Why AI will NEVER Replace Your Sales Job! - Stevie Case CRO @Vanta

Speaker A: AI is changing everything. And the best founders aren't thinking about how that changes just their product, but also their entire go to market function. Today I'm sitting down with, um, one of the best chief revenue officers in the valley, Stevie from Vanta. Stevie's awesome. And before that, she was the world's first female professional gamer. She gained a lot of notoriety in 1997 for defeating the creator of Quake at its own game.

Speaker B: What is sort of the number one characteristic that I exhibit? It is competitiveness. So that thread carries all the way through from pro gaming to today.

Speaker A: Today she and I get into the weeds on AI and the go to market function.

Speaker B: I view this heavy investment in humans who are getting super powered by AI as one of our biggest moats. We have, uh, built this incredibly powerful go to market engine that is a little bit unlike what else is out there in the world.

Speaker A: We talk about her favorite tools in the stack.

Speaker B: Favorite today is we talk about whether

Speaker A: you should hire more or fewer SDRs. We talk about which functions get the most leverage from AI Is sales, Is it customer success, is it rev ops?

Speaker B: I'm a big, uh, proponent of the idea that AI at this point is best used to supercharge human activity, not to replace it.

Speaker A: And then perhaps most importantly, we also cover how do you think about getting your salespeople to be co creators and builders and not just sellers. It's a great conversation for anyone who wants to build not just a great product company, but a great company overall in the age of AI. I thought we'd start off with the fun question, which is about are you gaming very much?

Speaker B: Not these days, no. Uh, I just do game of Revenue these days.

Speaker A: Game of Revenue. I mean, that's a good game. Um, well, cb, it's great to have you here. Thanks for joining us. Um, first thing I really want to ask is what did being a pro gamer teach you about B2B SaaS sales?

Speaker B: Honestly, I think it taught me a surprising amount. It's resource allocation. It's a little bit of human psychology, a little bit of tech, a little bit of strategy. So I find that there are more commonalities than you would expect.

Speaker A: Yeah, it's an adversarial, high paced environment. Absolutely.

Speaker B: If you ask anyone around me at any point in my career today, in the past, what is sort of the number one characteristic that I exhibit, it is competitiveness. So that thread carries all the way through from pro gaming to today.

Speaker A: And there's also very clear feedback.

Speaker B: Yes, absolutely, yes.

Speaker A: Is that scratching all the competitive itch

Speaker B: no, it scratches the whole itch. It's uh, I, I love to win, I love to build winning teams and I am 100% focused on doing that here.

Speaker A: That's great. Um, well I do want to dive into how AI is changing the go to market function. I thought first we just do a quick level set. Talk about the scope of the go to market for Vanta.

Speaker B: Yeah. So it is a significantly sized team. So at Vanta, we are an AI trust management platform. We sell across the spectrum, everything from first time startup founders running single digit number of employee companies all the way to large enterprise. So that org, we're 100% sales led. We have more than 100 new logo salespeople, we've got more than 80 SDRs and we've then got more than 150 folks in post sales across customer success and account management and that spread across the world separate. So we've got folks in uh, every territory, every geography covering the world today.

Speaker A: Yep. So 80 plus SDRs, 100 plus in the revenue organization and hundreds of new deals a month.

Speaker B: Yep, that's right. Hundreds of new customers every single month.

Speaker A: Yeah. And that's why I was excited to have this conversation because I think you have the right scope. You're not a huge, huge company yet, so you're small enough to be nimble, but you have high enough volumes where when you adopt these new tools and new functions you can actually see what it does.

Speaker B: Yeah.

Speaker A: Um, so I think maybe a great place to start would just be thinking about how you even design the org. Like how do you decide who to hire and why? And has the AI tooling landscape changed the actual org design?

Speaker B: Yeah, you know, we change so frequently. We're super agile as a go to market organization. Uh, because we serve such a broad range of Personas. One of the first things we have to keep in mind as we build the org is really specialization and getting the right product to the right Persona at the right time, sold by the right person who understands the talk track and understands how we deliver value to that person. So let's take a founder for example. When we're serving startups, we are serving a first time compliance use case and their considerations are going to be very different than somebody who might be selling to a chief information security officer running a huge program, uh, who have hundreds of people on their team and are trying to think about risk across an enterprise. So we have built an organization with pretty heavy segmentation. Uh, we segment such that our teams are focused on as little as possible at any given Time, really. I want one team of sellers focused on a single Persona, really a single product if we can, and then telling that story really cleanly. So we have a new logo sales team. And in the vast majority of our business, our team is set up to just sell to new customers and then we've got a team that serves existing customers. So we hand that off as soon as we close the initial deal. So that specialization drives a lot of the team design. AI hasn't entirely changed that design yet. I'd say it's changed the way We've built our RevOps organization more than it's changed anything else. What it's changed in the rest of our team with our sellers is our expectations of productivity and our expectations for how we look at their profiles. You know, we want people who are builders as a part of the team. It's not enough to just be a seller. We actually do want them to think AI first as well, not just be consumers of it.

