Stop Hiring GTM Engineers. They Already Work For You.

0:00 They're growing all-cost stuff, I think, at this point. It's pretty much wore itself out.

0:03 Like, I think those days are over.

0:05 My guest today is Ryan Vantret, VP of GTM Intelligence and AI Solutions at Handel,

0:11 a title that did not exist until he built the job that needed it.

0:15 In HR, finance, or sales, or wherever,

0:18 how do you pick the person that you're like, "Okay, this is going to be my AI person"?

0:22 It wasn't necessarily who, you know, has been using AI the longest or who has the most AI apps built,

0:27 as much as who's always looking for ways to solve problems and constantly looking

0:31 for ways to improve the team and the customer experience.

0:35 What do you think is more important?

0:36 Do you think it's strong, deep technical expertise on how to leverage AI,

0:42 or having strong, deep context of the business and what you need to solve for?

0:49 Definitely the context.

0:50 I think there's a growing conversation that people are realizing,

0:53 like, you know, the battle of models in Frontier versus Open and, you know,

0:57 the fables versus others, like, that's actually not the story.

1:02 Like, the story is what sort of context or ground truth is your model operating from.

1:17 Ryan, you said something at the end of our prep call really stuck with me.

1:21 The go-to-market engineer companies are trying to hire is already sitting inside their org.

1:26 They just don't know it yet.

1:28 What makes you so sure?

1:29 And what does that person look like before anyone ends up giving them the title?

1:36 Yeah, so I think that was coming off the back of, like, you know,

1:39 I was on vacation for a few weeks.

1:41 And prior to leaving, it was about three weeks I was out.

1:45 Historically, I've had to be the builder when it comes to, like, actual,

1:48 you know, production-level AI solutions.

1:50 And I had a few different folks in different departments where I was kind of testing this

1:54 theory that if I can kill a few folks in each department that actually live and

1:58 breathe the go-to-market problems, that they would actually come up with better

2:01 solutions that I could going in and trying to understand them from the outside looking in.

2:07 And when I came back, you know, after literally one or two-hour sessions with three different folks,

2:12 I was looking at three different pretty fleshed-out prototypes purpose-built to solve problems that

2:18 we deal with every day.

2:20 And so that's where I started thinking, like, you know, maybe it's not a one FTE hire, right,

2:25 that's out there with a title that you're looking for.

2:28 It might be, you know, a team, like a Tiger team, you know, if you want to put it that way,

2:32 of builders that live and breathe the problems every day, have that knack for innovation,

2:38 lean into technology and with the right tools, you know, the right guidance, the right resources,

2:43 you know, they can go out and build solutions that I would never be able to without, you know,

2:47 living, you know, the day-to-day jobs that they have to do.

2:51 What do you think is more important?

2:52 Do you think it's strong, deep technical expertise on how to leverage AI

2:59 or having strong, deep context of the business and what you need to solve for?

3:06 Definitely the context.

3:08 I think there's a growing conversation that people are realizing, like, you know,

3:12 the battle of models in Frontier versus Open and, you know, the fables versus others, like, that's

3:18 actually not the story.

3:20 Like, the story is what sort of context or ground truth is your model operating from.

3:25 And so we spent a lot more time trying to develop the backend systems and the infrastructure that

3:31 supports our AI rollout so that when I give somebody, you know, these tools, they've got

3:37 the same plugins, the same skills that I've developed and the same, literally, frameworks

3:43 of how they go talk to their team and gather information and project plan.

3:47 And so what I've tried to do is scale the way that we innovate within our go-to-market departments.

3:52 And, you know, that's less about, you know, giving them tools and hoping they're tech savvy

3:56 and more about letting them be curious and have good conversations, you know, with generative AI

4:01 that happens to have the toolkit, the compliance, the guardrails, and the skills that it needs.

4:07 To build, you know, one-shot high-quality artifacts.

4:10 And again, they're not coming out production ready with real data, but the fact that I have

4:15 builders and departments coming back with, you know, complete dummy data, you know, fully

4:19 fleshed out, you know, workflows, clickable prototypes, it's just showing that, you know,

4:24 it's more about the preparation and how you deploy the tools versus, you know,

4:28 finding somebody who's lived and breathed it for their entire life.

4:33 Well, and I like the way you put it, how you got to go get somebody pilled in each of the departments

4:40 and just spread the AI infection and let it take its form.

4:43 How do you go about looking for someone that you think is someone you want to really, really

4:50 implant this ideation into? And then how do you support them after? I'm just wondering, like,

4:56 okay, you go into a department, you're walking in HR or finance or sales or whatever.

5:00 How do you pick the person that you're like, okay, this is going to be my AI person?

5:05 I think part of it is just seeing who's naturally curious. I mean, when we were rolling out,

5:09 we weren't necessarily looking to throw licenses to every single person and just saying, hey,

5:13 guys, we have AI now. And so we were very strategic and we were picking departments.

5:18 I was working with specific department leaders and I relied on them to tell me, you know,

5:22 who are the people who lean into problems? Who are the people who come back and, you know,

5:26 go watch webinars and are naturally curious and always trying to share new practices with the

5:30 teams? Because those are the people who, if you give them good tools and a good context layer

5:35 underneath it, they're going to naturally engage with AI the same way that they do in their day

5:41 to day. So it wasn't necessarily who, you know, has been using AI the longest or who has the most

5:46 AI apps built as much as who's always looking for ways to solve problems and constantly looking for

5:51 ways to improve the team and the customer experience. And, you know, they're not necessarily

5:57 brought out as builders at first, but once you give them the framework for capturing problems,

6:02 converting that into potential like solution sets, thinking through planning and how you would scope

6:07 out the project and what done looks like and doesn't look like, they've never been trained on that.

6:12 But if you give them a playbook for it, that's really what allows them to go out and go from

6:18 curious, you know, naturally, you know, like information seeking people to people who can

6:24 actually take that and then execute building, you know, some sort of solution or workflow that you

6:28 can layer on top of it. Yeah, I found, we went through this, I'd say maybe December of last year.

