From Frustration to Innovation: RevOps in the Age of AI

Speaker A: Welcome to Revops Revolution, where we focus on changing the game for driving revenue. I'm your host, Jesse Morris. In each episode, we take a look at the different areas that affect change and drive revenue across your organization. If this is your first time joining us, please subscribe so you don't miss out on any future episode. Here's the revolution. Welcome to another day of the Revolution. I'm your host, Jesse Morris, and today I'm excited to welcome Elio Narchizo on the line, uh, and podcast. Super excited to have you. Welcome.

Speaker B: Thank you, Jesse. Happy to be here.

Speaker A: Awesome. So Elio and I got introduced by a fellow mutual connection in the VC space. Uh, and we hit it off while geeking out over agentic AI. Anybody knows me, man, I love that space. And so does Eliot. He's a serial entrepreneur, uh, currently, uh, co founded and runs an AI company called Scale Stack that I personally find fascinating. And they're solving some key challenges companies face around being able to utilize agentic AI and automation across an entire organization, which is a problem I've been trying to solve a lot, uh, as well. Um, Elio, super excited to have you here. Thanks again for joining. Can you tell us a little bit more about yourself?

Speaker B: Yeah, and thank you for having me. This is great. And yes, we did geek out a lot in our first meeting. So this is the output of that or one of the first outputs. Glad, uh, to be here. Um, my name is Elio. I co founded skillstack about, uh, three and a half years ago. Uh, originally from Italy, but, uh, I've been here in the US for the past 20 years. Came, uh, here for like to do my MBA and then love the entrepreneurial aspect of the US and the mentality of building and never left. Um, happy to be here.

Speaker A: That's awesome, man. So today we're going to geek out a little bit more for the audience around agentic and AI. And I think where things are headed, I think, you know, one of the things I personally and I think a lot of us do, maybe not everybody, but I would say I personally always look at entrepreneurs, especially in the AI space is kind of like the, the dream, right? It's the thing I would say a lot of us that are kind of more creative minds, problem solvers, you know, aspire to, you've done, you've done a couple of businesses now at this point, but in particular I'm really curious around Scale Stack because I do think, you know, it's, it's funny, I've been looking for six months for what you guys uh, have. And it's kind of random that you and I kind of ran into each other. So it's, you know, it's always funny when I come across these things. I'm really curious like when you started scale stack, like what led to the formation? What did you see in the market? What was the problem you guys set out to solve?

Speaker B: I think, I think I also agree that origin story is at the best. I was commenting uh, earlier this week on um, somebody else post about like you know, why did I, you know, do this? I think it's so important. Every, every origin story is so important. And so um, like many stories I think it begins with frustration. Uh, that is not like uh, uh, something that you experience like once or twice, but like that you regularly feel this frustration and then eventually say I just have to do something about it. And for me it was, I've always had like, you know, in all of my, the companies that I started, but also meaningfully at AWS where I spent uh, almost five years before founding Scale stack, um, sales, bd, go to market, partnership roles, right? So like a CEO, co founder or like uh, VP of BD and all of that stuff. And at uh, the companies that I started or co founded or like was involved with, they were like, you know, fast growing startups, whatever. The messiness of the go to market seemed like inevitable, right? Seemed like, okay, like I, you have to deal with it like nothing is perfect, like everything is breaking, you're growing, running fast, you're growing fast. It's like a normal feature. But then when I arrive at Amazon, um, that like, you know, everybody thinks rightly so it's an amazing company, you know, well run. My assumption was like all systems are gonna run perfectly and the data is gonna flow like you know, into my hands magically. And um, that wasn't the case. I had like this amazing role at uh, Amazon at aws, where since I had so much experience with startups, I was tasked with building uh, what's now called the AWS Global Startup program, which is a program to support the go to market of uh, mid to late stage. Uh, so like serious B and app companies, uh, that are built on aws, they wanted more help to grow faster. And so in itself the program was focusing on like go to market. And so that was interesting to see like how companies experience their go to market. But one of the problems that I became extremely frustrated is that like you know, this was an invite program, uh, so like you had to get invited by AWS and so the invite came from analyzing data. And so the idea was, okay, AWS has a lot of data. Our CRM Salesforce has like tons of data. So let me plug that data and see how to prioritize the companies that we want to invite. The data was awful. The data in the CRM was terrible. Despite the access to so much client data and consumption and information stuff, the data was super outdated, wrong duplicated, accounts invalid. And so what I ended up after trying to build reporting off of uh, Salesforce was I had my own spreadsheet with tens of thousands of companies that we wanted to monitor, my team and I, and then we built little zapiers, connectors to The Crunchbase, the LinkedIn, the Whatever, Zoom info and try to make sense of that data manually, basically on this spreadsheet. And then like uh, my um, manager and I would like review the list every like couple of weeks to decide like what companies should be invited. And it was literally like manually researching all of this data. And so that was the frustration. But the frustration extended also to the companies that we were supporting because I mean we were helping them in co marketing, co selling, and they all had the same problems. Uh, oh, like we need more leads. And they had a lot of leads in their CRM, but they were not like clean and you know, put together so that they could actually leverage them. Oh, we need like uh, to better understand our icp. But they already have spent like several years in market and had a lot of data they could not make sense. And so all of this was the frustration that is not just during Amazon, it was like it preceded, but like my assumption was it's just that like, you know, it's a fact of nature for early stage companies. Um, and so um, this was 2021 and uh, I started working with my co founder on like uh, an MVP and then left AWS in the mid 22 and solely focused on Skill Stack. And here we are.