Speaker A: Yeah, I feel like maybe in the AI adoption timeline, we're still early, even the tooling side of things. And maybe org design is one of the later areas where once you figure out the tooling, you start on the org design. I want to touch on the rev ops piece. What teams in the go to market function do you think today have seen the most adoption and utility from the AI stack?

Speaker B: You know, in our org, it's interesting. I think that the initial thinking and so many of the tools went this way, was that it would impact SDR the most. And it's definitely impacted our SDR Org and how we operate. It has changed the workflows, it's made them more productive. But the team that I think it has actually changed the most is customer success. The way that we are able to effectively serve customers is completely different because the way we're surfacing data and signals is much more dynamic than it ever could have been in the past. So we're not, I'm a big, uh, proponent of the idea that AI at this point is best used to supercharge human activity, not to replace it. So. So we're not.

Speaker A: So you haven't laid off your customer success?

Speaker B: Absolutely not. We're hiring like mad, actually, in all of our teams, including SDR and customer success. But those CSMs, they're just much more able to understand exactly where a customer is in the journey and then give them what they need to really realize the value of the platform. So it's just like the way we can serve customers is much more bespoke. It's much more specialized because we've got way better insight.

Speaker A: Yeah, I imagine they can handle higher ticket volumes which is something people can intuit. I also imagine that it's probably good at helping them understand and get ramped faster. When you hire a new customer, success, have you seen that take place?

Speaker B: That's been true in every function. So I do think this is one of the biggest ways that go market has already been transformed and we're seeing it go even faster. Is that when our folks on board in any role, we are leveraging AI to help them learn faster. Uh, they are. You know, everybody gets their own set uh, of tools when they get here. We also do expect them all to be builders. So we've given them access to an agent platform called Dust that we use. They have the ability to build their own agents and we've seen a lot of those agents come out of cs. We are seeing them ramp faster and we are also able to get faster insight into performance. So you know, in the past where you might have let things play out with new hires a little bit longer, now we can see very early if somebody's off track. And that's true for customers, it's also true for employees. So we're really able to get that early insight and then course correct and help people get back on track.

Speaker A: Yeah, I feel like it might be a little disappointing to people hearing they're like, oh, it hasn't replaced customer success. There isn't an AI SDR that I can just drop in. But if you're a fast growing company and your time to onboard is faster, you can get more good people in the door. You can also manage the bad ones out faster. So I think that's a huge unlock.

Speaker B: Yeah, we see it as a real competitive differentiator. You know, we are making what might seem like a very counterintuitive bet right now, which is we are hiring a lot of humans, we're hiring a ton of SDRs, we're hiring people fresh out of college, first time in tech, first job. We're uh, hiring a lot of people in CS and a lot of organizations I think are really hopefully trying to replace those humans with AI. But ah, maybe there's a future state where we get there today. The way I see it, our business is changing rapidly. We onboard hundreds of customers every month. That means we're seeing thousands of deals every month, thousands of opportunities. Those opportunities look different every month. Every quarter we're shipping new product. You know, we have to learn what works and we have to talk to customers to understand how they realize value. And if we tried to replace that with tech before, we had a deep understanding. I think that's kind of a foundational mistake. I view this heavy investment in humans who are getting super powered by AI as one of our biggest moats. We have built this incredible, incredibly powerful go to market engine that is a little bit unlike what else is out there in the world. And so what we see then is partners come to us and they see a huge advantage in being able to use us for distribution and reach customers through our engine. It's hard to replicate something that's got this many humans working so effectively in it.

Speaker A: Yeah, absolutely. I want to touch on something you said earlier, which is we require everyone in our go to market function to be builders. And I'd like you to make a pitch for why. I think now in this day and age at a B2B company, being in the go to market organization is probably the most exciting organization. Not just because you get to drive revenue, but because you get to build things. Like why do you think, especially for new college grads, why do you think it's so important and a great time to be in that org?

Speaker B: Yeah. I mean one, you get just tremendous exposure. So you're getting exposure to basically every company out there and what they're building, number one. So you're getting eyes on thousands of products all at the same time. You know, we're really lucky at Vanta. We serve a lot of the most transformative companies out there. So you're on our go to market team. You're working directly with some of the most innovative companies out there.

Speaker A: Yep.

Speaker B: And at the same time, you're not just sort of like serving them in a super technical rogue manner like you're getting to. It's a creative endeavor. You get to do great discovery, you get to learn about their businesses. You know, we're very focused on delivering value. So value realization is a huge topic here. So understanding how these innovative businesses derive value and how they think about the way they spend their time and investment is another part, uh, of the journey that's really valuable. The third is we do expect everyone on our team to be a builder. So, so we have given them the tools to build agents, to build assistants, to build their own workflows. And of course we're arming them with pre built workflows. But our top performers are the ones that are building their own stuff. They're armed to be able to do so. And so you get to use some of the most cutting Edge technology in your work as a builder and that can really differentiate you versus your coworkers, the rest of the team. And those are the folks that we are seeing accelerate so quickly. So you can come in as a first time SDR and you can new

Speaker A: college grad, first job ever.

