---
title: "Your AI Strategy Is a Data Model Problem"
episode: 122
podcast: "The LeanScale Podcast"
publisher: "LeanScale"
guest: "Jake Toepel"
guest_title: "Chief Technology Officer"
date_published: 2026-09-21
date_modified: 2026-09-28
duration: 00:45:52
word_count: 8464
topics: ["ai-in-gtm", "revenue-operations", "forecasting", "gtm-strategy"]
canonical_url: https://www.leanscale.team/knowledge/podcast/jake-toepel-leanscale-ai-strategy-data-model-problem/
source: "LeanScale Knowledge Hub — https://www.leanscale.team/knowledge"
license: "Free to quote and cite with attribution to The LeanScale Podcast."
---

# Your AI Strategy Is a Data Model Problem — Full Transcript

> Episode 122 of The LeanScale Podcast, with Jake Toepel.
> Published September 21, 2026 · 00:45:52 · 8464 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/jake-toepel-leanscale-ai-strategy-data-model-problem/

## 00:00 — Why we recorded the Updata CEO Summit talk

**[0:00]** So Jake and I were at the UpData Partners CEO Summit the other week and the presentation really,

**[0:08]** really landed. It was a room full of CEOs. We presented on the idea that your AI strategy

**[0:15]** is really a data model problem. And we felt like it resonated so well that we wanted to record the

**[0:21]** presentation, go through it, and then just share it with the audience and all of you.

**[0:26]** So relevant in the market right now where everyone's just throwing out Claude licenses,

**[0:32]** trying to increase their usage where they can and try to maximize the value that they get in return

**[0:37]** from that token spend. And I think it's transitioning now from how much can we do

**[0:44]** with these solutions like chat GPT and Claude to how optimal can we use them going forward.

**[0:50]** And summarize everything, semantic layer, write down your definitions, find a good place to store

**[0:56]** it that you can put your AI to, context graph, connect the dots of your data between systems,

**[1:02]** even within systems. Man, once mythos comes out, once chat GPT launches their new model,

**[1:08]** this is going to change everything. More horsepower isn't going to build the map,

**[1:12]** more intelligence isn't going to give you the context. And then making Usain Bolt run faster

**[1:17]** isn't going to solve the toddler leg. If you're in a $20 million plan with a 20% conversion rate,

**[1:23]** increasing the conversion rate by one point will give you another million dollars in the plan.

**[1:30]** Do not let AI author your business logic. It is not there yet.

**[1:46]** So Jake, thanks for being my wingman out in DC and then my wingman for this as well.

**[1:53]** And I'm really excited to get into it. Yeah, same here, man. Like you said,

**[1:58]** this is just so relevant in the market right now where everyone's just throwing out Claude licenses,

## 02:00 — "Which of my customers are likely to expand this quarter?"

**[2:04]** trying to increase their usage where they can and try to maximize the value that they get in return

**[2:10]** from that token spend. And I think it's transitioning now from how much can we do with these

**[2:17]** solutions like chat GPT and Claude to how optimal can we use them going forward. So this is kind of

**[2:25]** the first step in that. And I'm really excited to take this out to the broader market with you.

**[2:30]** Awesome. Awesome. Well, let's dive into it. We're going to go through the same presentation we went

**[2:34]** through in the crowd. So we'll just go through the slides. We also have a few giveaways at the end.

**[2:38]** So if you hang on for the end of the podcast, you'll have a QR code where you can go download

**[2:45]** a lot of the materials that we have access to here. And then just you can get everything going

**[2:52]** in your company. So first couple things we're going to go through. We're going to really anchor

**[2:56]** this into why this is important. So this again, in the context of revenue operations, marketing

**[3:03]** operations, go to market for B2B SAS and AI companies, we all inherently have the FOMO of

**[3:11]** like, hey, we need to be implementing AI. We know it's important, but let's remind ourselves of the

**[3:18]** why. Then we'll go into what it actually is and what we're talking about, break it down in a really,

**[3:25]** really detailed manner, show you the art of the possible. So once you have a really strong data

**[3:29]** model in place, what are the type of AI agents and workflows you can build and work on? We'll give

**[3:34]** you a full roadmap on how to deploy it, how to set it up in your company. We'll give a quick reminder

**[3:39]** of why it's important, and then we'll have the giveaways at the end as well. So before we fully

**[3:45]** get started, I think this was one of the inspirations. It was asking a super on paper, simple

**[3:52]** question, which of my customers are likely to expand this quarter? Which seems like a really

**[3:59]** natural thing. You would just type in a cloud and be like, hey, I'm connected to Salesforce,

**[4:03]** I'm connected to HubSpot. It even has access to, you know, Gong for my transcripts. I should be

**[4:09]** able to ask this and just hit go. And then I think what happens that I think we don't appreciate

**[4:16]** is there are a lot of layers to this question and a lot of things that the AI is going to need to

**[4:22]** know like, what is a customer? It seems like a really simple question, but where is my source

**[4:29]** of truth for what a customer is and what it isn't? What does expansion mean? Are we talking about

**[4:37]** committed contracts? Are we talking about more usage? What do we mean by expand? And then what

**[4:42]** drives likelihood? What in your world means a company is likely to expand? So all of those things

**[4:51]** are struggles an AI model will have if you're trying to ask this question. So Jake, on the

**[5:01]** presentation, this was I think my favorite part, you going through the metaphors, because I think

**[5:05]** it helps anchor like how wait, shouldn't the AI just be able to figure this out? But anyway,

**[5:10]** I'll kick it to you to go through some of these. Yeah, absolutely. And a lot of this, you know,

**[5:15]** is lessons learned the hard way, as Anthony was alluding to asking a simple question, the AI sees

**[5:21]** all of this data and all this information. But again, it doesn't really have the understanding.

