CRO Confidential: 0 to $600M in Under 4 Years. The ElevenLabs GTM Playbook with Carles Reina

The number one thing was like, am I gonna get replaced? Are we gonna get to a position where later on an AI agent would be able to do sales and I would essentially like be replaced by the company? And of course we never got into that space, but we were able to prove to team members that actually like by having an AI SDR that answers emails from customers and calls them straight away, or the customer can actually interact with them directly, then you end up having better conversion rates. You don't have to be that worried about like replying an email, like an inbound that comes in. Within the first 30 minutes because like an agent can take it from you, right? Or we started to prove that like, hey, like by having an AI customer success manager to focus on the upsells for the long tail account, like the SMBs and the mid markets, we could actually automatically unlock revenues and we will pay commissions to like the account owner of those companies, right? And I think like that helped us actually like navigate a lot of the initial concerns of the team and then start pushing.

SaaStr fans, welcome back. We are kicking off another series of CRO Confidential episodes, and we are coming out with a bang. We have Carlos Reyna, formerly of ElevenLabs, now of Valbab Ventures. Carlos led GTM at ElevenLabs for over 4 years. He was the 4th employee, first go-to-market hire, scaled the business to several hundred million of annualized revenue and an $11 billion valuation. Carlos is making the natural transition almost as we speak to helping startups scale with Baobab Ventures. There's so much to unpack here. Can't thank you enough for being here, Carlos. Welcome to the show.

Thank you so much for having me, Tim.

So the ElevenLabs run is pretty close to N of 1. Let's jump right in and let's start there. And when I say N of 1, there, there's a couple dynamics here. Joining as employee 4, Go-to-market hire one. The company is almost definitionally relatively unknown. You step in the seat, you build go-to-market from scratch, you scale to hundreds of millions of dollars of revenue. I can't think of many or any that have done that in the past. I think like, let's maybe start, what are the things that you and you all at ElevenLabs just absolutely nailed in go-to-market? that you attribute much of the overall success of that organization and that company to? Yeah.

And so for us, like a lot of it was building distribution from day one, right? And I remember the early days were like, it was all about let's nail one channel and we do really well on direct sales. And then you started going after resellers and then you start doing the cloud ecosystem and all of that stuff. And for us it was, okay, how do we actually get our technology as quickly as possible in the market, even in those markets where we don't even have people, right? And When you look at it, it's like, it's all about distribution fundamentally. It's like in some core markets you need to actually sell directly and you wanna do mostly that way. In some of the markets you might do essentially like resellers because you don't have team on the ground. And because when you look at the tax and like withholding taxes and the billing issues and all of that stuff, actually it's much better to actually go resellers versus like get direct sales. There was a lot of combinations of those things that we started looking at and we defined what was the actual mix for each one of the markets that we wanted alongside the thesis. So each one of the markets that I started to build and launch had a specific thesis. Why were we launching that market? How were we expecting to start doing go-to-market? And essentially, what were the results that we were expecting in the first 3 to 6 months, right? I think like that was the number one thing that we did that worked really well because it was all about experimentation. We didn't really know what actually would work in one market versus a different market. So when you combine distribution together with experimentation, that was the winning side. And When you look at the stats, for instance, we went from $0 to $100 million in ARR in 20 months. Then we went from $100 to $200 million in another 10 months. Then we went from $200 to $330 million in another 5 months. And then we went from $330 to like over $600 million in another 6 months, right? So it just gives you an idea, like when you actually do it at scale and you start repeating and testing a lot of ideas and experimenting with it, it has a massive impact on this rocket Like increase in revenues. And I think that was the number one thing that we did really well.

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That's awesome. So let's see, the absolute numbers are sort of jaw-dropping. Also, the revenue acceleration is also jaw-dropping. So you accomplished 2 things at the same time. A version of what you just described around distribution is something that I like to touch on a lot, which is Just the importance of creating a sort of demand or lead-rich environment that a business is operating in. You talked about experimentation. That's another thing that is like one of my favorite topics. And then sort of outlining a bunch of the experiments that we run, some of the posts that I had that were most successful on X or LinkedIn or whatever. They talked about like champagne campaigns and some of the things that we did at events. Is there like 1, 2, or whatever the right number is? What are some of the favorite experiments that you run or that you ran that might be helpful for those listening to Pattern Match? Like, oh, that's a really good idea. We may want to try something like that.

