What World-Class AI in GTM Looks Like | Kyle Norton, CRO @ Owner.com

Kyle Norton is the CRO at owner.com where he's running what might be the most AI enabled revenue team out there. In today's episode he shares how the right tooling can completely shift the economics of your sales team. If your BDRs now talk to 20 decision-makers a day and are booking those at like a 14 to 16% rate, now the economics of a BDR are completely different. The closed one ARR divided by the BDR compass like well over 10x now. But despite being heavily focused on AI, Kyle pushes back against the popular advice of simply buying your team AI subscriptions. And he shares his experience of how centralized implementations have been the most powerful way to create results. The production quality between what this applied AI leagals and what like a very AI savvy AE would build is not like 50%, it's not 100%, it's like an order of magnitude. Plus we cover the specific area of your funnel where you should be focusing 80% of your AI efforts today. And Kyle has a reality check for leaders who claim they don't have the head count or budget to innovate with AI. There are no excuses anymore. Your CFO, penning pinching is not an excuse, your CIO giving you friction about whatever is not an excuse like you just have to welcome to top line. Hello, welcome to top line listeners. Today we have a special guest. Yes, you all know Kyle Norton, the revenue leadership podcast. And of course I'm joined by my esteemed co-host Sam Jacobs, CEO of Pavilion, Austin Zaman, CEO of Sales Talent Agency. Welcome, welcome guys. How are we doing this morning? This is guy, I like this energy, AJ. I'm doing okay. I've got a board call in 22 minutes, so I'll be leaving a little early, but otherwise good. I got my first open-claw agent installed in my Slack, and I talked to it and it's very similar to talking to my executive assistant. And I don't know what that is about either. I had a clawed code this morning. I was copying tokens from GitHub, and I put it right into the chat. And then at the end it was like, you need to refresh your token. Don't ever do that again. Don't put that in the chat because apparently that just streams out to the world. So later in the conversation, it's like, hey, can you give me your token again? And I was like, wait a second, are you tricking me? And I said, no, I'm not going to fall for that. And I was like, ha ha, got you. And so it's so funny. The personality. Yeah. And then I was messing it around. And I was like, hey, clawed code, can you change the font to wing things? And it just said no. That was its response. And I was like, no, you're telling me no. And it was like, if you change the code based to wing things, it's completely Jewish trash. I was like, not the code, the font, come on. I like that it has a personality. It's cracking jokes with you. Yeah. Here we are. We'll use a.env file for tokens. Yeah, you have to do some things that I had no idea what I was doing, which is open up another terminal window and like create a URL. And then secretly do that. And then it's like random places that novices like the four of us are putting our credit card so that we can like spin up these instances. It's like I've got a dev ops thing with a Lestio. That's where my.env file is Kyle. And you know, somehow I gave my credit card to AWS at some point. It's just like, I was like, I might need something from AWS. So I just, it's funny. I'm waiting to see all the charges before I can test them. Yeah. Kyle, what are you up to these days? Trying to grow owner.com. That's that is focus number one. And the podcast is like a fun side thing. So I get to talk to interesting people every week and learn about what's going on. I'm a bad podcast businesser because we're poorly monetized and that that it's mostly an education project for me, but Austin's trying to make make that get a little sharper. Thinking about just things are doing right now in terms of AI. Like what's it? What's top of mind for you right now? Yeah. What's working? Like you're, you're probably running one of the most AI enabled cool market teams intact. Like on the SMB side, I can't imagine there's another company that's like right there where you are. And so you add the edge, which means you're figuring out what's working and also what's not working. So let's start there. Like what's actually working at this point for you? Like of all the things you've tried, which ones are giving results and which ones are like, that sounded cool, but generally go anyone. I'll start from the foundations because what we're doing today and getting value from is built on a bunch of foundation that you need as a baseline. The recommendation I give to everybody is start with data. You have to start with good first party and third party data. Without that, nothing really works because you can't give the models what they need to make smart decisions or give you good outputs. Do you mean like data on your ICP? So like, hey, who we're reaching out like contact data? Or do you mean like broader than that? Like more data than that. So there's two parts of data foundations. Third party data and first party data. Third party data is having a map of your entire market to start. So what is what does your entire tam look like? What is your sum within that? And then being able to go account by account into those sum accounts. So you're serviceable obtainable market. The companies that you think you can get on a call with and win today. Not your like aspirational fundraising tam, but the actual customers you want to work with today. And being able to enrich every single one of those accounts with the information that you need to decide who to talk to now and how to prioritize that market. And so that's going to be different for every business. But some of that is off the shelf data you could find from zoom info. We sell to local economy, like rest little restaurants. So that data is not in zoom info. We use