Speaker B: First job ever.

Speaker A: But you're AI native, you're hungry.

Speaker B: Yep. And you can accelerate so quickly. You know, you can be looking at a promotion every single year. It's an extremely interesting and lucrative line of work and it's really accessible.

Speaker A: Yeah. If you're a new college grad and you're like stress, you're like, what can I do in tech? How Like I don't have a PhD. I don't think people realize that like being an SDR at one of these companies, it could be Vanta, it could be another company. It's just such a powerful, powerful role.

Speaker B: It's huge. It's huge. And I mean it is, I really believe it is one of the hardest roles at the company. But it is also something you can do without experience. If you're curious, you're smart and you can learn, you know, you show up, we'll put you on the phones, we teach you the talk track and you will learn to have conversations with very savvy technologists and founders. And that alone is such a valuable experience. It really sets you up to be able to do anything. And you know we See SDR Sometimes SDRs move into a uh, long term sales career. We see them move everywhere into product.

Speaker A: This is a really career point. You join a high growth organization and then you have this career flexibility. So yeah, what are the, some of the moves you see people making?

Speaker B: We've seen all kinds of moves. So you know SDRs, we have a really well established program here because we have such a big SDR team. We put them through SDR University. We will teach them to become closers. So if they want to move into sales, they can move into that first closing role. But we've seen SDRs move into account management, into customer success. We've seen them move into solutions engineering if they're more technical so they can become Sesame. And we've even seen that path lead to the product team or product marketing. They're just kind of endless options once you're in the door and the exposure you get. Being on a high growth team opens basically every other door for you.

Speaker A: Yeah, it is funny because you think, oh, AI is going to replace sdr. So what you're saying is the opposite. We're hiring more SDRs. And the job that SDRs do is more interesting than it's ever been before.

Speaker B: Yeah, this, the tech now allows you to do the high value work work. And that to me is the beauty of it. We have really thought about AI and our investments here, number one, as a way to eliminate the busy work and the drudgery of some of these roles. You know, historically if you were in uh, sdr, you were showing up and you were calling and you were emailing, but you were handwriting a lot of stuff. You were doing a lot of manual data entry. A lot of that has now been removed. Which means the job is much more creative, it's much more interesting and it's much more focused on the human interaction and like the skill. So I think it's a much more interesting uh, and rewarding job than it ever has been.

Speaker A: Yeah, it used to be if you're competitive, like you had to go to sales, you want to build stuff, you had to go to engineering. Now you can do both and go to market.

Speaker B: Absolutely.

Speaker A: I want to talk about the Rev Ops Org.

Speaker B: Hm.

Speaker A: Why do you think Rev Ops has been one of the big beneficiaries of the AI stack?

Speaker B: You know, Rev Ops is such an interesting part of the transformation.

Speaker A: I feel like we're just maybe define RevOps. I mean people in sales are like, like people in sales two or three years ago. Like RevOps is not like building the things I need.

Speaker B: Yep.

Speaker A: But like where what is Rev Ops and why is that better than it used to be?

Speaker B: I mean it means something different at every company is one thing you will find. But the way that RevOps is typically organized, you'll have a team that is truly operations within RevOps. Those are the folks that probably most salespeople think of as RevOps. Those are your operations people that are directly supporting the field and your managers. And they're doing everything from helping with reporting to working through workflows to working through deal approvals. So they're really the day to day in the trenches with the team. Um, then we've got a couple of other really key functions. The biggest of those being Systems. Now within RevOps, that systems team historically was much more admin heavy.

Speaker A: Yes.

Speaker B: Think of a Salesforce admin. Somebody who might be customizing workflows but isn't necessarily writing code. That is the biggest point of transformation.

Speaker A: And a sales deal usually touches all of your. You probably have like 30 SaaS vendors, you've got Salesforce, Slack, email, phones. Like how does all that data get into the right point of context. That's how I think of um, Redlocks is like mapping all of that data together.

Speaker B: That is the key because what we are truly finding, and I think if you talk to any revenue leader who's really driving innovation that is AI centric, the data layer is the single most important part of that transformation. Once upon a time you could get away with kind of messy data in revenue and you could manage uh, it through CRM and it may or may not go back to uh, your data engineering team and to your data lake. But today that's not an option. You have to have clean data. You've got to be deeply integrated with data, data engineering, wherever that lives at your organization. So having clean foundational data is really the only thing that makes the rest of that AI transformation possible. So your systems team has to be super data savvy. You've got to have really people who,

Speaker A: and they used to be admins, now they can be builders.