Speaker A: That's awesome man. And I agree with you. I think the origin story, they're a lot of fun to see and, and you're right, a lot of times it's born out of the frustration side of it. And as somebody who has gone into multiple companies to fix the underlying data, I have yet to walk into a company yet that's like, you know, to your point where you'd expect it to be. And I think that's where a lot of the stuff, I think that what you guys are building and some of the things I've been working through as well is can Be really impactful obviously on the agentix side, um, to help streamline some of that. So I want to shift gears a little bit. You, you now been in business for a few years, you've started now seeing uh, you know, different companies trying to adopt AI and I'd be curious actually to look at kind of two different types of companies within that scenario. One, what is a common a company that you actually have landed and brought on board? You know, what are some of the challenges they have with actually you know, rolling out AI A And then second part of my question I can follow back up on it is what about a company that didn't come on board that wanted to do this? Maybe just said we're not ready yet. And what was some of the reasons behind it and what maybe advice do you have around that? It was a three part question.

Speaker B: Yeah. So let me start from the first and then maybe we need to help me with the other two. Like the first I think is super interesting and we have learned a lot. I mean like we are now in you know, two and a half years building on AI so like it starts to be a relevant amount of time. Right.

Speaker A: And,

Speaker B: and I think that the learning has been that there was a rush when like Geni first came on board to like oh, we're gonna like, you know, automate like the sdr. Oh we're gonna like you know, completely like uh, robotize like you know, the last mile. And we did that mistake too. And so together with one of our customers, MongoDB, um, we rush to build um, a framework which we now also sell. But it's changed like in the way it works called Spotlight. And Spotlight was basically jointly developed with MongoDB because um, um they, they wanted to use AI to help the last mile. Oh uh, we need to help like SDRs to send more emails by like you know quickly like putting together like uh, the text of the email based on insights that we have in our CRM and that um, maybe we can collect from other data sources. The end product was very interesting but it was essentially like you know, enabling SDRS to write like emails faster personalized for that company. Um, the problem were like several and the first one is always usually like connected to like uh, the human workflow. It required like uh, the reps to experience yet another interface, yet another screen. And that was bad. And that's like, it's, it went against like some of the closely held beliefs that we have a skill stack which is we want to be this middleware but because of the Excitement around AI. We also did that mistake and said, okay, let's build the screen that like rep can quickly like put together an email and like, you know, that puts spotlight on the key data points that you want to know about that company or that person and send that email at scale. The emails were really good, but the problem was adding the screen. And then the other big problem was that when we plugged like again like data from the CRM, it was bad. And so there was a lot of work that we had to do to like clean all of the lead data. And we were already helping mongodb on making sense of account data, so we were comfortable with that. But the whole lead area was like super dirty. Like you know, lots of wrong contact data so that like, you know, you would send emails to emails that don't exist uh, anymore or like wrong person or like, you know, the email field was in the name field and vice versa. So stuff like that. And so the learning from that was actually there is so much work, which we call busy work, manual work, like work that frankly is not that great and boring, but that needs to be done. And it is so essential where actually leveraging AI is great because you know, you can delegate to agents the cleaning, the enrichment, the uh, prioritization, the making sense of the data. And there was an artist that a, uh, few months ago went viral and she said, I want AI to do my laundry while I write my poetry and not vice versa. And I think it's very true. Like I forget her name unfortunately. I need to look it up. But the use of AI, ah, AI is faster, can process data, uh, like uh, at uh, incredible speed, never tires. And like there is no distinction between boring work or like creative work. It's basically the same. And so why not like concentrating and the efforts on AI, not on the last mile, which involves like engagements between humans and like after a while we realize that, oh, I can detect when an email is written by AI and the probability of responding to that email are like very close to zero. When instead we can deploy AI to fix the mess that exists today in like 100% of the companies we speak to, um, and concentrating AI there. And so that was uh, the learning for us. We rush like everybody else or like many other people into the last mile and then realize that the original hypothesis, which is we want computers and machines to work for humans and not the other way around, so that automating and leveraging AI to do the work that we don't like to do and that we always push back. And so that's how we end up with like bad CRMs. Because this is you know, like okay, let me remove the duplicates. Okay, shit, what is the logic? How do I implement it? And then like you know, all the fields that I need to properly enrich, getting data from multiple data sources and then maybe doing some research to fill the gaps, all of that. It's a fantastic use case for using AI. And that's where we are maniacally focusing right now.

