Everybody Has AI. Nobody Owns It.

Speaker A: RevOps AF, the only podcast for people who are RevOps AF and want to

Speaker B: learn all about things that are RevOps AF.

Speaker C: Uh, our goal is to give the revenue operations community that includes marketing, sales and CS for any of you not paying attention the skills needed to accelerate their careers.

Speaker B: Hello everybody and welcome to another episode of RevOps AF, the podcast where we sit down with people who are RevOps AF to talk all about things that are RevOps AF. I am your co host, Matthew Volum, CEO and founder of RevOps Co op, a community of 20,000 plus operators from all across the globe. And today we are going to be talking about decentralized versus centralized AI, along with some AI implementation stories. And we are sitting down with Brandon Bussey, who is a VP of Revenue Operations over at Advantiv. Brandon, thank you for being here, my friend. Why don't you first tell us a little bit about yourself, your professional journey and your current role at Advantiv, as I think that'll set the stage for the topic that we're going to be diving into today and then I'll start to pepper you with some more questions from there.

Speaker C: Love it. First, Matt, thanks for having me on. Big fan of the community. If people aren't actively extracting value from it, that's on them. Love, love all the, uh, content that you all produce. So thanks for that. So quick introduction to myself, I guess have a unique background. It doesn't feel super unique because it's mine, but I kind of grew up in the FPA space, so I grew up in finance and then had been dancing around revenue operations for the first, I don't know, 10 years of my career and then found Revops and kind of fell in love with it. So started my career at Concur Technologies when they were a cool, sexy company before they were acquired by the juggernaut and whatnot, and then spent a number of years at Amazon as well and got to see a whole different scale of revenue, which was a ton of fun. And then that's when I said, you know what? I want to get closer to the business. Dove into Revops and kind of started building Revops at multiple places. I was at Qualtrics Lucid Software and then did some consulting for a little bit. I realized I personally don't enjoy consulting. I'm an operator at heart and want to be part of something and a builder and that just wasn't my jam. So that's where I decided to kind of go back into the market and found Advantiv and Advantive We're a private equity roll up. So we are right now the amalgamation of. We just did an acquisition. So I think we're at Ah, 17 companies that have now been put together into our platform and we service, manufacturing technologies, ERP distribution, quality management software all fit into our platform and we're based here in Tampa Florida. So that's a quick introduction to kind of myself and my roundabout journey here.

Speaker B: I love it and actually very similar I'll say to my journey as well. So I know I started my career after college. I was in finance and accounting, did public accounting for a little bit, did corporate finance, strategic finance for a little bit. I too experimented with consulting and while I loved the variety of work that a consultant uh, job can provide, I needed to be, I needed to be in it for feel like I was executing and like getting stuff done. And that was the reason why operations was a big draw for me. And I think it's a big draw for, for a lot of people. And obviously I might be a little biased here but I think finance is the best background to have for, for revops. But it is actually very valuable because it allows you to truly connect the dots between the inputs that you see as an operator every single day in the business to the financial outcomes that ultimately make their way into the financial statements. And revenue is obviously probably the most important metric that majority uh, of businesses and companies care about today. Yep. So you brought up your journey through, through finance into revenue operations and the different paths that you've taken to get there. So today I mentioned we're going to be talking about AI and this whole notion of centralized versus decentralized when it comes to, I don't know, AI adoption, deployment within companies. There's a variety of way that people can handle this and I think the default so far that a lot of companies have chosen is a uh, decentralized approach. It's hey, AI is going to make everybody more productive. So let's like give it to everybody and make sure that everyone has access to this. And as I was thinking about that it sounds somewhat similar to this whole model we had with siloed operations teams in the past and now moving to a uh, centralized revenue operations teams and seeing the value that can be had. Like I said, a lot of orgs have started with this decentralized ops team. Now we've moved into consolidating centralized things into revenue operations. I mentioned what we're seeing with AI when it comes to workflows, agents, automations, things like that. So my first question for you is just me, or does it feel like history is repeating itself a little bit here? And why.

