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:
The year 2025 will likely be remembered as the moment B2B teams were given permission to spend aggressively on AI without a clear plan for what would actually work.
That grace period is over.
Business leaders are now demanding ROI from those investments, and they’re increasingly dictating where RevOps time and budget go next. Every CRO wants more accurate forecasts. Every CMO wants smarter targeting and faster optimization. And every RevOps leader is being asked the same question:
“Why aren’t we using AI for this yet?”
RevOps is under pressure to deliver while watching AI projects stall or fail across the industry. But the uncomfortable truth is most revenue stacks aren’t failing because the AI models are immature. They’re failing because the systems underneath can’t agree on how AI actually works with revenue.
Very few business leaders realize how much foundational work must happen before AI can produce reliable returns in go-to-market operations. The biggest obstacle isn’t data volume or model selection. It’s semantic readiness, or enabling AI to understand the context and unspoken relationships surrounding data.
A great example is the business logic required to get deals done safely—protecting margins, enforcing policy, and ensuring deliverability. It lives in people’s heads, spreadsheets, and disconnected systems. And that fragmentation is quietly eroding speed, confidence, and credibility long before AI ever enters the picture.
This article breaks down why failing to address semantic readiness is already expensive, and why RevOps teams are paying the price today, whether they’re using AI or not.
On paper, AI promises exactly what modern RevOps teams are desperate for:
Solving these problems would make revenue operators heroes in the eyes of Finance and the CEO. But these problems are often solved with humans today for a good reason.
The business logic, legal guardrails, and other miscellaneous rules that dictate whether or not a contract passes review live outside of the system being asked to gatekeep customer agreements.
That landscape has only grown more complex over the last five years:
This work feels like a normal cost of business because most of us haven’t solved the core hurdle standing in the way of introducing AI-led automation: semantic readiness.
According to a study of 1,000 companies in the UK, the most common pricing issues (including manual pricing logic and pricing rules living in disparate systems) are reported to lead to an approximate 15% loss in revenue margin (HubSpot).
What makes semantic breakdowns especially dangerous is how quietly they scale. Every new pricing rule adds fragility. Every exception adds review time. Every undocumented approval increases dependency on specific people.
Picture quote-to-cash like building a structure. Every time you tack on new exceptions to the rule, it’s like adding another staircase, door, or room to a house. Build these extensions over enough time, and you’ve got the Winchester Mansion: a giant house with a bunch of elements that no longer make sense as a cohesive unit.
Over time, businesses see:
We also must acknowledge that the C-Suite’s opinion of RevOps teams takes a hit every time they have to say no to things like flexible pricing requirements and packaging upgrades. RevOps can be seen as an inhibitor to growth because we’re stuck with legacy systems built for data collection and static rule sets – and it’s impossible to force them to solve problems they were never meant to tackle. Change is the only constant in today’s B2B SaaS company, and adapting to that change is table stakes.
By the time leadership asks for AI, the system is already strained. AI doesn’t fix that strain. It inherits and amplifies it.
If you’ve tried revenue agents already, this will sound familiar:
AI doesn’t eliminate the work necessary to prepare semantic data.
It amplifies the disorder.
If revenue logic lives in spreadsheets, Slack threads, and people’s heads, AI has nothing stable to reason over. The result isn’t automation—it’s faster confusion, convincing hallucinations, and copilots that need constant supervision.
Before AI can accelerate revenue, your systems have to agree on how AI with revenue actually works.
AI has historically failed in revenue environments because of:
Without correcting these three core issues, your stack looks like a pile of words and attachments with no shared meaning to AI.
Having data is not the same thing as having usable business logic.
Yes, you have stored data in your CRM. Opportunities, line items, discounts, stages, approval fields, and renewal dates are all very important.
But here’s the kicker. AI doesn’t run on raw data.
It runs on clear, consistent, explainable business logic.
What’s actually important to AI? It’s:
Only when we do the work of ingesting and structuring data living in disjointed systems and documents does AI have something stable to reason over.
Without semantic alignment, copilots don’t fail loudly. They fail quietly and convincingly.
A functional AI-empowered platform can enforce rules consistently, explain the logic steps used to arrive at a decision, and adapt logic without breaking workflows.
To do this, revenue operations must correct the three major hurdles that prevent an AI implementation from succeeding in a revenue stack.
Disconnected Data must become Connected Data. The gaps in definitions, missing pricing logic, and data quirks across systems have to be clearly documented and defined so your AI platform is grounded in one, unified revenue model.
Rules Living in People’s Heads must become Rules Documented Centrally. Revenue operators have to chase down the logic that lives with subject matter experts. A CFO’s rejection isn’t enough for AI. It needs to know exactly what about the deal led to the CFO saying No.
No Guardrails must become Every Guardrail Recorded. Approval logic that’s specific to product family, safe profit margins by line item, and acceptable payment terms are just the starting point. If these rules change at the end of the quarter to close the gap on an aggressive business goal, that has to be clearly stated too.
The takeaway from this article isn’t “don’t invest in AI.” It’s: RevOps must move fast to translate and record the business context and data relationships that are living outside of the system you want to activate with AI.
AI is a forcing function to invest in semantic readiness because it exposes the cost of ambiguity that revenue teams have been absorbing for years.
The good news is that the RevOps teams that do recognize this problem and fix it now will be able to adapt to flexing demands first. This is nothing short of a competitive advantage you can offer your revenue team, and it starts with identifying semantic weak spots and filling the gaps.
In Part 3 of this series in partnership with DealHub, we’ll shift from diagnosis to design. We’ll explore what it actually takes for systems to understand the commercial rules humans already follow.
The key to a successful AI implementation is understanding that it’s a tool that demonstrates and enforces consistency only once you’ve defined it.