**[5:26]** And we're going to talk a lot about that. And a lot of the times, what we thought,

**[5:31]** even six months ago, was man, once mythos comes out, once chat GPT launches their new model,

**[5:38]** this is going to change everything. It's just going to work. And what we found is that's just

**[5:43]** not the case. The model is already trained on all the publicly available information that's ever

**[5:49]** been created in history. It's a mind boggling amount of information. But what it doesn't have

**[5:55]** is the context and insights that make your company unique, how you run your business, how you operate,

**[6:01]** everything you need to guide it to that next stage. And we really think of this like an F1 car,

**[6:07]** it is a highly tuned optimized performance machine. That's what these providers are bringing to these

**[6:15]** LLM models. They're best in class, best in breed, and they're making incremental gains on things

**[6:21]** like coding in different areas. But an F1 car, if you don't give the driver a map, he's going to have

**[6:28]** no idea where to go, right? We could be driving in the complete wrong direction for miles before

**[6:35]** we realize, hey, we need to course correct here. So a lot of what we talk about today is going to

**[6:39]** be how you build that map in a very, very simple way. Another one that I really love, I really love

**[6:47]** this one, is you could hire the smartest person that you've ever met. And let's say you haven't

**[6:52]** met them. We'll use Einstein in this example. He comes in day one as a GTM analyst looking at all

**[6:58]** the information in your CRM. He doesn't know what to make of it. You ask him what deals are at risk.

**[7:04]** He has no idea. He doesn't know which ACME is the real ACME across all of your different systems.

**[7:09]** You've got ACME Inc. in your marketing automation platform, ACME Co. in your CRM, and just ACME in

**[7:15]** your product database. Are those the same client? Are they different? How was he supposed to infer

**[7:20]** that on his first day? And that's what these models do. They just make up the middle and figure out the

**[7:26]** path along the way. We're going to show you exactly how you steer them in the right direction. So when

**[7:31]** you hire Einstein that first day, you bring on the new model, whatever rolls out next, it has all of

**[7:38]** the information needed to be successful. And then lastly, I love this one as well. Anthony and I both

**[7:45]** have toddlers at home. So this one, this one hits deeply. You could have the fastest runner in the

**[7:51]** world in a two leg sprint. The first leg is run by Usain Bolt, whoever the fastest fan alive is

**[7:57]** nowadays. You're not going to make incremental gains by coaching him when the second leg of the

**[8:03]** race is being run by a toddler. What we see here is again, tripping, falling, stumbling over itself,

**[8:09]** getting distracted by other things and running down a weird path where you're chasing them and

**[8:13]** trying to pull them back to what you want them to focus on. Again, you need to coach that second

## 08:17 — Why this matters: yield from the go-to-market machine

**[8:19]** leg. You need to steer the toddler in the right direction. One more that I'll throw out here is

**[8:25]** essentially the Einstein example. You're hiring these AI models every single day. Every single

**[8:31]** day is their first day that you're onboarding them. So what we're going to show you is how you get

**[8:36]** out of the onboarding constant cycle of informing the AI, telling them what's going on, bringing

**[8:44]** them up to speed and really operating from a consistent foundation. So again, regardless of

**[8:49]** the model of the tool that you're bringing on, everything is operating off of the same foundation.

**[8:55]** Yeah, and I think just to reiterate what you've said, there's just so many people. I was kind of

**[8:59]** in this camp too, where it's like, oh, the model is going to get better and do the work for me.

**[9:05]** It's like, no, categorically, just by the sheer definition of what it is we're talking about,

**[9:12]** more horsepower isn't going to build the map, more intelligence isn't going to give you the context,

**[9:17]** and then making Usain Bolt run faster isn't going to solve the toddler leg. So I think just making

**[9:24]** sure people know that, oh, this isn't something that the model will just solve for me is really

**[9:30]** important. And then why is this important? Okay, we're all in go-to-market, we're in revenue

**[9:36]** operations roles, CROs, CMOs. Why do we even need to be deploying in the first place? And I think

**[9:43]** for us, it really all comes back to one thing, which is maximizing yield from your go-to-market

**[9:50]** machine. A lot of these companies, I know, Jake, you've seen a ton of them, they're investing

**[9:57]** millions, they're investing tens of millions of dollars into these massive machines and

**[10:03]** organizations that are designed to go out and get revenue, make bookings, grow customers,

**[10:08]** convert deals. And a lot of times, companies really aren't even measuring what the yield

**[10:13]** of this investment is bringing. And the most effective way to get more production out of

**[10:21]** the investment you've made in your go-to-market engine is to increase yield. So increase your

**[10:26]** conversion rate, reduce your time and stage. If you do this really well, I know people understand

**[10:32]** that the gains can be really massive. Sometimes I think people forget the magnitude. If you're

**[10:39]** in a $20 million plan with a 20% conversion rate, increasing the conversion rate by one point

**[10:47]** will give you another million dollars in the plan. And reducing the sales cycle by one day

**[10:54]** can help cycle in a quarter of a million dollars of revenue, which could be the difference of making

**[10:59]** your quarter or not. So I just wanted to share, I think this is people, they know it's important,

**[11:06]** but sometimes I think forget the magnitude. And especially if you combine both of these at the

**[11:11]** same time. So when we're talking about leveraging AI to get more out of your go-to-market and why

**[11:17]** you should invest in a robust data model in order to do that, this is why. It's how can I increase

**[11:23]** my conversion rate by a couple points? How can I reduce my sales cycle by a couple of days? And if

**[11:28]** you can do both of those at the same time, the results are really going to compound. I think

**[11:33]** unfortunately what people are missing right now is they're not getting everything out of the models.

