Speaker 1You wouldn't think that you'd have to tell an agent not to delete a company's source code if they're a developer of software. But that's actually happened. Yeah. So in this instance, you actually have to bring, you have to codify human behavior on a decision a human would have to make. Like somehow, how do you code in there that somebody's got to have a shaky finger before they press delete all versus the agent world? They don't have that experience of shaking. Are we sure we want to delete all?
Speaker 2Welcome to the Revenue Builders podcast, a weekly show featuring B2B sales leaders and executives. Hosted by five-time CRO John McMahon and Force Management co-founder John Kaplan, the show takes guests in the barrel, behind the scenes with the people who've been there. Done that and seen the results. Revenue Builders covers best practices for scaling and growing your business while sharing the pitfalls to avoid.
Speaker 1Welcome back to another episode of the Revenue Builders podcast. I'm John Kaplan with John McMahon. Today's guest is Ben Sabrin, a go-to-market leader at Arcade, a company solving one of the most overlooked problems in enterprise AI, agent authorization and security. Something that's been going on for a long time.
Speaker 3So, historically, if you think about most enterprise software, even early Mongo, we were going and selling to enterprise architects, the enterprise data architects, people that drove that platform shift and that change. And what we're seeing is a lot of companies have an AI platform team, especially the ones that are a little further ahead. And this is a team that's responsible for, how do I enable the organization to start to leverage AI in a predictable manner across the business? And a lot of times in that initial engagement, there is a bunch of discovery that we need to do in terms of, like, what are they using today? Are they building custom agents? Are they just, you know, leveraging connectors inside of Copilot? Or? Or Claude, or something along those lines.
Speaker 4Funny way, it's almost like the old RBAC for an agent. Yeah, 100%. What
Speaker 1do you mean by that, Johnny?
Speaker 4Role-based access control. I mean, that's what you typically did with humans. You'd say, okay, John Kaplan can do these types of things because of his role in the company. And these are specific systems, as Ben said. You know, what actions can Kaplan take across which systems? And Ben's saying the same thing for the agent. You know, which actions can they take at this time across these systems? And we. That's all RBAC was really doing. But everybody wanted RBAC almost for any application that you had. Because depending upon the information in the company, you only wanted certain people in certain roles to access certain information at specific times.
Speaker 3And now, like, add in the fact that. This quote-unquote user, right, is this non-deterministic, non-human, very intelligent resource that doesn't like guardrails, right? As we've seen, you know, stories that continuously come out over the last several weeks, right? Every day. You know, oh, our agents broke the guardrails again. And it's because you're putting. The control. When you put the control inside of the agent, right, they're not going to necessarily obey, right, those controls because they want to win. They want to satisfy the intention that was asked of them, right? And they're very, very smart. And they're getting smarter and smarter by the day. But it is interesting. You know, John, you made a really good point around RBAC, right? So I think one of the other keys. I think one of the other keys to any time you have a platform shift, right, there's always a. There's never a clean break from the past, right? You always have to have a bridge to how things happen, especially in the enterprise. We all know enterprise, they don't just, you know, break and say, okay, we're doing everything different starting tomorrow, right? It just doesn't happen that way. And one of the things that. One of the things that we made sure to be able to do was be able to meet people where they are. So in a lot of cases, most companies have an entitlement system. They have a source of truth, which is feeding multiple different systems within the organization. It could be a database entitlement system that's saying who can do what within different data structures. There's your application entitlement system that could live in a sale point or another identity governance system or ENTRE, etc. Arcade is. Arcade is capable of pulling all of these things in and being a policy enforcement point inside of the agent loop. And we did that very, very intentionally because we believe that if we walked into an enterprise and we said, in order for agents to do anything meaningful, you've got to take all of this work that you've done around governance and entitlements and you've got to completely rebuild it in the name of an agent, it would have been dead on arrival. Right. Yeah. So. And we shied away from having our own distinct policy language, but we're being pulled in that direction because the reality is people don't fully understand all the policies they need for agents. And the way they want to express those policies are much more in natural language than. How they may have expressed them in the past.
Speaker 1Well, you wouldn't think that you'd have to tell an agent not to delete a company's source code if they're a developer of software. Yeah. Yeah. So in this instance, you actually have to bring. You have to codify human behavior on a decision a human would have to make. Like, somehow, how do you code in there that somebody's got to have a shaky finger before they press delete? All versus the agent world, they don't have that experience of shaking. So when you're doing that inside of companies and you're trying to figure out how to meet people where they are, are you doing this with, like, forward-deployed engineers or. Tell us a little bit about that because we're hearing a lot about, you know, AI companies are now using these principles of forward-deployed engineer when Johnny and I were laughing a little bit and talking about. The old days, what that used to mean. What did you call it, Johnny? It used to mean your software doesn't work. Yeah, what it used to mean.
