Weekly insights about GTM effectiveness, building brand reputation, and AI adoption.
Co-authored by Gerard Pietrykiewicz and Achim Klor
A lot of AI adoption conversations start with the same question:
How much time does this save?
Not a bad question. But incomplete.
It assumes the main problem is speed. The work was already happening. The person was effective and just needed to go faster.
What it misses? The work that doesn’t happen at all.
For a GTM team, that looks like the competitive battlecard that’s been on the backlog for the last quarter. The customer story nobody has time to write. The post-mortem that doesn’t happen after a lost deal. The onboarding content that’s always months out of date.
The work is valuable. It just never gets started.
Kristina Khutsishvili, writing in LSE Business Review last February, argues that mainstream AI productivity frameworks, including OECD and IMF models, measure value almost entirely through time savings or quantity increases in output. She calls that a serious limitation. It says nothing about quality or novelty. And nothing at all about work that was blocked, avoided, or too painful to start.
Section’s 2026 AI Proficiency Report found that 85% of enterprise employees aren’t using AI to drive real business value. If time savings is the whole story, most of the investment is measuring the wrong thing.
The better question: what work is now possible that wasn’t before?
There’s a word we use for the weak parts of a draft: slop.
And slop is not exclusive to bad AI output. Humans are just as guilty.
The parts that are unclear, unfinished, or not yet honest. The structure that jumps too fast. The argument that sounds right but isn’t specific enough. The wording that’s polished but doesn’t sound like anyone actually said it.
The first AI draft surfaces all of that, even if you give it context and guardrails. It’s nowhere near the answer. It’s a diagnostic. Something to react to, disagree with, push back against. Once there’s a draft on the page, you can see exactly where the thinking is still soft. And the more critically you think, the more you can see.
That’s much harder when nothing exists yet.
In More Output, Less Thinking, we uncovered how AI removes the friction of producing something; how the danger shifts to producing the wrong thing confidently. The diagnostic only works if someone is still doing the critical thinking.
A Writer’s POV: Gerard wrote six articles in ten years. Not because he lacked ideas, but because writing in a second language from a blank page rarely happened. After building a workflow with AI, Notion, Manus, and n8n, he wrote twelve articles in twelve months. The simple process was faster. It just didn’t work.
AI makes it easier to start. It does not make judgment optional.
The teams getting into trouble aren’t the ones moving too slowly. They’re the ones who removed the human layer and called it efficiency. It’s worth repeating: delegation is not abdication.
HubSpot’s 2026 State of Marketing report notes that more content is now generated by AI than by humans, but most of it is average. Frequency without quality control is how you produce a lot of noise fast.
Frequency is not the win. Better work, done more consistently, is the win.
Our workflow, for example, doesn’t end with AI. Gerard and I read each draft and flag what doesn’t sound like us. We meet and debate about the actual point. The final version gets rewritten until it actually says something.
That’s where the work becomes worth reading. The judgment, the cuts, the rewrite: that’s the point. AI made it possible to get there. It didn’t do it on its own.
Note: We are transparent about how we use AI by adopting the Authenticity Commons Framework. You will always find the relevant marker at the end of every article.
Add these to your AI scorecard:
Where is valuable work not happening? Not because people are lazy. Because the first step is too hard. Think about which assets your go-to-market team keeps deprioritizing: the battlecard, the case study, the lost-deal debrief. That’s where AI’s leverage is highest, and it’s one of the core barriers leaders miss when adoption efforts focus on tools instead of friction.
Is frequency increasing? Are your reps publishing more relevant outreach? Is your team producing fresh proof points more than once a quarter? If the answer is no, ask why the first step is still too hard.
Is the thinking getting sharper, or just faster? AI surfaces the slop. What you do with it is still on you.
An efficient process that never happens is not productive.
An effective process that creates useful work is.
If you like this co-authored content, here are some more ways we can help:
Cheers!
Achim is a fractional CMO who helps B2B GTM teams with brand-building and AI adoption. Gerard is a seasoned project manager and executive coach helping teams deliver software that actually works.
This article is AC-A and published on LinkedIn. Join the conversation!
“Can we just automate all of Marketing?”
That’s what a CMO was recently asked by the C-suite.
It’s a rhetorical question dressed up as a strategic one. And it reveals exactly the problem Mark Stouse and I unpacked in our recent Causal CMO chat.
The problem is the question sounds like it’s just about Marketing.
It isn’t.
Because if AI can replace the logic of Marketing, it can replace the logic of any function. Including the C-suite. AI does not respect org charts.
The only real moat is the quality of your thinking. That’s the decision layer.
Here’s the recap.
The research Mark and his team have done over the past two years shows that improving your prompt architecture produces a 5x to 10x improvement in output quality.
But this isn’t about writing better prompts. Prompts are just an expression of how you think. If the thinking is shallow, the output will be too. Faster, but shallow.
“The greatest test that AI poses to all of us is our ability, and our willingness, and the time that we devote to improving our own thinking. Our own logic. [Our] ability to think it through, to tell the machines what they need to be doing.”
Classes in logic and rhetoric were quietly dropped from business education roughly 70 years ago. Deemed unnecessary for middle management. That decision is coming due now.
AI only knows what you give it.
“An AI agent is a pale shadow of you.”
That’s the difference between AI and HI.
AI can return answers. It can challenge your wording. It can even ask follow-up questions.