data lane for it. And then some of it we've built custom scrapers and enrichment flows. The prompt I give to folks. And I just had this conversation yesterday with somebody building the infrastructure is like, go sit with your very best reps and ask them when you sit down for a demo. When do you know you're like, I'm going to close this one. Like this is going to be awesome. Like what are those little triggers? I was like, well, when I go to their website or I look on LinkedIn and I see these three things or they have this role that they're hiring for. I know that it's going to be like very timely conversation. And you basically extract all of that out of your your like PMM materials. What is your ICP? And then what the reps says they want as like their perfect customer of every demo could look like this. What is that? And then you have to figure out what's the digital footprint of that? It's like, oh, I want companies that are rapidly scaling their engineering org. Okay, well, that's an easy one. You can you can look at how many open roles there are on their cruise page for engineers or it could be something that you're backing into a little more circuitously. So like we want to know how much volume are you getting on the third party delivery apps? That's not readily accessible. But you can see how many orders how many reviews did that customer have on month one? How many reviews did they have month two? That gives you the number of new reviews. And then I can say, all right, we'll 10% of people leave reviews. So I I'll take that review number divided by 0.1. And that's the number of orders that are getting placed. And so you have to build that system yourself. And whatever this looks like for your business with AI, you can you can make those decisions. And then you need the perfect mapping of who all the contacts are in those in those places. Mobile phone numbers is like absolutely essential in today's world. And then some detail to tell you for each of those people what is important to them in regards to the problems that you solve feels like an everyday iteration I would assume Kyle. Yeah. And not just everyday enriching and updating the information, but actually every day making that scaffolding better. So our our the first model that we built to predict how big or small this customer would be. We just completely overhauled it. We hired a new senior data scientist. And this was her first big project update that whole model and make it better bring in more data. And then we're we're creating new models for lead scoring. We built a model that can predict if a lead is likely to pick up or not or head of apply a AI. Built a score that tells you what leads are likely to pick up or not. And if you call high econnect leads, they pick up at like a 2.3 x rate, like a normal lead, which is insane. And tight next does this as a product. Can you frame like the net effect of this on your go to market? Like once you got this data foundation in place and you've been able to do some of these things, what was the actual like numerical benefit you guys had? Well, the original BDR BDR team that we hired would not have been able to ever do cold outbound. In our we are ACVs like 10 to 12k high velocity, but our original call to decision maker connect rate was like 3 to 4%. So if you made 100 calls, you're only talking to four decision makers. Maybe you book one opportunity a date, maybe. And then you know, you're closing your ACVs like a BDR motion, just the economics won't pencil out. But if your BDRs now talk to 20 decision makers a day and are booking those at like a 14 to 16% rate, now the economics of a BDR are completely different. The closed one ARR divided by the BDR comp is like well over 10x now. - This idea of AI being a use case for anyone for everything, it seems like you have and your team, AI ops, whomever have been able to build the AI foundational levels and then you're giving your team the BDRs whomever, the very specific use cases of how to use it versus giving them AI and giving them open book. Is that fair to say? Because you have to have like predictability and revenue. So giving them the use cases, so you know exactly what you expect to see and outcomes you see from those AI use cases. Is that because we've struggled with this where it's like, or we'll build something that's AI enabled and we're like, we wanna go give this to our team and Ryan or CRO is like, no, you have to have really specific use cases to the team, you can't just hand this over to them. - Yeah. So, and this is the newsletter that I wrote on my substack then we republished through Pavilion through the top line newsletter. - Everybody's interested in this. I'm getting text messages. I got a random text message from somebody saying, hey, what do you know about decentralized AI? - What the hell? (laughing) - I'm so ready for me. - We're shaping the conversation, guys. We're shaping the conversation. - Yeah, this is by far the most popular substack I've ever written in, in IA. Got so many messages about it and people wanting to talk. And if you're listening and I haven't responded, I just don't have time. (laughing) - I'm happy now, this is why this is the point of the podcast. - Yeah. So this was an emergent property for us. This wasn't a pre-planned strategy, but it just sort of like came out of how we approached it. And so there, I see there's two camps in how people are trying to bring AI into go to market. One is a very decentralized approach, which is like give everybody a lot of counts, tell them to build cool stuff, encourage them to be on the cutting edge and learn these skills themselves. Everybody in the entire company needs to be AI native. And that seems really good on paper. And I was trying to push AI adoption a lot, maybe 12 months ago. But what we really found in practice is that all of the best things, all of the most impactful implementations were all highly centralized. So it was basically our VP of Bizzops, me