Speaker 4You're essentially going in and writing code on the fly to make sure that, you know, when you do do a POV, it actually works. But, you know, it's different times these days. So it's codifying not only that. We're selling software that mostly works these days versus even 10, 15 years ago, we were selling software that didn't work.
Speaker 1But the difference is now that. People are, like, they're expecting it, like, I don't want it to work off the shelf. Like, I want it to work in my environment. Yeah. And I want it to make decisions like we make decisions. So talk a little bit about how you do that in this world today.
Speaker 3Yeah, no, look, I think you both make great points. And I think the reality is that we have a forward-deployed team and they build stuff on a very regular basis. And a lot of their purpose and their mantra. Because, I mean, Arcade is a middleware platform, right? So by itself, and when you sell, like, a middleware platform or a database, right, your product by itself does nothing unless somebody builds something on top of it, right? And so the forward-deployed motion really lends itself very well in that capacity because we're providing building blocks, right? And there's. Because the market, there's so much white space. Because at the end of the day, you know, I think we are collectively experimenting as an industry, right? And there's so much. People see, they see the potential and they see the possibility. And so there's this thirst for, like, how far can we push it? And so we spend a lot of time and, you know, and yeah, there's times where it may be a scenario where our software doesn't work the way we want it to. And there's some stuff that needs to be cleaned up. But in a lot of cases, it's really more of, well, it doesn't do this in the way that the customer wants it to, right? And it's not that our software doesn't work. It just, it needs to be customized for their environment. And that could be because somebody has a homegrown Kerberos-based entitlement system, right, that we need to hook into.
Speaker 4But what I really hear you saying in here, correct me if I'm wrong, is like. There's so much opportunity, especially for a company like yours and many other AI-native companies, that when you go into an account, you have to figure out, where can I give the customer the biggest bang for the buck? And where can I show the greatest value for my product? Yeah. And that's it, right?
Speaker 3Yeah, and a lot of companies, like, you know, at this stage, you know, you want your early customers to truly be partners, right? Yes. And the more you show the ability to build with and bring them in to that creative process, I mean, you sow amazing relationships that tend to last for a really long period of time. And our FTEs are empowered, so we operate our engineering org out of a single monorepo. All of our FTEs are empowered to write code that we merge into the monorepo, provided they follow our SDLC guidelines. And they've all, everyone on the team has so far built code that's now in our core product.
Speaker 4So let me just go back to that whole RBAC discussion. I haven't thought this entirely through, but you're basically doing RBAC for an agent, but I also may have an application where I still need RBAC for personnel, right?
Speaker 3Mm-hmm. Yeah. Yeah, and actually, the way we see it is, it's actually. It's an N diagram, right? So the tokens that Arcade holds, right, is every combination of user, agent, and application. action, right? Now, we don't mint those tokens. They're minted from either the upstream SaaS service or the IDP, but we hold them and we enforce those permissions at runtime. So when that agent is trying to go take that action against n number of systems, the first thing we're doing is we're doing a check to say, should this agent initially even be able to see those tools that it's trying to act on? Then should it be able to take the action that it's trying to take? And then there's a bunch of other policies that you can plug in along the way.
Speaker 4And then do you also work with application providers also? So if I'm building an application today where I want to make sure that my agents can do certain things, but not other things, do you work with application providers?
Speaker 3Yeah. So we definitely have some companies that are customers that are software companies. And even some of our big enterprise customers, like some big banks, I mean, a lot of them are software companies just disguised as a bank.
Speaker 4They have thousands of applications, yes.
Speaker 1It's that concept of the MCP though, right? Because you want to make sure that you're hanging on to your IP and your critical information and only giving out what needs to get out to whoever needs it. For me, in the simplest terms, when I read that Wall Street Journal article, I was like, I'm going to do this. I'm going to do this. This fundamentally is the number one reason why the adoption of AI and everybody squawking about the return on investment for AI is so low is because this hasn't been solved, is kind of what I was thinking in my simple brain. No, I think
Speaker 3you're spot on. And it's a problem that we are aggressively solving. And I think the stakes are very high and there's a lot of legacy incumbents to tell a story from multiple angles.
Speaker 1Is Anthropic trying to get it back?