Speaker B: They can be builders. Yeah. They're moving towards building. And this is something we changed in our RevOps org last year as we brought in our first uh, engineer into the team. So we've got a go to market engineer. We now also brought in a founder in residence. We really look at him as like our product guide, I think from first principles about how we build. So we are building what looks like a real product and engineering org inside RevOps today.

Speaker A: Yeah. And RevOps used to be pretty bottlenecked on engineering resources too.

Speaker B: Yeah, it used to be if you needed actual code written you were contacting another team. That was not something that happened inside Revs.

Speaker A: Very expensive San Francisco engineers. Like uh, I do not want to like do 20 integrations. No.

Speaker B: Trying to build the core product. You know, they need to be building that. Not doing integrations of Rev Ops tooling.

Speaker A: Yeah. And that's I think probably RevOps is the first function, but you have, in order for the other function to get value, you have to get all that data integrated first. And so I feel like maybe that would be an interesting thing to do is just like score, you know, like how you're using it in Rev Ops and how mature that is versus how you're using it in like SDR land versus mid market land. Like which of those is farthest along and where are you? Like oh man, I'm still really trying

Speaker B: to figure out, I would say that we're farthest along on that base layer of um, data. Like that foundation is the first thing we worked on as an organization and that's something we did in partnership with our EPD team, Data Engineering. That base layer is now really solid and clean. In terms of functions, we really started with our new logo salespeople because you know, when you're doing thousands of deals a month, hundreds of new customers a month, even small change there is transformative. So one of our first projects was we did a time study, uh, with our new business sellers and we identified where they spend time on busy work every week. And we set a goal to eliminate 10 hours per week per salesperson of busy work. Um, and we've actually just hit that goal and it's been transformed.

Speaker A: And how long did it take to hit that goal?

Speaker B: It took about three months to get that goal. So it wasn't that long.

Speaker A: That's pretty quick for 10 hours across your 80ish estimate.

Speaker B: That's kind of what's horrifying. When you peel back the onion is like the things they're saying 800 hours.

Speaker A: Yeah.

Speaker B: It's, you know, they're wasting a lot of time and it's stuff that makes sense. It's things like preparing for calls, doing research, pre call prep, um, sending follow up emails, updating CRM. These are very basic things. But when you total the number of hours spent, it's significant. And then the quality you get, particularly when you're hiring a lot of new folks can be very spotty. So some of your top reps may be doing very well. They might be sending really quality follow up emails. Some of your new reps might really be struggling and the amount of time it takes away from their selling time.

Speaker A: Yeah.

Speaker B: So knocking out that time, it basically the way we think about it is it drives up ARR per employee per rep. Yeah.

Speaker A: And also consistency, um, across the entire organization.

Speaker B: Absolutely. Yeah.

Speaker A: How has the AI stack changed hiring in each of these functions? Because now that people are expected to do a different kind of work and have a different set of tools to do that work with, you probably also need to hire different kinds of people.

Speaker B: Yeah, we do now include this. We're looking for people who are AI native, who are curious. Like there's a level of curiosity that is required now to do the job.

Speaker A: Yeah.

Speaker B: You know, when we started down this path with this transformation, we armed the field with uh, the ability to build. So we gave everybody a ChatGPT enterprise license, we gave everybody access to this uh, DUST agent builder platform and we set the expectation that they should start building. And we really did see the team break down into a few cohorts. We saw our AI native cohort just Immediately start building, started publishing, uh, all kinds of cool agents and use cases to their peers. We saw a next cohort who would adopt those things but wouldn't necessarily build. And then we saw a cohort that were our laggards.

Speaker A: Yeah.

Speaker B: And so really now what we do when we hire for every role is we are looking for people who have the curiosity, who are already experimenting with AI on their own as individuals.

Speaker A: Do you have specific questions or projects you ask as part of hiring?

Speaker B: Yeah, and it varies by role, but generally we're asking them, are you building with AI? Like, what are you doing in your life with AI? Like, what are. What are some things you've built? You know, and we're looking for people who have the natural curiosity. So if. If you're not playing with it in your personal life, you probably don't have the curiosity to build at work with it. And we're looking for people who have that native curiosity, so they're doing cool things at home with it. They're very likely gonna bring that to work.

Speaker A: Yeah, I feel like it used to be you could be competitive and maybe coin operated. Now it's like, competitive coin operated. And you have to be curious.

Speaker B: Yes, that's right.

Speaker A: And build stuff.

Speaker B: That's right.

Speaker A: Um, I would like to get super tactical. Like, what's your favorite AI tool right now?

Speaker B: Yeah, favorite today is clay. So we have adopted clay really deeply. And clay can be a lot of things to a lot of people. Its most basic use is for data enrichment. So, you know, once upon a time, you had to go out and work with dozens of different data vendors to get data based on what segment you're working in or what geo. So clay solves that out of the gate. You got one clean way to do enrichment. We then took that to the next layer, which was we sell to these very specific Personas. Founder Persona, uh, first security hire to a ciso, and we now drive enrichment for those Personas.

