---
title: "Your AI Strategy Is Really a Data Model Problem"
episode: 129
podcast: "The LeanScale Podcast"
publisher: "LeanScale"
guest: "Anthony Enrico"
guest_title: "Co-Founder"
date_published: 2026-09-30
date_modified: 2026-10-21
duration: 00:06:15
word_count: 995
topics: ["ai-in-gtm", "revenue-operations", "gtm-strategy", "forecasting"]
canonical_url: https://www.leanscale.team/knowledge/podcast/anthony-enrico-leanscale-ai-strategy-really-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 Really a Data Model Problem — Full Transcript

> Episode 129 of The LeanScale Podcast, with Anthony Enrico.
> Published September 30, 2026 · 00:06:15 · 995 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/anthony-enrico-leanscale-ai-strategy-really-data-model-problem/

## 00:00 — Ask your AI which customers will expand

**[0:00]** Which of my customers are likely to expand this quarter?

**[0:04]** Go type that into cloud right now.

**[0:06]** You're connected to Salesforce, you're connected to Gong.

**[0:09]** It should be easy.

**[0:12]** And what comes back is confident, well-written,

**[0:15]** and you'll have no idea whether it's true or not.

**[0:19]** I don't think that's the model's fault.

**[0:21]** Your AI strategy is really a data model problem.

## 00:26 — What's a customer? What's expansion? The definitions problem

**[0:26]** Think about what it has to know

**[0:27]** to answer that seemingly simple question.

**[0:30]** What's a customer?

**[0:32]** Sounds like a silly question.

**[0:33]** Is it the field on the account?

**[0:35]** Do pilots count?

**[0:36]** Does a company that already gave notice

**[0:38]** still count as a customer?

**[0:39]** I've watched four people on one leadership team

**[0:42]** give four different answers.

**[0:45]** Then, what's an expansion?

**[0:48]** Are we talking about an increase in committed contract,

**[0:51]** more usage, and what does it mean to be likely to expand?

**[0:56]** AI doesn't know, so it does what it always does.

**[1:00]** It makes up the middle and infers as best as it can.

## 01:03 — Everyone rents the same Formula One car

**[1:03]** The Frontier Labs have built a Formula One engine.

**[1:07]** It's extraordinary, it gets better every single quarter,

**[1:10]** and you and your closest competitor

**[1:12]** are renting the exact same one.

**[1:14]** Nobody in your market has a horsepower problem.

**[1:18]** What you don't have is the map, the corner ahead,

**[1:22]** the line that won the last 10 races.

**[1:24]** Nobody sells you that, and without it,

**[1:26]** the car just reaches the same corner faster.

## 01:28 — Einstein on your payroll: Acme Corp, Acme Inc., Acme Limited

**[1:28]** Or to choose a different metaphor,

**[1:30]** put Einstein on the payroll,

**[1:32]** smartest hire you'll ever make, and he starts Monday.

**[1:36]** Ask him which deals are at risk.

**[1:38]** He won't be able to tell you.

**[1:39]** Not because he isn't clever,

**[1:41]** not because he isn't intelligent,

**[1:43]** because he's looking at Acme Corp. in the CRM,

**[1:45]** Acme Inc. in the marketing automation platform,

**[1:49]** Acme Limited in billing,

**[1:50]** with no idea if that's one company or four.

**[1:53]** Intelligence is not the bottleneck, access is.

## 01:58 — Why bother: getting more yield out of the GTM machine

**[1:58]** So why go through the trouble?

**[2:00]** Because all of it serves one thing,

**[2:02]** getting more yield out of the go-to-market machine

**[2:05]** you've already paid for.

**[2:06]** Most companies pour millions, if not tens of millions,

**[2:09]** into an organization whose whole job is producing revenue

**[2:13]** and have never measured what it yields.

## 02:16 — One point of conversion is $1M on a $20M plan

**[2:16]** Do the math on a $20 million plan.

**[2:19]** One point of conversion, 20% to 21%,

**[2:23]** is worth a million dollars.

**[2:25]** One day off the sales cycle allows you to cycle

**[2:28]** in another quarter million within that year.

**[2:31]** No new headcount, no price change, not one extra lead.

**[2:36]** Same exact machine, higher yield,

**[2:39]** and the two levers continue to compound together.

## 02:43 — The model keeps climbing. Your usable context is flat.

**[2:43]** And I think this is the part that tends to get missed.