Speaker 3I mean, Anthropic definitely is doing some things, but I think we've all seen plenty of evidence that there's very few enterprises that are completely standardized on one LLM. And if you build control inside of one client, you're going to have to do a lot of things. You're going to have to build it inside of every client. And we allow you to just do it from a single place and plug the control into wherever your users are working. But you get, I mean, we get the competition from the identity guys and the API gateways and the network security guys and the hyperscalers because this is a problem that needs to be solved, right? And, you know, I think first principles thinking really matter. Right. And we've designed this from the very beginning. I mean, we, Arcade are, you know, probably our biggest claim to fame is we invented the how you do OAuth inside of the agent loop. So we, the URL-based elicitation that's part of MCP is something that we wrote. And we were solving the problem actually before the MCP protocol came out. And this is a fun story. So when the first version of MCP came out, it was November of 24. It was pre-NIPI. It was pre-NIPI being Arcade, but Arcade, they were already building, right? I kind of started consulting with Alex and Sam February of 24 or 25. And right about that time, Arcade was getting ready to, or contemplating publishing the protocol that was underneath Arcade as a competitor to MCP. And we had Langchain at the table that was all in on wanting to do it with us. And we even had OpenAI at the table. And OpenAI only agreed to talk because they made us promise that we wouldn't support MCP. That's probably the funniest part of the story. And so we had a blog post. We were getting ready to publish this protocol. I think it was a Tuesday and Monday, Anthropic dropped the latest rev of MCP because the first version was a desktop-only protocol. It worked on standard IO. It had no real, you know, authorization or authentication story. When they dropped the newest release in March of 25, it finally worked over HTTP. And so it could work remotely. And it had a very, you know, a little bit more of an authentication story, still really didn't have an authorization story. And we were already solving those problems, right? And so they dropped their latest rev on Monday. We kind of hit pause on the post that we were going to make. And we were like, okay, we're going to do this. We're going to do this. And then Thursday, the company who made us promise that we wouldn't support MCP, by talking to us, came out and basically said they were going to support MCP. And at that point, right, we believe that our brand wasn't big enough to go fight that fight. And so we decided to jump in and become active members with an MCP.
Speaker 1How great of a story that is, because, you know, when you just look at, and I don't want to make a political statement. I don't want to make a political statement. I don't want to make a political statement or anything about these companies. But there seems to be so much distrust of the intention of those large language model companies, that it kind of, it's kind of like perfect timing for you guys. I mean, it really is. Yeah, it's good timing, actually.
Speaker 3And it's, go ahead, John.
Speaker 4No, along those lines, many security companies have now added AI and LLM infiltration to their systems. And so, you know, we're, you know, we're trying to, you know, we're trying to, you know, we're trying to, you know, we're trying to, you know, we're trying to, you know, we're trying to So I was wondering, maybe this is an ignorant question or a good question. Can Arcade help you, if you're an application provider, limit through policies, what information Claude can access?
Speaker 3Yes. Yeah, great question. So we hook into DLP systems, we hook into PII detection systems, systems, any place that policy exists. And so a good example could be, let's say you're somebody in, in RevOps, right, or somebody within the revenue organization, and you're running, you're expressing an intention that you want to go pull a bunch of account information on a certain subset of customers, and you want to email it, right? And maybe there's something in a policy that says, you know, you want to pull a bunch of account information on a certain subset of because, you know, you cannot share any customer information externally. And one of the people on that email chain is somebody with an email address that is not at arcade.dev, right? If you had the right policy in place, we would block that, right? So we would, we would basically block the ability to send, it would say something like, you know, this, this contains customer information, and therefore it cannot be sent externally, right? And so, yes, that is absolutely something. And that all, it's all because we are sitting at runtime. So we are sitting inside of the agent loop and intercepting that call before it goes to
Speaker 1the model. It's out in the wild.
Speaker 3Yeah, the actual tool execution, right, is, is happening inside of Arcade.
Speaker 1So we're, or you guys are geeking out a little bit. I, I got my, I got my, I got my satisfaction out of this conversation because it really is kind of validating what I thought it was. So thank you for that. Now, let's talk about go to market implications. Because now when I look at you, and I'm like, okay, so Ben has, I mean, he's got, you got an incredible experience in just open source in itself. So that's kind of, that's kind of rare. Like you're one of the early open source, one of the early salespeople in open source, you know, JBoss and Mongo, one of the early people at, at Mongo. And, and now, you know, the implications of Arcade and, and, and dealing with open source. What are the implications of what we're talking about on building a go-to-market engine? So you've been in these category creator, you've been in these companies that are, you know, you've been in these companies that are that are creating categories. There's no category yet, or it's an early category. What are some of the implications on go-to-market?
