Philipp Stelzer: Hello, and welcome to another episode of the RevOps Lab Podcast. I'm Philip, and I'm here together with Ioannis. And our guest today here is Navin Persaud from 1Password. He's VP of RevOps there, but I'll let Navin introduce himself. Hey, Navin.

Navin Persaud: Hello. How are you? Thanks for having me, both of you. It's great to be on.

Philipp Stelzer: Yeah. It's great to have you. It's an honor, really. Navin, what do you do? How did you get into, you know, where you are right now?

Navin Persaud: Wow. What do I do? I feel like that's a loaded question. Today and right now, I'm the VP of RevOps here at 1Password and have been for the last five years. How I got here, twofold journey. IBM right out of university in sales, channel sales, realized I was horrible at sales, and then moved into a number of operational roles because I love data, I love building, I love fixing. But most of that is driven by, like, severe curiosity and, like, the entire go to market mission.

Philipp Stelzer: Awesome. Yeah. Okay. And I think, like, your passion for building things is also sort of, like, the topic that we're talking about here today in our episodes, and that is buy versus build in a world of AI tooling, I think is the title that we aligned on, and I think it's a very, you know, hot topic at the moment. If you go on LinkedIn, if you check your, like, feed really in any kind of, like, social network, like, will be, like, dozens of people who tell you all about all the amazing things that they build within just, like, just one prompt, really, or, like, how they have, like, a passive income of, like, twenty thousand dollars every month now, and how they don't need to spend like, you know, thousands of dollars on like expensive SaaS tech anymore because now there is AI, so yeah. I'm sure all of our listeners have seen those posts. That's part of like the the topic that we wanna talk about here today, and maybe let's just start with like, you know, just like a easy question for you, Navin, in the beginning. How much of the tech you use at 1Password right now, is is is built versus bought at the moment?

Navin Persaud: Very little is built. I'm I'm a big believer in configuring versus customizing, and I put build in the bucket of customizing. I think, you know, I agree. My LinkedIn feed is also littered with people who have vibe coded over the weekend and built a CRM or a governance platform or an enrichment solution. More power to you if you can figure it out and run a business and maintain that technical debt, if you can apply governance and compliance. Like, it's actually doing the opposite, I think, in my world where I'm constantly hunting for signal and I'm being bombarded by noise. Noise by building this, that, and the other thing. And blurring the line between what's the value you're driving in the market versus these side projects that you're layering into your business.

Janis Zech: Yeah. Like, when when you think about your RevOps team currently at 1Password, like, how is that structured? Like, basically, what I'm what I'm what I'm getting at is, like, is it even set up to to build, like, a lot internally, or, like, how do you optimize the team, like, the the structure and the hiring for it?

Navin Persaud: So we're not against building. Let me just put that out there. My team is structured in that way that I have a front of house team reporting analytics, forecasting, planning, and a back of house team that is generally supporting the go to market tech stack, all of the integrations. They have their plate full. We are a one stop shop for the IT administration, change management, integrations, etcetera. And the reality is if I were to ask that team to go build and replace some of that tech, I would turn it into a different vendor. It's just not practical for us because of the things that we need as a security company to manage compliance, to be audit worthy, and to really handle a business of over four hundred million dollars. It's not where AI makes the most sense to me. For me, I think AI should be the opportunity for exploration and for prototyping, but it should not be an area where I'm looking to displace core functionality in the business that I support to drive outcomes and reduce friction, and then from that, pull out a signal from the noise.

Janis Zech: Got it. So it's more like a early, like, prototyping, understanding the space kind of thing, or what's the signal to you that this is something that you should, you know, start your journey maybe with, like, building, like, a prototype, whether it's, like, using AI or not. I think that doesn't even matter here. And and when would you go directly straight to buying?

Navin Persaud: One of the biggest challenges that I think I want to leverage AI for is search and independence or democracy of data. I think we have a number of systems. Some things are siloed. Even in our CRM, you have to know how to run a report or to build a dashboard, or you're going to end up with data that might not be accurate. But what if you could democratize the way in which data is found and insights are extracted? All of a sudden, people are smarter about their books and their customers and their territories that RevOps is no longer the bottleneck. RevOps can then focus on higher order work of being the curator of knowledge, being the curator of systems, and actually incentivizing people to grow the business and identify friction. To me, there's a lot of value in that. Where I struggle to find the value is, 1Password's not gonna go build a billing system. We're not gonna go build a CRM. We're not going to go build an enrichment platform. These things, they are definitely core to what we need, but I'm not trying to apply AI into our core areas of need.