6:36 We had a handful of people who were like deep on AI and experimenting and leveraging stuff, but

6:42 it started to get a little bit more mainstream for us in December. It had our first like real hackathon

6:48 building stuff together. And it felt like to me, I'm curious your opinion. It was like,

6:53 okay, there are a couple major barriers we had to knock down. One was, let's just get an agent

6:59 platform installed. So back then you had to do it in terminal or you had to do it in like VSCode or

7:04 something. Now it's nice that Claude and OpenAI have really good desktop apps. I think they're

7:09 pretty much, unless you're doing some real advanced stuff, pretty much going to give you

7:13 everything you need, but just like, okay, let's break that barrier down. Then I feel like another

7:18 big unlock was when we got people, we use Netlify, but or like for sale, you could use too. But once

7:25 we got people hooked up with a Netlify account, it's like, okay, now you built this thing. Now

7:30 you can push it here. Now you could share it with people. Like those were some things I just felt

7:35 like they unlock them. Did you have similar things like barriers that you really had to knock down

7:42 from people are like, Oh, I'm not a technical person. I'm not an AI person. And what helped

7:46 them just like unleash? Yeah. Well, I mean, for starters, like there's like this weird sort of

7:54 tech debt that comes with like vibe coding and go to market, right? And this was something that I

7:59 got smacked with really hard. Cause I was, you know, I was like a kid in a sandbox, just like

8:04 playing all the time and building and innovating. And then all of a sudden, six months later, you

8:09 know, I had a project that just slammed to a standstill because data has a shelf life. And I was

8:14 like literally building the most complex HTML artifacts and nothing was hooked up to any

8:18 database. I had no idea what I was doing. And so what I ended up finding is, you know, like the

8:24 unlock comes from and you actually pull out like basic engineering discipline and then convert that

8:30 into a way that you can teach people like, why would you want to get hub repo to back up your

8:34 work? And why would you want to never put customer data in an LLM model that's doing model training?

8:40 And so like you want to avoid the wild west of innovation and AI adoption, especially if you're

8:45 in like compliance heavy industries like we are. So you, you really have to go in and like do the

8:53 dirty work first, right? Like put the back end tools, make sure that they can't blow things up,

8:57 teach them how to do basic project management and think like problem solvers and builders.

9:03 That's what was the unlock for us. When I came back in some, you know, I, I just told her what,

9:08 you know, get hub was. And I said, you know, I'm not an engineer. I just know cloud kind of told

9:12 me that I need to use it to keep, you know, version control and stuff like that. And it's a

9:16 way that I can share my projects. She came back with her own repo fully built out. And like here,

9:20 you know, here's my repo. And you know, that just comes from a conversation, but like her being

9:24 curious and seeking good information, went and watched a couple of YouTube videos, set hers up,

9:30 made sure it was, you know, kind of all good to go and send it over to me. And it's like,

9:34 you know, I don't think you can go higher for somebody who's going to take a one sentence and

9:37 go figure out how to execute and become a part of that process. But that's really where we started

9:42 being able to, you know, have a lot more faith that the work we were doing was in the guard rails,

9:48 you know, keeping the data, you know, compliant, making sure our sock tubes not being, you know,

9:52 compromised or anything like that, but still giving people the ability to innovate and build

9:57 in like a safe structured environment with really high level tools.

10:01 What's your opinion on? Because I've seen, I feel like there's phases to it. Okay,

10:08 got some evangelists out there. They're out there building. Is there a certain point in time where

10:14 this decentralized building of AI needs to get rained back in? Or is there some like middle

10:22 ground compromise? When you're really starting to take these things to scale across the whole

10:27 organization, how do you recommend structuring that on your teams? For me, it started with

10:33 understanding I needed to go speak with our CTO. I mean, I used to pride myself on, you know,

10:37 having production systems built and never pulling an engineering, you know, hour off of off of our

10:42 product. But after a while, you know, when you're not just using go to market data, and you want to

10:47 start looking at ways to incorporate usage data and product data, all have a ton of value, but we

10:54 have to understand like, what does that mean from a compliance standpoint? How do we make sure we're

10:58 using things, you know, appropriately? How do we sanitize different files before you ingest them

11:03 into any model? Like we had, I had to go through and just like, let myself be vulnerable and let,

11:09 you know, the adult in the room actually give me guidance on our posture and how to get aligned

11:14 with that. And so one, I would say is like create a bridge between you and the engineering team,

11:19 because they've, this ain't new to them, you know, they've been leaving and breathing this forever.

11:23 And we're new to the whole game. And that's where you that's where if I would have done that earlier,

11:27 you know, I wouldn't have had a wall of tech debt waiting for me, you know, by the time I was ready

11:32 to think about how do I scale these tools and make them a little more automated. And so those

11:37 conversations are really, you know, probably the first step is just create that bridge and leverage,

11:42 you know, the insights that those those teams have and the experience that they built over their

11:46 careers, and figure out how to translate that into to go to market motions and playbooks you can run,

11:52 right with with a non engineer. Yeah, that makes sense. And I, I think it's this balance that you

11:58 don't want to stifle people's creativity or hold people back from being productive. But at the same

12:02 time, there's some stuff that's sensitive, I'm starting to think access to things like cloud

12:08 codecs, getting hooked up on for cell and that with five, whatever that whatever your little AI

12:13 tech stack looks like, I'm starting to feel like that's the new Google Sheets, Google Slides. It's

12:20 like, yes, we shouldn't hold anybody back from having access to these things. At the same time,

12:26 there's a difference between you working in a spreadsheet, and the spreadsheet that the CFO

12:31 uses to do forecasting on the business. And it's like, okay, there's Yeah, you can build some cute

12:37 AI stuff and things that help you be a little bit more productive in your day. But then there's like

12:41 these business critical workflows and infrastructure systems that are set up that we need the

12:47 professionals to manage. Are you seeing it that way too? Yeah, I mean, in some ways, like, like,

12:55 in my role, I work cross departmentally. And my job is to make sure like the standards that every

13:01 department is set are met in any AI interaction that any of our reps have, right. And so like,

13:07 I'm not the person that gets to set our messaging framework. And, you know, sort of like our go to

13:12 market message positioning and how we draft emails and how long they can be in the format of them,

13:17 you know, like, I don't create the branding and design guidelines, I need to capture what the best

13:22 looks like, and then build that into the system that the reps interface with. So all it feels like

13:28 them is it feels like a 20 taller chat GPT license. But you've got connectors, context graphs,

13:34 intelligence, guardrails rules, right? You know, non trained model environments, because if you go

13:42 to enterprise and teams, by default, right, it's off. So you're not worried about, you know, rogue AI,

13:47 where somebody forgot to flip their switch. And now you've got contracts and customer data,

13:51 feeding an AI model, right. And so that's really what you have to look out for. You don't want to

13:55 stifle innovation, but it's also not worth, you know, losing a key marquee account, because

14:01 you had somebody do something with data that they shouldn't do. And now your software compliance

14:06 status, you know, is at risk. And, you know, there's a lot more of a business impact to getting that

14:12 wrong. And I wouldn't I wouldn't trade that for faster emails, or, you know, quicker copy or

14:17 whatever most people are doing with AI right now. Yeah, no, it makes sense. The risk and compliance

14:24 side of this equation is super important, especially if you're in a highly compliant

14:29 industry, where you need to be keeping that top of mind. You mentioned something when you're going

14:35 through that I think this has become the hottest topic in AI. The models are all fantastic, like

14:42 Opus and Sol, like they're good. That's not really holding it back. What tends to be holding people

14:48 back is that context graph layer. Can you walk through maybe your approach to that or maybe

14:57 actually just start with why is this layer so critical and important? Yeah, so I mean, I didn't

15:07 come from like LLM training, you know, engineering background. So when I just got into this stuff,

15:13 I was really just tinkering. And I didn't even know what a knowledge graph was or a context graph.

