and the step after that might take forever. Like, literally infinitely, because you're never going to close a deal because you weren't listening on the front end 100%.

Speaker B: You get. You get stuck in the next phase. You get, um, you suddenly open yourself up to competition. Because imagine, uh, the buyer getting kind of frustrated. Like, this guy didn't even listen to me. I'm going to start looking for alternatives. And now, all of a sudden, the sales rep could unwittingly create a competitive environment and really slow down the process. M. Yeah. Kind of pretty interesting.

Speaker A: So do you want to talk about negotiation or AI Next?

Speaker C: Um, so Dan's got some really interesting things going on with A.I.

Speaker A: yeah.

Speaker C: And it's, um. And to sort of tee it up, um, Dan, you and I travel in similar circles. And what we're seeing is that AI is swallowing a lot of professional services firms, but they are not swallowing your firm. So what are you doing and why is it working?

Speaker B: Yeah. So, uh, first thing is, if I think about professional services firms, whether they Thrive or not, it obviously depends upon the type of firm. So we're squarely focused in go to market consulting and we're an execution based firm. So I would say first and foremost we're starting from a pretty good spot as it relates to um, AI not being a threat to what we do. Really, uh, a compliment. If I were a strategy firm I would be a little nervous because nowadays you can train your, you can give your LLM context and do a lot of, lot of strategic thought pretty quickly. Um, and so I see the strategy firms getting really greatly affected in a lot of ways. But we're in that really good sweet spot of execution. Um, the other thing that's helping preserve what we do is the fundamentals. Kind of alluded to that earlier that the fundamentals of things like following a sales process, uh, knowing how to talk to a customer, all the fundamentals, uh, are true regardless of AI. And we spend a lot of time implementing those, building out those fundamentals within a go to market, uh, engine of a Portco. And then we help teach and coach and sustain the usage of those fundamentals long term. So again those are things that can be enabled by AI, uh, but not necessarily replaced by it. And so I think that puts us in a really, really unique space. Um, and then the last thing I'll comment on is, um, there's still this fear that AI is going to replace jobs. And maybe in a lot of ways that might happen. But we're, we're starting to see that jobs are shifting with their role description is changing or while some things get deprecated and sort of discontinued, new job opportunities are popping up as a result. But one thing is for sure is that um, if you're a firm that knows how to use AI, then as opposed to a firm that sort of shuns it, the ones that know how to use it are going to thrive. As long as we continue to embrace AI as this complementary skill set that helps us do things, uh, either better and faster, then that's going to help us preserve our place in the market. Those are the big areas that we see.

Speaker A: Uh, Dan, you have cited a number that I wanted to just ask about that only about 12% of PE operating partners had stood up a working AI organization in their portfolio. What did the other 88% get wrong?

Speaker B: That is such a great question because, um, so we experienced this over the last 12 months. So that, that was based on a 12 month snapshot that was taken I think last month. Um, and what, what we found was that all the other Firms either, um, try to do it themselves, but without any sort of centralized management of it, or worse than that is, they did nothing at all. And they said, listen, uh, right now is the Wild West. There are hundreds, if not thousands of AI applications kind of coming online. They're dying. Like, all this stuff is happening. And so a lot of the PE firms just said, hey, let's just wait and see where it lands. So the whole, um, do nothing, and then the other one, do it, do it yourself, uh, which I want to talk about here in a minute. Those are kind of the two things that happened. And now a year later they're coming back saying, holy cow, we just lost 12 months, now we need help.

Speaker A: Yeah, yeah.

Speaker C: So. So keep going. So what, what do you do to. Yeah, the guys who said, you know, do it yourself, or, um, or the, the. I mean, the do nothing is like. I mean, it reminds me of the Internet, when the Internet came out. Yeah, I mean, I don't know who was, who would have been the person who said, this is going to blow over, but there were people saying it, and I think that they're saying the same thing about AI. So, okay, great, head in the sand. Hooray for you. Um, but how about the people who are trying to do it for themselves? Like, yeah, how are you helping those folks out?