**[11:42]** The ceiling of what value they could be extracting out of AI right now is completely capped and

**[11:48]** limited by the data model they have in place. So I think all of those potential conversion rate

**[11:54]** improvements, time and stage improvements, really are being lost until you get this data model

**[11:59]** problem figured out. Okay. So now we know kind of why it's important and seen some numbers behind

**[12:05]** it, which is pretty illuminating. When you think about those just small incremental gains and how

## 12:10 — The semantic layer, or what we call the GTM brain

**[12:11]** much they add up to over time, I think it's super important to show exactly how you do this and how

**[12:16]** we see clients getting started with this. I think also there's a perception in the market that

**[12:23]** we need to fine tune a model or we need data science to come in and build kind of a custom

**[12:28]** distilled version of some of these models that are unique to our business. When in reality,

**[12:32]** it's much, much simpler than that. So first and foremost is the semantic layer. And here at lean

**[12:39]** scale internally, we call this our GTM brain. Essentially all this is to boil it all down and

**[12:45]** make it nice and simple is all of your business logic, all of your context written down codified

**[12:52]** in some sort of repo. We use GitHub internally. It's widely distributed. You can start for free,

**[12:59]** totally easy to set up and it integrates with all of these different LLMs nice and cleanly. They

**[13:04]** know exactly how to use it, but you can also use something like Google drive to house this.

**[13:08]** Wherever makes the most sense for your team and your agents to be able to access this data

**[13:13]** is where it should ultimately live. The main thing here, there's no real limits to what you put into

**[13:20]** this GTM brain semantic layer. We have everything from our brand messaging, our design kits,

**[13:27]** who we sell to and who we don't, what products we have on the shelf today,

**[13:31]** what good looks like to us for each individual project that we run. And then we also have where

**[13:37]** to find key business insights and really defining all of those at a super fine granular level.

**[13:43]** So this starts just as simple as saying, what does win rate mean at our company? How do we classify

**[13:50]** bookings and what value should we be looking at across different pipelines, across different

**[13:56]** segments, whatever that looks like unique to your business should be documented and codified here.

**[14:02]** The key thing that I'd really like to point out, and I'll talk about it a little bit later as well,

**[14:06]** but having one owner for these metrics is massive. You need to have one person who's really controlling

**[14:13]** the narrative on each of these. And that's to avoid many cooks in the kitchen and CS not buying

**[14:19]** into the way that sales is operating or marketing is operating. Everybody should be working again out

**[14:24]** of this consistent foundation. We've seen this across multiple clients where we ask a question,

## 14:30 — The $10,000 deal that reports as $30,000

**[14:32]** what's my forecasted ARR for this quarter? Super simple, nice and clean. The AI goes out,

**[14:37]** hits your back end to look at your product usage and start doing some forecasting.

**[14:42]** And what it does is it runs some very simple SQL. The SQL responds with data and the AI says,

**[14:48]** nice, I did it. I got some data. This looks good. These are actual numbers. I can return this to the

**[14:54]** end user. What the AI doesn't understand is the nuance in your CRM or in your data warehouse,

**[15:00]** where we're looking at opportunity products or product usage, and it's fanned out across

**[15:05]** multiple line items. So now a $10,000 deal looks like a $30,000 deal in your forecast.

**[15:12]** You're over reporting over estimating. And that's just one very small example of what we see day in

**[15:17]** and day out. Now the context graph, again, to boil this all down and make it super simple for everyone.

**[15:24]** All this is, is connecting all of the different dots that you have, all of the different data

**[15:28]** points that you've gathered through enrichment, through events, through outreach, whatever that

**[15:33]** may be. It's so important that you have this data model defined so the AI isn't guessing which

**[15:40]** records are associated to which. We really think of this in a crawl, walk, run format. And we think

**[15:46]** most should just start with making their CRM their true first context graph. All of the data

**[15:52]** should be there already. If it's not, bring in key product data, key usage insights. Whatever is

**[15:59]** missing to make it a complete picture of your organization, that should be tied back to your

## 16:00 — Tokenomics, and when the gravy train ends

**[16:04]** source of truth, which we ultimately think should be the CRM. So it's just making sure your contracts

**[16:11]** are associated to the right opportunities, which are associated to the right accounts,

**[16:15]** which have the right contact roles defined so that the AI can go in and very quickly understand

**[16:21]** what an account looks like, all the key players in the game, what a good renewal looks like, what a

**[16:26]** bad renewal looks like. If it's disparate and disconnected, you're going to get overinflated

**[16:32]** or underinflated numbers and not be able to address the root issue. There's also an aspect of this

**[16:38]** that comes down to tokenomics. Again, going back to six months ago, we had a big token leaderboard

**[16:44]** and all we wanted to see was that number keep climbing. And now what we want to see is the output

**[16:48]** relative to the number of tokens. So we're really looking at this from an optimization perspective,

**[16:54]** where your AI isn't going to have to index your entire CRM every time. If it has these connections,

**[17:00]** it makes very easy hops through the data. Instead of saying, here's this opportunity, can you go look

**[17:05]** at all the usage data behind this? Now it has to index this entire database and try to piece

**[17:11]** together exactly which of the custom objects are tied back. So this is where we see extreme value

**[17:17]** in getting your CRM dialed in. When cloud force rolls out, as we've seen with the native connector

**[17:22]** and all the skills that they've baked in, that's not going to save you on the token side if your

**[17:26]** CRM doesn't have the good quality associations to meetings booked to the tickets filed. All of that

**[17:34]** is so, so important to again, building that holistic picture of what's going on inside your company.