Speaker A: That's awesome. That's a good story. And I think when you look at that it is interesting. I haven't heard that uh, saying that that lady had said too about I'd rather it do the washing versus which actually makes a lot of sense and actually I'm going to use that going forward. I want to shift a little bit within this category though again of talking about maybe the clients or prospects that you've had that didn't move forward. What were some of the barriers to them being able to roll out AI within their organization?

Speaker B: Great question. So I think that um, we see uh, several factors. So typically we work with larger companies so mid market and app and our product has been developed with like the go to market ops people in mind. So people in rev ops, sales ops, marketing ops, that it's a category of jobs that is fast growing because of like uh, you know all of the stuff that needs to be done on systems, processes but it's also if done well a very strategic, strategic role. And so um, the companies where we win are typically the companies where the rev ups, marketing ops, sales ops teams are powerful and heard in the organization which means that they have a strategic role. They are not relegated to like being systems or like uh, or just systems or just processes or just like ticket handlers. Because what we observe is that like when the teams are like uh, perceived that way or maybe they don't have yet a strong leadership in the go to market ops team, they quickly like evaporate in an ocean of like tickets and priorities and quickly become overwhelmed. And it's very difficult for them to like lift their heads up and say hey, we need to like do something different if we want to obtain different outcomes. And so we have lost deals where like that was the situation where maybe. So the go to market ops team should be like equal partners with the sales and marketing teams should have equal voice to the CRO and cmo. If that doesn't happen, if they get crash and like you know the VP of sales treats like uh, their ops people as like hey, like you know, do this for me. Usually the results are suboptimal that like, you know, they relent, they say yes too much or they never say no. And maybe they build based on the inputs of like the sales and marketing teams. And then like um, you know, there is usually a bloat of systems and tools that nobody understands. There is no like, you know, enablement that like um, helps like uh, the teams to achieve their goals. So um, that's our observation because the reality of all of this is that the status quo is clearly not satisfying. And in the age of we use AI to build our platform and to get to where we want to get. But AI is also a huge trigger and motivator because in the age of AI, outcomes are disproportionately impacted by data. So maybe three years ago, four years ago, up to then, before AI, let's say, or the new iteration of AI, uh, answering oh yeah, my CRM data is terrible and it's like you know, out of date. And you know, I tried to do something like once a year when we do the sales planning was like an okay answer, like inevitable because also it was difficult to do it. You know, um, I don't think we could have done what we're doing at Skill Stack without AI. Just with automation. I don't think we could have done it. But now in the age of AI, you know, your next competitor maybe is going to actually clean their data, uh, you know, enrich it for real, aligning it to their icp, their go to market strategy. They're going to prioritize like really well all of their sales and marketing efforts and become very precise and so like convert better, grow faster and so you're left behind. And so we think that right now uh, is an unacceptable answer, uh, or like, you know, that's a fact of life, you know that my CRM is, is bad. It should be like what people talk about the, the CRM as like the source of truth, but they don't trust that truth. And so what truth is the truth they don't trust. Um, so that's our observation that um, the go to market ops team needs to be strategic. If they are strategic, they are equal partners in building for go to market and revenues with sales and marketing teams that they serve. And that's where we are winning. When uh, they are overwhelmed and always say yes, there is no way for them to think strategically and actually change the status quo.