Speaker 3Yeah. I mean, so look, I think it, it goes to the persona that you want to hire, right? You know, as you start to scale the, the revenue organization, you need a different type of seller than you would if you were selling, you know, something that was a well-defined widget, right? So when you, you know, anytime you're, especially when you're selling infrastructure software, when, you know, like I said earlier, when the product by itself does nothing, unless somebody builds something on top of it, right? You need reps that are incredibly curious. They're not afraid of the technology. They take a keen interest in understanding the customer's business, right? And how, what you're selling can enable their business in some positive way, right? Um, and you know, the good news is, is like on the research front, it's never been easier. Like there's, there is zero excuse at this point to not be insanely informed as a seller, right? Like a good chunk, like there's even a good chunk of your discovery that you shouldn't waste the time to ask because you already know you should be more validating, right? What you know, versus asking it like you've never understood it in the past. Um, and, and so I, I think to that end, right, I, I am always looking, you know, one of the questions I ask every seller, um, that I'm trying to hire right now is how they use AI. Um, and you know, the more kind of tinkering and, and the more curious they are, um, the more interesting. It is for me. And, and why is that right specifically for us is because we enable that reality, right? And so I want my, my folks playing around and building and, you know, being excited when they never opened their CRM for the whole week because everything they needed either in or out, um, they were able to facilitate be, you know, by asking, you know, their favorite model, um, to do things. Um, and so, you know, that, you know, people that say, oh, I, I use it all over the place. Well, they're usually the ones that aren't doing anything, right? Like I want, I want details.
Speaker 4Well, you're right about curiosity. I mean, that's the number one thing. And then especially since what you mentioned earlier, you can go into an account and probably have a hundred different potential opportunities, but which one's the best one for arcade and the customer at this time? Yeah. Curiosity and investigation, you know, comes into play.
Speaker 1Isn't that a go-to-market dilemma also, Johnny? It's a go-to-market dilemma. A little bit.
Speaker 4Yeah. But he's, I mean, Ben's trying to get the thing off, you know, really off the ground in the right way. So it is, it is, and it isn't because eventually he'll find some opportunities that he can repeat over and over again. I like to say. But where's the repeatability? How do I get that? Cause I want to try to, I mean, he's, I think you're in like essentially product market fit stage. We are, we're about, I would
Speaker 3say we're about there.
Speaker 4Yeah. And then I always think we move from there to, we find some consistency and repeatability and then you're like in this deal stage, Hey, we're getting deals and now we get deals consistently. And now you say, we got them so consistently and more and more reps are hitting the number. Okay. Now it's time to start to scale. So, and that's what he's looking for now. He's looking for this consistency. Repeatability. So it is an is, it is definitely an issue, but it isn't too.
Speaker 3Yeah. I, I, um, I'm a big fan of, uh, of some of the research that Mark Leslie published years ago. Right. The whole sales learning curve concept. And I actually have a, a little AI, uh, baked dashboard that I have, uh, my GROK bot runs for me every morning, um, on how, yeah, on how my reps. Yeah. Um, that I've hired are tracking on the sales learning curve, right? Cause you know, so where we are in terms of growth, right? So I hired the first rep in, so pre, pre us, uh, raising the series AI, I hired the first person underneath me. And the only reason I came there, I actually rolled my sleeves up and became the initial seller, um, is cause the relationship that Alex and I had, right. There was just tremendous trust and AI had gotten so good that I'm like, yeah, you know what? I'll roll my sleeves up and get it done. I'm, I'm incredibly comfortable in my own skin. Um, and, uh, and I love selling at the end of the day. I love, especially, especially when it comes to disrupting markets and creating a category, it's a lot of fun. And I also find that when you eventually get into the build stage, right? The fact that all those original logos are a good chunk of those original logos or stuff that you have your direct signature on, right? The ability to get people, uh, right. Uh, really marching, uh, behind you is, uh, is, is a little easier, right? Cause they know that you're not just, you know, spouting, um, things from, uh, from a playbook. You wrote the book and you know exactly how to get it done. But then we hired our second, like our first real cohort in June, right? So I hired four reps between June and July. And where we are right now is I believe I'm going to get all of them on the board with our first enterprise deal before the end of October. That's kind of where we're currently tracking. And once we prove that, then it's, uh, we step on the gas, right? Cause at that point I I've proven that we can bring on, uh, new reps. We can get them productive in a fairly short period of time. Um, there's more pipeline to be had from, um, that kind of population of companies where we now have good stories to tell. And, um, let's go do some world-class enablement, maybe a little command of the message, uh, and get everybody, get everybody in line and hire a couple of amazing, you know, frontline managers to go, you know, really drive the team in the right way. Um, and then just don't stop once we, uh, you know, once we're confident in the, in the repeatability and that's what I call, I mean, this is the stage where I call it going from art to science, right? The earliest stages are, it's a lot of art. You're throwing a bunch of shit against the wall. You're seeing what sticks. You're not afraid of experiments. Um, you know, I was, it's like my, I write my plan in pencil and I keep my eraser handy because the chances of being wrong are very high. Right. Um, but once you get to that, like, oh, okay, this looks the same. Right. And I'm starting to see it look the same over, you know, not just like a handful of deals, but like, okay, these 15 opportunities all look the same, right? The persona is the same. The pain is almost identical, right? With obviously some nuances. The metrics are very similar, right? Obviously also with some nuances, then it's like, okay,
