01
A confident AI answer you can't verify is a data model failure, not a model failure
Ask an AI connected to Salesforce and Gong which customers are likely to expand this quarter and it returns something confident and well written, with no way to check it. Anthony says that isn't the model's fault.
Why it matters: Before blaming or switching models, look at whether the definitions and connections the question depends on exist anywhere the AI can read.
Revenue ExecutivesFoundersRevOps Leaders
02
Simple questions hide definitions nobody has agreed on
Which customers will expand depends on what a customer is (the account field, pilots, a company that has given notice), what expansion is (committed contract or usage), and what likely means. Anthony has watched four people on one leadership team give four different answers to what a customer is.
Why it matters: Where definitions are missing, the AI infers the middle. If your leadership team disagrees on a term, the AI's answer is a guess dressed as an analysis.
Revenue ExecutivesRevOps LeadersCustomer Success
03
Everyone rents the same engine; the map is the differentiator
The frontier labs have built a Formula One engine that improves every quarter, and you and your closest competitor rent the same one. Nobody in your market has a horsepower problem. What you lack is the map: the corner ahead and the line that won the last ten races.
Why it matters: Model choice will not separate you from competitors. Your own definitions, owners and connected data will. Without them the car reaches the same corner faster.
FoundersRevenue Executives
04
Intelligence is not the bottleneck; access is
Hire Einstein on Monday and ask which deals are at risk, and he can't answer. He sees Acme Corp in the CRM, Acme Inc. in marketing automation and Acme Limited in billing, with no idea whether that is one company or four.
Why it matters: Identity resolution across systems is a prerequisite for any account-level AI question. A smarter model does not fix records it cannot tie together.
RevOps LeadersRevenue Executives
05
The point is yield from the machine you already paid for
Most companies put millions, often tens of millions, into a go-to-market organization and have never measured what it yields. On a $20 million plan, one point of conversion, 20 to 21 percent, is worth $1 million, and one day off the sales cycle brings in roughly another quarter million within the year.
Why it matters: That gain needs no new headcount, no price change and no extra leads, and the two levers compound. This is the business case for the data work.
FoundersRevenue ExecutivesSales Leaders
06
Model capability climbs; usable capability has been flat for two years
What frontier models can do rises every quarter, but what a company can actually use has been flat for two years, because it is capped by the company's context rather than by the model.
Why it matters: Every point of that gap is the company's to close. Waiting for the next model release will not close it.
FoundersRevenue ExecutivesRevOps Leaders
07
Layer one: a semantic layer in plain English, in one place
The semantic layer, which LeanScale calls its GTM brain, is business logic written in plain English in one place the AI can read: what win rate means, how bookings are counted, what qualified means. LeanScale's lives in GitHub so every model can connect cleanly, but a Google shared drive will do.
Why it matters: Neither this layer nor the context graph needs a data science team or a fine-tuned model. The tooling bar is low; the discipline is the work.
RevOps LeadersFounders
08
Every metric gets exactly one human owner, not a team
A definition owned by RevOps is owned by nobody. Within a quarter there are two versions of win rate and an argument about which is right. The owner is whoever has to defend the number in the board meeting.
Why it matters: Anthony says this rule matters more than any tooling choice. Assign names, not departments.
Revenue ExecutivesRevOps LeadersFounders
09
Layer two: the context graph, and your CRM is already the first one
The context graph connects the dots: contracts to the right opportunities, to the right accounts, with the right people on them. For almost everyone, the CRM is already the first context graph, so cleaning it up gives a solid version zero.
Why it matters: Skip it and errors pass unnoticed: products fan out across line items so a $10,000 deal shows up as $30,000 in the forecast, or an account with 150 touch points looks healthy until they turn out to be unanswered support tickets.
RevOps LeadersSales LeadersCustomer Success
10
Never let the model author your business logic
Ask AI what a good renewal looks like and it will grab standard industry practice and ship it, because that is the fastest path from A to B. Unless you run the most vanilla SaaS company alive, that isn't your business.
Why it matters: People write the definitions; AI converts the Google Doc into YAML, which it does well. The split keeps the logic yours and the formatting cheap.
RevOps LeadersRevenue Executives
11
Once the data model exists, the agents are the easy part
At Emailage Anthony ran customer success with more than 40 CSMs and hundreds of customers, and a health score he could trust was effectively impossible without a full data science team. With the two layers underneath, he says, it is days of work.
Why it matters: Start with agents instead and you reverse-engineer a private little data model for every agent you build. Build the shared layers first.
Customer SuccessRevOps LeadersRevenue Executives
12
A ten-week project, and half of it is agreement
Implementing this is realistically a ten-week project, and half of it isn't engineering. It is getting leaders to agree on what words mean, which has always been the hard part and matters more now.
Why it matters: The Monday start needs no budget: write down the 20 definitions every reported number depends on, put one name against each, and write down each number's source of truth.
FoundersRevenue ExecutivesRevOps Leaders