#97 AI-First RevOps: Achieving Autonomous Operations – with Tessa Whittaker, VP RevOps at ZoomInfo

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So, go to get weflow.com to start you a free trial today. [Music] Hello and welcome to another episode of the Revope SLAB podcast. I'm actually alone today for the Cahn join, but yeah, thanks God I have Tessa here from Zoom Influos, so hey Tessa, how are you? Good. Thank you so much for having me. I'm excited to be on the podcast. Same here. I've been following you for a long time. I think we've 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 Zoom Influos? And what have you done before? Yeah, so Tessa Whitaker, I'm currently the VP of Revenue Operations at Zoom Influos, and I've been at Zoom Influos 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. I 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. Yeah, awesome. I mean, I think there's many different ways into refops. And this is certainly one of them, being close to the sea level, understanding priorities and then getting started that way. Yeah, I mean, super. So I think today's topic is all about how do you use AI in the refops team? So not what kind of AI tools are you using to solve certain problems for the go-to-market teams, but rather focusing in on how do you drive efficiencies for your refops team? So maybe for context, how big is the refops team at Zoom Influos? How is it structured? What kind of responsibilities do you have there? So under my umbrella, and we're not completely centralized. So we, depending on the size of your company, really small to obviously we know large enterprises. Revops is going to look very different. And whether it's all completely centralized or does marketing opposite 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 agreement 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 I call it product managers that are really focused on the end-to-end business process that then automates into the system. So I 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 that the CRO go-to-market organization. So everything our sellers are using, our pre-sales 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 Zimindville. Obviously we've gone through a massive transformation as a company even 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, you know, you're the customer of the company. You are the persona that they want to 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 are go-to-market strategy of how we're going and talking and selling and supporting revenue operations professionals. Yeah, I mean, I think there's always this, like, you know, eat your own dog food, right? Like, me, 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 your 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 a reformed team efficiency at the moment, right? So not so much the tooling for the sales, CSMs, marketing teams, but rather, you know, your own team. So, and before we, you know, want to dive into that, I think you have this idea of like a maturity scale, right? So I'm curious, like, what that is and how you would outline that. Yeah, absolutely. So, you know, I think we can all relate to an experience or if you're a rev-ops leader, you're in rev-ops, the experience of having your ELT or your, you know, your CRO within the ELT or other DPSVPs and marketing ourselves 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 and Rev-ops. 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 are, what's everything that your team has done in the last quarter around AI? Put it in a list and there's like this fire drill or, you know, I've heard this company is doing this or that company is doing this, like, what are we doing? And there's this, I think, top-down comparison or uncertainty of like, 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 kind of sucking. And I woke up, I'm sure, 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 Rev-ops maturity scale came up, which is, you know, from zero to five, where do we fall on a Rev-ops 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 Rev-ops internal team, which we'll talk about, but also across data and go to market execution. So when we think about Rev-ops maturity, you have your data maturity as part of that scale, you're good at market execution against that scale. And then also your operational rigor or 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. So I can share a little bit more about how I think about this scale and really where, you know, we've where I benchmarked us and what we had to do from a robots perspective to get to where we are now. 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 focus in on specific process where you apply AI, but like, yeah, curious what that maturity scale, like high level looks like, because I think that's also something that probably a lot of folks listening are interested in, right? Like, what's like the different, yeah, different day I assume. Yeah, 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 I have zero is just being ad hoc and manual. And again, I'll kind of talk through this as I think about rev-up specifically internally as an org. One is manual but defined. So zero ad hoc and manual, you're kind of in this really, you know, 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 you as an organization. And then one is manual but defined. So you're still very, probably still pretty ad hoc and 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. This is, you know, some of our processes are starting to be written out and defined. To I call systematized rigor, which is you now have your processes defined. You might have some of them built into GERA. You might use some sort of project management tool, etc. But again, you're still pretty, it's still in that kind of reactive state. You're starting to become more proactive, definitely not yet using AI. And then three is AI augmented workflows, which is essentially now you started introducing AI to support some of those processes you define or those systems that you're using. Then we get to four, agentic assistants. 