Ask Claude which of your customers are likely to expand this quarter. It's connected to Salesforce and Gong, so it should be easy. What comes back is confident, well written, and you'll have no idea whether it's true. That isn't the model's fault. Your AI strategy is really a data model problem.

In this solo episode, LeanScale co-founder Anthony Enrico walks through why every company is renting the same Formula One engine from the frontier labs, and why the thing that actually separates you from your closest competitor is the map: your definitions, your owners, and a CRM that connects contracts to opportunities to accounts to people.

He lays out the two layers that close the gap without a data science team, the semantic layer (LeanScale calls theirs the GTM brain) and the context graph, the one rule that matters more than tooling, why you should never let the model write your business logic, and three things you can do on Monday with no budget.

WHAT YOU'LL LEARN

Why AI gives confident answers to GTM questions you can't verify

The three definitions hiding inside "which customers will expand?"

Why you and your competitor are renting the same engine, and what the real differentiator is

How identity resolution (Acme Corp vs. Acme Inc. vs. Acme Limited) breaks even a genius

The yield math: one point of conversion on a $20M plan is $1M

What a semantic layer is, and why it can live in something as simple as Google Drive

The one-owner rule: every metric gets exactly one human, not a team

What a context graph is, and why your CRM is already your first one

Why you should never let the model author your business logic

The three things to do Monday: 20 definitions, 20 owners, 20 sources of truth

CHAPTERS

00:00 — Ask your AI which customers will expand

00:50 — What's a customer? What's expansion? The definitions problem

01:25 — Everyone rents the same Formula One car

02:00 — Einstein on your payroll: Acme Corp, Acme Inc., Acme Limited

02:30 — Why bother: getting more yield out of the GTM machine

03:00 — One point of conversion is $1M on a $20M plan

03:30 — The model keeps climbing. Your usable context is flat.

03:50 — Layer one: the semantic layer (the GTM brain)

04:17 — The one-owner rule

04:45 — Layer two: the context graph

05:10 — Line-item fan-out and false engagement

05:25 — Never let the model author your business logic

05:50 — The health score that used to need a data science team

06:30 — A 10-week project, and three things to do Monday

ABOUT THE GUEST

Anthony Enrico is the co-founder and CEO of LeanScale, a tech-enabled RevOps and GTM operations firm that builds the systems, data models, and AI workflows behind B2B SaaS revenue teams. Before RevOps, he ran customer success with 40+ CSMs around the world and hundreds of customers. He hosts The LeanScale Podcast.

ABOUT THE LEANSCALE PODCAST

The LeanScale Podcast is where revenue leaders talk about how go-to-market actually gets built: RevOps, GTM strategy, AI, and the operators behind the number.

Get the deck, the worksheet, and 10 GTM agents ready to deploy → https://leanscale.team/mcp

Subscribe for new episodes every week.

Learn more about LeanScale → https://leanscale.team

KEYWORDS / TAGS

AI strategy, data model, semantic layer, context graph, GTM brain, RevOps, Revenue Operations, GTM, Go-to-Market, GTM Engineering, AI for GTM, AI Transformation, AI Agents, GTM agents, MCP, Model Context Protocol, Claude, Anthropic, Salesforce, Gong, CRM data, data quality, identity resolution, entity resolution, metric definitions, metric ownership, single source of truth, customer health score, expansion revenue, net revenue retention, pipeline forecasting, conversion rate, Sales Operations, B2B SaaS, Enterprise SaaS, LeanScale, Anthony Enrico

HASHTAGS

#RevOps #GTM #AI #LeanScale #LeanScalePodcast #AIStrategy #DataModel #SemanticLayer #ContextGraph #AIAgents #GTMEngineering #Salesforce #CRM #B2BSaaS #SalesOps

Your AI Strategy Is Really a Data Model Problem | Anthony Enrico, LeanScale

The LeanScale Podcast with Anthony Enrico

Your AI Strategy Is Really a Data Model Problem | Anthony Enrico, LeanScale