Speaker B: Yeah, um, it's funny, when we were at, when I was at ge, was when browsers are starting to hit the market, I remember someone asking me, hey, can somebody give me the user manual for the Internet? I always remembered that moment in the office and I thought, this is amazing. Um, anyway, um, one thing worth commenting, um, the do nothing is that if you're going to wait for the dust to settle and for stability to happen in the AI market, you are literally going to wait forever. So I think that we've got to get used to the fact that this is the new normal. The Wild West. Things changing is just the new normal. I mean, you and I have experienced, we've all on this call experience that, uh, six months ago, ChatGPT was the hottest thing, and now it happens to be Claude, and that that fight could continue or something else can pop up. I mean, it's just the nature, nature of the business. Um, but the do it yourself thing is, um, is okay if there is structure to it. And one of the biggest things that companies, uh, and leaders take for granted is that if, first of all, if I. You're sort of, you're stuck. Like if I let the. My employee base just kind of run like feral cats, you know, just go do your own thing. That's one extreme. The other one is I am going to lock it down. And this is the app you have to use. And if anyone uses anything else, we're going to get fired. You know, like this, this draconian kind of style, these two extremes, uh, ironically get you to the same place. And the reason why they get you to the same place is because, um, if you lock it down, employees are just going to go use their own stuff. Um, it just like the feral cats, everybody's going to use their own thing. And so what? One of the big risks there is that you start to leak information into the market. Your proprietary data sets, any sort of confidentiality, if you're doing, um, any coding, um, who knows what kind of, um, protected IP that your people are pulling out of those LLMs and putting it into your product. There's all sorts of risks there. So the ones who failed at kind of do it yourself, uh, and didn't get control in either one of those areas, they ended up with an environment where there's no governance. Really. You got to have some sort of rules in place just to protect things. Um, you also, uh, end up without a champion. So really the key is to identify people within your company that are not only passionate about AI, but they are doing all the research on your behalf. They've got their finger on the pulse and they will champion the movement from the grassroots up. That is absolutely critical. But the last thing I'll say is, uh, context. So I was listening to, uh, a podcast not too long ago, uh, a 16Z, um, which is a big, the VC Andreessen Horowitz out of, out of San Francisco, and talking a lot about the need for context to get pulled into those models. And context is your centralized database, whatever it might be, um, whatever that central point is, it's pulling in all the critical information and data relevant to your, your company and your market. So think of things like your CRM data, um, your win loss analysis, all those zoom calls or wherever you're, you're capturing transcripts, your icp, your buyer Personas, the list goes on. Uh, those who have succeeded are building and have built this context engine. And uh, that context engine then serves as this absolutely informative point of truth. So now when I put the large language model on top of that, I'm actually getting far better, far accurate results. When I start speaking to the market, when I start enabling my people, everything is within the context of the world that I've created. Uh, the ones who, that 12% who have been successful, they recognize that everything I just talked about. So they anointed, uh, the uh, champions internally. They had pretty good governance, not overly draconian, not too free, right in the middle. And then they started to build the context engine on which they've layered the LLMs. Um, that whole mechanism has been taken for granted, uh, in many cases, but if you can get that right, then all the other stuff kind of flows from that in a very, very successful way.

Speaker C: Yeah, it's um, the, the word that comes to mind is intentional. They're very intentional about what they're trying to do. They're not all the way one side, all the way the other. And we see the same thing in pricing strategy. It's like, are you being thoughtful about that? You could potentially do it yourself, but you have to be thoughtful about it, about how all these pieces are going to come together. And like, one element I'll add to what you just said, Dan, is that, um, don't think for a minute that this, all of this AI stuff that you're describing, you could equate the same thing to pricing is if you do it in house, don't you for a minute think it's free? Uh, that's what, that's what happens a lot is, oh, we'll just do that in house and that way we don't have to pay for it. And if you're being, if you're, if you're being intentional, then you're saying, I am taking this person and they're going to be in charge of this part. I'm taking that person and they're going to be in charge of that part and I'm ultimately going to pay for it internally. I'm going to build that context like you were talking about and I'm going to do it the right way. I want to do it. Fantastic.

Speaker B: Yeah.

Speaker C: But if you think you're going to do it for quote unquote free, like when people have an extra half an hour on a Friday afternoon and they're going to go build their go to market strategy or they're going to build their pricing strategy and now it's free. Uh, it's one, it's not going to be very good, and two, it's probably never going to happen because people don't really have that much extra time.