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 was important 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 probably 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 started applying those same principles to how you look at, again, I said your data. So is your, 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 and 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 in that scale? A perfect example would be, you know, if let's say a systematized rigor or manual but defined. So you know what your processes are. But you know, maybe you don't have them actually written out or documented. How are you going to 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 like 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, 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. Yeah, yeah, I love it. I mean, I think, I think obviously now we're touching on data, you know, business or like operation excellence for the actual teams, right? And then there's basically the ref of maturity scale, which he just alluded to. So obviously they applied to different kind of dimensions. I mean, I'm, you know, and just for context, right? I mean, ZoomM for like a billion dollar plus revenue company, right? 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, you know, 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 as 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 like ref ropes roadmap process, right? 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, right? 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, like what was the state before? What was this like ref ropes roadmap process? What was the intake process? And I mean, yeah, I look, I think we all know the good, bad and ugly. So, you know, don't hold back. Please, it's fine. That's right. And then, you know, where are we? Where are you going? And then we digest like, now 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 company. Yeah, no, absolutely. And I think, you know, to anchor on the point, like I am at a company that is doing, you know, that is a larger company that has over a billion in AR. And I think that in order to get to an AI first operating model for ref ops, there is a lot of re architecture that has to happen from that zero to three. When a rev ops org, I think it's harder to get there, the bigger company you are. And so, you know, I've been in role three years and I remember my first week coming in. For context, I came over from the sales strategy and operation site. 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 seeding growth company. We were trying to expand GoWaldewall into the enterprise. So it was really fun. And I really got the role of the lifetime to come over to Zoom info, but I had never run a technical team before. And so my first role, my first org, I think I had 70, I had the, all the Salesforce engineers and I had the team I had now since then. I think it was after a year and a half or so. We did move the engineers back into the engineering work, but I had them all at first. And my senior engineering leader left within a month of me joining. So I had a front line engineer's glory into me. So that was like totally drinking from the fire hose, an incredible, incredible learning experience. But coming in and saying, okay, well, I've never run a technical team before. But I know that 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 into that as I'm bringing myself up to speed as fast as possible. And it was like, I need to see like, what is everything we're working on? The current priority order, how are we prioritizing? What is our capacity and how are we running basically above the line, below the line on the capacity as the request come in? How are people requesting from us and then block me through our way execute? And that because that was like to me, that's the starting point. That's how I get to know the team. And it was like, well, we don't currently prioritize. And like, okay, well, what are we working on? It's like, well, whatever they ask us to work on. I'm like, all right, okay, I can take it in fact right away. And Laura, remember just saying like, okay, like show me what we're working on and that didn't exist. So it was like, all right, like it was like straight back to basics, pull out a Google sheet. I want everything that we're currently working on put in here. Just put a prayer, like 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 much of it how far through are we 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 person, I mean, what people can take how many story points and based on their level and expertise through a sprint, what's our sprint schedule? How are we doing release now? 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 skill like ad hoc and the annual they were maybe using Jura little bit. There wasn't much rigor 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 you request 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 Jura. Let's figure out a process of which we're doing that. Let's figure out, you know, do we have do, you know, what are we going to do? It's from a centralized intake perspective. Are we going to 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 changed the business priorities that we know that are going to 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 is systematized rigor. That's when you have that operating plan, right, that's sitting on top of this Jura with this ability, with really this ability to intake. We want centralized intake through one form that integrated into Jura. 