Speaker 1let's go where, where you guys are also like, I'm fascinated by this, Johnny, you hinted on it. When you, we just kind of brushed over, well, yeah, they'll get some, they'll get some metrics in place. They'll get some, but that's just because it's native to you and like finding a Ben or if you're out there wondering if you are a band and if you've come from like, and you've mentioned that a playbook or your own playbook, there's like art and science colliding here big time. Because what I'm thinking about is, you know, how do you not get in the wrong deals? Yeah. Like you got to have some experience because, you know,
Speaker 3especially when the pipeline isn't super deep. It's the biggest, it's one of the biggest challenges is, you know,
Speaker 1and what, and what is a qualified pipeline? I mean, if you get like, I know you guys do, you, you got some cool stories with like Goldman and you know, that obviously had to, they became a collaborator and a partner and the, but then everybody's going to want to do that. And some people are going to want to do it. Hey, come bring your four engineers in. Play around. Let me kick the tires with it. And then I want you to do this. Then they, then they think because of the stage that you're at, that I can tell you if I'm going to give you a certain amount of money that you'll go build it the way my vision is. Yeah. And that takes experience and moxie to be able to deal with a customer like that.
Speaker 3Yes. Yeah. You know, I'm thankful. I'm very grateful. So Alex, our CEO and founder is a great partner in that respect. It's very rare that you have somebody. He, uh, Alex has a computer science degree, uh, but started his career in sales at IBM and went through, you know, one of IBM's last like great sales training programs. Um, and, but he's been a, he's been a founder and he's got a, he's a product guy really. And, um, he loves to say no, but he also, uh, is not afraid to say yes. And, and really when, when, when there's the right pull from the market. He will lean in, in a pretty big way. And, um, I think that's, that's critical and I've seen both sides of it. Right. So if I think back to early Mongo, you know, one of the things that put us on the right path was, um, we had gotten in deal cycles with Goldman and city and met life and some other big banks fairly early. Right. And, um, at that point in time, and this is what, 2011, right. Um, maybe late 2012 is when some of the stuff really started manifesting, but we really got into it in 2011. If you didn't support, support Kerberos for authentication from a database, you weren't selling your database to the bank full stop. Right. And I remember like having the drag Elliot into that kicking and screaming. Um, but he did like, he wound up spending a bunch of time in Goldman's offices and, and, you know, they became an unbelievable design partner. And we did that because it was clearly repeatable. Right. It was this was, you know, while Kerberos was on its way out kind of as a as a technology, it was still entrenched enough that if you didn't play in that game, the banks and some of the big telcos were never going to use your product.
Speaker 4Now, Ben, do you partner with any other companies?
Speaker 3Arcade. Arcade. Yeah. So we're we partner very closely with Langchain. We also partner with Mostra. So they're also an agent builder from an SI perspective. Wipro is is a partner of ours. They're also an investor and our newest investor and partner. We actually announced today is Cisco.
Speaker 4Okay. Just curious, why would it make sense? Maybe I'm again, I'm on the wrong track. To partner with the cursors, the cognitions of the world where they're going in and modernizing applications like in big banks and automotive companies and all that and AI into those applications as they modernize them. Why wouldn't you want to partner with them? Because they're probably going in and almost doing some of the homework for you in the sense that the bank is how if it was a bank, they're picking the app that they really need. To modernize because it's costing them a fortune and they want to add AI in there. I don't know. Why wouldn't you try to work with those guys? Yeah, we have
Speaker 3made several overtures.
Speaker 1It's the mafia of the LLMs.
Speaker 3Yeah, it's we're still we're still little for those guys in some way, shape or form. Right. But if they're listening, we'd love to partner specifically. I love what cursor is doing. I love Grokbot. You know, I've got all of my tools wired up into Grokbot. And it does. It does a bunch of amazing things for me. Right. And our engineers love our engineers. We go back and forth. Like there was a time we were all in on cursor. And then there was a time we were all in on Claude and Claude code. And now everybody is like really loving codex. But part of that's part of the beauty of arcade is we we make that like optionality easy because you're kind of. It's like you have all the connectivity to all your tools. It's just a single URL, right, with all of the identity controls and governance and policy, et cetera, et cetera.
Speaker 1How do you manage from a CRO perspective the expectations like I have a lot of energy and desire and not a lot of applicable understanding of the market? And I'm sure you probably have people that invest and wind up on boards or advisors and they want you to go faster. They want you to do X. How do you manage those expectations being a CRO in a company like this?