Philipp Stelzer: Yeah. I think this resonates so well with me because I think we've all seen this post about the SaaS pullback, right? Destroying, I think, two eighty to one trillion in market cap currently since the beginning of the year. And all these ideas around you can build all this yourself. As you know, we are basically a revenue AI and intelligence platform. And if I think about just the compliance and security aspects of that, if I think about the depth of features and then most of these solutions, they not become good because you built the first version, they become really good because you iterated with customer feedback over years very specific things. Like, why would you go out and build this? Like, we use 1Password here at Weflow. We don't build this. And I think when you take 1Password as an example, it's very clear. Like, why would you actually build this yourself? Because, I mean, the just the potential risk is so high and, like, the security aspect is is such an important aspect for your SOC two type two, for your HIPAA compliance, for so many different aspects of SaaS companies. And I think that's just so interesting. When you when you think of, like, you know, experimentation, right, like, have you found areas where, you know, you you basically, you know, like, experiment and where where you have the highest potential to to to find out certain things that then that then you build like, you either buy or or incorporate into the some of these existing infrastructure systems?

Navin Persaud: I think that your sales teams, and as they grow and you build on a customer success motion, all of those interactions live in text, and they're very rarely curated in a way to understand the larger picture. What's going on at that customer? What's going on in that deal? And then when you win or lose, how do you learn from those actions? And I think for the longest time, those have been just fields in our report, and you hope someone can find the signal from all that noise. Now you can harvest that data to structured data. You can pull down insights and you can understand and re instrument your business on a monthly basis. To me, that's pretty powerful. Companies who are doing that understand like every go to market year, there are small pivots that can have outsized returns, but you have to identify those pivots to make that happen.

Janis Zech: Yeah. So, basically, like, unification of data as a as the infrastructure. Right? Like, taking the activity, conversation, CRM data, potentially web data, any other data sources you might have in your production databases, bring that all together and then being able to query those against certain, like, you know, win loss analyzers or competitor intelligence or objection intelligence. Is that, like, where you feel like there's, like, a lot of ways to experiment right now and and and, like, to to to to to drive value? Is is that a fair summary or, yeah.

Navin Persaud: I think go into any SaaS sales org and one of the top line concerns that a sales leader will have is I don't feel like I have a good enough pulse on what's going on in my business from a neutral standpoint, which specifically means I know what my sales leaders are telling me. I know what the data and the reports are telling me. I don't have like a copilot whispering in my ear telling me to look in this direction or fix this problem because what many people go after are the symptoms of disease in their go to market and very rarely treat the disease itself.

Philipp Stelzer: It's so interesting. I mean, so so I mean, we actually never talked about what we do, but it it doesn't matter in this context. But, basically, when everybody started building, you know, AI native workflow solutions, we actually focused on unification of data. Right? So activity data, conversation data, CRM data, because we felt like, you know, these are so often siloed in different systems. If you don't bring them together and you map them against your custom data structure in your CRM, it is actually really hard to derive insights. And then, you know, build the workflow layer on top so that RevOps teams can essentially orchestrate workflows. Right? But not querying just the, you know, field data in their CRM, but querying all the data. Right? The entire data pool. And I I I feel like this is almost like I I I, like, I hope, right, like, this is basically kind of right. Like, I think there's a big discussion around, like, how do you how do you have more strategic impact on the on the RevOps side? Right? Like, obviously, many topics around that. But I think if you can sit there and redirect kind of where people spend their time, how they do their work, because you have can access that pool of data and then you can orchestrate it through workflows. I feel like that that's like personally, I'm in a a vision I'm, like, very excited about building in. I'm curious what you what you think about that now.

Navin Persaud: I think the critical word that you mentioned there is orchestration. I think a lot of RevOps leaders want to be builders. I myself, for the longest time, and continue to be, wanna be a builder. But the reality is there are toolings out there now that you can leverage in your go to market tech stack that make you a better maestro or conductor or orchestrator of those things. And I think if you can elevate to that point, you can drive a lot more value. You can bring higher order functions through your RevOps teams, deliver insight at scale, find friction, and ultimately help your company grow.