Speaker C: Yeah, it's interesting. I definitely think like, AI is so approachable. Right. That's what makes it so incredible. Right. Like you don't. It's the first time people finance people, people finance backgrounds can do like really dangerous stuff like coding and things like that was never accessible to us before. So that's awesome. Right? And I love that. But then just because it's easy and approachable doesn't mean that that's where you should be spending your time. Right? The problem is this is where it really, as I've been thinking about this a lot of how we're. I think, I don't know, a good majority of companies, they've got either a license to Claude OpenAI, you name it. And then they just threw it out to the masses and said, here you go. Right. So. Which is great. I actually think that's the right first step. But then as you kind of go through the like maturity, you run into some, I would say, challenges. Right. So all of a sudden I've got sales reps building these agents. They're connecting to the salesforce, mc, a bunch of data and guess what? It comes full circle to where now I've got a sales rep publishing some like HTML dashboard that they've created. And guess what? They didn't use the right filters to based on our definition of bookings. Right. And there's a bunch of tools that are kind of popping up to be that kind of governance layer. But then it. Fine, we could solve that if we really wanted to. But then stepping back, do I really like. Is it really what I want are sales reps. I'm just picking on sales reps. It could be. Should they really be spending a bunch of their time building analytics? No, that's not worth their time. I need them focused on their job. Right. And now there's obviously use cases within that kind of that lane that they can go use, you name it, ChatGPT, Gemini, whatever your LLM, uh, of choice is. But it's just, I actually think it's. It's this distraction factor of. All of a sudden everyone actually thinks productivity, like the volume of things you're doing is like exponentially growing. But like the quality is like not. It's not focused and is it really driving kind of the ends that you need? Right. Are you selling more? Are you getting more booked meetings, whatever. You're kind of your seller. It's like we have very clear targets of what you need to do. And so I've started seeing a lot of these kind of use cases, or maybe not use cases, but issues popping up in lots of companies. Like, we're not the only one where you. You have that. So then, like, you can. I see then now this concept of fully centralized is. You see it with FP&A is a great example, right? In that they're a very centralized function. And one of the challenges that you see a lot of times in FP&A, as I was a central FP and a person at one point, is you just have no business contact either. So then you kind of like the pendulum swings too far to where you've now got this centralized AI team that's building agents and they just don't like it. They don't have the context. And then ultimately they might go build an agent. And it's this concept I'm kind of calling the agent graveyard, where they go build a bunch of agents, throw it out to the field, but then no one owns them and manages them. So it's like you've got these two extremes right now. And so trying to find that happy medium is where we're spending a lot of our time of how do we, like, really drive meaningful productivity? And I think there's like a world in the middle. So I'll kind of pause there. I kind of threw a lot at you.

Speaker A: Uh, no.

Speaker B: As you were sharing the example of the agent graveyard, I started thinking about the dashboard graveyard that a lot of people in RevOps are very familiar with, where you can get a lot of different requests from different people, executives. Everyone's that says, oh, like actually, like, I want to see the data like this way, right? Like by channel, by segment, by person, right?

Speaker A: Something.

Speaker B: Something like that. And you create all these things, right? And you spend time on all these things, and then they never get looked at, or they get looked at once and then they never get looked at again. And I think with agents or AI, uh, you can run into the same sort of thing where it's like, if you don't have that context, then you ultimately are just taking requests, building things without any business context. And people, more likely than not, maybe use them once, forget about them, and move on to the next thing. And so there is this place in the middle I think, that you need to find. And easier said than done. It sounds like you haven't necessarily found that place yet either.

Speaker C: Yeah, it's interesting. A couple reactions to what you said. One, like, I'm, um, strategically adding friction to a process can actually be very beneficial.

Speaker A: Right.

Speaker C: And I remember I used to work with an, uh, individual that he was like our salesforce for his title. But we'd go say, hey, Eric, go build this. And he would immediately pause and ask for business. He'd run us through this, like, list of what's the business impact? What's the justification? It always, like, frustrated me, but every single time it was such a worthwhile exercise because I don't know, 70% of the things I was going to ask for, like, we didn't build because it's like, yeah, okay, I'm convinced that's actually not worth it.