15:18 I didn't know what an MCP server was like, I barely understood APIs, if I'm being honest with you.

15:23 And so what I was doing is like, you know, a year and a half of organic go to market work and just

15:29 sort of saving things and organizing them so that I can get them back out later, right? Because,

15:36 you know, the context was the thing that just let me pick up these working sessions exactly where I

15:42 left off. And it turns out that's literally kind of like what this thing is called. And so it was

15:47 never really like starting out as like, I have a project and the project is to scope out a context

15:52 graph. It was just sort of an accumulation of being of service to the go to market team and just

15:57 collecting intelligence on all kinds of tasks, right? And so the part of a lot of people don't

16:03 want to hear is there's no automated way to keep it 100% accurate. Like a lot of mine is automated

16:09 at this point, but some of it's me every Friday afternoon, you know, doing a graph health, you know,

16:14 scale and making sure that I'm cleaning up some of the nodes and once a month doing a bigger pass

16:19 and making sure what's in there is still true. But that maintenance is what supercharges the

16:25 rest of the team to where they're getting grounded real intelligence back. And so I can actually show

16:33 you there's let me let me share my screen real quick just so you can kind of take a look at it.

16:39 Yeah, absolutely. I always say people who share their screens are the real pros.

16:43 There's people who talk about things and then people who show them so would love to take a look.

16:47 So I'm not going to dive into like that. This is what we would consider our go to market platform.

16:54 And what this is is all the gaps filled for any of the tech stack where if there is a solution

16:58 to a problem that we know exists, we've wanted to build it ourselves. So everything you're seeing

17:03 here is a live production app that we use across our go to market team within there is our knowledge

17:08 graph and ours has been built, you know, it's got a bunch of different nodes, you know, nine

17:13 different groups and categories. And I'm not going to go too far into it. But what I did do right on

17:18 in kind of anticipation of this is I took another project that I had been working on in a previous

17:24 webinar where we were basically testing this this AI native go to market approach to building

17:31 context before you build artifacts. And so what we did was we were looking at like a death care

17:36 vertical SaaS company that might work for, you know, funeral parlors or creamery places. And we

17:43 basically went out and we did all this research. So this is a fictitious company. But all of the

17:48 market research is real. And this is what that would look like, right? Just the research we did

17:53 for you know, to build like a fake company. There was 35 nodes, 123 connections between the nodes.

18:01 And we called it plot IQ, right? Like this fictitious business. But every node here,

18:07 along with, you know, the related go to market training material, this represents sort of the

18:12 same exact workflow that I built locally at the startup that I work at. So it's the it's just

18:18 reskinned to another vertical, another SaaS company, completely different buyers, personas,

18:24 competitors. But the underlying theme is the same. If I were to connect this as a connector

18:30 to our cloud environment the way that I do ours, every single node in here, all these connections,

18:35 you have a transversable graph that is grounded, where every answer is grounded in the intelligence

18:43 that is validated, constantly updated. And it doesn't live in, you know, two year old go to

18:49 market product files, or, you know, your Google Docs that were created last year, or the PowerPoint

18:57 slide that they showed you in one training, and nobody ever looked at again, it all lives in a

19:01 graph. So whether you're a new hire, you know, being onboarded, and you want to make sure that

19:07 they have access to the best information and a way to actually access the intelligence behind this,

19:14 every single one of those is essentially a node that feeds the graph, right? And so there's this

19:19 connection between, you know, where does all that tribal intelligence live right now, right?

19:24 Sometimes my nodes are created by me doing a zoom session with one of like our head of sales, who's

19:29 been at our company forever, and just letting him download his intelligence, his insights, what he

19:36 sees, and capturing that so that now if anybody ever talks to Claude, we have that sort of tribal

19:42 knowledge and feedback included in our responses. So that the challenge is really, you know, it's

19:48 not can I get the best LLM, do I have enough credits, it's am I am I hooking up a supercharger

19:53 to it, where the superchargers is literally fuel of top tier intelligence, that this wouldn't work

20:00 for any other company, like, you know, we couldn't take this and sell it as a SaaS product,

20:04 but it's perfect for what we do, you know, at the company I work with. And it makes everybody

20:10 better, right? Everybody is better because they have instant access to this information.

20:15 If somebody's listening wants to build this, let's assume they have very little AI and engineering

20:22 experience. Assume they're a chat GPTing AI person right now. How would they go about

20:31 building this out for their organization step by step?

20:37 I would say first is do a content audit, right? Go to your teams and see what you have, and then

20:43 you have to figure out what's true today, right? Because a lot of it won't be. And then once you

20:48 figure out what's true today, then what I was doing just sort of naturally was I was I learned

20:54 about this thing called a markdown file and you know how it was easy for AI to work with. And so

20:59 I just started creating markdown files and folders and just was sort of organizing the intelligence

21:05 as I was gathering it. If there was an old, you know, PDF document, a bunch of different like

21:11 videos with transcripts, old one pagers, testimonial, you know, white papers, anything like that, I was

21:18 just taking and I was having Claude ingest it for me. Analyze this document, review this transcript,

21:24 capture the messaging, and then fill it into my ground. And so I was just doing this sort of

21:30 naturally. Copy paste, copy paste, copy paste. Then when my company, you know, I was fortunate

21:34 enough to work somewhere where they like to invest in me. And so they sent me to get a certificate

21:39 last year at MIT. I'm doing one at Harvard Business School this year. And then I started learning,

21:44 right, about, you know, the concept of these knowledge graphs and data graphs and nodes. And

21:49 then I was like, oh, well, I didn't know that's that's what it was called. But I had just been

21:53 organically scraping everything that I could get out of all the different types of media that you

21:58 can think of. And then putting that into knowledge graph files, just markdown structure, organize,

22:05 and then eventually I was able to use Claude to say, hey, you know, I just learned about this

