Janis Zech: Hello, and welcome to another episode of the RevOps Lab Podcast. I'm actually alone today. Philipp can't join. But, yeah, thanks God. I have Tessa here from ZoomInfo. So hey, Tessa. How are you?

Tessa Whittaker: Good. Thank you so much for having me. I'm excited to be on the podcast.

Janis Zech: Same here. I've been following you for a long time. I think we scheduled this for a while, so super excited about diving in. Before we dive into our topic today, I mean, who are you? What do you do at ZoomInfo, what have you done before?

Tessa Whittaker: So Tessa Whittaker. I'm currently the VP of revenue operations at ZoomInfo. And I've been at ZoomInfo for almost three years. It's been an incredible journey. Before that, I was actually at Salesforce. So I was at Salesforce for almost a decade. Started in their San Francisco office. I worked in their London office. I was actually in New York and Toronto before becoming fully remote and being down in Miami. So I've been in tech pretty much my entire career. A lot of people don't know this about me. I actually started as an executive assistant and then found my way into operations and worked my way up.

Janis Zech: Yeah. Awesome. I mean, I think there's many different ways into RevOps, and this is certainly one of them. Right? Like, being close to the c level, understanding kind of priorities, and then getting started that way. So I think today's topic, you know, is all about, like, how do you use AI in the RevOps team? So not what kind of AI tools are you using to solve certain problems for the go to market teams, but rather, you know, how do you drive efficiencies for your RevOps team? So maybe for context, like, how big is the RevOps team at ZoomInfo? How is it structured? What kind of responsibilities do you have there?

Tessa Whittaker: Yeah. So under my umbrella and we're not completely centralized. So, you know, depending on the size of your company, you know, really small to obviously, we know large enterprises, RevOps is gonna look very different. And whether it's all completely centralized or does marketing ops sit somewhere else or are there specific strategy teams that are decentralized? So I'm sure depending on the size of the company of the folks listening, it might look a little bit different. So within my org specifically, I'm focused predominantly on the go to market. I've got a sales operations and partner operations team that are really focused on being that business partner to those executives. And then I have what I call the business process team. Some might call it product managers that are really focused on the end to end business process that then automates into the system. So think lead to opportunity, opportunity to contract, quote to cash, and then making sure that every process that any of our internal customers are going through has automation that is built back into the tech stack. And that's also obviously where we're thinking a lot about AI. And then I have a revenue technology team. So managing and supporting the tech stack that supports predominantly, again, the CRO go to market organization. So everything our sellers are using, our presales teams are using, our customer success teams, customer experience teams are using to support how they do business. So it's been very, very exciting being at ZoomInfo. Obviously, we've gone through a massive transformation as a company and really positioning ourselves as the go to market company, which has been fun because if you are the go to market company, your customer is revenue operations. And I think it's always the best job to have when you are the customer of the company. You are the persona that they wanna talk to. They're the persona that they're building for, obviously, in addition to the rest of the go to market. But I've become the buyer of the product that my company is selling. So it has been so incredibly fun to get more involved with product and sales and really our go to market strategy of how we're going and talking and selling and supporting revenue operations professionals.

Janis Zech: Yeah. I mean, I think there's always this, like, you know, eat your own dog food meme. I think that's obviously something I generally also very much enjoy with building this company using the product day to day. And I think it gives you a different empathy for the customers and then eventually also how you essentially run your entire go to market, you know, as you just outlined. So today's topic, right, like, I think you just introduced AI to drive RevOps team efficiency at ZoomInfo. Right? So not so much the tooling for the sales, CSMs, marketing teams, but rather, you know, your own team. And before we wanna dive into that, I think you have this idea of, like, a maturity scale, right? So I'm curious what that is and how you would outline that.