Janis Zech: Yeah. Yeah. I I I this so much. I I I mean, I think, you know, this, like made it so interesting. Everybody talks about AI, but then the biggest shift in, for example, top of funnel to me has been the ability to stack signals and redirect work towards, you know, higher impact work, which is exactly, you know, orchestration. Right? And I think the same is happening on the AE side, the same is happening on the CSM side. Right? Like and and so it's it's just so interesting because, like, like, there's obviously the, you know, AI native examples of Lovable that, you know, just only work because you have LLMs and they couldn't work without it. Right? But then I think in go to market tech, it's a bit more complicated, it feels. I like and the reality feels more like, you know, how do you combine good old software with the power of LLMs. Right? Like, to your point that most most realities live in text. But up until, like, three years ago, that text was buried in, you know, three, four, five different systems and was actually you couldn't even get any insights out of a long text field in Salesforce. So, right, like, now the reality is completely shifted. And that, I think, is the superpower, right, where experimentation suddenly makes a lot of sense, where you can dive into the data in a different way and you can use the data to have insights that you might have not seen before. At least that's what we are seeing with our customer base that suddenly there's things that they just didn't know and they find out and they're like, oh yeah, that's actually I had this gut feeling that maybe this is important, but now I know because I've seen this across twenty thousand emails, you know, and a thousand conversational transcripts. And suddenly it's like, yeah. Okay. I mean, like, we'd have to act on it. And maybe we have to change, you know, billing with our customers because it drives, you know, four to six million more revenues a year. Right? Like, just as a very specific example. I think that's just so interesting to to see, and it's it's just really a a big shift in my mind.

Navin Persaud: I had this analogy pop into my head as you were speaking. Like, I am not a carpenter, but if you think about your go to market tech as your material or your wood, your agentic workflows, your LLMs, all of those things can be the glue to bring those things to life for you. Right? So I agree. I think people looking to replace the material with AI, they're going to fail, or they're not going to scale, or they're going to risk compliance and security and audit and governance in their business. But those companies who can figure out, hey, here's the good mix that I can bring those agentic workflows through my go to market tech stack, those are the ones who are going to find the signal through that noise and are going to propel themselves to grow.

Janis Zech: Yeah. I love this.

Philipp Stelzer: Yeah. I love it too. And curious about, like, you know, if there's a is a team I I think, like, first of all, I think, like, maybe maybe one dimension I wanna bring in. I think there's different types of companies. I'd like so I think if you're, like, a early stage company, little resources, very little oversight, not that much need for auditing at that point maybe, I think perfectly fine to hack stuff together with an AI tool, like, or like, let Claude, like, code, like, a new product for you or stuff like this that you can use to put something together, or like these all these small business owners. Right? You run like a Shopify shop and you wanna adjust your theme or build like a plugin to do something. Makes total sense. Like, it's a use case. But would you build a Shopify? No. Right?

Navin Persaud: No. Exactly.

Philipp Stelzer: You would build like a plugin that plugs into Shopify. Right? Like, similar to how you can use Claude to do Apex coding, right, perfectly well, or like help you build a flow in Salesforce, but you yeah. Exactly. You wouldn't build Shopify because Shopify has all, like, I mean, I don't know, regulation across, like, the European market, the US market, shipping, tax. Right? Like, I mean, there's just, like, at that point, like, you don't need to, you know, run, like, an ecom business. You you basically run, like, a software business. So and then that's just you just it just changes your business. Right? So but my my my point is, like, so if I I think there are these businesses where it totally makes sense that, you know, you use AI to build some tooling for yourself. And then I think if you're, like, at the stage where you're, like, 1Password, also for us, right, I think at this point, like, we are SOC two type two certified, you know, ISO, HIPAA, GDPR, CCPA. No. I'm not gonna introduce, like, some hacky, like, product into the mix that's just gonna create, like, a lot of pain and problems for me further down the road, and it could cost me money, and instead just gonna buy, like, a solution that is also SOC two, you know, verified and or certified and helps me, like, stay true to the regulations. So yeah. Oh, sorry. Long winded, like, comment here, but, like, Navin, like, what I'm wondering, like, what are, like, some AI tooling use cases that you totally see being legit for, like, RevOps teams that they should, you know, should totally, like, embrace?

