---
title: "Stop Hiring GTM Engineers. They Already Work For You."
episode: 123
podcast: "The LeanScale Podcast"
publisher: "LeanScale"
guest: "Ryan Vanshur"
guest_title: "VP of GTM Intelligence and AI Solutions, Handle"
date_published: 2026-09-22
date_modified: 2026-09-28
duration: 00:58:19
word_count: 11244
topics: ["ai-in-gtm", "revenue-operations", "sales-enablement", "gtm-strategy"]
canonical_url: https://www.leanscale.team/knowledge/podcast/ryan-vanshur-handle-gtm-engineers-already-work-for-you/
source: "LeanScale Knowledge Hub — https://www.leanscale.team/knowledge"
license: "Free to quote and cite with attribution to The LeanScale Podcast."
---

# Stop Hiring GTM Engineers. They Already Work For You. — Full Transcript

> Episode 123 of The LeanScale Podcast, with Ryan Vanshur.
> Published September 22, 2026 · 00:58:19 · 11244 words.
> Machine-transcribed and **not diarized** — speaker attribution is inferred, so verify
> attribution against the audio before quoting a specific person.
> Structured breakdown: https://www.leanscale.team/knowledge/podcast/ryan-vanshur-handle-gtm-engineers-already-work-for-you/

## 00:00 — Cold open and intro

**[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.

## 01:17 — The GTM engineer you're hiring already works for you

**[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.

## 04:43 — How to find the one builder inside each department

**[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

## 07:46 — Vibe-coding tech debt: data has a shelf life

**[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 — Why the context graph is the layer that matters

**[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 — Screen share: inside a live go-to-market context graph

**[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 — Build your own graph, step by step

**[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 — 75% of it was outdated: the content audit

**[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 — The wave theory of AI adoption

**[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 — The numbers: cycle time, win rate, and ACV

**[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 — Where RevOps is going

**[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 — Build vs. buy: micro-SaaS and why he won't rebuild the CRM

**[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 — What the Salesforce-admin generation needs to learn now

**[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 — Making the resourcing case to your exec team

**[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 — Headcount: three BDRs instead of four AEs

**[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 — Wrap

**[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.