**[17:40]** Yeah, I think a lot of people don't realize. So you're mentioning like, yeah, lean scale,

**[17:44]** we were token maxing all day and a great time is fun. I think a lot of creativity, but

**[17:54]** we definitely are going to have to get more efficient. You know, anthropic opening eye,

**[17:58]** they're already starting to cut how, what your token limits are within some of their plans.

**[18:04]** So understanding, okay, how do I make this a more efficient process? It's kind of like a nice to have

**[18:10]** maybe six months ago in the future. This is going to be unbelievably important. And I think your

**[18:18]** CFOs are going to be all over this. So laying the foundation early now. Okay. We got the semantic

**[18:24]** layer in place, got the context graph set up. Um, there's different levels to this too, but

**[18:29]** getting that done early is going to save people a lot of money.

**[18:33]** A hundred percent. And as I always say, it's a matter of when not if we know that these providers

**[18:40]** are going to pull the rug out on these gravy train max plans that, you know, for $200, you get

**[18:46]** $5,000 worth of usage. And they know that they have all the insights on that usage and they are

**[18:53]** going to start cranking up the dial. So to your point, if you address this now, while you're still

**[18:58]** on the gravy train, it saves you from the fire drill, two, three, four, who knows how many months

**[19:04]** from now, when you get that first 25, $30,000 a month bill and have to go explain to your CFO

**[19:10]** exactly why you need to cut budget in other areas to support this overinflated AI usage.

**[19:17]** If I were to guess, it's going to be one quarter post IPO from either open hair and drop pick or

**[19:24]** whoever. So again, to tie it back and a little bit, I guess I'm a pessimist, so I used which deals are

**[19:31]** at risk instead of which deals are going to expand, but just to tie it back to a core business question

**[19:37]** that, you know, each and every one of us probably have questions about and have plugged into one of

**[19:41]** our LLMs in the past. So before it really started to look at how many touch points are on this

**[19:49]** account, right? And so a healthy account might have X number of touch points, but what it doesn't

**[19:55]** understand is the sentiment behind some of those touch points. Tools like Gong are great at this.

**[20:01]** They've been doing this for a long time. Sentiment analysis is so important, but when stuff comes in,

**[20:06]** let's just say through email, through Slack, you're not necessarily going to get that insight

**[20:11]** straight out of the box. So an account that has 150 touch points looks great until we start to

**[20:18]** look and say, no, those are support tickets that haven't been answered, right? We need to start to

**[20:23]** classify what good touch points look like, what bad touch points look like. And that's where the

**[20:28]** semantic layer comes in is it really flattens this out to say, what are all the really good

**[20:33]** opportunities looking like? What are all the really negative ones looking like? So we start to do this

**[20:37]** cohort analysis and really break down the data into these identifiable buckets that can then be

**[20:43]** modified over time. So this isn't something that's permanent set in stone. You can always modify this,

**[20:49]** but without it, you're really operating from a very vanilla assumptions-based inferred model,

**[20:55]** no confidence behind it. Now the context graph that comes in with the additional receipts.

**[21:02]** So that's going to tell you exactly why certain things happened, why certain touch points were

**[21:07]** negative, how many of those in the ecosystem there were, and then putting that all together,

## 21:09 — Never let AI author your business logic

**[21:12]** the semantic layer in the context graph in the response is something that a human can actually

**[21:16]** take action on. And that is the key. When a human can take action on the insights given from an LLM,

**[21:23]** that's again, when we see the maximum value coming from those tokens. Now, a key here,

**[21:30]** do not let AI author your business logic. It is not there yet. And I don't think it ever will be,

**[21:38]** as we've alluded to, it can be the best, most performant model in the world. That is going

**[21:43]** to take the fastest route from point A to point B, especially as it starts to optimize on tokens.

**[21:48]** It's going to say, yep, what are standard industry practices? Boom, ship it. We are good to go.

**[21:53]** Unless you are running the most vanilla basic lemonade stand SaaS company in the world,

**[21:59]** that is not going to work for you. What you need to do is just start writing it down in plain

**[22:04]** English. Just start putting pen on paper. Start typing it out in a Google doc where AI can help

**[22:11]** is putting it into a format that's good for GitHub and good for agents to access. It can turn it from

**[22:16]** a text document into a YAML file, get all of that aligned, and then push it to GitHub. But what you

## 22:21 — Start with the metrics you defend in the boardroom

**[22:22]** don't want to do is ask it, hey, go look at my CRM and tell me what a good renewal looks like. No,

**[22:26]** no, no, no, no. That all needs to come from your insights, your historicals, and your team that are

**[22:32]** working these accounts day in and day out. So as we've talked about, I mean, setting this up is

**[22:38]** pretty cheap. You can basically get started with your current stack. If you have an LLM license

**[22:43]** and you have a CRM, you're pretty much ready to go. Just start with the basics. Don't try to get

**[22:49]** every possible metric that you've ever calculated done at the same time. Focus on the ones that

**[22:55]** matter the most. And I always tie this back to the toughest meetings, the board meetings.

**[23:01]** That's what I always try and tie it back to is focus on those metrics that you know you're going

**[23:06]** to have to speak to in this quarter. Then we can start to build the ones that add additional juice,

**[23:11]** help us move the needle on certain conversion metrics, or move things out of a stage faster.