22:10 thing called a knowledge graph. Can you help me optimize this organic, you know, mess of

22:16 information and make sure that the top parts of these have the right, you know, keywords and the

22:20 relationships are there and that the graph is healthy and the connections are made. And so I

22:25 never really had to go through and like individually edit every single graph. The hard part was just

22:31 done because I worked for a year and a half supporting the go to market teams and was just

22:36 collecting it like intuitively day by day by day. So when I went back to ask it to create it,

22:42 it was there. That would be the same play I would run. Go to your teammates, go to where the tribal

22:48 knowledge is, extract everything, get it organized, find out what's true today and what's not so you

22:53 can get to a ground truth. And that ground truth for me, it was like 75% of all the intelligence

23:00 that I had was either outdated or no longer relevant. And so I was able to scrap a lot of that

23:05 and just use what was still true to form the initial graph. And now it's something where

23:10 there's a maintenance schedule for it. Like I know different types of work that I'm doing when to use

23:15 my ingest skills. So it knows, hey, tee this up for the graph and add a couple of modes. And then

23:20 I know when it's time to run my graph health exercises, where the skill literally goes and

23:24 checks all my nodes and make sure everything looks healthy. And I got a dashboard that tells me

23:28 exactly how healthy my graph is. And so eventually it becomes this more automated thing that

23:35 it's just always up to date and accurate. Never 100%. You always need human in the loop and some

23:41 maintenance to be done. But if you can get that down, every single thing you do off the back of

23:46 that graph, you could one shot high quality artifacts that you wouldn't even dream of.

23:52 Most people are used to 20 iterations of conversations and asking, what are you doing?

23:57 If you're getting to the point of frustration, I promise you, you have a context problem.

24:03 And a lot of times when people start building these, so, okay, you're taking transcripts,

24:07 you're taking artifacts, you're taking all these things and giving it to Claude,

24:11 it'll end up start building your graph locally. What was your strategy for making it accessible

24:18 across the entire organization so they could plug their AI tools into the context that would give

24:24 them all of those opportunities? Yeah. So again, very kind of caveman approach. My first thought

24:31 was like, well, I'll just make a bunch of markdown files that I'll create different projects for,

24:36 and the markdown files and the projects will have the context that it needs for the specific workflow.

24:42 And then, as I started thinking about the connectors, and we work with a partner called

24:47 Endgame who does a similar job, but it's more focused on our accounts, our opportunities,

24:53 and our contact intelligence that lives in our CRM and other systems, and they provide a graph.

24:58 And so what I realized was like, they're really good at giving me the intelligence I need to

25:03 know about our sellable universe, but what they lacked was this graph that I had that had our

25:09 playbooks, our strategies, our coaching manuals, our product, our positioning, our winning zones,

25:15 you name it, any sort of go-to-market intelligence, it was in there. And so I realized I needed to

25:20 combine those two, and then again, just using some cloud and asking some questions and being curious,

25:26 ran into a tool. There's a few out there, pine cones like one, there's a couple others,

25:30 where you can essentially leverage their technology to upload your graph to it,

25:34 and then turn that into a custom connector, right? So think of it as like turning on your outreach

25:40 or your gone connector, you just sort of built one that you host yourself and you allow the team to

25:45 hook up to it as well. So it was a very crawl, walk, run type of progression of getting to what

25:54 we have today, but it's something that anybody could replicate. And the quicker you do it,

26:00 the faster you're bottling up, what in a lot of cases is competitive advantage

26:05 in the tribal knowledge, your industry expertise, your experience, what you know about your

26:09 customers, and everybody gets smarter and everybody gets better. But you have to go through that dirty

26:16 work of control burning content, figuring out what matters, what doesn't, and then working

26:21 towards something that's a little bit more accessible and scalable for the rest of the team.

26:26 Yeah, and every single turn of this just continues to compound. So the longer you wait,

26:32 you're just missing out on that compounding interest equation of, hey, every time you do

26:36 more work, you can add to the graph, you can add new scenarios, you can add new things,

26:40 you can revamp something that's not working anymore in that context. So every single time

26:45 somebody does something, if you set this up really well, you're just getting that 1% better

26:51 every single day. And for those teams that aren't doing this, you're just burning daylight and

26:58 burning time on that compounding interest equation. Yeah, and you mentioned this wave theory that I

27:03 have where AI adoption starts from trusting account intelligence into deal acceleration,

27:11 and for us into pipeline monitoring, and now post sales. And so you see this customer lifecycle

27:18 wave, but most teams, they want to start with content, right? And write my emails,

27:23 create me my sequences. And that's what everybody's selling, so it makes sense. But the problem is,

27:29 you can't check an email, it either sounds good or it doesn't. And there's no right answer to

27:34 verify if there's no ground truth to work from. And so you can have a brand new rep using AI,

27:39 getting really, really quality outputs to their eyes, but they don't have the tribal knowledge

27:44 to know if this thing is hallucinating, right? And it hurts people all the time. And so people

27:50 don't trust it, right? And then they stop adopting it. And if trust never gets built,

27:55 it just basically gets its shelfware at that point. And we mentioned earlier, right? Context

28:01 is the bottleneck. It has nothing to do with your content. And the data behind that,

28:09 there's one of the case studies that was done, our H1 to H2 numbers. There was a benchmark that

28:15 we looked at where we wanted to try to understand, is what we're actually doing in terms of AI

28:21 strategy and some of these different changes we've made, like is it impacting numbers that we can

28:27 measure, right? And from H1 to H2 last year, I think I mentioned we were at like 12 account

28:33 executives to start the year, a little less than halfway through, we were down to about eight.

28:38 We never really backfilled, didn't even have a BDR team back then. We started them in October

28:44 of last year, but we still saw better sales cycles. We went from like a typical 12 to 18

28:51 month for big marquee accounts down to six to nine months from half one to half two. We saw like 9%

28:56 increase in win rates, like a 40% increase in average ARR for deal size. Like everything was

29:04 going up and to the right where it mattered. And we were able to actually go back and see it's the

29:09 intelligence. It's people are smarter. We're not getting, you know, kicking ourselves in, you know,

29:14 in the foot every time we go out and then we make some sort of claim to somebody because AI put it

29:18 in an email and nobody checked it. And so the context is really like it's, it's the premium

29:24 fuel for the system. And it'll show up later, like in the performance data, if it's actually

29:29 being adopted, trusted, and it's being used correctly. And if sales starts using it, marketing

29:34 starts using it, everybody starts to see that this intelligence we're gathering makes them better.