Speaker B: It's really, that's such an interesting point. Um, pricing is a, it's a big investment. And I think that, um, a lot of the C suite that we've dealt with. Um, they don't realize that it is in fact an investment that, that will create more value down the road. Um, in what ways are you seeing that sort of corner cutting happening with AI? What are they, are they trying to sort of replicate those voice of customer interviews by creating a customer bot and then interview that? Like, what do you, what are some of the things you're seeing? I'm super curious.

Speaker C: Yeah, I mean it, it's, I think even, I think if they did some of that customer research with their bot, that would actually be better than what they're doing. What we're finding is that it becomes very internally focused. It's like, oh, I know what our pricing strategy should be because a, it's part of my thesis. It was part of your thesis. Let's see what it is now. Or this is what we think the value that we're creating for everybody should be. It is. My salespeople aren't doing a great job. I just need to get rid of some of them and replace them with other people. But it's all like internally focused and they're doing the cycles with that instead of being really thoughtful about how their customers perceive their value. And when they do that, that helps them not only set the right price, but it gives them much more insight into the metric. So we have a lot of folks that we're talking to right now that are, uh, they, they used to be cost plus and, and AI is eroding that because they're getting so much more efficient. It used to be 20 hours to go do a project, now it's 10 hours. What do I do? Do I reduce my price? Like how am I going to keep my revenue where it needs to be? So there's folks who do cost plus and there's a lot of conversation, um, into us about value based pricing, outcome based pricing, how do we do that? And so they're, they're getting like this little slice from AI that's helping them feel like they're making progress in terms of value based pricing or outcome based pricing, which when in fact it's only given them a fraction of the picture and they don't have that customer context, to use the word that you use. That's great. Um, and so it's unsuccessful.

Speaker B: You know, it's really interesting and I'm glad you brought up the cost plus pricing. Um, so I would say that this is an area, uh, we ourselves are not a cost plus pricing type of shop. However, I know a lot in our peer group are.

Speaker C: Yeah.

Speaker B: So think about the perception of the market is that it's cost plus in many ways. So I'm a consulting firm, so thinking about going in now saying, hey, we use AI to help deliver this work. I'm m using less people. I'm going to do it in half the amount of time. The tendency is for, uh, the client or the prospect to come back and say, well, great. And it sounds like this time around it's going to be half price.

Speaker C: Exactly.

Speaker B: What is, how do you preserve that?

Speaker C: So you have to be having those value conversations about what you're actually doing for that client and what they're able to achieve because you're doing it. And if you're grounded in, I help you. You remember last year we did this, this and this and Your revenue increased 20%. Like that is meaningful. Oh yeah, that's right. You guys help us increase our revenue. Anchor them on increasing revenue or decreasing costs or sometimes minimizing the risk associated with their business model. That's where that conversation needs to reside. Not how hard I work to produce PowerPoint slides or to produce a marketing campaign or produce, um, your taxes at the end of the year, whatever professional services you want to tie it to. And I think that's, that's the biggest difference. And that's what we're, you know, for us it's, it's twofold in terms of education. It's, it's helping our clients get their heads around that. And typically if they're working with us, they're part of the way there. Yeah, sometimes there's a customer education component as well. And some groups of customers and the services they consume are more apt to sort of understand that. Okay, let's tie this to outcomes. And I can believe in that. Um, what I find is that if you want to talk about outcome based pricing with your customers, you need to be talking at a very senior level. So like CEO, cfo, those people can understand what you're trying to do and that you're partnering and you're working together to get them outcomes. If you're talking to a middle manager or the sourcing group, they're not going to get it. And they're going to say, look, the guy down the street is 30, 50, 70% cheaper because they're using quote, unquote AI and we're just going to go with them.

Speaker B: Yeah, that's really interesting because. Yeah, I'm glad you brought that up because these two buyer groups are, um, their, their personal success is measured very differently. You know, the, the, the, say middle management is going to be judged on their budget and how, how much of it they've been able to preserve. Are they able to cut costs? Whereas the CEO, um, in the C suite they're going to be, they are going to be judged on those outcomes. M. Well, one of, one of the things um, that, that we are hearing as um, measures of AI success. This is a really good segue into this topic of um, many PE firms and portfolio companies are saying hey, are AI successful? You know how I know? Because 90% of our, of our employee base is using AI. Um, there was that little trend there for a while token maxing where it was like, well we know it's successful because they just blew our token budget out of the water. Um, those are both in my opinion, uh, well, not in my opinion, it's actually kind of talked about quite a bit. Those are vanity metrics. Great. You're using it, uh, you're using tokens. The metrics that are emerging now, there are two of them. One of them is revenue per employee because what that basically is telling you is that you're able to um, get more productivity out of the same number of people or maybe less. So this whole revenue per employee metric is really emerging as a um, a leading indicator. And then the second one which is a, makes a lot of sense, great counterbalance is going to be net revenue retention. So nrr. And what that's telling us is that all right you're getting a lot of efficiencies but you're not sacrificing the quality of what you're giving to the client. And so we're retaining that revenue. We are still retaining. In fact they keep coming back because your service is still really, really solid for them. Those are two great metrics that we, that we're starting to think about from that outcomes based approach.