Navin Persaud: Absolutely. Around agentic search, I think my team spends a majority of their daily time answering questions. How do I do this? Where do I find that? What's the process for this? We need to better enable and arm our selling org to be able to understand, you know, how things work in the business, where they can go for help. Like, that first line of defense can't be a human or my function that is less than ten percent of go to market. We'll never get our projects off the board, we'll never continue to deliver value. So agentic search is a big one. Enrichment is the other one, like data enrichment. I'm living in a world where I have multiple enrichment providers. I am hoping that is not the world where I'm at in a year from now. And I think there's a massive consolidation in that space. I'd say the last one is like, how do we democratize that data and drive insights? I think a lot of execs are afraid to go into systems because it's hard to understand the underlying data from the dashboard. Is it configured right? Is it updated? Do they trust it? Or are those the right targets? Or even if all those things are true, what's it telling me? What's the three point headline that I should take away from it? And the ability to take natural language and get a question answered about what is being shown, super powerful. I would go to those three things. But I wanna come back to what you said, Philipp, around, you know, I like to believe there is a little truth in every big headline. So we are going through this SaaS apocalypse right now, and you talked about, you know, companies that are are building and evolving maybe in the last year from now, totally makes sense for them to lean on AI where they can until they have product market fit and drive efficiency. But the SaaS apocalypse to me is around companies today who have enjoyed selling a feature. Those are the companies that, you know, are potentially gonna be at risk because their feature can be easily reproduced. And if they are unable to find product market fit to develop a platform, develop a more intrinsic use case, that's where there's going to be a problem. So maybe a shout out to all of you calendaring solutions out there, those could be a problem for you. Maybe some of those enrichment solutions, that could be a problem there as well.

Janis Zech: Yeah. Yeah. Yeah. Sorry. Yeah. With the enrichment, I just wanna say, like, that one I'm I'm quite curious about because I think with the enrichment, the main problem is actually, like, getting the data sources and and ensuring the data source is always up to date. I never spend time, like, thinking about building, like, an enrichment service. I'm sure, like, there's, like, tons of, like, different services you can buy the data from. I think then the challenge is more, like, how do you verify the data? How do you make sure it's, like, accurate? Because, like, I know, like, there's all these companies that run tests, like, ZoomInfo against, like, Cognism, like, all like, and they perform different, like, depending on the geo and, like, the vertical and so on that you are, like, testing against. So I think that's, like, the that's it. Like, the tech part, like, the technology, I think, is easy. Buying the data, I think, is freaking hard. So I I remember just a little anecdote, like, two years ago, we tried to do a test, like, basically different data providers on mobile phone numbers. Right? And we had, like, free trials for, like, two weeks. And so we sat down and we had, I think, six or seven. And we tried to test the quality of the data. Right? And and we had some really surprising insights that, like, the coverage rate from some of the most known providers were a lot lower, but the reasoning was, like, the quality is higher. And, you know, how do you how do you comprehend that if you if you're on a annual but, you know, two year agreement is very hard. I think I really like what you said about, like, okay, you have to go multiproduct. Right? Like, with Weflow, we've always had this belief that, you know, we need to be great at data capture. Right? Like, unification of data, activity. I mean, in our cases, like, activity conversations, CRM data, bring that together, and then use AI to do, you know, ask Weflow AI or to orchestrate workflows across email stack and blah blah blah. Right? Like, I mean, revenue intelligence and so on. Right? Like but, like, I I I think that, like, you need that depth and breadth of the software, and then you need to become that orchestration layer. So I think of, obviously, Clay, everybody talks about it. But I think there's some truth to it because I think if you are able to sit there and become better at signal stacking, I think they suddenly enabled so many data providers to pop up. Oh, I'm really good at LinkedIn data. I'm really good at that data. I'm really good. Suddenly you have kind of best of breed and there's a lot of data providers that pop up that suddenly, oh, I also have this one data. I think there, long term, that's a very challenging business. The infrastructure layer, the orchestration layer is a very good business. And I don't think Claude or OpenAI will disrupt that business. I think that's a fantastic business. And this is something we deeply think about because we are basically thinking the same thing. Like, okay, are we getting disrupted by Claude or like ChatGPT, Gemini? And I think it's just interesting to think about those things from what are your kind of moats?