So it was actually like, we were running a lot of different things in parallel. Some of the things that like didn't work, some of the things that really worked really well. And one of the first ones that we, like, there was some very basic stuff. So like for the first 9 months of ElevenLabs after launching the first product, it was only myself selling to enterprises, like literally, right? So like one of the key items that I came out was this idea of our grants program. So ElevenLabs, we have an ElevenLabs grants program that we launched over 3 years ago, 3 years ago now. That was based on the idea that a lot of our competitors back in the day were focused on us. They were startups, right? So companies that actually were like relatively small, they were the same size as us, but essentially, and they had built their own foundational models and they were starting to sell to developers, right? And startups as well. So I went away for a few days with my wife to take some holidays. And then every time that I take some holidays is this idea of how can I kill my competition, right? This is like the only thing that I have to accomplish during the holidays period is how do I kill the competition? As of the companies that I have that moment in time. So that moment in time, because it was smaller companies out there, startups and so on, it was all about if we launch a grants program that allows startups under 25 employees to start using ElevenLabs for free for 3 months, then we could essentially move the demand from our competitors to us. You start building on top of us, you realize our technology, we make it public, we make quite a lot of noise on that. Can we essentially make sure that our competitors die on that end? And it was a gamble. We had absolutely no idea whether that was gonna work or not. And we ended up giving like tens of thousands of grants in a very short timeframe that essentially pulled all of the demand from the market into ElevenLabs. And we used that as an excuse to also later on upsell them. So for a very, very, very long time, like over 10% of the enterprise revenues that we're generating were essentially coming from the grant program, companies that actually have started building in ElevenLabs. And then essentially we were able to upsell as they were becoming bigger and bigger and bigger, right? That was one of the examples, like, but it could have gone like on the other side. that like and not perform.

It's an awesome example. Thanks for talking through that. When you say a grants program, just to add some teeth to it, I think what you're describing is you find like a hot startup that meets some qualification criteria and then you give them credits to use on ElevenLabs and they can effectively use the platform for free, which in which case would otherwise cost them many thousands of dollars or $10,000, whatever the size of that grant is. I think the category that this falls into, or the category that I place it into, is just making it incredibly easy to get started and realize value on your product. I think there's like a bunch of ways to potentially do this. You outlined one that was really effective for ElevenLabs, which is this grants program. That's sort of like a monetary example of making it very easy and attractive to get started. There are others. Jason and Amelia regularly talk about the sort of like onboarding process and level of involvement that somebody might have to go through and the like pain. And we've all experienced this with these traditional SaaS platforms where when you sign up for a new product, you have to get like external consultants and set up like all of the page layouts. It's just like you can't really even get started until countless hours of effort on your end, dollars going to third parties. And so then this is like in that category of you just want to make it as easy as you possibly can to do business with you. And this is a sort of like financial incentive way of doing that. I really love that. We just made the transition at Monaco from a platform fee that was a fixed rate for a year. You effectively had to sign a year contract just to get started. We moved to usage-based and pay-as-you-go. That's like a far more attractive model to encourage somebody to get started, which is maybe like our version of that. It's certainly driven more demand and less friction in getting signed up. We also try and do like as much of the onboarding as possible, which I imagine like ElevenLabs, not only is it easy from a financial standpoint with these grants, but also easy from a like, how do I get started and realize value? There's probably a lot of assistance, especially as we move upmarket on your side where you're like doing the work on behalf of the customer.

100%. And for you guys, like, it's even more interesting because you could play with like variations of like the business model. You could also say like, let's launch a campaign that is all about like outcome-based pricing, right? So you remove as many barriers as possible and then later on you are able to actually like reduce the cost of that outcome-based pricing if you sign up for a commitment, right? So there is variations of all of this stuff. There's like How do I reduce the friction on the onboarding side on companies to actually get started on seeing the value and essentially continuing to drive based on like your platform, 11Labs, or whatever is out there, right? I think like all of these items are fundamentally what we need to be testing as startups. Like we don't know what we don't know. The markets change a lot. What might be working in one market in the US might not be working in Germany, might not be working in Brazil. Some of that experimentation specifically in the early days is what's going to give you like enough momentum to just continue to drive, right?