Speaker A: Right.

Speaker C: And so I think, like, in this world, adding a little bit of friction can be a good thing because people can start thinking about it and really just through the kind of critical thinking of, hey, is this an agent I'm going to actually use or is this just a novel thought that I'm.

Speaker B: Yeah.

Speaker C: Um, how we are starting to literally, I just rolled out a role this morning on LinkedIn is we're going to try more of a. I don't know what the appropriate term is, like a hub and spoke type model. We have a centralized team of agent builders here at Advantiv, and we're starting to see some really good results. But one of the things that we're going to introduce is almost like I'm, um, framing as like a PM or someone that is that intermediary layer. And so someone that will sit on our kind of go to market or commercial operations team that will start to funnel requests and then they can build or they can throw to the central team to build, but they, like, are the gatekeepers. And then once something's built, they own the agents going forward. Because that's the big problem, I think so many companies aren't thinking about is like, build an agent, you name it, and then it just vanishes somewhere, right? It vanishes or it starts to veer off and like, you got to keep, like, it's like an employee, right? You got to keep it in check and monitor it, make sure the output's still quality and those types of things because the business changes. But two, like, we all know there's just like natural drift, I think, with the agents that they're going to start doing goofy things if not monitored and those types of things. That's how we're thinking about building this kind of. I don't know if it's a hub and spoke model or how we want to think about it, but someone that's almost like a project Manager, or, excuse me, a product manager for kind of go to market AI agents.

Speaker B: What's the job title for that role and will they roll up to revenue operations into your team or somewhere else?

Speaker C: Yeah, so we've called it a go to market AI specialist I think is the official title. And yeah, they're going to roll up into revenue operations.

Speaker A: Yeah.

Speaker B: So a couple things on what you said. So first you mentioned the, the uh, friction that you can insert into processes. I was having this conversation just yesterday with someone. We were talking about the natural tension that can exist between sales and marketing teams. And they were asking me a lot of questions on how to drive better alignment between their teams and remove all this tension. And I was like, hey, tension's okay, it's not. And friction's okay. These aren't bad things. Your job is to not completely remove them because they, they can serve a purpose and they can add value. So what you need to do, whether it's friction or tension, as we're talking about here, you need to find the right balance of those things so that it's healthy friction or it's healthy tension. Like you said, the friction that was introduced in the process you brought up probably eliminated 70% of stuff that would have otherwise gotten build and disappeared into some, to some to some graveyard. And then the, the other thing that, that you brought up on this, especially with this role that you're hiring for, is I think the best revenue operators are going to be the ones that do view, whether it's the tooling, the systems, the agents, the process, the stuff that they're building as a product where they have end users, people or agents. Right. That they're serving and that's what they own. And I think just generally the ease of which people can vibe code and build things today doesn't mean if you don't have that product mindset, you're just gonna build a lot of garbage. And that's also why I believe a lot of people are talking about how coding is gonna kill SaaS and software and all these things. And it's like, I don't follow that. I don't believe in that. I don't think that's true. Because you uh, like it's so much more than what you build. Like you said, there's a product experience that you need to wrap around this stuff. And if you don't have that, it doesn't matter how easy it is for you to spin up an app and afternoon, if there's no product view or product ownership or product approach to it.

Speaker C: Yeah. One thing that's interesting, and this is another piece that I've been. We've been spending a lot of time thinking about is like building. One of the things in this job description is I want them building agents where reps are working. So let me give a little bit of history and context and I'm just picking on sales. But this applies to all the kind of revenue teams that we support. About a year ago, when AI was really taking off, we wanted to evaluate our tech stack because we had a lot of contracts kind of coming up for renewal and those types of things. And being more private equity and my finance background, I'm always looking at costs and those types of things and I've seen how much we're paying for some of these tools. And I said, let's look what's out there and let's also look one from a cost perspective. But then also like AI forward thinking. Right. And so we ended up like completely not overhauling our whole tech stack. We're still Salesforce Shop and those types of things. But we brought in Glyphic, which is now Airspeed. They're a, uh, call recorder that is very. The LLM is like central to the user experience. And then we just recently brought in Letter AI, which is our, uh, content management session. Excuse me, content management system as well. Like, we replaced some of the kind of household names that many people are used to. And sure we were able to reduce costs, but also, more importantly, it took a like a user experience that they were very. That reps were used to and had understood and then basically put AI on top of it. Sorry, give me one second.

