Welcome to our blog series challenging how we view QTC in partnership with our friends at DealHub. Here’s a brief outline so you can jump to other articles in the series:
If you’ve ever been in, or covered for, Deal Desk, you have seen how quickly revenue control breaks down.
A salesperson submits a deeply discounted deal at the last minute of the quarter.
The CFO is busy closing deals and not checking approval requests.
And suddenly, you are nominated to track them down and get the deal approved before the clock runs out.
Layer in enterprise deals with redlines flying back and forth between legal teams. Exceptions stacked on top of exceptions.
Now imagine a world where AI understands:
In that world:
But that vision only works if AI can see and reason over the same logic humans use today. That requires groundwork.
Before AI can help, your systems need to understand the rules humans already follow. That starts with commercial logic.
Commercial logic is not strategy decks or pricing theory. It’s the operational reality of how deals get done safely, predictably, and without reliance on human memory or informal exception handling.
Commercial logic includes (but is not limited to):
You may be saying to yourself, “Cool! Most of that is in my CRM approval routing logic.”
Not quite.
They were not designed to incorporate or adapt to the real-world context that determines how revenue decisions are made in the moment:
So when leadership flexes rules to close a quarter, that logic never makes it into the system.
It stays implicit.
To support AI-driven logic, you need to explicitly document things like:
Humans learn this logic over time. Systems can’t—unless you know the rules yourself and intentionally teach them.
AI can’t reason over what isn’t explicit.
Once commercial logic is documented, the next step is bridging the gap between the data living across your systems.
AI can’t reason across disconnected systems unless those systems share definitions, identifiers, and meaning.
At a minimum, AI needs to understand how these systems relate to one another—and have access to both the raw data and the rules that govern it.
In a traditional setup, that typically includes:
Most RevOps teams assume this data is “connected” because systems are integrated, but integration alone isn’t enough. Data flow is not logic flow.
If one system defines a “deal” differently than another—or uses different product definitions—AI has no stable foundation to reason from.
To be clear: this logic layer must be semantic, declarative, and owned by the business, not embedded in code or hard workflows.
A more modern approach should marry, or even consolidate, many of these elements—especially CPQ, supporting documentation, billing and finance rules, and as much contract data (including exceptions) as possible.
Because of the complexity involved, we recommend exploring purpose-built solutions rather than attempting to layer AI across these tools manually. For an example of how DealHub customers unify CPQ, contracts, and billing rules into a single logic layer, see their case study with MotorK.
This is where most AI readiness efforts fail.
Teams assume documenting logic is enough. It isn’t.
Where your logic lives—and how clearly it’s defined—determines whether AI can reason over it.
If your rules live in:
AI cannot reason with them because code executes logic. It does not explain it.
This is where most teams realize they don’t have a tooling problem—they have an architectural one.
The Business Logic Layer stores commercial rules declaratively, not procedurally, and understands how those rules apply consistently across systems. It functions as an applied semantic model.
This is not a dev abstraction. It’s a governed, semantic execution layer, typically CPQ-native, that translates commercial intent into something machines can reason with.
When logic lives in this layer, AI can reason over it without guessing, hallucinating, or breaking downstream workflows. Rules are no longer opaque instructions buried in code—they become explainable, governed inputs AI can safely navigate.
For a deeper look at what this architectural shift entails, DealHub outlines the concept clearly in its Guide to Semantic CPQ Architecture, which breaks down how commercial logic can be unified into a governed, AI-readable model that supports end-to-end Quote-to-Cash execution.
This distinction matters:
Only the latter allows AI to enforce policy consistently—without constant human supervision.
Centralizing commercial logic does more than enable AI.
When rules are centralized before automation:
Automation doesn’t create order. It amplifies whatever already exists.
When rules are sensible, explicit, and accessible to AI, you reduce risk, increase consistency, and dramatically improve your odds of success.
AI-powered CPQ doesn’t replace judgment. It institutionalizes policy, so your revenue systems enforce strategy, not just reflect it.
This is where platforms like DealHub focus first: turning policy into something systems can actually execute—not just document.
In the final article of this series, we’ll show what Quote-to-Cash looks like when it actually works.
Until then, the mandate is clear:
Before AI can help you, your systems need to understand the rules humans already follow.
That work starts by turning commercial decisions into governed logic and putting them in a system that can execute them without friction between your sales and Deal Desk teams.