and our VP of Rebops for a long time were by far the most interested and on the cutting edge of learning this stuff. And we would just come up with these ideas. And this is really like our VP of Bizzops and data who did most of the building. And we would build something centrally and then just deploy it to the team in the experiences that they already use. So we didn't like vibe code a pre-call prep app because I don't want additional surfaces. There's already too many surfaces that a rep lives in. We would just give them that information in Salesforce, in Salesforce. Like now we've got this apply AI lead who builds all this stuff. The production quality between what YG builds and what like a very AI savvy AI, AE would build is not like 50%, it's not 100%. It's like an order of magnitude. What YG builds is 10, 20 times better than what the really savvy AE might build. And so you just get a completely different result out of it and you can properly enable your whole org to adopt it. And you don't have this like soup of custom gems and custom GPTs and all of this stuff. And we have found that that has had driven like far better business results. You see this like enthusiasm. So if you're lucky, you have people that understand that they have to play with AI and they're trying to wrestle with it. And that's fantastic. You have the right people. But if that's all you're doing is this decentralized thing which you end up getting other benefits of like a really pro-consumer. Like it's the consumer benefits of AI that you really feel inside your businesses. Your emails get written. You know, maybe if the person has good taste and they're not sending bad emails, maybe they're sending worse. Like you get like the meeting notes off. You get like debts being built a bit faster. You get like certain things happening. Sometimes faster, sometimes better, sometimes a little bit of both. But you don't get like real business use cases. Like the ones that you can say, ah, this moved the needle for the business in a really substantial manner and is ready for prime time. Like that ready for prime time thing, you just don't get it. I totally agree with you and I'm sure what Kyle said is correct. But also one of the things I'm seeing inside my organization is you do see speeds changing. And it is getting strange when you have certain people who are, you know, you can call them cloud native or co-work native, who are able to process output, just regular work output. Like what's the outcome of this? I miss a good example of this, but yeah. And like the bits and it's weird because when you have the disconnect of some people that haven't, they haven't adopted it personally. They're now slowing down the people that have. And so there's a group of people that read, I forwarded the ClareVo article, AJ about, you know, if you can't do something in a day, you're not gonna make it. And you feel this tendency for certain people to want to defer, you know, they think things should take weeks instead of days. And even though I do agree with Kyle, like there should be centralization so that you can build high levels. - You need both. - High leverage roles where you're making decisions that have a broad impact. So if you're in revops, enable men, if you're in marketing, you have to become AI native just to do your job at a different speed. But when it comes building the core systems to accelerate workflow, that should be centralized. And so I don't really need my account executives and BDRs spending a bunch of time in cloud code and building stuff because they are a role that has like a multiplicity. Like there are 60 of those people in that role or 40 of the people in that role. Those are core systems that need to be built centrally. If you are the sales enablement manager or director, you need to be able to figure out how to make your organization AI native. And it's interesting like almost everything that gets produced now, prototypes, like the initial versions of things like are all cloud code artifacts. Like in our organization, we wanted to cut a bunch of data to make a smarter decision about how we're managing the launch organization. And so the revops person pulled everything together and the output was a cloud code artifact that you could like pull the dragger, you could change things and it would just update. So it's like a better version of a spreadsheet now. A way more usable version of like a massive spreadsheet. And that does need to become the default if you are in a role of any leverage. So what should you build centrally? What could be on the edges and just like vibe coded throwaway apps or artifacts? And this extends to build versus buy. I think like the core intelligence of the organization needs to be centrally built and managed. And I don't think you want to buy those solutions. And so I was at the Clay CRO summit on Friday and this was a big topic of conversation of like what are people building versus buying? What should I have internally versus not? I think you have to own the intelligence. You have to build the intelligence internally and buy things that are like workflows and user experience. And so the example on the sales engagement side, I haven't seen any of the sales engagement providers build an AI product that does any of the intelligence stuff. Like, oh, this is the next best action or you should do these things with these deals. I'm yet to see those companies produce something that is like all that viable. I'm curious if you guys have seen anything different. But they're a good place to inject the outputs of the intelligence layer that you built internally. The deal health score or your estimated win rate that prioritizes leads. You inject those things. You do the intelligence. You print that somewhere on a lead record. And then the sales engagement platforms, those are good experiences. I wouldn't try to recreate an experience because you need rock solid stability. You don't want to vibe code the thing