Speaker A: Specifically, what does it mean to enrich a Persona?

Speaker B: Yeah, so really what it means is if we identify a target account, we're going out, we're hitting Clay. We're saying, find me in this account, uh, people that look like. Like xyz. And then we're populating those contacts onto the account. So, you know, once upon a time, you could do that in really simple ways with titles and all of that. But with Clay, you can do it in some much smarter ways that get you, like, the right buyer Personas. So then we're serving up the right contact to our SDRs. And our AES. And then the beauty of Clay is it's a whole platform with an agent builder called Claygent. So we're now building tons of AI workflows in clay from there. Everything from we've done, uh, automated follow up emails that are following up. We're basically pulling the transcript of a call and the Persona of the buyer. And Clay knows our value prop and pitch, putting those things together and then crafting an email after every call for our rep to use as a follow up. Doing everything from that to updating CRM to doing unique custom outreach sequences. Uh, it's, it's a.

Speaker A: And I could imagine for an SDR, even in a, the hours you would spend trying to find the right contact at the org, who they are, what they do. Like it takes a time.

Speaker B: And now you say to them and yeah, so we have really, the way we kind of figured a lot of that out was we looked at the ways our best reps and SDRs were operating and we copied their best plays and then we turned that into automation for the whole team.

Speaker A: Yep. I'm curious if you've also been able to use AI as a sales coaching tool, talking about taking your best people and then trying to make everyone more like them.

Speaker B: Yes. Yeah. And we're doing that in a few ways. So one is we're using a tool called Hyperbound and Hyperbound does. It's essentially like an AI bot that you can talk to, learns you and your talk track and what calls sound like. And so that's helping us ramp SDRs in particular more quickly. They practice with the Hyperbound bot, they talk to it. It's like you can do mock call calls with it, uh, which is something that you used to have to do with another human. Now you can do it with the bot. Much more, uh, efficient. Um, but then we are also essentially democratizing the best of what we see reps doing. So everything from taking the messaging that's working for reps and then turning that into more of the standard for everyone to publishing some of those best case workflows flows, We've really been able to ramp people much faster because they are learning from the best right out of the gate. We do call scoring. Uh, discovery is such an important skill in sales. Obviously our reps are taking thousands of calls a month. So if we can increase the quality of discovery, it changes outcomes, change contract size, it shortens deal cycles. So uh, doing this kind of dynamic call scoring based on the quality of discovery, we have found to be a really impactful thing.

Speaker A: So those are two tools, Clay and what was the other one?

Speaker B: So hyperbound is the bot, like, for practicing voice calling, you know, superpowering our SDRs that way. We're actually using, um, for scoring some of the calls. We're using call scoring and scorecards in gong. So gong's got some native functionality there. Um, so, yeah, we, uh, we've got a pretty classic stack. We've got Salesforce, we've got gong, we've got Outreach, Um, and then we're using Clay for a lot of our AI automation. We're using Dust as our agent builder platform and we're using Clari for forecasting.

Speaker A: Yeah. How do you decide what tools to introduce to the stack? Because I'm sure you are overwhelmed. People are enriching Stevie's profile and saying, I would like to pitch this AI product to Stevie, so she will be my customer. So how do you decide what products you're going to use? How do you empower your team? Decide if, uh, they're allowed to purchase, Purchase the product. How do you evaluate if the product is being successful? Post implementation?

Speaker B: Yeah, this was a real learning process for us. You know, we started out without a strong point of view on this. So to really kickstart these efforts, about a year ago, we essentially took a demo of everything. We would demo any piece of software, any AI tooling, demoed it all. Um, and we piloted set several and really we found a few things. Um, one is we developed the point of view on how much of our stack should be built versus bought. And what we found is we have a real bias for, uh, tools like Clay that can be more like a Swiss army knife that you can really customize and integrate into your stack versus things that come with a set point of view and are used in one way. And are two opinions opinionated. Yeah, we don't necessarily want either something, uh, too opinionated or too broad. We want to build like we got a unique point of view on what works in our business. And so platforms that allow us to build are the ones we are more likely to adopt. That's why Clay and Dust have really resonated. The other thing we found is that particularly with AI tooling, it's got to be bottoms up. If you just are dictating from the top, top down. What we find is those workflows get less adoption. We have way more success when it's coming from the ground up. So often what we're looking at is what workflows are our builders building, what are our ICs doing, what are they asking for? We do let them go out and pilot. So that was the other.

Speaker A: So you empower purchasing at the lowest level of the organization because you believe in bottoms up.

Speaker B: Yeah. And we really have had to change the way we allow people to pilot. So we've opened the doors to much more piloting and we like to see that bottoms up success and then we'll turn that into something bigger. If it's working for the team.

Speaker A: Yeah. And so the takeaway there is basically empower your team, demo lots of things, figure it out, and develop a point of view on what's working. There's a little bit of a meta point here where as a venture investor I hear like, everyone is willing to demo everything and I think, oh man, what is Churn going to look like? Uh, it's a little scary.