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 going to talk to you when requests come in and we need to depravertize. And here's how we're going to communicate and share what we're working on. And it was funny because I remember coming in and look and again, I'm going to 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 the culture of Zoom info is you need to have quick wins. What are your quick wins of how you're going to impact the business? It was very much like, sure, you do have longer strategic wins, but how are you coming in as an executive and showing right away your 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 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 going to 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 and work and rearchitect complex check 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 rev off 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, you know, running a PMO North American ride 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, you know, you have this enter AI, yes, we have a lot of initiatives in an external roadmap to how we're supporting the go-to-market and that again could be a whole other conversation. And there were that 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 system-atized rigorous way using agents if we hadn't gotten to the place where we were today or at least, you know, inserted them in a very transformational way. Hey, Philip here. Are you enjoying this episode? Well, good news, because you can find more free red ops and go-to-market resources on get reflow.com/redops. Access over 20 cheat sheets, reports and guides that will help you become a better revenue operator. Or join over 2000 subscribers who already get the latest resources right into the inboxes without a free newsletter. Just go to getbiflow.com/redops. Yeah, I mean, I think it's a process that everybody goes through and I mean, Philip and I were 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, you know, 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. And, you know, like, these are the things that we believe have the biggest impact. Unless you start systemizing 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 know, you tie that back against your strategy, right? One question I have is like, how do you deal with, you know, the noise, right? I think you call it like below the line, above the line, but I think that is typically in serotonous. I know it's like kind of the decision-makers versus, you know, kind of the people that you also need to convince. I think in the year, like, in reflubs, right? Like you obviously have people that, right? Like if the CEO says I have a bought meeting next week, you know, 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, how do you, right? 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 it's just curious, you know? 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, we let's apply similar, you know, maturity scaled to your RevOps organization. So if you are at a startup, smaller company, you've got, you know, a number of RevOps people that are all wearing 20 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 needed at or new skills 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 rules within a RevOps organization. So the folks that are business partners who are doing forecasting or pipeline or memos or etc and are actually more business facing are going to 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 going to be doing these things, right? And so I think first and foremost is really making sure that if if you have the right, you know, depending on your org size, if you have the ability to do so, how are you segmenting your team to have different roles and responsibilities, the always on tasks, right? And maybe the more transformational initiatives, etc. 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 realize that our SPs 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 Zoom info. 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 Zoom info? And then underneath that, we introduced in partnership with our CRL, what are the top CRL priorities, which is across all the SPPs 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 assails and my SVP partner and DAS and my SVP 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 to East Go 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 going to allocate 30, 40% of our business to run the business, which is enhancements, changes and tech debt consolidation and all the different things that we have to do. I'm going inconsistently and that was a major unlock. Now we're doing something with AI on that, which I'll be excited to share kind of when we when we pivot our segue into that. But I think the getting the buy-in on we're going to work to up above probably two and above our capacity always because we are a hard working 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 I've heard things like people are coming to me and talk about prioritization and capacity. And I don't think that the business I just want to be clear. I don't think that prioritization and capacity conversations should slow down anything. I actually think it's up when you do it properly. It's the opposite is 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 of all and really rooted in data and backing up that data with actual sprint data coming directly out of JIRA that 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. Yeah, I mean, look, I think I think there's this 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 it's not just the prioritization and the discovery and then 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 sea level, let's say, look, I mean, this is what we agreed on. Right? And in a month, we can agree different. But this is exactly what we agreed on. And we tie it back to the outcomes. And you allocate for smaller items like, you know, tap, tap. And this is how good product orcs are run. This is how good refops orcs are run. And I think this is rooted in product management and R&D development processes because typically 40% of the PNL is in R&D. And so, you know, if you want to, I mean, the reason of the product manager exists, obviously, we want to know what we build and how to best build it. Right? So there's a whole discovery process to understand how do you build it. And this is the true, the same reason it's for refops. Right? There's a problem, but there's 10 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 resource, right? And execution resources are always scarce. So it is super important. It doesn't matter which scale you are. And I think I really like how you outline this maturity level because we haven't even touched on AI, right? 