Philipp Stelzer: Yeah. Like, I mean, I'm, like, surprised. Right? Like, seeing HubSpot at like, I think they are now valued at twelve billion, make, like, three billion in revenue. I mean, I don't wanna give stock recommendations here. Like, that's not the podcast for it. But to be honest, right, this feels like a pretty good opportunity to me. I find it crazy. So I don't know. We're going off here. This is probably the most unscripted, but I love it.

Navin Persaud: It's like, I don't know. I would then know like, hey, I'm not anti AI. I fully believe the train is departing and you need to get onto it. I'm just thinking about where it makes the most sense from an ops lens. Crawl, walk, run is my my way to making experimentation easier. I'm just not sold it's gonna replace some of the fundamental things that I must trust, rely on to be able to run the go to market business.

Philipp Stelzer: But look, really didn't know what you're gonna say today, right? Because we didn't meet before this episode. And I hope that this would be what you're saying because everything else to me, I just really can't comprehend it. I think we know Salesforce very well, right? And there's no way you can vibe code Salesforce. Even if you start doing it, it becomes — it took HubSpot ten years to build the CRM. Ten years in house build. HubSpot is a good company and they still lack fundamental capabilities to Salesforce. They do other things really well. A big fan of both companies. They're impressive companies. If you start a company and you do this, this is generational. Right? But, like, I I just, like and then I see all these posts, and I'm like, oh, no. Hopefully, we're on the same line.

Navin Persaud: Yes.

Philipp Stelzer: I'm, like, really happy, actually. I also think, like, the sole SaaS apocalypse is, like, I mean, there's a lot of reasons why, like, stock went down for some of these companies, and there's still a halo effect from, like, all the overhiring that happened, like, a few years ago. Right? Like and you can see it, like, I I saw somebody post, like, a LinkedIn post on Fiverr, and then they just, like, say say, like, hey. Look. Exactly at this point when ChatGPT was introduced, like, the company stock went down. But, actually, like, if you zoom out, like so, like, he cut it off at, a very convenient point in time, basically, like, the the the chart as you do when you make, like, a, you know, crazy post on LinkedIn. But if you zoom out, right, like, basically, the stock went up, like, during COVID, and then once COVID was basically close to being over, like, the stock started to go down. And, like, the the the end of COVID and the beginning of, like, GPT models, like, being deployed, like, in bulk, like, and and really getting adopted, coincidentally, it's pretty close. Right? So it's not that simple. Right? Like, I think, and and and you can see that in the guidance reports also. So, yeah, this whole SaaS apocalypse, other than being also terrible words to pronounce for me as a German speaker, is is, yeah, just yeah. I don't know. Like yeah. Not that not that not that well thought through, I think.

Janis Zech: Yeah. Yeah. Maybe I mean, anything to add from your side? Otherwise, would love to ask you a closing question.

Navin Persaud: No. I mean, maybe I can just wrap and say, listen, don't be shy about leveraging it in your business. Absolutely experiment. Hire people in your RevOps function, go to market engineers, people who can actually mix good understanding of how go to market engines work and where AI can actually then be folded in and make that business flow. If you don't do those things, you're gonna get left behind. And so it's a matter of getting on that train and understanding where you can drive impact in your business.

Janis Zech: Yeah. I love that. I love that. Look, we always ask our guests one final closing question. What's the book or research report you would recommend?

Navin Persaud: I'm a big fan of this book here. I read it a year ago. It's by Mark Manson. I won't give you the title name because I'm not for bad language in podcasts, but I think that what resonated for me about this is that I really only have so many of these to give in a certain lifetime, and I need to choose them wisely. Because in RevOps, it's full of context switching. It's full of emotionally charged decisions, and it's up to us to stay unemotional, to stick to the black and white, to be the truth sayers. So I really have to pick my spots when I really, really need to do that. So definitely a good read. Highly recommend it. Keeps you on an even keel.

Janis Zech: We'll put the link in the show notes. This was awesome. Really enjoyed it. Thank you so much for coming on.

Navin Persaud: Thank you for having me. Have a great day.