I think there are maybe 2 things that I would reinforce as takeaways that I just sort of enthusiastically agree with. You've used the words sort of like experimentation and try new things, and you've even said like some of these things are going to fail. When these things don't work, chalk that up as a win. Like you've actually learned that something doesn't work and you can move on. There's this, there's this meme that I saw that really resonated with me. And it was like people shooting free throws. And the first person was like really measuring the distance to the basket and taking it really scientific and, you know, measure like 10 times, cut once approach to shooting a free throw. And then the second person shot a free throw, immediately missed it. And then it was like depressed, like really upset that it didn't work. And then the third person was just like constantly shooting. And eventually they got far better because they were shooting the shots. They were trying the thing. And if you try and experiment more often, your feedback loops are just going to be far faster. And that really resonated with me in this concept of like experiment. If you're not failing and trying new things, you're not experimenting enough. And then the second thing that was like quite tactical is the make it so easy to do business with you. And not only just do business with you from an onboarding and financial standpoint, it's time to value. How quickly can we get a customer to some valuable outcome with the most limited amount of effort on their end, whether it's money or time or however you want to frame it? And like, you only get one chance at a first impression. If you make a really good first impression, that's going to pay long-term dividends. And so those are maybe the 2 things that come to mind as major takeaways.

And you know, Sam, like I always used to tell the team all the time, I only need— you need to test 100 things, but I only need one of those 100 things to actually work to give me another $100 million in ARR, right? I only need one. I don't need 5, I don't need 20. I need one single thing to actually work, right? So when you actually look at it, it's like, well, if we don't test 100 things to get one thing to work, then every single other thing that we do as a business needs to be perfect. And we know from a revenue perspective that statistically some things will go wrong. Like, Some customers will churn, some customers might not upsell at the pace that you wanted, some things will not work. And as such, if you assume that statistically some of the things will fail, then why not to invest in things that are potentially much more new also as well, right? So like you end up kind of setting each one.

Very insightful. Hopefully lots of takeaways in that line of thinking. Super helpful. So thank you for walking us through that. Let's do the inverse. This is always a fun one and I can certainly do it at Brex, Zenefits, and others. There's always like The one thing that, gosh, had we just done this differently instead of being at, I don't know where you landed, Carlos. You said like something, something like $600 million of ARR or something like that. We would have been at a billion or whatever it is. Is there one thing that you gravitate towards that's like, if I could redo this, I would have done this slightly differently, that is more seemingly universally applicable to other folks that might be listening?

100%. Yeah. I mean, there's like lots, like lots, probably tens of things. I think the top 2 for me that are like the most insightful ones is number one, the enablement side. So sales enablement, I think it's one of those items that is overlooked usually where you end up hiding sales enablement folks like much later on down the line where like you already have a wide, a bigger team and then you need to make sure that people are being onboarded and things are happening in the right way. And I think if I was building it again, I would have hired sales enablement much sooner. To make sure that like things were in order and we could scale much quicker. And the number 2 item is hiring a lot more like senior salespeople. I think like this common understanding among starters, you wanna hire young people that are hungry, that are very driven, energetic, and all of that stuff. But it also means like you're starting most of the time from scratch, tackling, like tackling companies when in fact you could go much quicker on the go-to-market side by having A lot more very senior people and having a mix of it, right? So people with 20 years experience in your teams that come with a lot of connections that have been selling for the past 15 years, and they know when you talk to a specific company, they know the procurement people, all of that stuff is a more of relationship problem item that you can essentially factor in by having very senior people in the team, right? And I think those 2 items, if we had done it much sooner, then for instance, we will have already passed a billion dollars in revenues, for instance, right? Or like getting close to that billion dollars in revenues. Of course, like not everything can be perfect, unfortunately, right? But it's one of those things that you keep learning and learning.