Speaker A: Got it. Really helpful and I mean it makes sense. At the end of the day, you Know I think both points you said one around the data quality piece which actually I think is where agentic to your point around like most of us find that super boring. I actually think you could leverage Agent Tick to really help clean things up as a potential first use case. But secondly, I mean you're right around the influence piece, right? Which is you know is revops viewed as a partner, uh, and potentially even right hand versus essentially uh, an assistant, a glorified assistant operations which I think is a key piece of it. What are some of the other use cases that you're seeing have substantial and high impact within those three categories?

Speaker B: I mean the four use cases that we map out typically are CRM Hygiene which involves lots of things. Involves also do I have the right map of hierarchies between the accounts? Parent, child, sibling, Do I have like a clean slate, no duplicated account so that all contacts flow into the right account. So it involves many things. So CRM hygiene is one. The other is account prioritization. So that like involves also many other things like territory mapping, time calculation, distribution, uh, of like the, you know, the accounts across reps and then like you know prioritization of those accounts on a regular basis. An area that I've been like surprised that is uh, growing very fast for us is the lead prioritization. Because my assumption was there are a lot of solutions for this that over the years have been coming to market. But really the same problem exists uh there because I think what is the key innovation of Skill Stack is that orchestrating the data or unifying the data or making sense of data is not an abstract concept. It only makes sense or even enrichment, the word enrichment, it's usually oh like let me buy Zoom info and we love zooming for it's a great partner and stuff. But enrichment is not. Let me buy data from Zoom Info and let me put it into the CRM because like I only want some data from Zoom Info, some data from Crunchbase, some data from LinkedIn and then associate that data to my ICP which zooming for doesn't know, right? Or 6sense doesn't know. My ICP is my ICP which is different than yours. And so the innovation of Skill Stack is really to like associate a uh, wide variety of data points to your, to the data points that you care for the companies and leads that you care. And so I thought that the leads actual space was actually sold. You know there is a lot of lead companies I have to actually lead in their name, but it is not. And, and the Reason is because like even making sense of lead data it requires the understanding of what's really important for you which involves also like the type of companies uh, that you want to sell into and there are part of your icp. So it's not the buy only the buyer Persona is their team is the buying group and is this the right company and are they showing the right signals? And so all of that are part of the lead prioritization. So that's a huge category for us and I still cannot disclose it publicly. But a uh, massive company that um, basically generates lots of leads via uh, forms like form filling for other customers. Uh just signed up on skillstack. So we're going to be embedded in their product to automatically enrich users of their customers who have filled forms. You know like maybe you fill like three or four like you know five fields in that form and then what we append is like number of attributes which are relevant for that customer for their go to market automatically all agentic. And so this is going to be embedded into their product and it's super exciting. Uh, hopefully we can talk publicly about this like by the end of this year. Um and that's very interesting that you know the market is responding to that particular area. So I would say those three as the, are the key. So account, I mean sorry, uh, CRM hygiene account prioritization and lead prioritization.

Speaker A: Anything that you're seeing at all around quoting in that process, the quote to cash process.

Speaker B: We are, we uh think that there is so much to do just in the top of the funnel to keep the troops well aligned that by the time uh we get so down the funnel um, we will eventually explore those things as well. But it's an area that we haven't yet. Um, our platform is totally modular and so like you know we have integrations in and out of the go to market stack. We keep adding them. So like we are like an API first company. So uh, integrating new things is natural and it's part of the design of the platform. Um, it is an area if we do a good job in solving the top of the funnel, naturally we'll go further down the funnel.

Speaker A: Let's talk a little bit about the evolution of agentic and just AI uh in general. Where do you see the next one, three and five years And I put it in those categories because I think things are changing rapidly. You know, high level. What do you see some of the biggest adjustments that are going to happen during those times and you know, I know get some of this is prediction based, right? Which you can always get yourself in trouble on. But I'd be curious from your viewpoint, you know, you're seeing things firsthand, uh, you're not just hearing about them. What, what would you, how would you categorize those things?

Speaker B: Uh, I started my career in uh, mobile. Uh, so in the early 2000s. I, you know, as I told you, I was born and raised in Italy. I started my career not in Italy, I never worked in Italy. Actually it's interesting, after college I moved to Spain and started my career there as a consultant, but working on many different engagements all over Europe and Latam and other places. And it was a company that was specializing in telecom and media. And so this was at the beginning of the mobile revolution, like you know, late 90s, early 2000s. And back then we made like all sorts of predictions for our customers that some of our customers were laughing about. Like one of them was, oh, like there will be more phones than people. And at the time, like the penetration, it's called penetration of mobile phones was, I don't know, 10%. So only 10% of the people had a mobile phone. And we were saying we think there's going to be more phones than people and customers. Like ah, this is impossible to base the business case on this. The reality is that like there are more phones now than people, meaning like connected devices and like, you know, everything is connected. So they are like, I don't know what's the penetration now? It's probably like you know, five times or more.