that your reps use every day, even though it could be a little better. Because you need rock solid stability. You need the support ecosystem. You want something that's familiar to them. So I think it diminishes the value of the sales engagement platforms because it's just like a dumb UX now. But I think that is the way that companies should think about this interplay. What do I build versus buy? You want to build internally the core intelligence. Yeah, the sales engagements are really interesting point given that their data all lives inside the CRM. So they don't really have data as a mode because they're not really doing the enrichment aspect of it. But the workflow distribution part of it, I think is what is being still very undervalued in the market in terms of what SaaS really means in 2026. And it's, this is just a really fascinating conversation in the sense that for a quota path, and similar to how you all are thinking about it, you have this central core intelligence engine. And then you basically bring everything in with the MCP, which is. APIs for agents, just API, if you bring it in. And so now we have dust frames for the pre-sales demo, dust frames for the handoff notes, dust frames for the executive, I'm gonna talk to one of our customers and I have the total timeline there. And so you kind of templatized the data and all of the use cases very specifically for the organization, but the workflow management tools we'll even call Gong as one of those, those remain because that's still a really important and central part of the workflow. And so I completely agree there. - What's interesting here is that these are three very different sorts of businesses. So, Carl, you guys have raised a lot of money, you have lots of revenue and so you have resources to invest into this. And so sometimes people will listen to you and be like, yeah, you can do it. But what about us, right? Like at an earlier stage in the life cycle, but ages businesses, you know, at a different station at Slive Cycle where they're earlier on in their revenue journey and they've been able to make a lot of headway. So, AJ, can you talk? And then there's ours, which is AJ, you have venture funding that you can invest into this with an eye on the future. STA doesn't have that, but we have our profit that enables us to also have some sort of a centralized view on this. And so we've also stood up a centralized operation to think about the intelligence there as a central component and building something that is actually modeled a bit awful what you've done, Carl. Like a bootstrap profitable business version of that is what we're trying to build. And it's really interesting. I say you can do a lot more than you think and the appropriate thing for your stage. So, AJ, can you speak about, you built that AI optional early on, you've done all these things, you've invested different to Kyle from a magnitude perspective, but you've gotten a lot of ROI. What has it taken? Like give us a flavor what it took to get what you've got. A lot of existential crisis by a founder at night, honestly. I mean, I'm serious where I say like every day I'm having to reinvent and rethink and relearn everything that I'm doing and so is my team. And that's really an important part of it because at the end of the day, I don't think a quota path as a product today will be the main revenue driver for the organization in 12 months. I think the product that we're gonna release in the next two months will be the thing. And I have to communicate to my team that this innovator's dilemma is not just really, it will happen. And that's what's ultimately the most challenging part of this whole entire thing is how do I tell a team of 70 people, including individual contributors of like, hey, we're gonna die. Sorry, like that's just the reality of the situation. If we don't innovate, I mean, that's all startups, but today that's all roles. That's all responsibilities. Every company is going, has to go through that. - Maybe insane for us. - The idea to think that the way that we do things today, two years from now, three years from now, that's the job. That's where the imagine in our world of all the things we do, where does the value of crew, like what are people actually paying us for? That thing will completely change. And so if we don't change, then we'll miss the boat. And it's happened in previous platformships. 10 years from now, I want to build a company that sustains boss, my career. So 10 years from now, do you think SDA is making money doing what it does today? Highly unlikely, highly unlikely. That's a really strange time, right? Life's strange things to think about. - I sort of want to challenge the premise of like the previous question a little bit. Like, oh well, owners raised a lot of money, so it's different or wereboots dropped or whatever, because AJ's got it right. If you do not become AI native in terms of the product you offer and how you operate as an organization, you will just get swallowed up. Because either it's like, AI native competitors come and surpass you or competitors that make this pivot, or incumbents that can build the feature that is your company so easily. And I think you need to become the intelligence layer as much as you can. And I think for quota path differently than the sales engagement tools, there is a differentiation opportunity in terms of the intelligence that you have across all of these customers to provide unique insight and unique intelligence that is not just a UX. If you are just a UX, if you're complaining to drag and make drag and drop easier and see pretty visuals, yeah, that's not the way of the future. But if you can-- - Well, the thing that you said that's so interesting when I went earlier on, like, I haven't seen a sales engagement platform that does the insights well. Now for quota path, we release Atlas and it will ultimately say, these are the three things that you need to change with your comp to change