I'm similar to you. You know, there's sort of like countless at a tactical level things that, you know, seemingly on a daily basis, there's something that I wish I would have like done differently or a change or a decision I would have made that was different. There's, The first one, interestingly, that you highlighted, you know, most of my career I was at a company called Zenefits and then Brex more recently. At Zenefits, it's the same as your first one. We invested far too late in go-to-market operations and enablement. And this is one where I've like never heard a sales leader say like, you know, we went too big on revenue operations or go-to-market operations. And so then like I'll reinforce that, just like start as early as you can with this category of go-to-market operations that incorporates enablement also. It's one of those things, and I don't know if you're similar to me, many sales leaders, certainly me, I'm like very performance and execution oriented and I came up through sales. And one of the things that I'm like, somewhat deficient in, and I think this is pattern matching relatively true across sales leaders, but not maybe painting with too broad of a brush. Many people need the operations support. It's like the, I don't know, a very complementary skillset to oftentimes a sales leader. And so I'll like reinforce that, that first one that you mentioned emphatically. The one that I had at Brex that I think is like, it was a little bit unique to Brex at the time, but it is now becoming more universally applicable. Brex was always usage-based. Like, we effectively generated revenue on card spend. And, you know, at the time, this is 2018 when I joined Brex, most technology companies, with some exceptions, though, like Snowflakes, Databricks of the world, most technology companies were sort of like contract license-based, platform fee-based. And so the lesson that I had from Brex was We really want to incentivize the outcome that we work towards. And at Brex, we started with things like activations. So like number of logo customers that we signed up. You know, there are a lot of like SDR incentives that were around like meetings, that sort of thing. The moment that we switched reps and then eventually SDRs to revenue just ripped from on like a per rep basis, on a per account basis. And so now that we're in this world where like most AI Companies are usage-based. Apply that to your business where like rather than incentivizing these inputs, can be meetings, can be activations. What is the thing that ultimately we want to drive the business towards? What did Carlos talk about earlier on? He talked about the revenue number. We got to $100 million in this amount of time. We got to $200 million in this amount of time. What Carlos didn't say is we got this many meetings by this time. We got this many customers by this time. Of course, those things are important. You want to track them. Incentivize the thing that you really want. That was my sort of like Brex learning that, gosh, if I had done that earlier, we would have been off to the races a lot faster.

100%.

And actually, it's very interesting, right? Because like we went through this phase in AI 3 years ago where the majority of companies thought like, oh, you just build the best products and like companies will buy it, right? So like you don't really need to put like very large incentives, like the commission structure, all of this stuff. But at the end of the day, like AI gives us the ability to actually create product overnight. So you end up having like tens of competitors like that within a week of you launching are going to be competing with the same customers, right? So how do you really incentivize people on the actual number of revenues, like on the recurrent revenue side, not the actual like total revenue, but like in the recurrent revenue portion so that they feel that like they're empowered, like they want to make a killing, right? For us, we established that like we would compensate, we pay commissions on recurring revenue contracts, but not in one-offs. So if you sign a POC and you pay for like 2 months or like 3 months or like 2 weeks, you would not get any commission on that. It didn't really matter if it was like a POC for like $20K or it was like a POC for like $20 million. You would just get zero because it was not adding value to the business. Like literally we were adding value to their customer, But not to ElevenLabs, and our valuation was not going up. And I used to tell people, for every $1 million in revenues that you bring, it's another $33 million in extra valuation for the business. So literally that POC amount might be fantastic, but I cannot put it in my numbers to actually like investors. And as such, your equity story is like not like fully built just because you are signing a POC. So let's work on signing this long-term contract to make sure the companies actually see the value of our technology, right?

Yeah.

Again, like it's building on top of you by saying, hey, like revenue number is the one that we need to incentivize always. Let's be generous on those like commission payments because if we don't get to those numbers, don't really need to have like even technology because like you're not going to be able to survive as a business.

We could spend an entire show on sort of like pricing incentives. What are the like revenue numbers that actually matter? Is it sort of like gross profit, top line, right? Like all of these things that Interestingly, like probably very business to business. There is something you were on Harry Stebbings podcast. I think the like most shared clip was something around the like revenue contribution relative to compensation. So it was something like reps were generating 20x the revenue that they were receiving in commission payments, something like that. Talk us through the way that you compensated reps. And then for those that are listening that are like, oh my gosh, we need to have our quotas that are way higher, how should other interpret that? Because it's certainly not like one size fits all where you can like universally apply that everybody in a sort of like AI scenario should be generating 20x the revenue that they are paying their sales reps.