**[23:17]** Those are all great. But the ones that you really want to make sure you have confidence

**[23:21]** on are the ones you have to defend in the board room. So I always try and emphasize getting that

**[23:27]** first set of metrics done codified and with a set owner is so crucial going forward.

**[23:34]** Then we start to look at ways to optimize the spend. And I think that's where the tokenomics

**[23:38]** come in and getting really clean data. So you're not going through and indexing your entire CRM

**[23:45]** every single time. And there's a lot of tools that can help in this space as well.

**[23:49]** ETL tools are now coming up with these unique ways of surfacing data in a blended fashion,

**[23:56]** where, again, you can pull in your campaigns from HubSpot, your gong calls, your clay data,

**[24:01]** and your CRM data to form a unified model that you're not bringing into your CRM.

**[24:07]** Or it's a distilled version of that. So only the key fields that matter to your company

**[24:11]** when you're doing forecasting. And I'm not having my AI, again, dig through 500 custom opportunity

**[24:18]** fields that we see. It's like a very distilled set to get the most juice out of those tokens

**[24:23]** that you're spending. Yeah. And I know there's quite a few tools that are coming out. One we're

**[24:30]** partnered with closely, Polyatomic. I think that's a really good option with their data harbors

**[24:35]** model so they can support this. I know their founder CEO is going to be giving a talk

**[24:43]** at OpStar coming up. So I think that's a good option. And I'm sure we're going to see more

**[24:48]** of these too as time goes on. I think this is going to be a huge need for a lot of companies

**[24:54]** that are trying to maximize the value of their AI and data model. 100% and have some sort of

**[25:00]** middleman because we see that all the time of the fear of connecting an LLM directly to your CRM.

**[25:06]** How do you control the data that it has access to? How do you make sure that reps aren't stealing

**[25:11]** each other's accounts and overriding the ownership? There's all of these dilemmas that need to be

**[25:17]** solved. And you alluded to one, Polyatomic is a great tool. We use it internally as well. And

**[25:23]** it allows us to really bifurcate systems and only allow the agent to access the data that we've

**[25:29]** approved. So again, we're distilling it down into this harbor. And then the harbor actually has its

**[25:34]** own context layer so you can put skills in there. So then anytime anyone connects to that, they're

**[25:40]** running the same consistent skills, the same consistent output on the same consistent data.

**[25:46]** I think consistency is kind of the name of the game here. Yeah, another platform too. And it'll

**[25:52]** kind of fit this maturity curve. I know you'll go through it a second. But Vasco. Vasco is a really

**[25:57]** good go-to-market platform that pretty much forces the function of building your semantic layer and

**[26:04]** will give you the context graph all in one. So as you go through the motions of setting up Vasco,

**[26:09]** you get two for one and giving that setup. And then you can point your AI just to Vasco and ask

## 26:14 — The maturity curve: centralized, resolved, defined, retrievable

**[26:14]** the questions you need to ask. So it's I think a couple of good options there. And I think there's

**[26:21]** more to come. And I think hopefully it'll make this process a little bit easier in the future too.

**[26:28]** Totally, totally. Yeah, and tracking to go is so important. There's just so much functionality

**[26:34]** out there that I think most teams haven't quite started exploring yet that's going to be really

**[26:38]** exciting over this next quarter. So on the maturity curve side, I think this one's interesting.

**[26:45]** There's definitely a shift happening right now. I know a lot of the companies that we used to work

**[26:49]** with, you know, we're evaluating a data warehouse, they might have like one little cluster that

**[26:55]** they're tracking their product usage in, but no CRM data flowing between the two. And we're seeing

**[27:01]** that shift earlier and earlier in the growth stages to where companies are investing pretty

**[27:06]** heavily in this data infrastructure early on, because they know the value down the line of

**[27:11]** tying this all together. So really what we're seeing is a lot of companies are at this centralized

**[27:16]** level one zone where they have the BI layer, they have the data warehouse, they've got all

**[27:21]** the infrastructure in place, but we still haven't defined where everything lives throughout that

**[27:28]** ecosystem to build that confidence with the AI. So that's when you move to level two, resolve those

**[27:34]** identities, really flatten out your duplicate problem. I know, I know it's a problem for

**[27:39]** everybody. It's a problem for us. We've actually developed agents and tools around this because

**[27:43]** it's just such a prevalent thing of duplicates, misaligned data, right? I'll go back to the ACME

**[27:49]** example, but even something like Amazon, you've got Amazon, you've got AWS, you've got Amazon

**[27:56]** logistics. There's so many different options in that one given account that it gets really hard

**[28:02]** to tie that all together or the AI is looking at AWS when it should be looking at Amazon because

**[28:07]** the domains are the same, really flattening that out, getting to that canonical identity model is

**[28:13]** huge. And I know I'm using a lot of fancy words there, but all that really means is deduping your

**[28:18]** CRM, getting one consistent key to identify an account, whether that's the domain, whether that's

**[28:23]** the record ID and the CRM, just making sure you have that across all your systems. So that flows

**[28:29]** all the way to the data warehouse. That's where we get to the definition metric. That's where you go

**[28:34]** through and say, if you're looking for win rate, here's the column you should look at and here's

**[28:39]** the filters you should apply. We're going to talk a little bit more about what that looks like,

**[28:42]** but I think this is where we're really trying to push our clients to, is getting them to this point

**[28:48]** where they have all their 20 to 30 key metrics defined, agreed upon, and version controlled.