Tessa Whittaker: Absolutely. So I think we can all relate to an experience, or if you're a RevOps leader, you're in RevOps, the experience of having your ELT or your CRO who's in the ELT or other VP, SVPs in marketing or sales or whomever, you know, coming to you to talk about AI and feeling that panic or fear or uncertainty of, are you doing enough? I think we can all relate to that. Every single person I talk to feels behind. I have not talked to anyone — and if you're out there, reach out to me — that's like, nope. I am ahead of everybody. I'm killing it. I am absolutely the first in line of doing the absolute best in the go to market with AI in RevOps. If you are that person, reach out to me. There's a lot to talk about. But when I would get messages or questions about, you know, what's everything that your team has done in the last quarter around AI? Put it in a list, and there's this fire drill. Or I heard this company is doing this or that company is doing this. What are we doing? And there's this, I think, tops down comparison or uncertainty of, are your teams doing enough with AI in comparison to your peers if you are in those ELT-like positions? And I didn't really have a way to baseline that to say whether or not we were really good or we were actually just kinda sucking. And I woke up unsure if we were doing a really great job or we were behind. And so I knew that I needed to create some sort of framework to benchmark myself and my team and really show, you know, what we had to do to get to the point where we were and then what we needed to do to continue to progress down this scale. And so this idea of a RevOps maturity scale came up, which is, you know, from zero to five, where do we fall on a RevOps maturity scale? And in my opinion, introducing AI only starts at step three because there's these prerequisites that you need to do to even begin to get to a place where you can start building on AI. And what's interesting — and definitely open to having another conversation about this because it's even grown since we've talked — is that I started building up a scale not just for my RevOps internal team, which we'll talk about, but also across data and go to market execution. So when we think about RevOps maturity, you have your data maturity as part of that scale, your go to market execution against that scale, and then also your operational rigor, operational excellence, whatever you call that against that scale. So I've really built out this framework to start benchmarking and really thinking about the maturity of the organization, which has been great. I'll pause there if you have any questions, but I can share a little bit more about how I think about the scale and really where I benchmark us and what we had to do from a RevOps perspective to get to where we are now.

Janis Zech: Yeah. I'm obviously super curious, like, if you would maybe just walk us through the different steps in the scale or, like, what are the different maturity levels you would think of? And then we can maybe, like, focus in on, you know, specific processes where you apply AI, but, like, yeah, curious what that maturity scale, you know, high level looks like. Because I think that's also something that probably a lot of folks listening are interested in. Right? Like, what are the different layers?

Tessa Whittaker: Absolutely. So I would say when I think about the maturity scale, like I said, I have five steps on it. And I have it as zero being ad hoc and manual, and I'll share this with you as well after this conversation. But zero is just being ad hoc and manual. And, again, I'll kinda talk through this as I think about RevOps specifically internally as an org. One is manual but defined. So zero, ad hoc and manual. You're kind of in this really — you're kind of spinning in circles. You don't have defined processes. You don't have a way that you're really prioritizing. You're very reactive to what is being asked to you as an organization. And then one is manual but defined. So you're still very probably still pretty ad hoc in how you're operating. You're not really using systems or technology to support the organization. You are starting to define how you operate. Okay. This is maybe how we intake. This is our sprint cadence. Some of our processes are starting to be written out and defined. Two, I call systematized rigor, which is you now have your processes defined. You might have some of them built into Jira. You might use some sort of project management tool, etcetera. But, again, you're still pretty — I'd say still in that kind of reactive state, maybe starting to become more proactive, definitely not yet using AI. And then three is AI augmented workflows, which is essentially now you've started introducing AI to support some of those processes you defined or those systems that you're using. Then we get to four, agentic assistance. So you're actually leveraging AI agents. I would say we're between three and four right now. And four is when you say, okay, we have defined processes. We know what we need to be doing. We're in a more proactive, less reactive state. And in some cases, we can leverage agents to do some of the work. And then five, which is AI first, and I think this next sort of important step is autonomous operations, which is essentially you have agents working for you autonomously, which I don't know how we get there yet. I think that still we're in a place where AI needs to be heavily monitored. But I think the future, you do have some sort of processes and systems that maybe have some sort of AI monitoring on top of them, but are working completely independently. And so the framework, which I built that zero through five just to specifically talk about RevOps — and I can share with you a little bit about what the organization looked like three years ago compared to today. But really, I've started applying those same principles to how you look at, again, I said your data. So, you know, what's your maturity of your data from zero to five as it pertains to RevOps, which obviously we know is so important because you can't build AI on top of bad data. We know that. Garbage in, garbage out. And then go to market execution, which I'm really thinking about, like, your processes in your system. So zero to five, where do you fall on that scale? A perfect example would be, you know, if, let's say, systematized rigor or manual but defined — so you know what your processes are, but maybe you don't have them actually written out or documented — how are you gonna build out AI workflows on top of that if you don't actually have the processes that exist today documented in the first place? So there's a lot of prerequisites to get to a place where you can start building AI workflows. And then the operational piece, there's also just operational rigor on, you know, you can build out AI, but do you have a rhythm of the business? Do you have defined forecasting? Do you have ways that you're doing your monthly or quarterly business reviews? Are you doing account planning? Are you doing big deal reviews? Obviously, this is all sales examples. But there's so much, again, you have to do from that zero to three to really define, to document, to set your processes in place before you can introduce AI.