I can say like the 20x quota, that rule was, was very controversial. And I got like people like online messaging me being like, you're slaving people for asking them to bring 20x. Someone, people like messaged me being like, where do I sign up? It was very controversial, but also like it generated the right debate out there, right? So the entire logic was very simple. It's like, look, if you want to have a base salary of $100K, you're going to need to bring $2 million in revenues every single year, right? You have uncapped commissions. It's like OTE out of the gate is going to be $200K because we will double like the base salary and all of that stuff, right? But there were 2 main items that came for this. Number one is I was actually, it was based on my experience selling as a single seller for the company for 9 months. It's like, how much can you actually sell as an individual? How can we extrapolate that? Right? Number 2 was based on we need to make it fair. Like the market is evolving very quickly. We have a land and expand model. We want you to go hunting new logos and we wanna have like a customer success team that like actually goes and expands those logos. And for those 2, I will actually double compensate people. Because I do believe that if I'm incentivizing to go hunt deals, then I want to keep you commissions for that deal expansion for at least 12 months, and in some cases, 24 months or longer, right? And what we found out is when I hired the first 2 people, 9 months in, I told them like, this is going to be 20x quota, right? So both of them were like, what? That is, there's no way we get that. And I told them very clearly, I don't know if you're going to get there or not. But let's keep an open mind on all of this. What I can promise you is that we will make it fair. If in the next 3, 6 months we see that like you're not getting there, I will lower the commission, I will lower the targets, and then we'll reevaluate all of these things. If you get there in 6 months, but in a year's time the markets have changed, we'll reevaluate as well and we will drop it and make sure it is fair for everyone. I can give you my commitment that it's gonna be fair, right? And then of course, people like those 2 first joined, They were signing contracts during the first week at ElevenLabs, right? And of course they realized something is actually going differently. Like the way we're selling is slightly differently. The market adoption is slightly different. 20x did not seem that bad at that point in time. And to this point in time right now, like 3 and a half years in after started selling the first product, I can tell you we have like account execs doing 600% quota. We have account execs and it's not uncommon. We have account execs doing like a lot of them doing 300% quotas, right? People found a way to actually generate pipeline. We attracted the right profiles that actually were hungry, were like driven, wanted to go hunt, and also were willing to actually work with the customers. And some of them have a lot more patience because they're focused much more on strategic deals, which might take like 9 months to close and things like that. Some other ones are actually focused on like the mid-market and SMB segment, which you can close a deal in a single week. So that hybrid approach, and that meaning that like the average quota attainment on a per quarter basis is across the entire go-to-market team is 167% every single quarter, right? It means that everyone is very motivated and I would rather have a smaller team getting very well compensated than having a bloated team that is just not reaching quota, complaining and demotivated. And that was the theory behind the compensation structure. And I can also say like in some markets, like if there's been a quarter where like we suffered most because the market fundamentals had actually changed. changed, we would actually review at the end of the quarter and then give quarter relief. So in some cases we did 50% quarter relief because like the market had to change and we had to actually readjust the way of selling. Great. Fantastic. Or the pitch or the way of doing things. Great. We've done it and we've done it like across the board, like in any of those markets that were difficult. And that was the commitment that I gave to everyone. Literally give us enough time to understand what's happening. We'll make it always fair.

This is all super helpful. There are a few things that stand out as maybe takeaways. You know, there used to be in like the SaaS world, there used to be this aspirational quota to compensation ratio that businesses would strive for as 5x. If you were paying a rep $200,000 a year, you wanted them to contribute $1 million a year of ARR. And it wasn't like, you know, some companies were a little bit lower than that when they got started, but that was like the number that everybody strived to get to. I think there's a couple things that stand out from your sort of specific example at ElevenLabs, but it can be more, more universally applicable. The first is like all bets are off in the world of AI and what works for ElevenLabs at 20x quota, meaning if you are paying a rep $100,000 a year, they need to generate $2 million a year. That works for ElevenLabs. It does not mean that we pattern match that to all other startups the way that we might that 5x revenue contribution in this sort of like SaaS world. So, and there isn't like a rule, I think like there's no rule in AI. And so then it is sort of like individual or business specific. Here are like a couple other things that I would take away. One is you want to, as a company, as a revenue leader, as a founder, if you're the one that is setting these early on, You want to earn a reputation of being fair and you want to compensate salespeople well. So earn a reputation of being fair and doing right by your salespeople. You also want top performers to be overachieving in such a way that everyone on the team sees their success and it is hyper-motivating. Carlos described people at 600% of their target. Now, if that person is on a $250,000, you know, OTE type comp plan, they're making a lot of money. You want some reps depending on the, depending on the size of your team, depending on how much money, there's a lot of variables here. But like, gosh, paying a rep $1 million in 2026, that is, that is something we should be celebrating, not something we should look at as a line item on our expenses and thinking like, oh, we're paying reps too much. Inverse is true. And then lastly, And this is all like, you know, easier said than done. And one of those things that you need to balance, but you also want to like push the envelope a little bit in terms of what's possible. And to Carlos's point, you can always come back or even like mid-game shift the goalpost.