Speaker 3Yeah, that's a great question. I mean, I think I think scaling prematurely, especially from a go to market perspective, before you have a semblance of repeatability. It's just lighting money on fire, right? And so if they're in the business, it's
Speaker 4like you as a CRO up for failure.
Speaker 3Yeah, totally. Like, I mean, if they're in the business of lighting money on fire and, you know, they're they're going to give me a stay of execution.
Speaker 1You never get that. Oh, right. You never get the stay of execution.
Speaker 3No, of course not. It's the easy, you know, it's, you know, this I always joke with people. I mean, this is one of the loneliest places you could be when you're the CRO of an early stage company. It's the easiest place. The point the finger is at you. Right. It's never a product problem. It's never a delivery problem.
Speaker 4It's always a sales problem. Man on an island. Man on an island.
Speaker 3Right. But I've got I've got broad shoulders. I'm used to it. Right. Well, it's one
Speaker 4of the phenomena in any company I've been in is everyone thinks they can do the sales job, but most of the sales guys don't think they can do any of the other jobs.
Speaker 3Yeah, I would agree. I mean, you do not want. Well, in today's day, I could write some code, but no, you don't want me. The building product.
Speaker 1Tell us a little bit about. You made reference to it. I'm intrigued by the. Grokbot and just in general, like what problem were you trying to solve? You wanted you wanted to apply the learning curve to your and if you could just give some people some spirit around, you know, a lot of times when you're doing interviews 12 months ago, people would give me answers that, well, my company doesn't allow me to use. That's why I say that those answers are out the door now. So regardless of what your company allows you to do, people are still using AI. So tell us a little bit about how you. In your position. You had a gap, whether it be research based or information based or productivity based or what have you, and then you solved it with what was available to you. Just walk us through that a little bit to encourage folks.
Speaker 3Yeah. So there's there's a bunch of different ways that I. Use the machines for me. I think like if we rewind, right, and if you think about how we needed to do things as a leader in the past, now I'm even going back, you know, probably pre call recording. Right. You know, in order to really coach reps, you had to be in a lot of calls. Right. You had to you had to physically be there sitting, sitting, you know, in the room and doing your best to shut up. Right. And observe, which not is not always the easiest thing, especially if somebody is veering off course. Right. But with with really good. I mean, we were we recall record any call we possibly can. And I have a skill that I then use and I've and I've given it to my reps so they can also coach themselves so they know exactly the things that I'm looking for. Right. And it's basically it's analyzing their calls on a weekly basis and it's looking for across several different things. Number one is in here adherence to the message. Right. Are they delivering the message the way we want them to deliver the message? And there's still a lot we can do in order to crystallize and make that better. The second is I'm a I love I love MedPick. But I also love. From a behavioral perspective, the concepts that came out with spin, you know, I'm an old dude. I was very fortunate. My professor in college is the guy who hired Neil Rackham to write spin. He was at Xerox at the time. And then he became a professor. Well, I know the story. Well, yeah. So Dick Canada was my professor at Indiana where I went to school. And so I'm looking for seeking behavior versus speaking behavior. Right. And that's one of the things I'm consistently tracking talk time as a as a metric and then adherence to MedPick. Like, are they are they following when somebody opens the door to be able to easily figure out who the economic buyer is or my reps walking through it? Or are they, you know, not recognizing the opportunity? Are they not getting to metrics or or even the beginnings of starting to map the value equation? Right. And so I have that skill. It runs. I use it. I get a report that kind of gives me the high level themes and it scores them right across each of those different metrics and they can see it and they can run it themselves. I encourage them to write. I'm like, don't wait until our, you know, biweekly one on one to try to get better. Right. I've delivered this to you. Go be your be your own. Best critics. That's that's one area. The other is on the kind of like how we're tracking in terms of moving deals across stages and building pipeline. I have that that runs every morning. I get a good snapshot that's looking at, you know, how many new conversations, how many, you know, stage twos converted to stage three, because one of the key metrics that we use for how we're tracking along the sales learning curve is. Can they how many of the new reps are have active deals in tech evaluation? Right. So we've moved out of qualification and we're truly in like an actual deal. Right. And and how many are they stacking? Right. In that. So that's I have that that runs every day. It's looking at, you know, pipeline across all different stages. And then I have another one that's looking at intent. Right. So I want. I want to make sure that we are effectively prosecuting hot opportunities. And so we use a bunch of different tools under the covers because we have a product that somewhat caters to developers. There's a really good signal based product called Rio Dev. That's pretty powerful. Also, some ball is a really good product that has like good buying group information plus my my call recorder. And so I have like four different systems. Then we also have the swarm on top of it, which helps you map. like where's my best point into an account. And so every day, um, I