Speaker B: I think that's real, you know, and I think we are going through that phase now. I think some folks are starting to see it that there's a lot of experimental budget early and you know, for me, one of the biggest takeaways here because I invest at seed as well in some of these platforms and you know, you really still have to deliver value. You have to get customers using your platform and your tech. You have to realize value for them. You have to, to help them achieve the mission. I think that like, early on there was a lot of that shiny toy syndrome of like, oh, this thing is cool, I want to try it. It's still got to deliver value. And I think that's one of the harder things for these newer, uh, AI tools and platforms is that they're going to market very AI forward. And I think what those who have gotten enough traction have learned is they still have to build a real go to market machine to distribute that technology. It's not enough to just run an AI GTM playbook.

Speaker A: Yeah. Um, and I kind of wish you had the stat of how many pilots you cancel.

Speaker B: Oh yeah, a bunch.

Speaker A: A bunch. And that's going to be interesting for this whole ecosystem to work out. Another big question in the startup land is does a new platform shift benefit the incumbents or the startup? So the new AI tooling stack, do you think it benefits you more as a young company or does it benefit the incumbents? You know, and why? Because I think people usually buy in that. Yeah, the young companies benefit. But like, why is that the case?

Speaker B: I don't think that's necessarily the case. I think it benefits those that are willing to change, whether they are brand new and small or they are an incumbent. But those who just continue down the traditional path regardless of the size will suffer and they will just not be able to compete. You can see with newer, smaller companies, your ability now to accelerate revenue is completely different than it was a few years ago. So you have to change your mindset. And this is uh, one thing I find is so common is companies that were around before two, three years ago have this mindset about what's reasonable or what your expectations should be for whether that's the pace of innovation or revenue growth rates. You kind of have to set all that aside and be willing to think completely differently. I think the mistake that a lot of incumbents are making uh, is that they're thinking about AI as more like incremental improvement. Like how do I make a uh, 5%, a 10% improvement here? The company's regard, regardless of size that are going to win are the ones that are thinking about how to change things 10x100x.

Speaker A: Yeah.

Speaker B: They're not thinking incrementally.

Speaker A: Yeah. One thing you said that I think is very clearly in your favor is can you imagine enterprises saying we're going to empower bottom up procurement of new AI tools? Our call center employees are now empowered.

Speaker B: Right. Uh, it's a little scary if you're a legacy company like that. And I think the key is you got to do it responsibly. This is, we're a security compliance company. So one of the things that was foundational for us is that if we were going to empower people to pilot things or to try things, we had to have a safe way for them to do that. We had to have a process such that we can vet the security of that tooling, the way data flows within it. So you do have to build process around it. It can't just be wild west, it can't just be anything anywhere. You have to be thoughtful. But if you build those processes and you empower the field, you will start to see those AI native and more creative folks pull away from the pack and they can then become the model for what's possible. So you got to really support that kind of transformation and make it possible. And if you allow them, those kind of folks will transform your org.

Speaker A: Yeah. And I think it's huge. It's a huge advantage to be able to build a better go to market function. So it's not just better product like go to market also matters. And startups are pretty interesting.

Speaker B: Absolutely.

Speaker A: Place to do that. Um, I'm curious, going more on the competitive landscape, what competitors do you look at and you say they're doing a great job in terms of. And these cannot just be competitors, they can also be colleagues or folks you respect in terms of how they're building and adopting these tools at their organizations.

Speaker B: Yeah, I mean I really look at much larger organizations that are moving in quickly to learn from um, you know we've got a substantial go to market team at this point. So I look at companies like Datadog for example. I think that the way they operate is super interesting. They are you know, one very data forward, data centric. So they have always had this like very uh, intense operational culture and I think that that set them up really well for this type of innovation and this next next phase of how go to market is going to get built. They also have this very interesting top

Speaker A: down and they probably did a good job with rev ops.

Speaker B: Yes, yeah, great job with data, great job with revops. And you know they have to sell to engineers but they also have to sell to executives. And I think any go to market organization where you have to do both of those things already has an advantage because you've had to think about how to build a machine that is really operationally sound but then you've also had to think about value and like the human aspects of how you go to market. So I think organizations like that are really set up to win. And you know, sometimes if you're 100% PLG or you're kind of more in this like legacy only approach, I think those orgs are going to be at a disadvantage. But these hybrid kind of tops down, bottoms up where you can drive innovation based on data, those are the orgs that are really going to do well. And I think Datadog stock is doing that particular interesting.

Speaker A: Yeah, I guess the takeaway would be if you're purely a top down sales, it's about relationships and positioning and your product doesn't have to be good.

Speaker B: Yeah, that's right.

Speaker A: If it's purely plg it's like just build a great product.

Speaker B: Right.

Speaker A: Don't worry about the go to market. I mean you still do at some point.