Yeah.

Let's go to 20. Let's see if we can do it. Let's see if we can in, in this quarter, let's see if we can drive $1 million of revenue on a per rep basis. And gosh, if we get like 2 months in and we're trending towards $250,000 or whatever the number is, We can adjust it in quarter to, to, uh, you know, make it more motivating for the team. The last thing that you want is like approaching the end of the quarter and reps like trying to get to 20% of their number. Like talk about a depressing sales environment to be in. We can, unlike a football game, we can sort of like create our own scorecard, move the goalposts in ways that like, you know, you can't do in a sport or something like that. Those are the things that stand out. I think that was great to unpack. Let's move on to the, like, topic of the day, which of course is AI. Gosh, it's been the topic of the day for years now and probably will be for years to come or something like that. Okay, so here's how I'd like to frame the next few minutes. I think of the introduction of AI into technology startups Similarly, in a way to like, there are companies that are at a bit of a disadvantage, those that started in a world that was like pre-AI having such a major impact. Like if you're starting today, you're at such an advantage relative to a business that has systems in place, process in place that is all like from an era of, let's call it even like 2023, 2024. There's just been so much advancement over the course of the last really 12 months. And so if you started your foundation far prior to that, in a lot of ways you are sort of like not AI native. What are some things that come to mind? You went through this process of incorporating AI and leveraging it more over the trailing 4 years at ElevenLabs. How did you fully take advantage of AI in an environment where like you weren't really built to be AI native? You know, the Claude skills and everything that people are using today, they didn't exist 4 years ago. When you were GTM hire number 1 at ElevenLabs, how do you make that transition? Where do you start for the companies that are like a couple years, 2, 3 years already started go-to-market? We started in a world where like AI isn't as advanced as it is today.

Yeah, I think definitely you need to convince the team and show them your goal is not to actually replace them, it's more about enhancing their productivity, right? And that was the pitch that I was doing to my team. I'll give you like, for instance, a story here, but like 3 years ago, like 2 and a half years ago, we were all in a mini company offsite in Switzerland. It was like less than 30 of us at the company. And I went to Matti and Pia, the founders of ElevenLabs, and I pitched them this idea of like, I want to build an AI go-to-market, like, person. I want to build like an agent. I want to build an AI, like, account exec, an AI, like, SDR, an AI customer success manager. All of these like AI tools, because I do believe that we can increase productivity by a lot for the team. And the wider company, like everyone was like, no, the technology is just not there. Like forget about it. Let's continue to build like the go-to-market and hire more people. We need to go faster and all this stuff. And I kept insisting. And then a year later, like they, they allowed me to actually hire the first developer that was only going to be focused like on the go-to-market side so that we could start building the agents. And like, I actually pitched the entire team, like the go-to-market team. It's like, hey guys, like we are now gonna be as part of the vision, we're gonna be launching AI agents. We're gonna be incorporating them to the team. They're gonna become your assistants. You can do a lot more things with that and so on. And while the team was like very like interested in learning and experimenting and pushing the boundaries because it had an impact directly on their productivity and their commissions, at the same time, there were like some skepticism with like a lot of people because the number one thing was like, Am I going to get replaced? Are we going to get to a position where later on an AI agent would be able to do sales and I would essentially like be replaced by the company? And of course we never got into that space, but we were able to prove to team members that actually like by having an AI SDR that answers like emails from customers and calls them straight away, or the customer can actually interact with them directly, then you end up having better conversion rates. You don't have to be that worried about like replying an email, like an inbound that comes in within the first 30 minutes because like an agent can take it for you, right? Or we started to prove that like, hey, like by having an AI customer success manager to focus on the upsells for the long tail account, like the SMBs and the mid markets, we could actually automatically unlock revenues and we'll pay commissions to like the account owner of those companies, right? And I think like that helps us actually like navigate a lot of the initial concerns of the team. And then start pushing. I do still believe that like AI agents are not that good today actually at doing something that is very relationship driven, but they're getting much better at it, right? So like the problem is when you look at LLMs, they look at the distribution side and they give you results within the distribution. If you truly wanna do proper sales and like smash it out of the gate, then you actually need to look outside of the distribution, right? And that's where like some of the LLMs, like the LLMs just struggle today. It's probably gonna get solved over the coming months and all of that stuff, but today there's some, something that element, but like LLMs are fantastic today at like analyzing a lot of data and a lot of the go-to-market team members should be using them, whether they use Perplexity to research into companies or use LinkMonaco. There's many different variations of like tools that you could use out there to actually like enhance how quickly you can understand your customers and essentially like offer them personalized solutions as well.