the behaviors of your team today right now. Quarter to quarter. What we're seeing with-- and I'm gonna write the top I newsletter on this, AI native companies and commissions are changing their comp every quarter. There is a 250% higher chance that they will change their comp every quarter because their pricing models change every quarter. And that is the thing that is dynamically the world is changing. - How are they changing their region? Like what variables or things are they playing around with the most? - The usage and credits and outcomes based pricing is what's changing the most in those organizations. They're still trying to figure out margins in terms of what makes their business actually viable. - Are they actually viable? So is the structure of the comp plan the same with the percentages of what they're playing around with a little bit? - Yeah, there's basically what we're seeing is that a lot of these AI native companies are going to enterprise a lot faster. And with a CFO and an enterprise, you have to have much more prediction around what you're gonna be paying and they don't like usage based as much. - We will also-- - Another thing we find in is that there are a basket of AI companies that are just growing so absolutely quickly that they don't have to offer commissions at all to their salespeople. - 71% of companies enter the year without quotas. 71% of all SaaS companies. The reality is, AI native companies sometimes don't even have quotas. Like they're, and I've had this conversation with Matt Braille as well where you have sales cycles that are like going from 12 months to three months because the buying in the budgetary is opening immediately. And they're not even being able to set quotas. So they're setting rates and percentages of it, but not as a-- - I don't think that's the variable that's affecting whether you pay commissions or not. Whether you pay commissions or not is just dependent on one plane is the value of your stock options. So if I can give you stock options that it's just growing at an astronomical rate, imagine and profit, right? We know people that are in profit that have just made like the sort of money that a CRO would make after like the one of the largest exits ever. - And they'll make that as an AE. That's the sort of equity that-- - It's a CRO Monday.com, Monday.com infamously did this very first. They didn't pay commissions even as a public company. - But that was like-- - Or-- - Yeah, yeah, but their equity actually wasn't valid enough for that. So were you able to attract the best people and retain the best people if you were Monday.com with that stance you want? Like it didn't work for them as well as it does right now if you're in profit or open AI. So there is a basket of these AI companies that have the sort of growth where they're like, listen, you'll have a really solid base will give you equity and that equity is just from a CRO at astronomical rates, you will be rich, chill. And that that can work. If you are like anything below the fastest growing AI companies in the world, you can't do that. - In history. - In history. - Yeah, in history, you have to be one of them. Otherwise it doesn't work. You're gonna see talent density drop. And so everybody else has variable components but not how do you structure it. Then that's where that conversation happens. So you need to reinvent yourself as a business that is a prerequisite but you have to make these investments into the AI capabilities in order to do that. And so people will hear me talk and like, oh, I can't afford to get higher like a fancy apply day eye leader. And we've got, I don't know, three or four people in apply day eye now. We've got a ton of open roles for that team. We're just stuffing people into that. Okay, that might be a little unique to the owners and vanties of the world. But you can have one person. You can find head count elsewhere. You have to force yourself to do this. Go and do the stack rank of your organization and figure out who are the people that if they walked out the door today, you'd be like, oh, okay, we'll be fine. And you're just gonna have to use those folks who-- - S.T. is it to do it? Like if S.T.A. can do it. Which is we don't have venture. We are a business that has no referring revenue. Every quarter starts at zero. Like we can find it. We were able to find it. And it's great. What's the walking approach to this? And then once you start seeing momentum with it, then you can build around. Now you're like, oh, I can add a second now. 'Cause like the first one's the most important investment. After that, it kind of like just convinces you on its own. - It's so obvious. - It's obvious. When you look at the projects that that team is shipping for us, it is so obvious. We will just hire people as fast as we can into those seats no matter what. There will be no budget conversation. However many people we can bring in to that have that type of skill set. And let's talk about what skill sets to be in that team. But like I'm gonna channel my Inter-Jason Lemkin on this. Like there are no excuses anymore. We've been doing this for coming up on-- - You were dying. - Yeah, I'm a deli. - Like that, yeah. - That is, yeah. - Like you have to go carve out the budget and people are like, oh well, you know, my CFOs type with Per strings or blah, blah, blah, blah, it's like, I don't care. It's like go, go find a way, like make, you know, make room in your org chart, move some people out that are your bottom performers and spend the money on somebody who is truly world class at this stuff. It will be the best returning investment that you make by bar none. With this, this interesting question of centralized versus decentralized, but then debatement versus company, right? So like, do you centralize it company wide? Do you centralize it department wide? Like how do you think about the intelligence layer between those two components? Where do you centralize it? - It's gotta be company