**[28:56]** Now the agreed upon part is big and Anthony's going to talk a little bit about that as well,

**[28:59]** I'm sure, but getting that alignment is really the core problem. Sometimes difficult discussions

**[29:05]** need to be had. We work with a ton of different companies. I think all of them recognize revenue

**[29:11]** just a little bit differently. So it's really important that you come in and just define those

**[29:16]** owners, define what these mean to you. And going back to the Amazon example, one of their values

**[29:22]** is disagree and commit. And I think that's so important to bring into these meetings,

**[29:27]** trust the owner, even if you say, no, no, no, no, we've been using this spreadsheet over here and

**[29:31]** our calculations look much better. Well, that's great, but let's try this. Let's commit to this

**[29:37]** and build on this foundation and bring your insights in. Let's get to an ultimate foundation

**[29:42]** that we all agree upon. Level four, we move into the retrievable. So that's where a lot of the

**[29:48]** polyatomic harbors we talked about getting really good, clean associations in your CRM. So you're

**[29:53]** not indexing the entire database every single time is so important. This really comes down to

**[30:01]** highly retrievable, quick insights that don't cost a lot of spend. That's the next evolution. And

**[30:07]** again, you can get really advanced in this with the extra credit mode, going with a vectorized

**[30:12]** database that sits alongside your product warehouse. You can even do this in a cloud

**[30:16]** database, something like super base, using like a Postgres. But really, we think most should just

**[30:22]** start cleaning and associating in their CRM. That's where you're running your business today.

**[30:28]** That's where you're going to get the most juice out of all of the teams are working out of that

**[30:31]** platform. They don't need to connect to an additional database. And ultimately, you'd

**[30:36]** probably write a lot of this info back from that vectorized database to your CRM. So we really try

**[30:42]** to focus on using the tools you have today and getting the optimal usage out of them when it

**[30:46]** comes to AI. And this is a lot of work. It's not super easy to get all this set up. And I think

**[30:54]** it's really the hard part. If you think about, okay, there's two different camps. There's get

**[30:58]** the data model set up. And then there's build the agents, build the workflows. I think building the

**[31:04]** agents and workflows is so easy. That's the cheap part. If you have a great robust data model, all

**[31:10]** you have to do is ask Claude, hey, what type of agent can you build me for XYZ? Yeah, point to

**[31:14]** this data set. So when you do have the data models set up and everything ready to go, that's when,

**[31:23]** okay, yeah, we can line up all the sales agents, the marketing agents, CS agents,

**[31:28]** all the rev ops agents that we need to spin up. You can do things like forecasting. You can count

**[31:34]** on the forecasting analysis it's providing. One of the biggest things, so Jake and I, before we

**[31:41]** found our way back at LeanScale, we worked at a company called Emailage. And I, my first half at

**[31:49]** that company, I actually ran customer success. And I had 40 CSMs all around the world. We had

**[31:57]** hundreds and hundreds of customers. And it was almost impossible to get what every head of CS

**[32:05]** wants customer health. And it's because you're dealing with so much qualitative data, you're

**[32:10]** dealing with so much data, and there's so much analysis and sentiment to try to read that is

**[32:14]** nearly impossible. You'd need like a full data science team just to do it. But now if you get

**[32:19]** your data model set up, you can get a very robust customer health model and something you can rely

**[32:25]** on. It can help identify problems early on. It can help tee up companies that are likely to expand

**[32:31]** so you can maximize those opportunities. But there's just so much you could do. And a lot of

**[32:35]** these just won't take very long once you have a data model that can support it. If you're trying

**[32:40]** to start with the agent first, inevitably, you're just going to have to go reverse engineer the data

**[32:45]** model it needs to run, rather than, okay, why don't we just build this whole thing out that we can

**[32:51]** then run any agent on. You'll be able to deploy agents in half a day, ones that are production

**[32:59]** grade, you can share with your entire enterprise, they're scalable, reliable, trustworthy, where if

**[33:05]** again, if you just keep going to let's build the agent first, it's going to have to manually

**[33:10]** connect to all of the different sources of truth, you're going to be building hundreds of many data

**[33:14]** models for every agent you want to spin up. And just to double down on that point, just real quick.

**[33:19]** Yeah, I think it also democratizes the build. And for me as a CTO, that's so important, because for

**[33:26]** a long time, it was like, Jake is the vibe coder of this company, right? Like, if we need to do

**[33:31]** anything, it's got to run through Jake. But after we've built this out, and we've gotten everybody

**[33:36]** connected to GitHub, and we have these design kits, Anthony's thrown in a ton of PRs. I mean,

**[33:42]** he is cranking stuff out, like, if you would have told me six months ago, even that I'd be approving

**[33:46]** Anthony's PRs, I would have told you that you were crazy. But here we are. And it's amazing.

**[33:52]** It's awesome. And I know I can trust the output. I don't need to go back and reinvent the wheel

**[33:57]** every time. And so for us, it's really democratize the build of these, where each individual person

**[34:02]** can build their own agents or build their own automations with the confidence that it's

**[34:07]** operating again on that same foundation. So just to double down on how important it's been to us,

## 34:08 — Half of this is an alignment problem

**[34:13]** me personally, if I'm being honest, but it can be so impactful across organizations.

**[34:19]** It really has been impactful. And it has unlocked a lot of builders at LeanScale to be able to

**[34:26]** build something, like you said, that's built on data that you can trust and the results it's

**[34:33]** giving you you can trust and the way it's working. But also, then it's very easy to say, okay,

**[34:38]** that's awesome. Let's now get that exact same thing into the hands of the entire company

**[34:42]** so they can use it too. I think once you make the investment, you'll definitely see the value.