get a full view of like, here's the intent snapshot, like here are the 20 kind of warmest opportunities based on a multitude of different factors. And here's our path in. And then each individual rep gets their specific version of that, um, that they can, you know, from a single button then act on, right. So they can go launch a campaign in Lemlist, uh, directly from that kind of intent report, um, or go fire something off on LinkedIn, et cetera, et cetera. So those are some of the ways that we, uh, you know, another great example, like John, you guys will both probably completely relate to this. So, um, who likes like building a new, uh, pricing model? I mean, it's never, never a ton of fun. Um, if, if you do it well, right. And so like a couple of weeks ago, I was at a point, and this also speaks to where we are, like we're starting to get into more repeatable patterns, right. We're starting to enable some resellers, right. When you enable a reseller, you need a price sheet that is, you know, very understandable and predictable. And so historically, if I wanted to kick off a project like that, I would bring my, my product marketing or marketing leader, somebody from product, one of my sales engineers or my lead sales engineer and rep or two, we get around the table. We're like, okay, here's, here's my thoughts. Here's what I want to go do. You know, you own this part, you own this part, you own this part, you own this part, go do your research. Let's come back. Right. Yet Claude and I, I spent about, I don't know, five hours and we built out a, a model, had about a seven tabs, a spreadsheet, got down to margin, a calculator for reps to be able to use, a one pager, something that I could push out to partners. And that took me like a day. And part of it was like, it went through all of our call recordings, all of our existing deals, our pricing that's, that we publish our, you know, our basic SAS pricing, any proposal that we've sent out, it then went and did a competitive look out in the market to pull out anything they can from somebody who looks similar. Right. And now we have like a really good enterprise pricing model. And like, that's the, the microcosm of the kinds of things that you can really do with, with AI.
Speaker 1You know what I love about that journey that you just described. And I, if I, I can call a day a journey, so congratulations. That's amazing. But if you didn't know the problem you were trying to solve, this is why I really encourage people to not be afraid. You know, the problem you're trying to solve, you know, that complexity is a problem in pricing. You know, that you need some type of calculator that's easy and definitive, you know. And so what I try to encourage people is to use the technology, as a thought partner, not as a thought replacer, if that makes sense. And once I did that, I'm just being completely transparent. The intimidation factor for me a couple of years ago or whenever it started to go away was when I used it as a thought partner, it's, it was never as smart as me on the stuff that I'm talking about on what I'm trying to automate. And, and I think that what you just went through there is a great example of, you know, solving a problem that helps, and Johnny and I are really, really big on this. Like, I think when you're, because everything you just did, you're, you're solving it also for sellers. And there's just not enough of the technology in the stack today that is really built to do that. It's to keep an eye on a seller, it's to keep an eye on, you know, and it's run by people that haven't sold and they're looking at it and they're saying, this is a quality product. And they're looking at it and they're saying this is a quality deal or not a quality deal. I just think we're in the best time to be alive is that to use that, you know, codify your knowledge with a thought partner and create less friction for your sellers is the name of the game.
Speaker 3Yeah. I mean, it's, it's like, I kind of said this earlier, right? We're at a point where if you can clearly express your intention, right? Yeah. Yeah. Yeah. Yeah. Yeah. And you can, you can provide good direction, right? And the prompt doesn't even have to be that good anymore because the models have gotten really good, right? It can truly be a stream of conscious intention, not structured as a very well-written prompt, right? And then I'll always in that, you know, basically put in something like, what other questions do you have for me? Yes. In order to, you know, succeed. And, you know, then it gave me like this list of seven, you know, here's seven things I want you to answer before I can go research more, right? And you can, you can get some pretty good outputs, but we've got a, I think the other thing that you made a good point, John, there's, there's a lot of folks that the biggest thing that could create, you know, I would say negative return in the world of AI. I would say negative return in the world of AI is when people aren't kind of doing what, what we just talked about, right? And they are just relying on the AI. No original thought. And they're not reading what the AI writes. Like we've got an internal use policy that, you know, if you're going to share something with somebody internally that AI wrote and you didn't read it, nobody else is going to read it. Yeah. Right. Why should, why should we waste our time? Right. When you're. When you didn't, you just, I could have just generated that same thing myself.
Speaker 4Yeah. Well, it's when you understand the domain really well. And you ask the LLMs a question that a lot of times the answer comes back and you're like, that's not right. Yeah. Yeah. Now, 90% of the time it's right. But this many times where you're like, that's not right.
Speaker 3Yeah. You're missing, you're missing.
Speaker 4That's the domain that I understand. I can't imagine the answers that I get back in the domains. That I don't understand. And then I have to assume that, you know, 90% of it's right.