Speaker C: I love that.

Speaker A: Tell us a little bit about. So we've talked about some of the trends that we're seeing broadly with AI, but we've talked a little bit about very senior level buying groups and it sounds like you, you engage with a lot of, of um, CXOs all uh, over the world. Um, what are some of the less obvious trends or concerns that you're seeing before we kind of wrap up in the next couple of minutes here?

Speaker B: Um, one, we've got a couple of them. One of them we alluded to earlier and that is just a trend that adopt, not that adoption isn't happening, it's just not being done the right way. That to me is quite troubling. So specifically around, uh, not just providing a company sanctioned tool, but, uh, making a tool that's better than what somebody could get in the open market. If I think about the choice that an employee has, look, either I could use whatever Cortado Group has mandated for me, or I could just go use uh, my personal cloud account. The only way I'm going to get them to use my tool is if my tool just gives them a heck of a lot more value. So one less than obvious trend and concern to me is that companies uh, aren't catching onto that quick enough. I got to make my internal tools more valuable. The other thing is, uh, the lack of adherence to the fundamentals. This, this notion that AI is kind of this magical shortcut. I don't have to worry about the fundamentals, um, but the reality is that they matter no matter what. And those fundamentals being starting with the problem. Um, Brian, you alluded to it like be, don't, don't be inward focused. You got to start with, with the problem, the buyer's point of view. Um, and so that, that would be the really one of the second biggest trends that is a little worrisome is the abandonment of the fundamentals.

Speaker C: Yeah, yeah, that makes sense. That's great.

Speaker A: Absolutely. And. All right, last question. We've touched a little bit about how jobs are evolving. I read once a number of years ago that the kids who are being born today, the jobs they're going to have as adults don't even exist yet. And we can't fathom them. So what bringing that back to this conversation, what is the belief that you had about this dynamic today that you had five years ago that you maybe don't have today?

Speaker B: So, um, you know, five years ago I would say that I would have thought that growth for a firm, um, especially my firm, let's look at it from a Cortata Group point of view. I would have thought that growth would come through adding Headcount. And in many ways that's true. You got to have more and more experts. Um, but it's, it's, it's not just Headcount that knows how to use Excel really, really well and develop really beautiful PowerPoint slides. It has to be smart growth. And so the shift is obviously going toward AI savvy headcount, uh, for sure. Um, from the, from a private equity point of view, I thought five years ago, during the roaring days of the early 2000s, is that PE was just going to go on forever. Um, so what happened? And actually Brian, you alluded to this earlier with zombie funds. I mean, interest rates rose, buyer behavior changed, cash is just more restricted, and there's this huge downward pressure on pricing. Hold periods are getting elongated. Um, and so a lot of cash is being kept on the sideline. Um, so those trends are, are happening. Um, but I would say that it's, um, kind of a good time for AI then.

Speaker A: Absolutely.

Speaker B: You know, to be able to fit into that model.

Speaker A: Absolutely. Dan, thank you so much for joining us today. How should listeners get in touch with you if they want to learn more about you and what the Cortado group does?

Speaker B: Well, they could certainly connect with me on LinkedIn. Uh, that would be great. Just, you know, tell, tell me how, how you heard about me on this on this particular podcast and connect with the ambernoski on LinkedIn. And, um, that would, that would probably be the quickest way. Um, they of course could email me as well directly.

Speaker A: Fantastic. And Cortado is www.cortadogroup.com. this was fantastic. If you got something out of this, follow if Prices could talk wherever you listen from and you can come find us at Holden Advisors. We will see everybody next time.

Speaker B: Thanks so much for having me.

Speaker C: Thanks, Dan. It's been great.