**[34:49]** So I want to go through how to deploy it. Okay, how are you going to actually get there?

**[34:53]** And Jake alluded to this earlier, literally half of this is an alignment problem. So the engineering

**[35:02]** here is not that difficult. It's something that I'll walk through the types of roles you'd likely

**[35:08]** want to lean on to get it done. There's a little bit of technical work to do, of course. But most

**[35:14]** of this is, okay, can we get on the same page? Can we all agree on what the definitions mean? Can we

**[35:19]** agree on what the source of truth is for all of these definitions? And then can we agree on the

**[35:24]** person who owns what this definition is? And if you can do that, then okay, now let's codify this

**[35:31]** and house this somewhere. And then let's make sure our data is connected meaningfully and clean it

**[35:36]** up, deduplication, all that stuff. Then you can go have fun building agents. But I don't want to

**[35:42]** diminish the fact that the hard part of this, like most things in life in general, is going to be

**[35:48]** alignment and communication between the teams. So starting with the semantic layer, the way

**[35:54]** you might want to think about it is, okay, every metric, every definition has to have an owner.

**[36:04]** And on the sales side, we need definitions on what a qualified opportunity might be.

**[36:09]** On the marketing side, oh my goodness, I can't tell you how many different definitions of a

**[36:14]** marketing qualified lead I have seen. You need to figure it out for your company. So write it down,

**[36:20]** agree on it, and then just stay consistent with what an MQL is in your company. Customer success.

## 36:26 — "What is a customer?" and other questions nobody agrees on

**[36:29]** I've run into this so many times too, which would probably be shocking to most people. What is a

**[36:33]** customer? And how do I know? Is it this field that says customer on Salesforce? Is it they have an

**[36:40]** account and our ERP? Is it that they've had an invoice? Do we count pilot accounts as customers?

**[36:46]** Do we count customers that have put in a churn notice still as customers? Or do we wait until

**[36:51]** the churn time comes? What is a customer here at this company? You'd be surprised at how many

**[36:56]** different definitions you can get on that. And then there's all kinds of company-wide ones like

**[37:00]** what's annual recurring revenue? For those who are listening, most of your company is valued on ARR.

**[37:07]** That's just the way people value companies, especially early on. So what is the definition?

**[37:15]** How do you articulate what your ARR is? Especially if you have usage-based products and a usage-based

**[37:20]** service too. So anyway, getting this all on paper, codified, and then stored in one spot,

**[37:26]** super important. Now I'll kick it to Jake to go through quickly, just technically, like okay,

**[37:34]** where does this live? But again, most of it's going to be that alignment. Totally, totally.

**[37:41]** And just to double down there, this isn't a new problem. This isn't something that AI invented

**[37:47]** with this lack of definitions around an organization. As you alluded to earlier at emailage,

**[37:51]** having 40 CSMs around the world, if we didn't onboard them properly and we just threw them

**[37:56]** into the wild, do you think they'd be successful? Probably not. So this is just really highlighting

**[38:02]** the importance of getting this stuff on paper because again, you don't want to be Adam Sandler

**[38:07]** in 51st states playing the same video every morning for your AI and trying to tell them exactly what

**[38:13]** bookings means to you. So not a new problem, just AI has definitely accentuated the need for it

**[38:18]** across all these different business units. So the semantic layer, I promise I'm not going to make

**[38:24]** you look at too much code here. Sorry if you just threw up in your mouth a little bit. This is

**[38:29]** YAML on the right and then just a simple folder structure on the left. So as I talked about

**[38:33]** earlier, we use GitHub for this. It's very native to all these LLMs. There's nice clean connectors.

**[38:39]** It's free to set up, free to use. You can house things privately. You can share them amongst the

**[38:44]** team. Very easy to configure, very easy to add automations around as well. So we're big fans of

**[38:49]** using GitHub for this, but you could even use Google Drive with the correct folder structure

**[38:54]** in just a shared drive format. So on the left is just that exact folder structure. It's the

**[38:59]** semantic layer first, the different models that we have, all of the different entries and metrics

**[39:04]** that we would want to capture. And one example of a metric is on the right there. So this is

**[39:09]** the YAML file. You'll see the metric at the very top. In this case, we're looking at win rate.

**[39:13]** The next line is the owner. And this is so important. We got a great question when we

**[39:19]** presented this live of how we think about who should be the owner. Ultimately, we're going to

**[39:24]** talk about some of the roles that are involved in this as well. But RevOps is kind of becoming that

**[39:30]** pioneer of change when it comes to AI and being kind of the first AI enabled team,

**[39:36]** making sure that we're rolling it out responsibly. But ultimately, your owner per metric, and I go

**[39:41]** back to this time and time again, is who's going to have to speak to it in a board meeting? Who

**[39:48]** is the owner of what that metric means? Because if you've ever walked into a meeting and tried to

**[39:52]** defend a number that you don't have context on, that is not a fun razzle dazzle game. Let me

**[39:57]** tell you, I've done it a couple times. I'll never do it again. It is not fun. You start sweating.

**[40:03]** So whoever is going to be speaking to that number should have full confidence in exactly how we

**[40:07]** defined it, how we got there, what it means to us, and be able to back that up. They also approve

**[40:13]** any changes. So like I said, and Anthony alluded to, there's tons of different ways to qualify MQLs.

