Many of us are still in the early stages of AI adoption, experimenting, testing, and trying to make sense of how it fits. But the pressure to move beyond pattern-matching is building. In Part 3 of The Causal CMO, Mark Stouse explains that Causal AI isn’t just a tech upgrade—it’s a new layer of accountability. It recalibrates forecasts, reveals the impact of GTM decisions, and removes the guesswork from budget conversations. This article outlines what GTM teams need to prepare for as Causal AI becomes mainstream.
One of Mark’s biggest points was to embrace being wrong. Be effective instead of being right. If GTM teams want to get ahead, they need to let go of trying to control everything.
We’re already seeing this pressure hit big consulting firms. Demand for their AI services is off the charts, but clients aren’t paying what they used to. It’s forcing layoffs because staff aren’t fluent in AI. This is what the 2028 reckoning looks like in real time: not a tech crisis, but a credibility one.
Causal AI doesn’t care about any of this. It calls things as they are. It adjusts automatically based on what’s going on around you.
Mark calls it a GPS for your business.
“If things start to degrade in the forecast, it tells you what to do to get back on track. That’s why we modeled Proof Analytics after GPS.”
Unlike forecasting the weather, which looks at past patterns, Causal AI tools like Proof Analytics measure cause and effect in real time, based on current conditions and the actual levers you can pull.
Mark outlined four categories of AI:
The leap from correlation to causality is the break point. GTM teams stuck in attribution are falling behind. Those preparing for Causal AI will be ready when it becomes standard.
“We have about three years to cross the river. If you don’t, it’s going to be very hard after 2028.”
Unlike attribution modeling, which relies on correlation and weighting, Causal AI directly isolates impact.
Causal AI forces a choice: keep pretending we’re in control, or start navigating with truth.
Mark compared what we control across different grading scales as illustrated below.
In each grading scale, what counts as success depends on how much is actually in our control.
In school, we control most of our grade because it’s based on the work we hand in. In baseball, a .350 hitter strikes out 2 out of 3 times but makes the Hall of Fame. In surfing, a world-class pro may wipe out 94% of the time. In war or pandemics, almost nothing is in your control.
“Business is more like baseball. If we start grading it that way, we end up with a lot more truth.”
Same goes for marketing. As much as 70% of GTM performance is driven by external factors.
“If you don’t know what the currents are, you won’t know how to steer the ship.”
That’s why your job isn’t to be perfect. It’s to reroute.
Causal AI detects shifts and tells you what to do. It zooms from big picture to ground level, depending on what the decision calls for.
There’s a quiet fear around AI, especially in creative and strategic roles. The idea that if a machine can see something you can’t, your work might not matter. That’s just not true.
If the tools are properly learned and configured, they amplify creativity.
Take GenAI, for example. If you dive into ChatGPT without training it on facts or setting expectations, it’s like hiring an intern and never giving them a clear job description.
The problem isn’t the tech.
“If you pick up the tool, you have purpose. That’s not loss. That’s awareness.”
Mark also shared how his son, a private chef, uses GenAI in the background while cooking. It helps plan menus, tailor preferences, and provide real-time input. It doesn’t replace his job. It makes him better at it.
It’s like that for marketers too.
“Marketing is a multiplier. It doesn’t need shared revenue credit. It needs causal proof of lift.”
Even before the first Industrial Revolution, tech has been a multiplier. It has expanded human capability, freeing people up to focus on innovating and creating. If you’re still defending multi-touch attribution (MTA), it’s not your data that’s outdated. It’s your mindset.
You can’t hide behind attribution dashboards anymore.
During interviews with Fortune 2000 firms, Mark uncovered a common thread: C-suites aren’t frustrated by skill gaps. They’re frustrated by teams who can’t explain impact.
“They come up with total BS programs to justify spend. Do they not know that we know this is stupid?”
Fortune 2000 CEO
Attribution models are weighted and easy to manipulate. Change the weights, change the story. Everyone knows this. That's why MTA charts get ignored in the boardroom.
Causal models run continuously. They adjust to change. They reroute you when conditions shift. Causal AI works like a GPS. It gives teams the heads-up they need to adapt.
A lot of GTM teams are stuck on a productivity treadmill. Budgets are cut. Expectations stay high. Nobody knows what’s actually working.
AI will expose that. Early on, it will cut 30-40% of activities. Not because it’s ruthless, but because that activity wasn't creating impact in the first place.
“We’ve just always been doing it. With AI, everybody will know.”
In other words, if you’re not using AI with a causal lens, you’re optimizing noise.
My conversation with Mark made a few things very clear:
In Part 4, we’ll talk about what it looks like to operationalize this shift.
Stay tuned.
Missed the LinkedIn Live session? Rewatch Part 3.
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Cheers!
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