29:39 The sales handoff, the client success kickoff, the onboarding, there's no drop in intelligence

29:44 from team to team. You really start to separate yourselves from like typical vendors who

29:50 are bolting on AI and, you know, copy and pasting a bunch of stuff blindly and, you know, doing a

29:54 bunch of slob versus the teams who have more intelligence about companies than any individual

30:00 that we're selling to over there with clear, you know, perspective and a ton of intelligence to

30:07 back the reasons why we're reaching out. And that all comes from the graph and the work that we did

30:12 in the first half of the year. Can we go back to those results? Because the way you rattle them

30:17 off and to those listening, it may sound like, oh, those are incremental improvements. When you say

30:24 nine points on conversion rate as an increase, reducing sales cycle from nine months to six

30:30 months. Those are dramatic improvements in the go to market leverage of your entire team. And I

30:39 don't know if people listening really have an appreciation for how powerful movements on those

30:45 numbers can be. Only a couple points on conversion rate can increase your bookings by millions,

30:52 tens of millions at scale. And there's huge risk if you don't maintain those go in the other

30:57 direction as well. And then if you can reduce sales cycle, I mean, reducing it by a third

31:02 is absolutely insane and can get your pipeline turnover so much quicker to where you can get

31:10 more in the year and getting more in the year moves up your growth rate, which the difference

31:17 between 80% growth and doubling could mean multiples on your multiple. So I just wanted

31:25 to take a pause because those are huge improvements, not just minor ones. And I think

31:31 when you're pointing all of this productivity towards improving those metrics, the revenue

31:37 results are dramatic. So I was just wondering if you wanted to double click on those a little bit.

31:42 Yeah. And sometimes we have it easy in sales in some ways, because a lot of people have to

31:46 prove like, what's the ROI on this AI stuff we're doing, right? I think, you know, like customer

31:50 support and then like, I think that's probably the easiest place to prove it because the number of

31:55 tasks you solve and tickets you close and that kind of stuff with AI, like it's pretty easy to

31:59 measure. And sales is kind of similar because we're a performance, you know, like organization,

32:05 like it's all about numbers. And so like, yes, we could have 100% adoption and people

32:09 asking questions to the AI, but if it's not actually driving revenue, you know, accelerating

32:15 pipeline, you know, helping us multi-thread into accounts and understand businesses in ways that

32:20 we just couldn't do it before, you know, you don't get the same sort of downstream impact. And, you

32:25 know, I always think of like leading indicators, lagging indicators, and what can you see in the

32:30 middle that gives you a good idea about where you're going to end up. And for us, like the 70%

32:36 ACV increase wasn't just like, oh, well, that's cool. We don't know how that happened. The 9%,

32:42 you know, win rate improvement, like that was a very specific thing that was going into like our

32:47 business cases and us really being able to tell an intelligent recommendation to somebody where

32:52 it just makes total sense for us to do business together. The shortening of the sales cycles was

32:58 really the convergence of the go-to-market teams working these accounts together, you know, and,

33:03 you know, the numbers look good. I don't want to like, you know, go through like our public numbers,

33:07 but we published that case study. It's out there. Actually, I'll give you the link, you know,

33:11 in the follow up just in case you want to include it. But it talks about how we leveraged specifically

33:17 some of these context graphs to be able to gather the intelligence needed to go and compete in a

33:22 market where we're not the big incumbent. You know, we're a scrappy startup and we're taking

33:26 business from some of the biggest players in the space because our sales process is differentiated

33:31 from the experiences of other vendors. It's just not the same thing. And so when you're, you know,

33:36 competing for a dollar spend and you've got a business case that is a clear cut, you know,

33:42 couldn't be any more tailored from what you had in the conversations and comes from only the

33:47 intelligence of the sales process. No copy paste, no random artifacts that you give to everybody

33:53 else. No, no blanket marketing proposal that everybody uses. It's just a different type of

33:58 experience that you provide. And it makes you go from tech vendor to pure consultative seller. And

34:04 it's for us, it's been one of the ways that we've really been able to distinguish ourselves in the

34:09 market that's pretty competitive. I'm curious, with everything going on, where do you think RevOps

34:17 is going in the future? I think RevOps, especially in like my experience, probably holds a lot more

34:23 of the folks that you're looking for to fill these go-to-market engineer seats, just because the

34:28 backends, like I talked about, you know, leveraging some engineering discipline, like my counterparts

34:33 at our company that are Salesforce certified and know our core systems inside and out are in some

34:39 ways more critical than I am to the types of building that we're doing on top of like our core

34:44 tech stack. And so I think, you know, RevOps is going to be a department that gets a lot more

34:48 investment. And instead of going, you know, all in on FTE on sellers and the folks on the floor,

34:54 I think the more people realize, you know, the enablement operations and, you know, the go-to-market

35:00 deployment of resources is really where you're going to start to see leverage. Like it's the

35:04 reason why we started at 12, we're at eight by mid-year, and we're still able to hit the numbers

35:10 and have a record-breaking year, you know, without having to backfill it. In most cases, a company,

35:16 you know, 10 years ago when I was running my old startup, if we had a couple reps go out like that,

35:20 you know, somebody had to make up that pipeline. You know, it wasn't like the investors were just

35:25 like, oh, that's cool. We'll go ahead and, you know, dock that from the expectation. And so those were

35:30 scramble exercises. And for us, we just hunkered down on more enablement, more intelligence,

35:36 better ICP, you know, better pre-qualification, and really just put the resources into investing

35:41 in the people who were high performing. And I think that's the motion more people are going to

35:46 start to adopt, right? You don't need to double your sales team. Sometimes you just need to let

35:51 them do a little bit more with less. And AI is the perfect tool to do that if you know how to

35:55 leverage it. One of the things I saw on your GTM operating system, which I thought was really

36:02 interesting, it was a lot of custom built tools. And I think now more than ever, it's so much easier

36:10 to build than it ever has been. I'm curious your take. What type of tools do you say, hey,

36:17 these are pretty straightforward to build in-house. And then when do you want a professionalized

36:23 vendor to bring into your tech stack? So from your perspective, what's worth building and

36:31 what's worth buying right now? The way that I kind of describe it is like what I would call like

36:37 microsass, right? It's not necessarily something you put an ARR, you know, go to market effort

36:42 behind and build a company around, but they're like these really, you know, intricately built

36:48 solutions that only work with your tech stack, right? And you know, they're sort of filling gaps

36:54 that exist that would make your team better. But you know, if you went to Salesforce, they'd be

36:59 like, okay, cool, we'll take the feature request, but you'll never see it because it doesn't have

37:04 any, it's purpose for our business, right? It would only work for us. And so that's kind of the way

37:09 that I distinguish it. Like if it's, I'm not trying, I've seen a lot of people talk about,

37:13 counsel my CRM and build my own CRM, zero interest in doing that. Not any interest at all.