**[40:19]** You can change that quarter over quarter. If you say, "Hey, we were over-qualifying a little bit.

**[40:23]** We want to tweak it down." Perfect. Submit a PR. Because the owner is in that metric defined in

**[40:29]** the code, they're going to get a notification to let them know, "Hey, so-and-so just submitted a

**[40:34]** new PR. They want to update the win rate metric. What do you think of this?" They can pick and choose

**[40:39]** and say, "Yeah, yeah, yeah. I really like that new lead scoring model." Or, "No, no, no. I don't want

**[40:43]** to change. We always want to MQL hand raisers." Whatever that looks like, they have a chance to

**[40:49]** interject and then stay in the loop as well. Because as we've alluded to, they're going to

**[40:53]** be speaking to these metrics. So super important that you just start writing these down. I know

**[40:58]** the code makes it look a little bit aggressive, but really you just need to start documenting this.

**[41:06]** Okay. The context graph. I'm not going to spend a ton of time talking about this one.

**[41:11]** Really, what I want you to understand is this is just connecting the dots in your CRM. But as

**[41:17]** important as it is to connect the dots, it's also just as important to say, "What are we not going

**[41:22]** to throw into this mix? What are we not going to tie together because we don't want that insight

**[41:26]** or it doesn't belong in this individual context graph?" So I think taking some time to really look

**[41:32]** at those associations, understand where you need additional, where the gaps exist today,

**[41:37]** and then where maybe things are overinflated as you click into some of those calls and you see

**[41:42]** internal calls being associated to a company record because they mentioned their name,

**[41:47]** those types of things that need to be flattened out and fixed

**[41:50]** before you have that full confidence in your CRM as your first context graph.

**[41:58]** So I can hop in here. There's a handful of roles. The ones I really want to focus on,

## 42:00 — Five roles, a ten-week roadmap, and how it goes wrong

**[42:03]** you have to have an executive sponsor to push this through. There needs to be some budget

**[42:07]** put here. You're going to need some tools. You're going to have to carve out people's time to make

**[42:10]** sure people know that this is important. So whoever that is, typically it's going to be your CRO,

**[42:14]** maybe even your CEO. Like Jake mentioned, RevOps is going to be a huge person to lean on. They are

**[42:20]** already owning a lot of the business context and I think they are a perfect linchpin between the

**[42:28]** teams to pull all this together. Now, if you have an analytics engineer and platform engineer on the

**[42:34]** team, I think that would be fantastic as well. And then whoever's running your CRM. So if somebody's

**[42:38]** running your CRM, that's a person that you're going to want in the room when you're making

**[42:42]** these decisions. If we were to space it out, everything's going to look a little different.

**[42:47]** What does your data environment look like, et cetera. But in general, you should be able to get

**[42:51]** this done in about 10 weeks. And if you notice, more than half of it is mainly alignment on

**[42:58]** definition, semantic layer tools you're going to use. And then it moves into the engineering side

**[43:03]** and the agent building side. But this is a 10-week roadmap that you can copy for your organization.

**[43:08]** And I think Taylor for your needs. A couple of things, ways this could go wrong. I don't

**[43:13]** want to harp on all these too much, but I think the biggest one is not having a single owner for

**[43:18]** definition. That's really where the wheels fall off on this. So, hey, your VP of sales

**[43:22]** owns what a qualified opportunity is. Your VP of marketing owns what an MQL is. I wouldn't have

**[43:28]** RevOps own many of these. I think most of these will live with a functional leader. And then one

**[43:36]** that we highlighted starting with the agent building. It's like, yes, you can try to build

**[43:40]** all the agents you want, but at the end of the day, they all need data to access. So,

**[43:44]** sequencing this is really important. And then just a reminder, hey, if you do this right,

**[43:50]** if you invest in the data model, you build the agents and you start to shave off a few days of

**[43:55]** the sales cycle, improve conversion by a couple points. These results continue to compound. And

**[44:02]** this is where you make the investment back. Just to strip the jargon and summarize everything.

**[44:07]** Semantic layer, write down your definitions, find a good place to store it that you can point your

**[44:12]** AI to. Context graph, connect the dots of your data between systems, even within systems.

**[44:19]** Just make sure the AI knows when you're asking a question and it goes to a particular person

## 44:21 — The giveaway

**[44:23]** or account, it knows what other data is connected to that. And if you think about it that way and

**[44:30]** make it pretty simple, I think you'll go pretty far. And then the last thing, this giveaway,

**[44:34]** this is all for you. So in here, you're going to get this presentation so you can download it,

**[44:38]** PDF PowerPoint, convert it to Markdown and throw it into cloud, whatever you want to do,

**[44:43]** you can have it. You'll also get the metric definition worksheet that you can work with

**[44:49]** your team. This should give you a head start. We have 10 pre-built agents that you can download.

**[44:54]** Now, none of them are going to be that effective unless you invest some time in your data model,

**[44:58]** but you're going to have them. And then also a way to stay connected with LeanScale. We actually

**[45:06]** put everything into our MCP. So if you go to leanscale.team, you can get connected to that.

**[45:12]** We have our playbooks, we have benchmarks, we have studies, we have podcast transcripts,

**[45:19]** if you want to know what CROs and CMOs are actually talking about. And you'll have all

**[45:24]** of the materials from here. And then if you're interested, we can also offer up a diagnostic

**[45:31]** to take a look under your hood and see where we can help. So I hope this was helpful. Jake,

**[45:37]** thanks for being my wingman as always. And I hope this helps whoever's listening,

**[45:42]** invest what they need to into getting their data model ready to get the most out of their AI.