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 am 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 equal important part, especially in refops, I think, because the stakeholders are very strong, I'd say on the go-to markets, they're very different to product because these are the customers. They typically don't sit there, right? But in refops, they actually sit there and they actually approach you and they come to you every day and want something from you. So I think, you know, being really well aligned is super important. So now, you know, I know we don't have much time there. But like, what do you do with AI? Yeah. Yeah. Yeah. Now this is, so this is painting the furry and this is good. And I think, I think you're seeing the, you know, 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, you know, without a purpose, right? And instead of being building blocks of creating this incredible AI first operating model in OR, 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 CoriMI team is 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 rev options 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 stock. And then after they take that note stock and they write a gap, they have to make it to a requirement stock. 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 are going and creating MLR decks. You have conversations with their stakeholders. And then they're having prioritization conversations and they're moving things around. And we just put those and put them in a list. And then basically try to quantify how many hours all these things were taking. And go through and kind of identify where there are quick wins in there. And what could potentially be the most time saving if we could not only automate the building 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 want to talk about how we're going to do it. But if we could do it, what order would be the priority and what that 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 want to 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. Yeah. Yeah. You got a director in sales and a VP of sales and maybe some IC sales. And you've got two people on rev-offs because it touches L to O and maybe O to C. And then we go back and then we have to set up another meeting. And then there's more questions. And next thing you know, for meeting and 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 waste full time that goes into the gathering the requirements process. And not all requirements stocks are created equal. Not everyone on my team is as technical as each other. So how do you equal the Pilling 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 going to send my CEO or CEO around there. But majority, the 85% and 90% of people that are coming to me. Maybe, maybe, or maybe you'd probably have 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 Revots who's ever gathered requirements will tell you that when they go to the business, you have to be really good at flipping the situation because the business isn't going to come to you with a solution. And we shouldn't give a shit about their solution. What we need to do is what they're trying to solve so we can figure out what is the best technical solution with the least amount of technical debt that'll solve their problem in a scalable way. But the business less to solution. And so the agent can actually go, they say, okay, we need to pick this, it's like, what are you trying to solve? What is the business impact? What is the objective? And asking all these things, right? And so the idea just started with, okay, we have the centralized intake, that's built with an agent in a chatbot, and it's gonna pull out and build, it'll have a requirements document that is based on what is best in class requirements document. It can highlight what is missing very quickly. It can help write a user story, and it's gonna integrate directly into JIRA. So like right there, that's hundreds of hours, if you multiply it across your teams, but very quickly, I was like, that's actually not the end of what I think this agent can do. The agent can also have context of what is the top company priorities. What are the CIRO priorities? What are priorities or initiatives or change the business initiatives that are already in flight or is this a net new ask? And actually in a way that is super objective, shoot out a prioritization score of what they think this should set in a way that's not just whoever's shouting the loudest, but really based on business impact, is it an existing priority, does it lend you a company priority, all of those things, to give us a baseline of where we think we should prioritize it against. And so we've introduced that, which is really exciting, and we're starting to work through the modeling of that. But my vision, my complete vision of like, where do I want us to be, or start to be closer to the end of the year, is someone goes, we have this running scrum list. Someone goes to ask us something. They fill out an agent that requirements build a user story that integrates into JIRA. We have our list of what's in sprint, slated for in sprint, slated for that, or that's in our backlog, and that this agent could then say, like, hey, for this upcoming sprint, this is how much capacity years, this is the estimated capacity it's gonna take to do this. Based on this prioritization that we've done, we actually think it should slot for this sprint, or that sprint, and this should come off based on their prioritization score. Like, I actually see this being a dynamic way that we can slot and prioritize things