Janis Zech: Yeah. I love it. I mean, I think, obviously, now we're touching on data, you know, business or, like, operational excellence for the actual teams. Right? And then there's basically the RevOps maturity scale, which you just alluded to. So, obviously, they apply to different kind of dimensions. And just for context, right, I mean, ZoomInfo is like a billion dollar plus revenue company. Right? And you had, like, three to four. Right? Like, so I don't know how we get to fully autonomous agents just yet, but I think it's probably some steps in between that will be fully automated. Right? I think Intercom is a great example where you have Fin that automates some support tickets and that is fully autonomous. But then the rest is actually quite manual and, you know, obviously well defined. But that's more like a tool perspective, I think. But it applies here as well because I think one thing that you've been introducing is like, okay, so what is the general RevOps roadmap process? Which I think every team runs, whether you want to run it or not. And the maturity zero is like everybody throws stuff at you and you try to survive. That is obviously not a good place to be in for anyone. But as you're between three to four, right, so you obviously have a lot more sophistication on that and you're already applying AI in that specific process. So maybe before we dive into what you're doing today with AI, what was the state before? What was this RevOps roadmap process? What was the intake process? And I think we all know the good, bad and ugly. So don't hold back, please. It's fine. Then we can digest, like, how did you change that very specifically? Right? So I think people can take this and essentially, hopefully, just copy you and introduce it to their own companies.