Speaker B: Yeah, all good.

Speaker C: Okay, let me start that one more time. So I apologize. Uh, we should be good from now on. As I was mentioning, we pivoted to letter A and Glyphic now Airspeed, and our reps have quickly grown accustomed to those platforms and interfaces because they're very AI forward. You can build agents directly in there. And so rather than having them go do their call recordings, review call recordings, but then jump to other platforms to do really tangential things or, excuse me, things that are very similar, we have them do it all in the platform where they're doing kind of the job to be done platforms. For example, we have agents in Glyphic Airspeed where they do all their call prep, right? It goes and pulls in summary of all their notes, pulls in like Salesforce data, and then puts together a prep sheet right there.

Speaker B: Right.

Speaker C: A lot of companies are solving this with a claw job or some sort of ChatGPT agent, you name it. Right. And so it's sure that's. I'm not, that's not necessarily the wrong way to do it. But when you create agents, like being very intentional about what, because you can create them in lots of places, but being very intentional about where you create them because it should fit naturally into the rep's workflow. And for a lot of these things, a, uh, lot of these agents being very critical of saying, hey, this is what it's going to do, this is where it fits into their workflow, this is the tool stack they're going to be in during that workflow, then build it there as opposed to just one centralized resource. And so that's one of the things we've been really. And this role is going to be really intentional about is thinking about where they build agents and being somewhat like have to be a little tool agnostic. Right. Because we want to build these agents where they work because that's where they're going to get used better.

Speaker A: Yeah.

Speaker B: It's interesting and correct me if I'm wrong, but it's interesting because there's kind of a contrast between what we were talking about before of moving to a, uh, like a, uh, like a centralized model or approach when it comes to like building around AI. But then what we're talking about here, with how you implement it, at least as it relates to using sales as the example, is implemented in the tools or the systems or the places where people are already working. So not necessarily saying, hey, we're going to put everything in just one spot and we're going to all the agents and this is where you're going to build the agents, use the agents, do things with the agents. Right. But it's saying, no, let's take how people are actually doing their work today, which could be say, spread out across a couple of different tools, systems, processes, whatever it might be, and let's actually build the agents in those tools in those systems where it makes sense. So it seems at contrast with what we talked about before. Right. Is that fair?

Speaker C: Yeah, yeah. And I think it's going to be, look, this is point in time, fast forward six months and I'll probably be saying something different. Yeah. Because maybe, maybe there's a world where I think fast forward maybe even 12 months, the AI becomes the unification layer of all tech, but we're just not there yet. Right. There's no perfect platform that kind of unifies everything for right now. This is very much how we're thinking about it. And we'll definitely evolve probably as the tech gets even better and better.

Speaker A: Yeah.

Speaker B: So I always like to also talk about, because we talked about some of the things that have worked, how you're approaching things I always like personally, at least for me, I love to share things that I've done that haven't worked. I think those are also interesting stories. So as you reflect over say your time at Advantiv, especially around AI and revenue operations, are there things that you've tried that haven't worked or things that people can learn from? And especially I think the main thing here to hit on what you just mentioned is that these could be things that might work in six months or nine months. Right. Because of how fast things are evolving and moving. But any. And uh, I don't like calling them failures because I think like when things don't work we just learn from them and we move on. That's not a failure. But anything that you've tried that hasn't worked that would be relevant for people listening.