in and out of sprint. And then also, in my vision, again, this is vision, 'cause I haven't quite figured out how yet, I want this agent to have full understanding of the capacity of the actual executors, assistant admins, the engineers, and be able to tell when things are taking longer, or not, or maybe went faster, and can understand when we can actually slot more in, or we need to push, or we need to pull more out, and actually really take out all the waste, and the very interesting human behavior that comes into executing things coming in and out of sprint, and monitor capacity and get to a point where it is all quantitative and not qualitative, because I think that's really the future of how we're going to execute technically. - Yeah, especially off the intake process, because I think the requirements, the other challenge you outlined is very time consuming, and I think this, just asking why, and how, and what you actually want to solve for, is something that essentially, make sure that the person requiring something has a deep understanding and thinks deeper about what they actually want to solve for. And so I think that's super interesting. - Yeah, sounds like this could be a real product, and you could found a startup with it, but that's a different discussion. - That's a different question. But I think that was one of our, and then we've got so many other use cases, right? So we've got, we have our, everything from, we did a process documentation. So I believe that if you have processes in the go-to market, built into any sort of system, they only need to be documented. And so we've built, built, and/or, in some cases, bought AI tools as well, where we can do all the process documentation that doesn't need to be manual anymore. Obviously there's process monitoring. There's agents that we have that, we don't have to build slides anymore. They build our slides for us. So when we're going into business reviews, we don't have to do that. And there's several DevOps agents that we've been building, not just this intake one, which I think is actually going to make the biggest impact as we evolve, but several agents that we have now built in order to support this. And I know we're getting close to it at time, but I'll share this is I knew that in order to drive to being a truly AI first, revenue operations organization, I need my, need in my people to think AI first. And I would say that I made every mistake in the book of like trying to figure out how I started to get them to think AI first. I think I tried to scare them, give word and rev up so we don't know AI, we have a job and he's like, "I didn't really work." I, you know, we tried to gamify it. So we had all these courses and learnings. And if you took them, you entered a raffle. And that didn't really work. And it wasn't until we did this incredible thing internally, which is we built out our own Zoom Info Chat and the ability for everyone to build agents that I saw a shift. And basically what I did is I made it mandatory to create an agent I did a hackathon where every single person in the org had to create an agent that supported a internal workflow to make us more efficient or productive. And that was really when I saw the biggest unlocks happen. - Interesting, yeah, very interesting. Look, I think we had time. Thank you so much for coming on and sharing all this. I think really very operational and something that, I really love because I think other folks can just take this and then either contact you or, you know, like just implement it, right? And I think it's not just AI, it's also the stuff we discussed around the maturity scale and the process rigor and the operational efficiency, which always matter in what we all do. So much appreciated. Before you go, is there a book research report, community, anything where you can learn you would recommend for Refops, Super Design? And I'm not throwing out the community because of Refops Chat actually just as a disclaimer. I think we're advertising this well everywhere, but like anything, you know, you would recommend. Yeah. - Yeah, so I recently read the AI first leader, Molly Baudin-Steiner, she was on your podcast, recommended it to me, is a very good book. I will say though, I think the number one thing that I've shifted this year, and there are a lot of communities like I'm in Pavilion, I'm the co-traptor head of Miami chapter. I'm in, you know, I'm in your new community. You know, I'm in chief, I've been in the Refops Cop, like I've done all of these things. I would say the thing that's been most impactful for me is finding my four or five, I probably have five right now, really tight community friends that all have my job, VP of Revops, at Tech Companies, that I've built personal relationships with, that I text with several times a week. And we talk about personal stuff, sure, but we're constantly asking each other questions or bouncing ideas. And yes, these communities help you, but leverage them to find your tight-knit people that are dealing with the same problems who have the same challenges at a different company. And it is such an unlock. And I'm fortunate that I have that, two of those folks have been on your podcast. And I'm in a group chat every day with folks that are trying to solve the same problems. And I think what you putting is what you get out of those communities and find your tight-knit circle, and that's my best advice I can give anybody. - Sounds like we have to invite the other two that are part of that group as well to the podcast. Tessa, thank you so much. This was awesome, really, very much enjoyed it. And yeah, wish you a great day. - You too, thanks. (upbeat music) - Thank you for listening to the Revops Lab podcast. If you enjoyed this episode and would like to support us, share it with a Revops friend or try us five-star rating, right now. And if you have feedback, questions, or guest ideas, just send a message to Janice or me on LinkedIn. Thank you and see you next time. (upbeat music)