A lot of GTM teams are overloaded. New tech. New tools. New hype. All promising transformation, but rarely delivering clarity. In this recap of Part 4 of The Causal CMO, Mark Stouse explains why operationalizing Causal AI isn’t just about buying another tool. It first requires a mindset shift. A hard reset on how GTM teams define risk, read signals, and move forward.
Before you operationalize anything, you need to think differently.
The first step isn’t modeling or tooling. It’s dropping the need to be right.
Causal AI is only effective if you’re willing to look at what’s actually happening, not what you hoped would happen.
So do you want to be right? Or do you want to be effective?
This is a key distinction, especially for Go-To-Market teams. Instead of constantly trying to prove they’re right, they should ask what they need to do next.
That shift is already starting to happen.
Proof Analytics, for example, no longer looks like a traditional SaaS product. Most of Proof’s clients now rely on software-enabled services because self-serve just doesn’t work when teams are overwhelmed.
It’s not a tech problem. It’s a saturation problem.
“Teams today are saturated like ground that’s been rained on for too long. They can’t absorb anything new. The water just runs off.”
Too many GTM teams are stuck on this treadmill. They’re still chasing efficiency because it’s easier to cut cost than drive growth. But efficiency without proven effectiveness is meaningless.
And that’s where Causal AI comes in.
As we discussed in Part 1, Multitouch Attribution (MTA) assumes linearity and doesn’t account for time lag. It only focuses on the dots, not the lines in between.
Dashboards typically treat data like a mirror. But as Mark pointed out, data only reflects the past, and past is not prologue. Like crude oil, it’s useless until refined.
“There is no intrinsic value in data. Only in what it gets refined into.”
Mark shared a story from a meeting where a CIO showed how easy it was to manipulate attribution weights. Then he had various leaders at the table do the same. Same data, four outcomes. Each one reflected a different bias.
Guess who had the least credibility?
Yup. Marketing. The CIO said to them:
“Of everyone in the room, you arguably have the most bias. Your outcome is dead last in terms of credibility.”
Causal AI mitigates gaming the system. It tests patterns for causal relevance and recalibrates in real time. If the forecast starts to degrade, it tells you what to do next.
It doesn’t care if the news is good or bad. It just tells the (inconvenient) truth.
Before the model even begins, Mark’s team maps the external environment. They model the headwinds and tailwinds first. Only then do they plug in what the company is doing.
This is where most teams fall short.
Too often GTM is treated like an isolated system. But it’s not. It’s subject to risk, time lag, and external forces that marketers rarely model. And it shows.
According to Mark, the effectiveness of B2B GTM spend has dropped from 75% to just above 50% since 2018. That’s not a tactics problem. It’s a market awareness problem.
“The average B2B team is frozen in their perspective. They’re not thinking about the externalities unless it gets so bad they can’t ignore it.”
The result? Poor decisions, reactive guidance, missed opportunities. And an inability to plan for value because no one knows where in the calendar to look for it.
One of the most powerful features of Causal AI is the ability to model counterfactuals. What would happen if we made a different decision?
Until recently, this required expensive, synthetic data. Now, GenAI tools make it accessible. With a detailed enough scenario prompt, teams can simulate outcomes, measure impact, and prioritize programs before spending a dime.
It’s like an A/B test for strategy. No need to touch real data. No risk of tripping legal wires. Just clarity.
“Most stealth efforts start here. The counterfactual model shows what’s probably happening. Then you go get the real data to prove it.”
It’s also the easiest way to build internal buy-in. Teams can explore alternatives without asking other departments for access or permission.
You don’t need a top-down mandate to operationalize Causal AI. In fact, Mark recommends the opposite.
Start small. Keep it quiet. Don’t even call it a transformation.
“Carve off a small budget and a couple of people. Model and learn for 9 to 12 months. Don’t say anything. Just execute.”
As the team starts learning and adjusting, people will notice. They’ll feel the shift before they understand it. Then, when the time is right, you explain how it happened.
“If people feel the improvement first, they’ll accept the facts. If they hear the facts first, they’ll resist.”
There’s no manipulation in this. It’s psychology. Let results speak before you tell the story. It’s like asking for forgiveness instead of permission.
Causal AI is not a marketing or revenue tool. It’s a business system.
Mark says the best clients are already thinking in terms of enterprise models. Finance teams often lead the adoption. They use causal modeling transparently across departments to see what’s working for the business as a whole.
And leadgen is not the goal. The board doesn’t care about leads. They care about cash flow, growth, and risk. In other words, bigger deals, faster deals, and more of them. Causal AI connects those dots and the lines in between.
“You can’t get to efficiency unless you know if it’s effective.”
Effectiveness is not a tactic. It’s a lens.
Causal AI doesn’t ask if you were right. It helps you get better at seeing possibilities and becoming more effective.
We’ll explore that further in Part 5 as we dig into investment decisions and boardroom conversations.
Until then, ask yourself:
Are you willing to be wrong long enough to get it right?
Missed the LinkedIn Live session? Rewatch Part 4.
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