Speaker C: Yeah, this is maybe a little cliche but um, I think the biggest one where we've had to kind of dive in and pivot a little bit is this concept of an AI sdr. I think there's, we started not quite a year ago and I think our use case was a little bit all over the place. And so I think this is, I'd say the age old story of, of like most AI projects is you come up with this super grandiose idea of what it's going to be and then it doesn't work. Whereas it's. Now this is how I approach most of my agent building is like where it gets really good is when you have an agent given a very specific task, it's okay, do this and do, then another agent does this, then another agent does this and then you can have an agent that then runs those agents thing. And I think that was like a bit of uh, how we approach this AISDR is we wanted to fully create some solution. We brought in a third party software and promised things and ultimately uh, I think we over tried to stretch it much further than it could and ended up not really working. And so we've completely kind of gone back to the drawing board and we're starting to kind of reevaluate the market and approach it a little bit differently. And now we're having the kind of age old conversation of build versus buy.

Speaker A: Right.

Speaker C: Of do we buy a tech, do we start to build it? Because it's. The tech has come a Long way. There's come a really long ways in the last even six months. Right. And so it's like, with something like this, do we look at a third party vendor or do we start to leverage some more? Especially now that we have some of the infrastructure internally to build? Because that's the other. That's the other problem too is like a lot of these people, like I won't go down to the build versus Buy, but a lot of people want to build, but they don't have the architecture, the scaffolding to actually build in a meaningful way.

Speaker A: Yeah.

Speaker B: So I do want to ask you just about Build versus Buy, because as you were talking, I was thinking back a couple of weeks ago, I was the MC at this event called Rev Star, which was In Toronto, about 150, 200 like Rev Ops folks that were there from all over North America. And one of the speakers was Kyle Norton, the CRO over at Owner. And his whole session was around like how he approaches build versus Buy. And Owner has done something crazy like over a year, two years, has gone from like 2 million to $100 million. And so like they've seen incredible results. And my main takeaway from what Kyle brought up is the way he approaches Build versus Buy is basically he doesn't want to buy something that involves like core intelligence to their business. So if there's a uh, like an intelligence sort of layer, he wants to, basically he wants to own that he doesn't like buying things or there's like a black box where it's like stuff they do goes into this black box and then results come out and you have no, like, you have no view into it. So he brought up the example of a call recorder. Right? He's. And to your point before, he's like, could we build this ourselves?

Speaker A: Sure.

Speaker B: Would it be any better than what's already available on, out there on the market? Probably not. Is there anything unique about say like the, the intelligence and like some of the other stuff that we do and actually, I'm, um, sorry. Call recorder, it was a power dialer, not call recorder. So like a power dialer for their outbound reps. He was like, so could we build it? Yes. Is there anything like fancy or unique that we would do that we couldn't get in an existing product like a Nooks or like any of the other power dialers that are out there? Probably not. So is it worth our time to invest internal resources to build that? No. So they decided to buy it. But other examples where there is maybe more Of a like an intelligence sort of piece. Those are the things that he wants to own, I guess for you, does any of that resonate? Do you have a different viewpoint on how you look at build versus buy and just think about that as it relates to AI and revenue operations?

Speaker C: Yeah, I totally echo that sentiment. Is like right now it's all about getting access to your data. And like I'll talk about the call recorder. For us it's really important that uh, we've got great MCPS with Cliffic interface. Like we can get the data in and out very easily. If they said hey this is our data, they wouldn't. But if they were to use that as their competitive moat per se and say this is our data, I'd be like I'm out. Right. No thanks. Right. I definitely agree that having access to the data is really critical. Right. But then the second piece is. And this is where just the finance roots comes in. It's like the ROI calculation of like. I don't think people think about it.

Speaker B: It's.