We view the world at least similarly and maybe like the exact same way. And I'll sort of like distill down my version of everything that you just articulated, which is something like agents are better than humans at many specific tasks or workflows.

Yeah.

Examples would be things like building a TAM, so total addressable market of accounts that we should go after, and then scoring those accounts based off of some set of criteria that we feed the like models in a prompt, overlaying signals. So like monitoring things like websites and contacts for certain events to take place, leveraging those events to create a like message in real time to surface to a human to maybe review it or to send automatically. These are all like non-directly customer-facing activities. There are like 2, and then there's a bunch of other things, updating opportunity records, forecasting, so many things that like models are just going to be very good naturally at ingesting a bunch of data, taking real-time action, working 24/7. There are 2 things that stand out above all else that these models and agents actually should give us more leverage to lean into that humans today remain far better at. The first is developing relationships with buyers or customers. These are things like, Carlos, you talk a lot about getting on planes and meeting with your customers in person. Especially for like larger deals, more complex deals. Customers do not want to buy from an AI. They still value the relationship building that comes with meeting someone in person, meeting them over a Zoom, the like connection that exists in the meetings that a lot of the sort of like agents and offline orchestration from AI is oriented around getting for us, getting us more meetings so that we can build the relationships. So that's one. The second is, and like agents are maybe getting better at this, but still not the level of humans. It's the like, try new creative things. What are the really creative campaigns that we want to come up with and experiment with? We talked about this earlier in the show where Carlos like said something that really resonated with me. It's like the experimentation stuff, the like, let's get in a room, let's whiteboard. What are some ideas we can come up with? One of the things ElevenLabs came up with was a grant program. We're going to give the, like, hottest startups $10,000. They're going to onboard to us. They're going to get 60 days of value. That is, like, very difficult for an agent to come up with. That is where we should be spending our time as people. Let the AI and the agents do all the other stuff. And so if you find yourself as a sales rep, A sales leader, a go-to-market organization, spending a lot of your time doing things like trying to find new companies to add to our database, overlaying signals or researching people to find characteristics to give us a reason to reach out to that person, dropping people in sequences, updating our CRMs. These are things that like agents are better at and you are behind. You should be spending your time meeting with customers. You should be spending your time coming up with creative ideas that agents can't do. And that is sort of like the, I don't know, my interpretation of your exact talk track there, Carlos, about how you sort of adapted to an AI world early. But that's sort of my version of, of what you described.

And then like 100%, and like when you add on top of that, like, Like something that we did, and I'd recommend always to my portfolio companies, like if you're going to be adopting like AI for like go-to-market, 100% you should, by the way, is like also comp the sellers, the owners of those accounts. It's the best way because like, yes, you're double compensating, you're paying for the AI agent to actually do those sales or like do upsells, like find signals and all of that stuff. That's amazing. But Compensate the person so that like you remove any friction through the process because still like selling is a relationship problem. Like you're not gonna be able to sign a big deal with a company if you don't go and camp in their offices, if you don't meet them, if you don't actually like understand the problems, the constraints, the concerns, all of that stuff, right? So like dedicate your time to continue building the relationship because the next upsell might not be a very small upsell. And you know, like sellers, they want to be spending time like with people, they wanna They don't want to be researching all the time, like accounts. They don't want to look at like 25 different signals. Like they just want to have like a prioritization element, like something that actually makes their life much easier because that for them, it's all about the relationship. So let's optimize for the relationships like that.

Carlos, I think this has been an episode of like our minds melding. Lots of just like philosophical agreement on leading sales organizations and what works and maybe like lessons learned. We both went through maybe like some of the same mistakes. Hopefully, like, we prevented some others. Invest early in go-to-market operations is maybe like one that we both, you know, in enablement specifically for you, that we both like learned the hard way. This has been awesome. Thank you for taking the time to be here. I hope everyone enjoyed the episode and we'll see you next time. Take care.

Thank you.

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