cow, right? - Well, I don't know, but product and go to market are so different. - Yeah. - Okay, that's separate, yes. That's 100% separate. I think like the AI you're applying to how your product team works and the AI you build into your product that is a separate endeavor. And basically it's the point, like if you're not a totally AI-pilled engineer now, like you're gonna lose your job imminently because everybody else who knew is gonna-- - If you're still writing code like manually, like if let's say, if 20% or more of your code is handwritten still, you are very behind the market. Like that's where you are. Like you're kind of at that point, where if you look at the 10% of the best engineers in the world, they're not writing code manually anymore. Like they're just not. - Yeah, I don't know what the specific benchmark is, but-- - It's literally that. Like it is that if you're doing more than 20%, it's a problem you shouldn't have to. - Yeah. And so let's just say like, okay, product is over there and then everything market facing from marketing all the way through the customer support is a separate basket. I do think that that needs to be quite centralized and you need central data management and intelligence building that can serve a bunch of these purposes. One of the other reasons that I don't want my intelligence to be in these other products, I don't want the intelligence engine to be in the sales engagement tool or be in my CS platform is I wanna manage those centrally and I want everything to be able to come back to the same sets of skills or context files or prompts that then get shared centrally. So that you have this-- - So that you've been available on this in a services business. So like contrasting product versus services businesses. I think I've thought about this a lot. If you're a product business, your ages, I think you have to separate these two things. So you think you centralize by core department of the business. So you say product is its own thing, this is its own thing, you don't centralize. It doesn't operate from that. And you wanna give people the chance to experiment on their own, but there's gonna get interesting ideas from like the edge of where people are experimenting. Your most AI build, whatever is gonna bring ideas to the table, but you wanna centralize things that go into production. If you are services business, you probably need to centralize centralized a lot more because your kind of your product is the people that are going into the market and solving that problem. So if you're a censure, if you are a law firm, like your product is the humans and it's like intertwined between product and go to market. Like in the services business, those two things are highly intertwined. And so you centralize even further over that. And so that's an interesting distinction on how like you would effectively build it here versus that. How are you picking the problems that you decide to focus the resources into, right? Like you've got, and at this point, you probably have all these ideas of like really impactful stuff that could happen. And so how do you pick between like A versus B, this is what we don't go after? Is that just taste? - So the framework really needs to be identifying the most important business problems to go solve. Like people will randomly be like, "Oh, like I saw this cool thing on X," right? So I got this idea from a buddy. And, or like they just thought of this idea on their own, like I could use AI for that. And so you end up doing a lot of these quality of life, like little itty bitty things that are not all that useful versus forcing yourself to think about, okay, what are the most important strategic priorities for the business? And then what are the ways to solve those business problems? And then where can I apply AI to that? And so I have this like five P's framework. So first you wanna map all the possibilities. Like what are the problems you wanna solve? And what are the possible ways to solve that problem? What's the payoff? So if I choose this possibility versus that one, what's the, if that works, what's the payoff of it? Is it a million dollars and more ARRs that's saving 200K? What is the probability that that works out? And if you take payoff times probability, now you get expected value. A lot of this is like a riff on anti-dukes stuff from how to decide and thinking in bets. And then you have to think about the perspiration. Like now what is the effort required to make that, make that initiative work? And then you can pick, then you can choose what you wanna do 'cause you obviously want high payoff, high probability bets, possibilities that have low perspiration. And so that you can either use as an actual framework or is just a heuristic and you keep back to yourself and you think when you're thinking through problems, okay, like these are the options and like, well, like what's the outcome if we get that right? Well, it saves every rap like 15 minutes a day. Okay, like no, that's not that compelling versus, and I think 80% of your AI efforts in your early innings should be pipeline focused. Pipeline is the, you talk to any CRO, any founder other than the fastest growing companies in history, what's their problem? If they had more pipeline, things would be better. And AI is a really good pipeline, pipeline tool. And so once you get your first party and third party data foundations in the right place, now what are the options to go do that? Is it about picking the absolute right accounts and knowing what time to reach out to them or knowing what to say to them on a cold call or in an email or using AI to generate like compelling artifacts to send to those customers? Like we have this AI website greater that's like the world's best lead magnet. That's an example of building AI as a pipeline driver. - On time, at that point, your AI initiatives is it relates to pipeline? Like how often are you looking at the different channels? And are