Speaker C: Yeah. Is it feasible we could go build a call recorder? Sure. And maybe there's a time where that equation changes. But the like at least how it works right now it is like the ROI is just very much there to buy. And the other thing too is like you have to think. Sorry, I'm getting a little bit on a tangent but like a good example is you have to think about this, the AI fluency of um, the users of your tool. Right. Sure, I could probably, we could probably code something together. But what's so great about Airspeed glyphic is user interface. Right. And that still matters for these people. Fast forward 18 months we'll see as their AI fluency changes. But like that user interface is really critical. If I were to just basically turn it into some um, Claude agent that records everything and they've now got a query and some of these sellers that are not as AI fluent will just lose it. Right. And they'll be like I don't know how to interact with a, with a prompt or things like that. I think there's a lot of things to consider. I think it really comes down to like roi. Are you going to get the use out of it that you need? And then to your point like you've got to have access to the intelligence because that's your, that's what makes it so critical. Right. Like the thing that we're, I mean our AI vision around kind of our go to market team is like that call data or just that intelligence is so central to everything we do. Right. It drives our enablement, it drives what strategic decisions we're doing. And so making sure that like data is accessible and easily accessible and queryable, we can analyze it is just so central to that.

Speaker A: Yeah, yeah. You, uh, so I guess you brought up a couple of things. I think. One, um, the fluency of the team is super important. I saw a meme the other day where someone was like, why wouldn't I just vibe code this myself? And the response was like, because it took you three minutes to figure out how to turn the audio on for this meeting that we're having. Right. And it's not everyone out there is super familiar with the tooling and the technology that's out there. And then the other thing is the ROI piece. I think for, especially for a lot of us that. And I don't have an engineering background, but now these days I do a lot of stuff with, with cloud code. And it kind of gets back to the product mindset that we were talking about before is that if you've never built stuff before and now it's super easy to build things, you can have this false sense of, oh, I just got to spend an hour to build this. And then like it's just there and it's going to work and it's not going to break and everyone's going to know how to use it. But there's so much more that goes into building a product and maintaining a product, deploying a product, enabling your team to use a product. You got to do all of that stuff as well. You got to think through the UI and there's a lot of costs that can come with that. If you go down the build route that people just simply don't think of because all they think is, oh, wow, look at like it's been so easy for me to build this thing.

Speaker B: It took me two hours and now look how great it is.

Speaker A: Right? But there's so much more when it comes to software products in that.

Speaker C: Yeah. And I think the bigger thing is I run really lean robots teams and this is the age old finance and accounting term of opportunity cost. Right. Of, uh, yeah, we could go build a call recorder, but we've already got one. It works just fine. It works great. I'd much rather focus my team on much higher value work than rebuilding tools we already have in our tech stack that generally have a pretty good roi.

Speaker A: Yeah. And don't get me wrong, I love building stuff and being like, hey, look at this cool thing that I built, right and things that can get used and everything like that. But yeah, if it's not to your point, if it's not gonna. If it's gonna be just as good as other stuff that you could go out and buy, and if the time that you would spend building that would be better off spent elsewhere, your to do list will never get shorter, it will only get longer, it will only grow. So do the things that'll actually make an impact.

Speaker C: Yeah. And that's one of the things that, as I mentioned, we run really lean teams. The reason we're able to do that and be successful is we have to ruthlessly prioritize, right. What we're focused on. We do that by essentially a loose ROI kind of, uh, type, I would say calculation, but it's like mental math, right. Of like I'm going to do. Here's these 18 things I want to go do. Which three are going to have the biggest return?

Speaker A: Yeah, yeah, 100%. All right, so one more question for you before we wrap up asking you to get out your crystal ball a little bit here, but if you were to fast forward three years, what do you think will separate companies that, uh, have successfully scaled AI from those that haven't? What will the difference look like and what will the main reason be?

Speaker C: 3 years feels like an eternity in these days and it's like almost impossible to see what the difference would look like. But I think it's very much a, uh, just. Maybe I'm just gonna take this from a financial lens. What's gonna happen is the amount of output and quality output that just any knowledge worker can produce should be 10, 20x. And sure, salaries and the expenses will adjust to an extent, but the reality is you should be able to drive much higher profitability as a result. And so I think what you're going to see is the companies that can't operate in that model are investors and things like that are going to just demand higher and higher returns. Right. We hear the rule of 40 all the time.

Speaker B: Right.