Tessa Whittaker: No. Absolutely. And I think, you know, to anchor on the point, like, I'm at a company that is a larger company that has over a billion in ARR. And I think that in order to get to an AI first model for RevOps, there is a lot of rearchitecture that has to happen from that zero to three. With a RevOps org, I think it's harder to get there the bigger company you are. And so, you know, I've been in the role three years and I remember my first week coming in. For context, I came over from the sales strategy and operations side. So I was at Tableau. So I was at Tableau post acquisition by Salesforce. I was running strategy and operations for their global enterprise team. Tableau was a seed and grow company. We were trying to expand go wall to wall into the enterprise. So it was really fun. And I really got the role of a lifetime to come over to ZoomInfo, but I had never run a technical team before. And so my first role, my first org, I think I had seventy. I had all the Salesforce engineers and I had the team I have now. Since then, I think it was after a year and a half or so, we did move the engineers back into the engineering org. But I had them all at first. And my senior engineering leader left within a month of me joining, and so I had five frontline engineers reporting to me. So that was like totally drinking from the fire hose, an incredible, incredible learning experience. But coming in and saying, okay, I've never run a technical team before, but I know that, you know, one of my superpowers outside of just being able to get things done is operational excellence. And so I came in and I'm like, okay, leaning in to that as I'm bringing myself up to speed as fast as possible. And I was like, alright. I need to see, like, what is everything we're working on, the current priority order, how are we prioritizing, you know, what is our capacity, and how are we running basically above the line, below the line on the capacity as the requests come in. How are people requesting from us? And then walk me through how we execute. Because that was, to me, like, that's the starting point. That's how I get to know the team. And it was like, well, we don't currently prioritize. I'm like, oh, okay. Well, what are we working on? It's like, well, whatever they ask us to work on. I'm like, alright. Okay. I could make an impact right away. And so I remember just saying like, okay. Like, show me what we're working on, and that didn't exist. So it was like, alright. Like, it was straight back to basics. Pull out a Google Sheet. I want everything that we're currently working on put in here. Let's put what we think the priority order is. You know, let's help me understand the level of effort or the complexity of these things. Help me understand, like, how far through are we on these things. And then it was going through and saying, okay. Like, how many engineers do I have? Okay. Let's take story points as a measurement. Okay. Let's go by level. How many story points can people take based on their level and expertise through a sprint? What's our sprint schedule? How are we doing release notes? So it was just really down to, like, very, very much the first step was — and that's where, you know, I talk about the maturity scale of, like, ad hoc and manual. They were maybe using Jira a little bit. There wasn't much rigor or consistency. And so it was just really back to the basics of like, what are we working on? How much can we work on at a time? What is that priority order? How do we come to that priority with the business? How much can we take in and out of sprint? How do we do above the line, below the line when new requests come in? And, okay, let's define all of that. That was zero. And then going into one, once that's defined, it's like, okay. Now let's build that into Jira. Let's figure out a process by which we're doing that. Let's figure out, what are we gonna do from a centralized intake perspective? Are we gonna have standard monthly operating reviews with each of the stakeholders to go through what we're working on and make sure we have the right priorities, to share what we accomplish, what's on the roadmap? Let's start building a roadmap because before that, we weren't looking beyond two weeks of sprint. And really understand like, okay, have the business priorities changed that we know are gonna be several months out? Let's get them on a roadmap, and then let's save capacity for run the business. And then going into, I would say two, which was systematized rigor, that's when you have that operating plan, right, that's sitting on top of this Jira with this ability to intake. We want centralized intake through one form that integrated into Jira. Again, all manual for the most part, but it was like, here's how we work. Here's how we prioritize. Here's how we measure capacity. Here's how you come talk to us. Here's our monthly review to share that with us. Here's how we're gonna talk to you when intake requests come in and we need to deprioritize. And here's how we're gonna communicate and share what we're working on. And it was funny because I remember coming in and — and again, I'm gonna pause there because that's really till the end of before we enter AI. And that's the first really two years of my role. But I remember coming in and I was told, like, the culture of ZoomInfo is you need to have quick wins. Like, what are your quick wins of how you're gonna impact the business? It was very much like, sure, you can have longer strategic wins, but how are you coming in as an executive and showing right away you're making impact? And I remember being a broken record that I kept talking about all the things I was doing for my team. And I think that, you know, I don't know for certain, but I would say that perhaps there was a perception or an opinion that, like, it would have been better if I had focused first on, like, what was I doing to the go to market to make an impact? But, like, I've always been a very, very strong believer. And as a really strong operator, that I can't impact the business if my team isn't working as efficiently as possible because we're never gonna be able to execute fast enough to support the speed of change. And by focusing first to make sure that I am running my team like a machine, then the ability to execute and do best in class work and rearchitect complex tech debt and to consolidate technical tools and platforms and to restructure contracts with vendors and to be able to output best in class — I had to have my team operating like a best in class RevOps team. And honestly, if I think back to my entire career, again, the almost decade I was at Salesforce starting as an EA, convincing them to internationally relocate me and make me an ops manager, to running a North American wide PMO, to going over with a global role with Tableau, like, it was always my ability to, yes, get shit done, but have the utmost operational rigor. And so I first applied that to my team. And so when you enter AI — yes, we have a lot of initiatives and an external roadmap to how we're supporting the go to market. And that, again, could be a whole other conversation. And there were those zero through three steps we had to take to get to that point. But I don't know how I would have introduced AI in a systematized, rigorous way using agents if we hadn't gotten to the place where we were today or at least inserted them in a very transformational way.

Janis Zech: Yeah. I mean, I think it's a process that everybody goes through. And, I mean, Philipp and I, we're both more product guys, but, like, I mean, fundamentally, it's the typical product process where you have a certain set of capacity, you have a discovery process of what should you actually do. Right? And this is very important because the projects you select to work on, right, they are always trade offs. And so if you don't have a roadmap you can basically show to the executive today and say, look, I mean, we can do this, this and this, but we can't do all of it. Unless you start systematizing it and, you know, like writing it down and tracking it and having a clear capacity model, it's actually very hard to have everybody understand that there are trade offs, and it's very hard to actually understand that you need to make decisions, and these are trade off decisions. And then you tie that back against your strategy. One question I have is how do you deal with the noise? I think you call it below the line, above the line. And I think that is typically in sales — how I know it — is kind of the decision makers versus the people that you also need to convince. I think in RevOps, you obviously have people that if the CRO says, I have a board meeting next week, often people jump. Right? And whether that's good or not, I mean, that's a different discussion, but, you know, that is often a reality. I'm curious, like, how do you allocate certain capacity to the nitty gritty small things versus the more strategic items? How do you deal with that? And that's fully outside of AI, but I'm just curious.