Speaker A: And if you think about watches, um, you know, anything. Right?

Speaker B: Watch anything. Yes. So like each of us so. Exactly. So if you think about the person like each of us has like multiple devices.

Speaker A: I mean it's just. Yeah.

Speaker B: And so another thing was, oh like you know, people will consume content on their phones and like watch movies and I remember we put together these slide decks which were like, you know, unfortunately I don't have a lot of that stuff. But like I remember like visually the visuals were like, you know, terrible but they were trying to imagine the future. Remember like phones were not like uh, this device. Then we're like, you know, flip phones really bad. Like, you know, they were super small also. No, so like the trend has been like the phones are bigger because we wanna. And so the point of all of this is that all of the things that like the wildest things that we were imagining back then in mobile phones have happened and more. And so I have no reason to believe that the wildest things that we are imagining On AI won't happen. Maybe they will happen in a more refined format. Maybe they will happen like, you know, in a slightly different way. But I do think that this is like a technology that will change completely the way people work. Um, I think as humans, I mean, I'm an optimistic obviously, you know, since I'm building this, I'm less concerned. Like even like for instance, like uh, if I think about robots, we start imagining robots as like a uh, person, right, with arms and stuff. And it's sort of like, I think that it's, it's actually assuming a polyform way. Like, you know, the robots that we're seeing, like we serve a company, Pat Robotics, they build robotic cars. So it will be different. It won't be like the robots, uh, like, or like a person that walks. It will be a different type of thing. But so my point is that like everything will happen, it will happen faster than it's happened in mobile. And so my message is, we better get ready. So we have now adopted AI, like deep into the company. We expect every team to leverage AI to do every work that they're doing. And the way we are hiring also is changing. We are hiring people that are like, maybe not engineers, but they are very curious about building because like, everyone needs to build in, in this new world. And like, that's how you're going to continue to add like value, uh, for yourself and for the company. You need to build stuff, you know, even me now, I have like a built workflows for like, you know, recording my calls and then like extracting the insights and then having my little like, you know, agent that like suggests and recommends ideas and I becomes, I become much more powerful in like, you know, engaging with customers filter through my brain but like with tools that like empower me and make me go faster and you know, work better. And so my advice is that like, you know, for instance, I'm from Europe. Europe instinct is like, oh, uh, let's put like constraints on AI because we are afraid that like, you know, impossible, uh, inevitable, like Europe will be left behind if that's the mindset. So embrace it. Learn like, you know, use it, learn how to use it in, you know, a lot of different, you know, like, uh, aspects of your life. Because it's inevitable.

Speaker A: Yeah, I think it's some. I love you hit on a couple interesting points. I think it's good you paralleled back to, you know, the, the mobile phone revolution and some of the predictions there. Um, and I do think you're right. I mean the rate of change now is substantially faster.

Speaker B: Yeah, I think in the next five years it you know like uh, completely changed. So like I don't know, like I'm surprised like you know, so you asked me one, two, three years. I'm surprised about like what we are able to do now that we couldn't do six months ago.

Speaker A: Yeah.

Speaker B: And so I, I start to see like you know basically agents talking to other agents to agree on a course of action. Um, and super agents that coordinate the work of other agents. So it's uh, it's and like you know, let alone like the building of like you know the cycle of product development going much faster and iterating. So I think the death in the next year, so like speed and acceleration in product development like we've never seen uh, two, three years is like complete pervasiveness in all aspects of the work uh, of a company. Um, you know, uh, we still spend, I mean I have my podcast as well. I think we do still like way too much work manually. And like I just started like fidgeting with TikTok. I mean you can already see like on TikTok they give you like it's basically like a suite of editing capabilities pre built, you know. So uh, I think the next two, three years will be like a complete change in the way we work. Uh, you know I think I read it or heard it from someone like software like uh, change and transition people from paper to digits and so meaningful change. No, like you know we used to have a ton of paper on our desk uh and now we don't. And that's how software change work. Now it's like going into like manual people. Human workflows are going to be completely automated uh, by AI and so um, that's what we need to be prepared for. Like you know, how can I leverage AI to become smarter, to go faster, to build better. Uh, because that's, that's basically how it's going to happen if I still do things like the old way. Like uh, it's just not going to be the same level of quality.