37:20 Maybe why? I want to dive a little deep on that because I think I agree with you,

37:27 but I have talked to people that have completely built a homegrown CRM, even a homegrown sequencing

37:34 system. And I was like, I thought the workflow part might be the part that's a little bit more

37:38 difficult to replicate and you need to rely on it, but what goes into your equation as to why

37:46 I'm not going to build the CRM versus maybe some of these smaller things? Well, a lot of it starts

37:53 with compliance for me, right? Like if we're SOC 2 certified, like I don't have the skill set,

37:59 even at my level today, to ensure that everything that I'm doing and building is going to be SOC 2

38:04 compliant, right? And a lot of these ERP, CRM, foundational source of truth type tools, a lot of

38:12 people, the reason why they want to leave them is because they're garbage cans, right? They've never

38:17 been able to turn it into a goldmine. And so if that's the way you feel about your CRM, then sure,

38:21 you might want to build your own and start over and make it something that's a little more manageable.

38:26 For us, I use it as a database, right? Now I'll tell you this, I do as much as I can to keep my

38:34 reps out of Salesforce because it's one of the most cumbersome tools to click through and update

38:39 and it takes a lot of time. I have ways that I leverage that data and give them other tools

38:44 using the same information, but in a way that's more conductive to their workflow, like their day

38:49 to day. So it's not like I'm championing keeping people in the CRM. All I'm saying is there's a

38:55 database that already exists, that's already compliant, that has an integration economy built

39:00 around it, and it's connected to all of our go to market stack. For me to take on, you know, do I

39:05 want to rebuild the CRM, I actually have to think through the lifeblood and the connections, you know,

39:10 of the entire stack, not just, you know, whether or not I like a CRM, right? We work with a lot of

39:17 vendor providers, marketing has their tools, success has their tools, all of it's integrated

39:21 to Salesforce. So for us to leave, you know, that project is probably a bit different in scope that

39:26 maybe a startup is trying to decide, do they want to invest in the CRM or build themselves?

39:31 Totally. Don't make sense. I just think some of those things where I was like, I wouldn't even

39:36 think this would be a question or somebody would try to do this, but then I'm seeing people do it.

39:40 I'm like, okay, well, what goes into that decision tree of building versus buying? And I think you're

39:48 right. I mean, at a certain level of scale, also I always think about it as opportunity cost. Okay,

39:53 you're going to pull, you mentioned like, hey, I try to not pull my engineers off of building

39:57 the core product as much as possible, because every hour investing in your core product that's

40:03 going to market, that's likely going to give you a better return than building internal tools.

40:08 So I think of it as an opportunity cost thing too, like, hey, you'd actually need some pretty

40:12 serious engineers if you want to replicate what all of these companies have done.

40:16 Totally. Yeah. And again, in that compliance aspect, like what kind of data is in there,

40:20 right? Are you buying or building compliance, right? Like that's a whole different, you know,

40:26 ballgame. Like if I've just got a bunch of a lead list that anybody can get and everybody can buy,

40:30 great. But if it's got customer data coming back from system and production, invoicing data coming

40:35 back into their billing section, it's like, how am I going to make sure that I've accounted for

40:41 all the stuff that that system's already handling for me? So I pick and choose my battles in terms

40:46 of building. I prefer to look for the things that, you know, I wouldn't even ask any other vendor

40:51 provider reuse to build it. I would, because it wouldn't work for any of the other customers.

40:55 But if you've got APIs, MCP servers and ways that I can access your data,

41:00 that's literally all I need with the plugins and the toolkits that I use.

41:04 Totally. No, it makes sense. For some RevOps folks that are, I'll say, let's say they came up

41:12 through the Salesforce admin kind of ranks. I know there's an eclectic background of how people fall

41:18 into RevOps. But for some of those that might be in that more like legacy type of mindset with the

41:26 RevOps role, what type of skills do you recommend they need to start gaining immediately? And what

41:34 types of things do you think they need to be learning immediately so that they can stay

41:38 relevant? And how existential do you think it is for their career?

41:46 I mean, maybe not in the near term, but there's, there's going to be a whole, you know, group of

41:50 professionals that come up behind the generation that are in the seats today that all they're,

41:54 they're going to be AI native, since they were kids, right? Like, we're not talking about trying

41:59 to learn it for a business. We're talking about people using it in their day to day lives, you

42:03 know, for, let's call it a decade. When they show up into the work environment, it's one of those

42:09 things where if you're an engineer that hasn't went back to school or got the latest CAD updated

42:12 training, or, you know, have it went back and got your continuing education hours as a nurse,

42:16 you're probably going to be falling behind the industry. Things are changing very, very quickly.

42:22 I mean, I proactively seek certifications, webinars, invest quite a bit of time and money

42:27 to try to stay at the cutting edge of what's happening, because it's the best way that I can

42:30 support, you know, the team that I'm serving. But even I, you know, at some times feel like

42:35 there's just no way to keep up. So one, don't try to learn everything, you know, focus on the things

42:40 that actually matter and understand, if you're coming from a RevOps background, or, you know,

42:45 you're a Salesforce admin, you've already got technical skills, like, that's not what I would

42:50 be looking for in a, in say, a go to market engineer, then that's why I think they're already

42:54 hiring in the building, what you need to look for is like, you know, how can you develop this

42:58 knack for, for enablement, right? Not the traditional, you know, LMS, you know, knowledge

43:05 base, training material, PowerPoints, enablement, but like, how do you actually go and help people

43:12 be better at their job and make them more efficient, make them more effective, improve,

43:17 you know, the internal customers that we have, improve their life and their workflows. And so

43:22 the technical part is, you know, it's not the most important thing, it's the curiosity that I look

43:28 for, because that's not teachable, right? So, you know, if you're technically inclined and look at

43:32 things in ones and zeros, you probably want to start, you know, going and looking at some design

43:36 thinking courses, or just some different ways to approach problems other than, you know,

43:42 product scoping, you know, solution, prototyping, like, you have to be a little bit more open-minded

43:49 and not just rely on the back end behind the scenes work and get out in front of the

43:53 teammates that you're serving, you know, find these problems and understand what you have at

43:57 your disposal, right? Like a lot of people maybe, you know, aren't as, as, you know, I would say

44:02 entitled as I am in some ways, because we lean so much into AI, like I have all the tools at this,

44:07 at my disposal that I can ask for, there's no limitations, you know, to me being innovative