Tessa Whittaker: Yeah. I think there's a couple ways how I handle that, but also how I think about that just from, like, an architecture of an org design. So I think it's really interesting. So when you have — let's apply a similar, you know, maturity scale to your RevOps organization. So if you are at a startup or smaller company, you've got, you know, a number of RevOps people that are all wearing twenty different hats. And so it's really hard when you think about fire drills coming in or board meetings coming in or this is coming in. And also you have some sort of Salesforce pick list that you need to add or a new SKU that has to get created, and you have the same people doing everything, it becomes very hard. Right? Because, you know, you are constantly going through that prioritization exercise. I think as you become a larger organization, one of the things that you see from an architecture is the actual segmentation of roles within a RevOps organization. So the folks that are business partners who are doing forecasting or pipeline or memos or etcetera and are actually more business facing are gonna sit very differently than, let's say, your business process team or your product management team or whatever you call them who are going to be gathering requirements and building user stories and working back with your technical execution team, system admins that are gonna be doing these things. Right? And so I think first and foremost is really making sure that, depending on your org size, if you have the ability to do so, how are you segmenting your team? Do you have different roles and responsibilities? The always on tasks. Right? And maybe the more transformational initiatives, etcetera. So I think that becomes really important. And then for us, one of the biggest things that we did, obviously, we introduced this idea of a monthly operating review with the stakeholders. We still realized that our SVPs were competing for resources against each other. And so we knew we needed a more centralized way of saying, like, what are our top change the business initiatives, and how do we bring that more centrally together? You know, we're fortunate that we do have a goal setting process at ZoomInfo. So we have, like, what are the top company priorities? Right? Like, what are the four things that we're trying to go out and do at ZoomInfo? And then underneath that, we introduced in partnership with our CRO, what are the top CRO priorities, which is across all the SVPs where should we secondarily be allocating resources. And then underneath that, what is the stack rank centrally? This is a new thing we did because I had my SVP of sales and my SVP of partner and DAS and my, you know, SVP of customer success. I had their priorities in order, but, like, that didn't work anymore. Like, I needed a stack rank of them centrally. And so we started doing that where we had, okay, we had a meeting two weeks ago with our CRO and his directs, and it was, here's the company priorities, here's the CRO priorities, and let's all agree on the stack ranking of the SVP priorities. And we're gonna allocate, you know, thirty, forty percent of our business to run the business, which is enhancements, changes, and, you know, tech debt consolidation and all the different things that we have to do ongoing and consistently. And that was a major unlock. Now we're doing something with AI on that, which I'll be excited to share kinda when we pivot or segue into that. But I think the getting the buy in on — we're gonna work to up above probably to and above our capacity always because we are a hardworking RevOps team. But unless we have that stack ranking, the person who is always shouting the loudest will end up winning. And I think we've all been there. It's funny because I'll listen to podcasts or conversations of our ELT, and they'll say stuff like, we need to operate like a startup. And, you know, I've heard things like, you know, people are coming to me and talking about prioritization and capacity. And I don't think that prioritization and capacity conversations should slow down anything. I actually think it's the opposite — when you do it properly, it's the opposite — in that I can make sure my team isn't wasting their time working on things that aren't important, and we're moving the needle on the biggest, most strategic priorities to and above our capacity in order for us to meet our top goals as a company. And so I think a lot of people use prioritization or capacity as a way to push back. But when you make it completely quantitative and not qualitative at all and really rooted in data and backing up that data with actual sprint data coming directly out of Jira, you can actually show that you've been able to make your team more productive and driving more impact if you build this right. But I do think that there are people that use those words priorities and capacity to push back instead of just using data.