Speaker C: I don't foresee a world where you're talking rule of 100. Right. Or something crazy. Right. It's because they're going to demand productivity and you should be able to do it right? Now that doesn't mean, I think, uh, I don't want to turn into the naysayer here. I don't think AI is going to replace all our jobs. I think our jobs become even more critical. Right. Because at the central of it as a human, AI allows us to do 20x or 10x kind of a thing, just taking a pure financial lens. That's kind of how I think companies are going to have to start thinking about, like, how can you drive 100 million in revenue with 20 people? That's maybe an extreme, but those types of things of, uh, like how do you do some of those types of things? And then how do you create a really good customer experience? Because that doesn't go away in it, in that type of environment as well. That's probably where I'll put my crystal ball away. It's like I just. It's going to. What we see even in the next 12 months is going to be totally different. Right. In three years, who knows? But I just think, like stepping back, I think that's the biggest. And you're seeing it already.

Speaker B: Right.

Speaker C: Investors are starting to request they want to see more and more profitability, not just revenue growth, but it's EBITDA margin growth, things like that.

Speaker A: Yeah. And I think the company is again, a year, two years, three years from now. I think the ones that'll win and be at the top are going to be the ones that view this on these really short timelines and are willing to look back and be like, hey, you know what, what we've been thinking for the last three months, six months, like, things are changed and different now. And so we need to change our approach to the point of what we were even talking about before, of our opinions right now on decentralized versus centralized and building agents, where people do the work versus actually building them in maybe like a centralized hub or something. Right. Like you said, in six months there could be something new within AI that could totally change the game. And we've had these kind of like the golden era of SaaS and software over years. Right. And a, uh, decade plus where we've been able to get comfortable and do the. Call it like, same playbook, same stuff, works across different companies, I think. So we've maybe fallen into a little bit of, uh, a trap where it's like we can get comfortable and think we can exist there for years.

Speaker B: Right.

Speaker A: But the companies that in a year, two years, three years are going to be the ones that succeed are the ones who say to themselves, we can be a team of 20 generating $100 million or. Yeah, those things like that thing that I talked with Matt about six months ago, you know what? I've changed my tune on that. Given the, um, environment and the stuff that's out there today, that can be really hard for people and for companies to do, especially given the history that a lot of us have.

Speaker C: Yeah, I think the biggest use case or I guess example of that would be. It's like the. I feel like it was the mid-2000s, that kind of concept of. Now I can't think of what it's called. It's like the revenue pipeline, but it was the old Hammer 100 accounts. You have this many open rates. It's the funnel approach. Right. That, uh, has always existed. Even still this day, I see people on LinkedIn publishing or they still have that old formulaic approach where hit this many accounts, this many convert. It's the calls, calls made, call connect, convert to opportunities. Is that whole funnel. That's. It's kind of. I don't want to say it's dead, but it kind of is. Right. We've got to think of. And I now, when I see companies approaching using that same framework, how many LinkedIn messages and emails do you get that are like, slightly better, they're more customized, but it's still the same playbook, right?

Speaker A: Yeah.

Speaker C: Just hit more and they can hit them more at scale. It's really like it was the birth of the outreaches and the sales loft.

Speaker B: Right.

Speaker C: That did this. And people need to be innovative and creative and figure out new ways to reach their target audience. Right. And who's looking and those types of things. So I think you're going to the companies that. And that's just one example from a sales side of those that can be innovative and think differently. Uh, are the ones that are going to win.

Speaker A: Yeah, yeah. 100%. Brandon, that's all I got for you, my friend.

Speaker B: Thank you very much for being on the podcast, for dropping some knowledge on all things AI and revenue operations today. But with that, I will let you run. But like I said, thank you for joining and being here.

Speaker C: Thanks, Matt. Appreciate the time.

Speaker A: Thanks so much for tuning in to today's episode, everybody.

Speaker C: For more great content like this, join our community@revopscoop.com and check out the links provided in the show notes.

Speaker A: Keep it RevOps safe, y'.

Speaker C: All.