44:12 and serving my teams, you know, some people you have to build that trust, and you have to go out

44:16 and show that you can do it on a small scale first, and it doesn't take, you know, an enterprise-wide

44:21 operational, you know, project, find your own wedge product, right, within your department, build

44:28 something that works, improve that, that you can be trusted to help, you know, improve the lives of

44:32 your teammates, and then you'll be able to start, you know, sort of rolling out for there. So, you

44:36 know, don't go swing for the fence, but also, you know, you got to rely on something other than your

44:41 technical background, because people like me have, you know, built these teams, train these teams,

44:46 work with people, we didn't just work with systems, and anybody in a go-to-market engineer role is

44:50 going to have to figure out how to marry both the technical aspect, which I've been trying to get

44:54 better at, right, and then the enablement aspect, those are going to probably start merging fairly

44:59 soon. Yeah, it does feel like the value of the technical skill set is starting to drop a little

45:07 bit, at least like the deep specific manual technical skill set, and then the context and

45:13 creativity is becoming more and more valuable. So, I just think it's challenging, I think the

45:19 RevOps role is becoming really, really dynamic, it's becoming really challenging, I think the

45:24 expectations in the bar have risen by 10x of what people expected maybe five years ago, and I just

45:32 see it continuing to grow, and what people will be demanding out of their RevOps teams and RevOps

45:40 functions is going to be quite a lot. Yeah, we're not just looking at integrations and systems

45:46 connecting and putting in custom fields anymore, we're looking at real operational

45:51 transformation that can be happening, and they can be at the forefront of it, but they also have to

45:57 understand that that old role is, like a good example of this, is we have a consultant that

46:02 works for us, he's super good at what he does, and we've had this really weird Salesforce bug

46:06 for a while where an address gets changed every once in a while, and we've went through everything

46:13 you could possibly think of, digging through workflows, and looking at connectors, and calling

46:18 Salesforce, and looking at every provider, and trying to troubleshoot, and luckily all these

46:22 providers that we've tried to see who it is, they've got MCP connectors, and they've got APIs,

46:28 and I didn't know this was going on, they kind of brought it to my attention, and then going through

46:33 and just saying, "Hey, hook up to these connectors, and you tell me, go look in the log, see what this

46:38 is, when was it triggered," and it literally reverse engineered down to what the actual issue could be,

46:43 gave it to them, and they said, "Oh, that's interesting, let me try that," and then it fixed it.

46:48 I've never used Salesforce before, I don't even know how to go back there and look at what's going

46:51 on in Salesforce, but if I can deploy an AI solution towards it that can see all the systems,

46:57 and look at these logs, and figure out what's triggering the automation, and where it's coming

47:01 from, nobody knew how to do that, not even our consultant, and we're really good at what we do.

47:06 We're not incompetent, it was just something that was very complex, and that's a skill set,

47:11 where even having a consultant, and a dedicated full-time resource, and me throwing myself at it,

47:16 and somebody trying before that, the problem persisted, and that the requirement for us

47:22 to fix it was just giving a better system, all the information and tribal knowledge we've

47:26 accumulated about the project, and literally watching it solve it within about an hour.

47:33 That's crazy, and I think if that's not an example of where everything's going,

47:39 I'm not really sure what is. One thing that I think is going to be really important for

47:44 people to wrestle with, too, that I'm curious your take on, the capabilities have gotten so good,

47:50 the efficiency and effectiveness of RevOps people have

47:54 tenexed, I think, over the last couple of years. At the same time, the needs, I think, have gotten

48:00 even bigger, and I think it's put people in a situation where on the surface it looks like,

48:07 "Hey, you can run super lean because there's all these new capabilities," but what's actually

48:11 happening is the ceiling of potential work has just risen so high that anything could be done

48:15 at any time, so now there's even more work to be done. How do you navigate the resourcing

48:22 conversation with an executive team to say, "Hey, this is? I need you to invest in more

48:29 headcount or more tools and more things because of XYZ?" How do you navigate that conversation?

48:37 Any recommendations you have on how much a company should be investing in their RevOps org?

48:43 It's tough to answer. I could answer it from the universe I'm from, which is your enterprise,

48:48 B2B, SaaS, but it depends on what your go-to-market archetype is. If you're doing

48:55 SMB community-driven motion or you're doing product-led growth, there's a couple different

49:00 ways that you could approach that, but for me, it usually comes down to what can you

49:04 tie it on in terms of revenue, and can you actually measure the impact, like me going

49:11 and saying, "Hey, I want to invest in this end-game context graph because everybody keeps asking me

49:16 to go and download all these transcripts and go through Salesforce and gather intelligence and

49:22 create these account plans." There's a tool that can do that and it's connected to everything,

49:26 and so yes, I thought about building it, but then it was like in compliance,

49:30 it was already SOC 2 certified, I didn't have to worry about checking that box, so that was a win,

49:35 right? But then it integrated into everything that we needed, and so when an executive comes

49:40 to me and says, "We have a problem to solve," usually it's either training and better information

49:45 and leveling the team up, or we're building or having to buy a solution that fills the gap.

49:52 And so for me, what I'm looking for is like what actually solves the problem they're asking me to

49:56 solve, right? But here's a good example. One of the very first problems that I got from our CEO was

50:03 all the reps are doing dials, but they're not showing up and being logged in the CRM, right?

50:08 And you would think, "That's a pretty easy problem to solve." The problem is a lot of them were

50:12 happening in the field, they're getting calls, stuff like that. So yes, I could put a training

50:18 together, yes, I could force everybody to go and manually enter their calls, they may have

50:23 varying degrees of notes, and maybe they'll put the outcome and maybe they won't, like yes,

50:28 you can force some sort of training function to solve that. I found, like there's a lot of them

50:35 out there probably at this point, but like a really decent dialer, right, that worked with our system,

50:40 but that also analyzed the call transcripts, added the notes, added the disposition, and logged it in

50:45 Salesforce. And so all it was was a little tool, not very expensive at all, a decent overlay on our

50:51 outreach platform so you can make your calls efficiently, but it actually, you know, not only

50:55 did it make the reps more efficient and made them, you know, better, you know, gave them better

50:59 information, but it also solved the problem the CEO had was, you know, we're having conversations

51:04 and we're losing the intelligence. And so that was a very easy, "Hey, you said you have this problem,

51:09 primary benefit, here's the secondary benefits that I've identified for our team and how it

51:13 makes us more efficient." And so if you're solving a business problem with a solution that also makes

51:19 the team better, I think that's an easy conversation to have. It's when, you know, if I go and just say,

51:24 "Hey, you know, there's this really, like clay, for example, I've always wanted clay. I don't think

51:29 we're gonna get clay," you know what I mean? It's really expensive, but you know, I've always wanted

51:34 it. I'm not just gonna go over there for the sake of saying I want clay just to see what I can do

51:38 with it, right? So you have to sell the business case internally. Like I literally run the same

51:42 sales process. I teach my reps internally. Like you're multi-threading, you know, talking to

51:47 different department leads, seeing if there's cross-departmental value, figuring out what use

51:52 cases we can actually check boxes off if there's any overlap with the current tech stack. Got to

51:57 know about that. And so you're just doing a sales process inside your own organization. And then

52:02 when you build a business case for it, you'll know before anybody else, if you want to present it,

52:07 like, you know, you'll know if you can actually have an ROI story to tell for an investment that

52:11 we're about to make. And I force my vendors to go through that process with me. Like I make them

52:16 help me develop my business cases. Most people don't do that very well.