**[2:47]** What the Frontier model can do climbs every single quarter.

**[2:50]** What you can actually use has been flat for two years

**[2:54]** because it's capped by your context, not by your model.

**[2:58]** Every single point of the gap is yours to close.

## 03:02 — Layer one: the semantic layer (the GTM brain)

**[3:02]** Two pieces to closing it.

**[3:03]** And neither one needs a data science team

**[3:06]** or a fine-tuned model to accomplish.

**[3:08]** First is the semantic layer.

**[3:10]** We call ours the GTM brain.

**[3:12]** It's your business logic written down in plain English

**[3:15]** in one place your AI can read.

**[3:17]** What win rate means here, how we count bookings,

**[3:20]** what qualified means.

**[3:22]** Ours lives in GitHub.

**[3:23]** Every model can connect cleanly,

**[3:25]** but even a Google share drive will do.

## 03:27 — The one-owner rule

**[3:27]** One rule matters more than the tooling.

**[3:29]** Every metric gets exactly one owner, a human, not a team.

**[3:35]** A definition owned by RevOps is owned by nobody.

**[3:40]** Inside a quarter, you've got two versions of win rate

**[3:43]** and an argument about which is right.

**[3:46]** Who's the owner?

**[3:47]** Whoever has to defend the number in the board meeting.

## 03:50 — Layer two: the context graph

**[3:50]** Second is the context graph, which is a fancy way of saying

**[3:54]** connect the dots between your data.

**[3:57]** Contracts to the right opportunities,

**[3:59]** to the right accounts, with the right people on them.

**[4:01]** For almost everybody watching,

**[4:03]** your CRM is actually your first context graph.

**[4:08]** It's already there.

**[4:09]** So if you go clean it up, you have a great V zero.

## 04:13 — Line-item fan-out and false engagement

**[4:13]** Two things can happen when you don't.

**[4:16]** Products fan out across line items.

**[4:18]** So a $10,000 deal shows up as $30,000 forecast.

**[4:22]** Nobody catches it.

**[4:23]** Or an account has 150 touch points and looks fantastic

**[4:27]** until you look closer and they're unanswered support tickets.

## 04:31 — Never let the model author your business logic

**[4:31]** One hard rule, never let the model

**[4:33]** author your business logic.

**[4:35]** Ask it what a good renewal looks like

**[4:37]** and it'll grab standard industry practice and ship it

**[4:40]** because that's the fastest path from A to B.

**[4:43]** Unless you're running the most vanilla lemonade stand

**[4:46]** SaaS company alive, that isn't your business.

**[4:49]** You write it down, let AI turn the Google Doc into YAML,

**[4:53]** that part it's great at.

## 04:55 — The health score that used to need a data science team

**[4:55]** What really surprised me is that once the data model exists,

**[4:58]** the agents are the easy part.

**[5:02]** When I ran customer success at a company called Emailage,

**[5:04]** I had over 40 customer success managers around the world

**[5:09]** and hundreds of customers.

**[5:11]** And the one thing every head of CS wants,

**[5:14]** a health score you can actually trust

**[5:16]** was effectively impossible.

**[5:18]** You would have needed at the time

**[5:20]** a complete data science team to even attempt answering them.

**[5:24]** With the two layers underneath, it's days of work.

**[5:27]** Start with the agents instead

**[5:29]** and you'll reverse engineer a private little data model

**[5:31]** for every single agent you build.

## 05:34 — A 10-week project, and three things to do Monday

**[5:34]** Realistically, if you wanna implement this in your company,

**[5:36]** it's a 10 week project and half of it isn't even engineering.

**[5:40]** It's getting your leaders to agree on what words mean.

**[5:43]** That's the hard part, as it always has been,

**[5:46]** but especially now.

**[5:47]** Three things you can do on Monday that requires zero budget.

**[5:51]** Write down the 20 definitions,

**[5:52]** every number you're reporting depends on,

**[5:55]** put one name against each,

**[5:57]** write down where each number comes from,

**[5:59]** their source of truth.

**[6:01]** For everything I'm talking about today,

**[6:02]** the deck, the worksheet, tens of agents,

**[6:05]** it's all at leanscale.team/mcp.

**[6:09]** Your model is fine, go build the map.