you measuring then cost of acquisition to that? Are you adjusting them on a weekly basis and the different channels? Yeah, you're constantly looking at this. Has you feel like it's revved up more as a CRO prior to AI? Or is it about the same? - Way more. - It feels not the same way more. - We have tests that are happening every single week in terms of new ideas. And I would say 80% of what our AI lead does is pipeline-focused. We actually had multiple tests happening last week. You know, small things. Like what hours of the day should you be calling? And so we like built some tests to figure out, okay, how do we control for dial volumes in person and figure out exactly what times of day should we be calling? How do we structure the team so that then we put meetings? Like, you know, everybody has to have some meetings. We put meetings in the parts of the day that are low connect rate. And we really make sure that the team is like, as revved up as possible for those high connect time periods. And you can just take every little piece of your funnel building motion and figure out how to improve that. Like time to first touch. When somebody fills out a lead form, how fast do you get to them? How do you use AI to make that better? Well, make sure that the rep doesn't have to do any research before picking up the phone and calling. So AI does the research and fills in the information and hand delivers to the BDR exactly what they need. Just the three things, not 20 things that you could say on this cold call. They just need like two or three. And then you can experiment like, okay, let's try a different pattern interrupt. Let's try a different offer of value. And you can start to get really intelligent about that system. One more thing, like you can't do that unless your first party data is good. So we talked about the first party data piece, which are the third party data piece, which is understanding your market, the accounts through to call contact information. First party data is, do you understand what's happening in your customer journey in a deep level of granularity so that you can do these tests? And that's where we use momentum so that it scans every call and fills in all the fields and we can write prompts and then back tests and backfill from previous calls to have this level of intelligence of like, when people ask these questions, what happened? And when we give these answers or we rolled out new pricing so we've got all of these prompts that are set up to tell us if the new pricing is working or not, you need that infrastructure as a baseline. And I like to tell everybody use momentum if you're like slightly bigger. - Do you still tell them, by the way? - Congrats also as an advisor, but I'm just kind of being facetious and joking about that comment. - No, I actually think we use momentum. It's free product, of course. - I actually think the Salesforce acquisition [BLANK_AUDIO] And I was like a big supporter of that on both sides of the fence actually because it'll just help them get deeper into making Salesforce be like the best system possible and they'll get a bunch of advantages now over all the other call recording tools that's that's going to be a big block. I went from being a bear on Salesforce 24 months ago to being like very bullish on Salesforce now which I never thought I would say because you know they bought bluebirds momentum qualified in formatica they're all in on AI and I think the way the ecosystem is going to move is actually advantages Salesforce as like this pivot point of where all the integrations are where all the partners are where governance and roles and permissions all sit I actually think it's going to work out pretty well. It's interesting but just they have so much revenue like to move a 40 billion dollar business you know if you want to grow that at 30% 20% like that's a lot of new data fine. And so that doesn't mean like if in the public markets they aren't able to show the sort of growth that is going to be amazing in the short term for their stock it doesn't mean that from a value provided to a CRO and a Robin ops person and hence the sales team the big part figure out really really good. Intid creation of all of this stuff and hence product for the team I think on one side you have to be bullish on the other side like that so probably have a tough time in the market for a bit that's the key. Many off can make the call and say we're all in on this we are that's true for I think so many startups is going to be really interesting to see what happens to the P companies or venture companies that have installed CEOs that are very much the predictable revenue drivers and they've done it for decades because that's good. I don't think it's true like I think it's like they're more companies that were founder led that have failed and succeeded there's more market cap that's been created by non founder CEOs than founder CEOs and so I think it's like there is advantages when you're the founder you're willing to like take certain risks and bets. That maybe the other person is going to be more measured about but I think it's undeniable that on the other side you have CEOs of all sorts that you do that as well like I think satia sooner like these guys at different points have shown the founders or mentality so then a founder CEO says they're like me. They say no you are just a ready to see you and they're really good CEO it's not like you're a really good CEO because you're a founder you've just figured out how to be a really good CEO and that guy is doing the CEO job really well to it's kind of that that's where I think it is just say how like the founder mode thing was like complete nonsense is like. And it overtilted it went into like this wrong area like it started like Ben Horowitz spoke about this he's like it started happening where you found companies that were actually not hiring the sort of people they needed to hire to be able to go solve the problems in the use