52:21 What about on the headcount side? I think that makes a ton of sense on the tool side. And then

52:25 you could do opportunity costs, like you mentioned, like, "Hey, I could go build it. I could go code

52:29 something. And I could do the enablement. That's going to be, you know, 20, 40 hours of my time if

52:34 you want to do that. I'm not going to be able to do these things." What about on the headcount side?

52:38 Would you ever get the pushback of like, "Ryan, can you just do it all? Get you some more

52:42 cloud tokens and a few more tools?" What do you need more headcount for?

52:47 Yeah. And I mean, honestly, I think one of the things my leadership loves about me is like my

52:53 first solution that comes out of my mouth is never we need more headcount, right? It's almost

52:57 always how can we be more efficient using what we have to get more, you know, how do we get more

53:02 toothpaste out of the tube if you want to put it that way. But there are times where like, you know,

53:08 if we don't have market coverage, right, and we've got accounts that aren't being worked on and,

53:12 you know, they're stale and we don't actually have enough people to work the market and the

53:18 identified ICP that we have, then yes, there's obviously some situations where you need to

53:22 backfill. But, you know, we've been trying to make the case, you know, when we went from 12 to 8,

53:28 yeah, they want us to backfill. But, you know, and their preference is we put people back in those

53:35 seats. But for us, we were able to do some account shuffling, brought in a couple of BDRs and put

53:40 together a different look at our motion that allowed us to be more efficient without having

53:45 to go back and get a bunch of account executives and ramp them up. And so sometimes it's not,

53:49 you know, sometimes it's like, are we doing, are you talking about the right FTE? Because you guys

53:54 want us to get four new account executives. I think we can bring on three BDRs and actually

53:59 supercharge pipeline for the people who have already been performing and are still here.

54:04 Right. And so I don't want to invest in their competition and watering down territories and

54:08 accounts. I want to invest in resources with a different look, a different strategy. But,

54:14 you know, that was something that had to be discussed among the execs. It was something

54:18 that had to be sold for three, four years. They believed outbound does not work for our

54:24 industry and our company, and they've never had a BDR team. But good data, right, seeing some things

54:30 early with our account execs and knowing that there's finite numbers of accounts presented

54:34 the business case. And it turned out, yeah, you know, the resources make sense to backfill some

54:38 roles, but not in the way, you know, that the traditional, you know, workflow would happen.

54:43 Oh, somebody goes down, we fill it. We've got market coverage. We stopped asking those questions.

54:47 Now we're trying to ask, you know, what is the role we need? Like, what's the business today and

54:52 what does it need to be in 12 months? And are we the team that it's going to take to get there? And

54:56 if we're not, who else do we need? And if we don't need anybody else, how much better do we have to

55:00 get? But we kind of, you know, think through those processes before we just assume throwing bodies

55:05 and problems is, you know, the best approach. That growing all cost stuff, I think, at this point is

55:10 pretty much war itself out. Like, I think those days are over. Yeah, yeah, they've been over for

55:15 a little bit. It was a fun party, though. I got to enjoy a lot of it in the 2010s. I have an exit

55:20 during that time. But I was lucky enough to be on one of those. I think my founder there timed it

55:28 perfect. But it's tough for some teams to know, do I need one RevOps person? Can that one RevOps

55:37 person help me when our sales team is 20 people, 50 people, 300 people? Like, when do you start to

55:44 need some more people? But I think, yeah, starting with, hey, how can we be more efficient and solve

55:49 this in a creative way is great. Just giving people a gauge. Like, there is a point in time where it's

55:56 like, hey, I can't do everything that the business wants me to do. There's an endless task list and

56:02 endless project list and things that could really move the needle. And we need to bring in some more

56:06 people. So just wondering if you had a gauge for where that is. But I know it's a complicated,

56:10 complicated thing to try to gauge out. Yeah, I mean, for us, it's pretty clear because we have

56:15 a finite universe to sell into, right? So ours is activation rate. Like, do we have good activation

56:19 and coverage across, right? And if we do, then we don't need to bring any more bodies on. And usually,

56:25 you know, it's like, even if we lose a couple, you know, could we do more enablement, more

56:29 optimization, you know, more operation support to let the people that are still here, that do perform,

56:35 that have a high level skill set, you know, can we just give them more opportunity, right? And that's

56:41 sort of the approach that we've taken. Now, if you're SMB mid market, and you've got thousands

56:45 and thousands of thousands of accounts, like, yeah, there's probably a different thought process

56:49 and strategy that goes into it. But for us, it's really about, you know, do we have the adequate

56:54 coverage of the market? And are we enabling and putting our resources into the right people?

57:00 100%. Well, Ryan, this has been an amazing conversation. Thank you for sharing everything

57:08 that you brought to the table. I think everything from how do you get your team enabled and AI

57:14 pilled as much as you are, so you can start to really get that across the organization and start

57:20 getting the benefits of that. Then I think I really appreciate you bring a demo to context graph and

57:25 walking through step by step how to start building that out. Because the AI strategy is a data model

57:32 problem. It's not an LLM problem. They're already, you already have way more horsepower than you'll

57:37 ever really take out of them. It's really how can you get the right context fed to them so that way

57:41 you can make the most out of it. And then I really appreciate you walking us through where you think

57:45 RevOps is going, and how to think about building out your team. Because I think in this new world,

57:50 the role is changing. The expectations are changing. Everything is really starting to

57:57 change in this industry. So having some gauge of how should we be approaching this is super helpful.

58:03 So Ryan, I appreciate everything you brought to the podcast today. I can't wait to watch

58:08 what you and handle do next and follow all of your success. All right, man. Appreciate it. Thank you.