Speaker A: Couldn't agree more. And I think you're right. I think timeline wise it's, it's amazing. People that haven't aren't actually like looking at it right now I think would be shocked at what's possible today. And to your point. Well it wasn't possible even six months ago. I think this agents, you know, managing the agents and communicating the agents, that, that capability just actually you know, just made things even more exponential even in and of itself. So, um, last question I have for the day and this has been really insightful and fun to talk through. You know, you've. You started at quite a few companies. Now talk to me a little bit about like, lessons learned. Some of the keys to success that you've had within doing that. I mean, now you're on. I think this is your third startup or fourth startup. I can't remember.

Speaker B: Fourth? Yes, fourth.

Speaker A: So, yeah, talk a little.

Speaker B: Two exits. Two exits, one failure and skill stack. The jury's out, but I think we're doing fine. Um, lessons learned. I think that, uh, I mean there are many. But I, uh, would say like, for like, uh, for people that are starting, it's, you know, you've heard this already. But like, uh, the. I suffered because I started a company that was not a painkiller, but it was more of a vitamin. And that's true. Uh, I think that like companies, I mean startups are companies eventually. So like, if they succeed is because they become companies and not just startups. Right? So. And a company needs to have a business model, meaning that someone needs to pay for whatever you offer and if the probability of paying are lower because the pain is not so like big, so widely spread and you know, um, but it's more like, oh, like, yeah, I need to take some vitamin every once in a while. And there is a huge vitamin business industry out there. But like, uh, uh, it's very different in size than the painkiller industry. So think about that first, second. Um, I think always and especially in the age of AI, like, you know, where we can build so much faster, always work backwards from customers and not from like uh, your ideal building. I think that another mistake that I've made is like to get enamored with technology and then build towards like something while like, I should actually. You should actually always start from a problem, painkiller problem, and work backwards leveraging technology. So this second, third, I don't know, like, I've done both. I think, like, um, you know, now there is like, how you can build, like solo, uh, founders and stuff. First of all, like, it sounds boring. I mean, it sounds like, you know, I'm a social animal and I prefer to be with people that alone, maybe that's me. But eventually, like, when, you know, things like uh, become more complex and complicated and like you feel like lonely building by yourself. So my advice is actually to always have a co founder. I've done stuff with our co founders and like, you know, the results have been less exciting. And probably suboptimal than when I've had a co founder. Um, and then maybe the last one is about the fundraising. Um, it feels that there is like, uh, this pressure to fundraise, um, that I find silly because investors are super important and they help you accelerate. But, like, be aware of like, the money that you accept because it's very costly and like, you know, it can distract you tremendously during the process. And even after managing investors, investors is like, you know, part of the job. Um, so I would say like, uh, yes, raise. If you, you need to raise, raise only the amount that you need for the next iteration. Uh, and not a dollar more. Because the vanity metrics of like, uh, oh, I raised like X, you know, will come. And I mean, all of the companies they raised in 2021 are struggling, or a lot of them, you know, close shop. Even if they raise enormous amounts of money because it was totally. And they did not need that money for the level of development they had back then. So those are like, I mean, before insights that I can share the scars to prove it.

Speaker A: Those are really good though. And I, you know, I love that you hit on a couple of the key areas. And it's interesting because I would agree with you on the, you know, I started my own tech company and 10 years ago by myself and I vowed I would never do solopreneur thing again. So, uh, completely agree of having other people in the trenches with you can take things a lot further. I think it's just the teamwork mentality. Uh, and I like the not being too enamored with the technology, focusing more on the problem. I think that one, especially even in the stuff we've been talking about today, you and I could geek out on this stuff for days, like, oh, this looks cool, and we could go down this route. Uh, and that's fun. Uh, but it doesn't always lead to value and outcomes, which is some of the big ones we talked about today. So appreciate you sharing the insights. Uh, thanks again for joining today. This has been a lot of fun.

Speaker B: Thank you, Jesse.

Speaker A: For those of you listening, uh, if you aren't following the podcast, please click, like, subscribe. Uh, also, Elio is on LinkedIn. He also has his own podcast. What's the name of the podcast again?

Speaker B: Revenue Engine Masters.

Speaker A: So, uh, you know, check that out as well. But, uh, thanks again for joining and I hope everybody has a great rest of the day. Thanks again. Talk to you guys later. Cheers.