of the source of founder mode became an impediment to growth within the injuries portfolio and they had to push really hard to try and break that mentality within their portfolio that was really interesting. I still think founder mode is is like really important the anything in the extreme can be detrimental you're going to trigger on that one Kyle because I know this. I think if you look at founder mode as like you want a founder who has a certain like I don't know a certain energy to them and certain sort of proactiveness to them sure great. But at the same time the theory was framed so it's like the framing of founder mode is that effective or ineffective I think it's like one of the most ineffective things they they publish because it's it's hard to understand and implement it correctly is one of those theories that always gets you always stand when you implement it so if if it's implementation is traditionally over extended it's not a good theory it didn't get light understood. Yeah, it was fair. What do you think as you as you're at the edge of this you're noticing like as model improvement has happened new use cases have become available. What are you excited about in the next 12 months beyond the pipeline stuff where you're like this is not doable today but I think it'll be possible at some point. Co work is a really interesting first step in this where but you know the last 12 months you could build a bunch of the stuff we're building now it just was way trickier and you had to like I had to learn how to use terminal and set all this like other stuff up and and there was a pretty big learning curve and I think co work and what cloud announced yesterday which is sort of like their their open clock competitive push like dispatched. I think it is where you where you can have like mobile access I think we're just going to we're going to take. I don't know whose camera whose code it was like the future the the future is here it's just not evenly distributed yet. I think we already live in this where there's a lot of people that are completely living in the future and are just doing like things that are that are were once unimaginable like my VP of Rob ops was like he's like. Yesterday I accomplished what I would have done in two weeks like two years ago and he sent me the list of what I was like to tell me more like what did you do and he sent me the list of these like six things and it's like wow that is crazy and but Steve is living in the future he's got his open client instance and like all of the context files and skills and it's API into everything but I think bringing what. He people like him and people what like my apply day I leader is doing to like many many more people and I think the average employee in the next six to 12 months will be much more like a Steve and we're seeing this in our business where the people that have adopted the tools just jump out of slack at you like you know something goes to the enablement team and then they spin up an output that is like super high quality and I'm impressed by. And it was three hours later as like this is multiple days of work and so those people are just so much more valuable and they do so much more and they're going to rise the ranks so rapidly that it's not that everybody's going to be like you're not a I need of your fire it's just going to be like it's so drastic the difference in output between these two people but I think the co works and dispatches and you know somebody building like a easy to use open claw will come to more people and. And that I think is going to unlock much more productivity and we'll probably start seeing that in like GDP numbers over the next 12 to 24 months and would also expose like another group of people like you know the ones that that latch on to it like that's an accelerant for them and the ones who don't it becomes like it's push it it's poses that person greatly as well like to know but I'm a bit scarier for a lot of people to as a result. I think so Kyle you made me feel slightly better about what we're doing at quote a path but it's still I'll still have the existential crisis tonight it happens every night I just go through this I'm like sit there and I'm like oh my god what are we doing right now we have cloud code and cloud bought and. I co work all running at the same time and it's just like I that part of it is a mess and we are also trying to figure out how do you reconcile all these different context files all these different skills how do you give people access with the right governance and permissions to like what parts of this. You know that is an unsolved problem but I think age of your theme is such a good example of like you guys raise venture dollars but you're not on the like you know growth at all costs like spend spend the most trajectory and you have a team that is like AI build and building real stuff you do the goal be owner and spending you know the the the burning the amount of capital that we are playing our game you can do it in I'm both you guys are running organizations like this and and I think the message to leave everybody as we wrap is like there are no excuses your CFO be penning pinching is not an excuse your CIO you know giving you friction about whatever is not an excuse like you just have to go run road maps being months and years is not an excuse like there are no just if you're in position of power like just leave that company like you're in the wrong company then if you're not in a position of power and you're noticing that sort of like hesitation leave and if you are then push hard like if you're in a position about it suppose that is like the thing that's going to be the thing that makes us fail and if you're the founder CEO fix your or like you have to make this word. This has been a great episode of top line what I learned from it is that founder mode is very real and we're meeting well as he had to hop but thanks for joining us today. Yeah, appreciate it.