Janis Zech: Yeah. I mean, look, I think this is important wherever you work and whenever you work, and it's obviously the ability to essentially know what has impact to the company KPIs, being aligned in terms of the views across the entire revenue function. So not just the prioritization and the discovery and how you slot it in, but how do you stack rank it and how do you make sure that there's no resource fight going on behind the backs? So if you have one central meeting where people come together and you have executive buy in from the C level that say, look, I mean, this is what we agreed on. In a month, we can agree differently, but this is exactly what we agreed on. And we tie it back to the outcomes. And you allocate for smaller items like that. And this is how good product orgs are run. This is how good RevOps orgs are run. And I think this is rooted in product management and R and D development processes because typically forty percent of the P and L is in R and D. So you want to — I mean, the reason the product manager exists, obviously, we want to know what we build and how to best build it. So there's a whole discovery process to understand how do you build it. And this is the same reason for RevOps. There's a problem, but there's ten different solutions. So you need to find out how to best solve it. But then you have to execute at most efficiency. And so obviously engineering resources and execution resources are always scarce. So it is super important. It doesn't matter at which scale you are. And I think I really like how you outlined this maturity level because we haven't even touched on AI. We want to actually only talk about AI. We haven't even touched on it because these things you have to do before you introduce agents. This is the table stakes you have to do and you have to do them really well. And if you don't do them, do them. It is worth a lot. And I'm sure there's many who already do it. Then it's obviously, I think, the stakeholder management, the buy in. Right? That is also an equally important part, especially in RevOps, I think, because the stakeholders are very strong, I'd say, on the go to market side, very different to product because these are the customers. They typically don't sit there. Right? But in RevOps, they actually sit there and they actually approach you and they come to you every day and want something from you. So I think being really well aligned is super important. So now I know we don't have much time left, but like, what do you do with AI?

Tessa Whittaker: No. So this is painting the picture, this is good. And I think you're seeing the picture that I'm painting, which is it is a scale. And there are these steps you have to take in order to implement best in class AI. And if you jump ahead or jump around, it's like AI without a purpose. Right? And instead of building blocks of creating this incredible AI first operating model in an org, you're kind of just throwing blocks into a pile, and then you're not sure why it's not adding up or it's not driving impact because you're not really building anything. You're just creating a pile. And so what we did as a first step is we went through — I partnered with Corey on my team who's essentially my scrum master. It's kind of the center of excellence, incredible person. And we went through and we said, okay. Let's work with the team and create a list of every single thing they have to do to execute their jobs as a RevOps person from start to finish. So we had, okay, they review intake, and then they set up a call with a stakeholder, and then they gather the requirements in a note doc. And then after they take that note doc and they have to make it into a requirements doc. And then they're writing a user story. And then they're meeting with the tech team to go through and groom. And then they're doing user testing. And then they're going and creating MLR decks to have conversations with their stakeholders. And then they're having prioritization conversations and they're moving things around. And we just put those in a list and then basically tried to quantify how many hours all these things were taking and go through and kind of identify where there were quick wins in there and what could potentially be the most time saving if we could not only automate but build in AI assistance. And so that's really where we started from start to finish of like, if you could do anything, so get out of the box. If you could say, here's everything I could do. And we were able to do anything. I don't wanna talk about how we're gonna do it. But if we could do it, what order would be the priority and what would that look like? And I think that's really important that I've had to kind of break with people is that I don't wanna talk about the how yet. Let's just talk about the vision. We'll get to the how later. What is the vision if anything was possible? And so we started there. And one of the themes that kept coming up is, you know, you go to a meeting with a stakeholder to gather the requirements, and they haven't even really thought through what they needed in the first place. And now all of a sudden you've got a director as well.

Janis Zech: Right. Yeah. Yeah.

Tessa Whittaker: You got a director of sales and a VP of sales and maybe some IC of sales. And you've got two people on RevOps because it touches lead to opportunity and maybe opportunity to contract. And you go back and then you have to set up another meeting and then there's more questions. And next thing you know, for what might be an enhancement, you've got six hours of meetings. You go to the tech team, there's missing requirements, and you have to have another meeting with them. And there's this crazy amount of wasteful time that goes into the gathering the requirements process. And not all requirements docs are created equal. Not everyone on my team is as technical as each other. So how do you level the playing field? And so that was really the very first initial idea is how do you create an agent that gathers the requirements? And so if someone needs something from our team — now, obviously, I'm not gonna send my CEO or CRO there. But the majority, the eighty five percent, ninety percent of people that are coming to us, not yet. I'm sure Andy wouldn't look at it. Maybe. Maybe. Or maybe you tell me. He probably would have feedback on how to make it better, but for a good reason. Always be better. One percent better every day. But you go through and the agent just starts asking questions, qualifying questions. And it'll continue to kind of gather the requirements and probe the person to answer things about business impact, about what they're trying to solve. Anyone in RevOps who's