Speaker B: You probably want to eventually and that's the interesting thing. So many of these new AI tools that have come to market, they have started that they've started almost like prosumer, very bottoms up, very plg and they're able to get to kind of mind blowing revenue numbers before they really have to think about building go to market. But I think the easy mistake for those companies to make is to think they're gonna never have to do that.

Speaker A: Yeah. I think you're aligned, that that is actually a great way to start. Cause you're like, that's literally how I want my teams to buy. Right? Plg.

Speaker B: Yep.

Speaker A: But then how do they, how do you know? Let's say you're one of these companies, you're going through plg. How do you know when to flip the switch and be like, we should have to go to market.

Speaker B: Yeah. I mean the reality is I think it's earlier than anybody ever think thinks it is or wants it to be. You know, I think that a lot of these teams resist it. Like, oh well, revenue is growing really well, we'll just wait. But the reality is, even you know, Vanta, we're a, uh, series D company. I need now to talk to a salesperson. I need to work through vetting the security of that software before I can buy it. Like there's a process I need to run. And that's very true. As you get into enterprise selling, you have to have a real go to market team.

Speaker A: Yeah.

Speaker B: So I think the earlier you do that, the bigger an advantage you're gonna have trying to resist it and think, oh, we're gonna be prosumer or PLG forever.

Speaker A: What would be some good rules of thumbs for when it starts? Like, oh, you sell your first hundred grand contract, you sell your first three enterprise. Kind of like you get your first CISO doing a review of uh, I

Speaker B: think it comes back to who you're selling to. The first time you sell to an organization with more than a thousand employees. Employees, you should know like, we gotta get some salespeople in here.

Speaker A: Yeah.

Speaker B: And you know you can get away without doing it. But I spent six years at Twilio before I was here. Very heavy PLG. When I joined Twilio, it was 99% plus PLG. The growth of Twilio over the five years that followed that was so heavily driven by salespeople. You know, I helped build the enterprise business business there. When I joined our enterprise customers were Netflix and Amazon. Not really traditional enterprise. Uh, when I left, we had a billion dollar run rate enterprise business. And those dollars were all touched by a salesperson. So if you want to build a strategic upmarket business, it really does take humans. So you'll close some deals, you'll get it done without it, but you will just see much, much better results. Much higher, higher retention if you accept you need to build that machine.

Speaker A: This is why I'm so excited about the ecosystem. I feel like in the 2010s. It was like, we're better at product engineering, but like, oh, uh, go to market. How embarrassing that we have salespeople, but now we're like, oh, wow, like product engineering and sales. And that's a great foundation for getting everything we're building here out in the rest of the world.

Speaker B: It can be a huge differentiator. And I think that is. This is a big part of why I think Vanta is winning. We do both exceptionally well. We have a great product, we have the best product in the market, and we have the strongest go to market machine out there. And those two things together, it's. You know, I deeply believe that execution is the only moat. We have built something very strong and we distribute it better than anybody else does.

Speaker A: Yeah, that's great. How do you think about international go to market and know, uh, when it's. Is this the first time you've helped scaled internationally directly?

Speaker B: Yes. So I've worked closely with our international teams in the past, but the first time I've gotten to build it myself. Um, you know, we started our international go to market around like 20 million in ARR. I actually think that's a little late. So when I. Yeah.

Speaker A: Uh, and this is what I'd love to hear, especially because this was your first time. I have a lot of founders who are like, okay, you know, I'm getting to this threshold. I know I'm going to do international.

Speaker B: Yep.

Speaker A: How do I do it? How do I know when to do it? Who do I go to to learn from? Because, like, does Vanta map to these companies or those like, so what was your learning journey to decide what you wanted to do?

Speaker B: Yeah, I mean, we talked to everybody and anybody that would talk to us. We were very fortunate thing number one. Yes. Call your friends, call anybody that'll talk to you that's done it before. Um, get that point of view, you know, One of my biggest learnings is every organization's international go to market has to look pretty unique. So you can learn a lot from others, but you still have to run your own playbook. One of my biggest takeaways is you should go international earlier than you think. When I work with founders these days, I typically advise them to do it around 10 million in ARR. It takes a long time. It's complicated. You know, there are a few things that we learned along the way that I think can be replicated. Like one is you should absolutely send people from your US Uh team to wherever you're trying to build a team, have them do a one or two year cultural ambassadors. Yeah, send them as your ambassador, establish best practices. That's one. Uh, the second is be really opinionated about what markets are best fit for you and that's going to be different for every company. So, you know, take those inbound signals, really think about the problems your product solves and think about like what markets you're going to enter. For us, as a company focused on compliance and security, it's actually backwards of what you would see from a lot of typical SaaS. Companies like heavily regulated markets are great markets for us. Germany, amazing market. Japan, great market. So it's different from traditional SaaS for us, but you have to be opinionated in that way. The people that helped us a lot along the way. We have great investors in Europe, frontline, uh, ventures who have been incredible advisors for us on that. You know, they have done everything from help introduce talent to advise on location strategy. Like you know, just best practices.