Speaker 1Yeah. Then take us out. As we land the plane here, take us out five years, not only for to the best of your ability to kind of like where you think what you're doing and what arcades going to be doing and what's going to be normal in the enterprise environment. Tell us also what. What do you think. The sales. Industry might look like in five years. There's no right or wrong answer. We're just, we love that.
Speaker 3Good question. Look, I think five years. Feels like an eternity. From now. But, you know, I've learned that time just keeps moving faster and faster as we get older and it's mathematic. Right. You know, when I'm one a year is 100% of my life. When I'm old, like me, it's like a 2% or so it just keeps moving faster and faster. But look, I think in five years, the browser's dead, right? I don't think people work predominantly through a browser. I think, you know, we're, we're basically, you know, app apps are built on the fly based on, you know, an interaction MCP apps are kind of an emerging thing that are, that's coming in that, in that respect. Um, I think that there's, um, any, any work that is repeatable, like, like process that isn't just very repeatable is done by machines, right? Uh, without, you know, human in the loop. When it comes to sales, I still believe that, um, people buy from people. And as long as people in some way, shape or form are a key part of the buying process, then I think people will still be involved in the sales process. Now, I think the, the, the yield curve could look very different, right? I think we, we should be able to theoretically, right. If we can, if we can remove all of the busy work that we put our reps through, right. And most of what reps are doing is in they're in deal mode. Right. And because they don't have, you know, the agents are kind of doing all of the busy work behind the scenes, you know, whether it's writing their proposal based on all the conversations that you've recorded and the notes that you've given it, um, based on the frameworks that you have and, and, you know, putting things through CPQ, right. Because it's got all the specific terms and sending things to legal and, you know, all of the stuff that occupies a lot of cycle. We theoretically should be able to get a higher yield per head, right. If we can make them more productive because we move. whatchamacallit, we remove a lot of that burden. But I still think it's a very human thing, at least in enterprise sales, right? If you're selling something small and the agent is going to be the person, the thing buying it, well then do you really need a human on the other side of that?
Speaker 1Probably not. The buyer will always dictate that. Correct. And the buyer might be an agent in the future on some of that lower end stuff.
Speaker 4Any of the commodity stuff, yeah. But if it's brand new enterprise software, no one's ever seen it before. To Ben's point, I think you need a human involved. And, you know, humans are unpredictable. For sure. You know what Ben's going through now where he's starting a company, he's trying to search through different organizations, try to find the right persona to hit, gain repeatability. Some of that AI will help with, but you need a human involved to talk to people. Totally. Amazing. Sales has always been part science and part art. Yes. The science is going to get better. You won't pick up the art part.
Speaker 3I think the science part, I mean like modeling, you know, the calculation and the modeling side will just get very easy and automated, right? But, you know, the stuff where there's still a decent amount of subjectivity, right, which is a lot of sales, right, I think is going to remain very human.
Speaker 1Yes. It's funny, though, if you took that concept of a forward-deployed engineer and plopped it into a go-to-market organization and looked at what people are actually doing and struggling with, you'd think you were looking in a cave because it's going backwards.
Speaker 3It's going backwards. Yeah. You know, it's funny, too, and you guys probably see this. There's a lot of these new AI native or AI, you know, modern CRMs. None of them are being built by salespeople.
Speaker 4Right. Big red flag. When you look up the founders, it's always a couple tech guys with no sales experience that are building sales tools. Not only CRM, but sales
Speaker 3tools. Yeah, across the board. Yeah, I agree.
Speaker 1It's fascinating. So, Ben, I know you've got a lot of stuff. You've got I know you've got a tight schedule. Thank you for responding to my email to you. I was intrigued by what I read in the journal. I'm now even more intrigued. And, you know, we're thanks for sharing some of the secret sauce. How can people follow you? Can they follow the company? Can they follow opportunities at the company? What's the best way to do that?
Speaker 3Yeah. So we've got a very active LinkedIn company page. I'm fairly active on LinkedIn and I'm pretty easy to find. I'm the only Ben Sabren you'll find. And we are hiring and we will continue to hire in a very calculated way until we have that true repeatability and then we'll still be calculated, but a little more aggressive.
Speaker 4Yeah. Ben Sabren, thank
Speaker 3you so much. Jon, guys, I appreciate you having me. It's a long-time listener, first-time caller. You killed it, buddy.
Speaker 1You killed it.
Speaker 4Thank you. Thank you, too. And thanks to everyone for listening to another episode of the Revenue Builders Podcast.
Speaker 2Thanks for listening to today's episode. If you enjoy the content, please subscribe. Rate and review the show to help us reach more people. This show is brought to you by Force Management, where we help companies improve sales performance by executing the growth strategy at the point of sale. Check out forcemanagement.com for more information.
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