Speaker A: Yeah.

Speaker B: And then, you know, my last piece of advice on international is don't lower the bar. Some folks will tell you like, oh, when you go to Europe or you build your teams in, in Asia, you know, set your quotas lower or just, you know, it's different. Like don't accept that at face value. That does not have to be the way. You can maintain a high bar and really set this expectation of greatness globally and then drive execution from headquarters, but with the expectation that everybody can deliver.

Speaker A: Yeah. So talk to a lot of people, but be opinionated and filter what you learn. Start earlier than you think. Maybe around 10 million arrows. Open local offices staffed with, um, cultural ambassadors. In terms of opening local offices, I'm curious about the EU expansion because that's a pretty, that's a common thing. US EU. Do I need offices in all 30 European countries? Do I need one just in London? Do I need one in London and Germany but like Spain? Like what, how did you think about what is your office footprint and how did you decide that was the right answer?

Speaker B: Yeah, our office footprint footprint today. So we've got an office in Dublin which is where we started and now we have a larger office in London and those are our only two offices in Europe today. It's very dependent on your business. So one of the realities for us is we started as a very down market business, so focused on startups and SMB. When we went into Europe we wanted to replicate that play. While we were moving up market in the States, we wanted to focus first on startups in Europe and because of that we wanted to be close to them. Um, but we didn't feel like we had to put people in every country.

Speaker A: So London makes sense. One of the biggest cities, English language, big startup ecosystem. Why Dublin?

Speaker B: Dublin's actually interesting. So Ireland historically the advice was always go to Ireland first. Dublin for tax reasons among others. Um, Dublin is particularly interesting because there's a lot of down market talent there. So there is ah HubSpot's got a huge presence there. There's sort of this cohort of like big SMB talent so SDRs, Stripe has an office there.

Speaker A: I don't know how much of their go to be go to market SMB function is there.

Speaker B: Yeah, they've got a pretty material presence. Google has a big presence so you get a lot of down market talent there. London is great. You get more multicultural talent. You got a little more as you start to like you want to cover France with French speakers. You can do that more effectively out of London than you can out of um, Dublin. So think of it more as a phased approach. Eventually we will have people in many more countries. M but we have really felt like keeping people centralized in an office together has a huge amount of value. They learn from each other. We are able to cross pollinate best practices with everybody sitting together.

Speaker A: Yep. And have you looked at APAC as well? And then um.

Speaker B: Yeah. So we started our APAC journey out of Sydney. So we've got a big operation there now we are really uh serving all of Asia Pacific. But we focused first on English speaking markets. We felt particularly with our platform we're focused on compliance the regulations that we're helping people uh become compliant with matter. We felt we could do that first in Anz most effectively. Ah and you know we help people write policies for example to be compliant. Those are English to start with. So Anz market was our initial focus. We opportunistically took inbound from elsewhere and where we've gotten inbound and succeeded at closing has driven where we're now focused. So now we're moving to really more intentionally cover Singapore, to cover Japan, to cover you know a few of these other markets with local language speech.

Speaker A: Yeah, one open ended thing to end on. What are the questions around the AI tooling and landscape that you're kind of struggling with and thinking through at the moment?

Speaker B: Yeah, one of the biggest is really how to resource it. Right. You know we have transformed the way we resource this but I think that like this paradigm of systems as a subset of of revops is probably uh, a legacy concept. I think that systems generally for the company have risen in significance and importance. And I think that means that you have to resource it in a much more meaningful way. So we're thinking a lot about, uh, not just sort of the, the type of talent, but also how big should that team actually be? You know, all the legacy ratios don't really mean anything anymore. So thinking about how we appropriately resource systems is a big one. The second one is really, if you're going to have people in your Rev Ops team or your systems team writing code, how do you do that in a way that is thoughtful and consistent with the quality expectations of your.

Speaker A: Do they have a GitHub profile and generate pull requests and get like, what do you do?

Speaker B: These are the questions that have come up and the answer we've come to is yes, they do. But is it appropriate for that to live and go to market? Does it need to be?

Speaker A: So some of your SDRs have a GitHub profile and are pushing not SDRs

Speaker B: yet, but we do have members of RevOps who suddenly they're like, okay, so we actually need access to the AWS instance. And that starts to raise lots of interesting questions about who controls code quality, who writes code, where does architecture and strategy live. So. So I think those are the things over the next couple of years that we'll really start to sort out.

Speaker A: Yeah, that's a super interesting question on all these different fronts. Um, awesome. This has been a really great conversation. I think my big takeaway here I want for people, so I'm just going to tell it to them again is we think about AI as something that's changing product and it is, but it's also changing go to market and you should be excited about that. So thanks so much for sharing your story at Vanta for what that looks like.

Speaker B: Thanks for having me.

Speaker A: Awesome.