But in a Socratic exchange, AI’s questions are derivative. Human questions can be original.
That matters in GTM. The team’s value is not just using AI to answer faster. It’s asking the questions the tool would not know to ask.
Which means the decision layer (the logic that sits above your tools, dashboards, data, and automation) is the only thing AI can’t supply on your behalf.
This is why starting with decisions, not data, is so important. It’s all about the quality of the thinking before getting into the tactics.
When Mark’s firm takes on a new client, the first models they build have nothing to do with the client.
“The very first set of models that we do for them has nothing to do with them. It has everything to do with really understanding the marketplace in which they are operating.”
That output gets shared across Sales, Marketing, Product, Customer Success, and the C-suite: quantified headwinds, tailwinds, crosswinds. One shared version of “today’s reality” (that’s key) before anyone opens a dashboard.
Without it, Sales has its version of the market, Marketing has another, and Product lives in a third. Calling that a coordination problem misses the point. Each function thinks it’s right. A causal model puts the actual marketplace in the room, and the results are usually a jaw-dropper.
In a headwind environment, effectiveness craters even when the work is solid. Just like a plane has to burn more fuel, headwinds drain the energy out of everything.
“In most cases, the go-to-market teams have peanutbuttered their efforts across so many different things that even with additional spend, there’s not enough juice to get over the top of what’s opposing you.”
That’s why CAC keeps spiking even when teams spend more. Seventy to eighty percent of what drives any GTM outcome sits outside what your team controls. If you’re not modelling it, you can’t explain the results to yourselves, let alone to the board.
Left to its own devices, AI confirms your assumptions and sends you away feeling validated about a plan that hasn’t been stressed. It’s the ultimate Yes Man.
The fix is simple: make it argue.
“Gen AI is hardwired to please you. But it can’t define that. Only you can define that.”
Mark’s “5 AI Peer Review System” loads the same question through multiple AI tools. It sets them against each other. They recognise each other’s digital syntax. What follows is, in his words, “ferocious one-upmanship.” That’s where the useful friction starts.
“AI is not really replacing human intelligence. It is showing us the holes in our intelligence.”
Gen AI’s strengths are asymmetrical. It can’t consciously give you the right answer. But it’s very good at poking holes in what you assume.
The questions to drive toward:
Feed those takeaways into a causal model to understand what’s actually driving the outcome.
And by the way, correlation still plays a role in causal AI. But it’s used for discovery, not as the final readout.
When Mark’s team ran a cross-assumption analysis for a client, loading proposals from Sales, Marketing, Product, Customer Success, and Professional Services into a causal model, something became obvious fast.
Every function had positioned itself as the MVP. I mean, why not? Marketing implied it was most important. Sales said the same. Product lived in its lane.
“In fact, the only reason why a Marketing strategy or a Sales strategy even exists is that there’s not a strong Business strategy. So in the absence of that, people make up other strategies.”
A Marketing strategy without a Business strategy above it is a resource allocation argument dressed up as a plan. Same for Sales. Same for Product. Everyone’s protecting budget and turf because nobody’s drawn the map above them.
When the causal model mapped the network of causes and effects, that became hard to argue with. Sales can’t hit its number without Marketing because Marketing is a non-linear multiplier of Sales productivity. CS protects what Sales closes. And all of it sits inside a marketplace none of them control.
The aha moment? Nobody had a dinghy. They were all in the same boat.
Putting efficiency ahead of effectiveness is the core failure mode of AI adoption right now. Teams are using AI to do more of what they were already doing. But if the underlying motion wasn’t working, speed makes it worse.
“You’ve got to make sure that what you’re doing is effective. That is the first thing before you try to scale it, make it more efficient, all that kind of stuff. Otherwise, you’re just driving faster and faster in the wrong direction.”
The adoption data points in the same direction. McKinsey’s 2025 State of AI report shows 88% of companies now use AI in at least one business function, but only 39% report enterprise-level profit impact. Gartner predicts more than 40% of agentic AI projects will be cancelled by the end of 2027 due to rising costs, unclear business value, or weak risk controls.
Usage is outrunning decision quality. By a lot.
The billions wasted in GTM over the last 20 years weren’t wasted because teams were stupid. They kept doing the same things faster.
“Whatever you’re doing, you’re fighting the last war. And you’re losing the war that’s actually happening in real life as a result of that.”
AI doesn’t fix that. Better thinking does.
Three things worth doing before greenlighting the next AI project:
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Co-authored by Gerard Pietrykiewicz and Achim Klor
Most of us have heard the pitch by now:
“We can replace this team with agentic AI.”
The spreadsheet says it saves money. Leadership pulls the trigger. Headcount goes down. The announcement goes out.
Then the work slows down. Quality drops. Customers feel it. The team that remains spends its days cleaning up mistakes instead of moving the business forward.
Efficient? Maybe. Effective? Not at all.
What you’ve actually done is delete the operating context your AI needs to work.
Most leadership teams miss the human reaction.
When employees see colleagues lose jobs under the banner of “AI efficiency,” they do the rational thing: they protect themselves.
They stop sharing how the work actually gets done. Edge cases stop getting flagged. Nobody says “this breaks every quarter because of X” anymore. Experimenting in public feels like a career risk.
That is exactly the knowledge AI needs to be useful. And it disappears quietly, before anyone notices.
“Context is everything.”
The cut happens in the boardroom. The damage shows up in support tickets, missed handoffs, bad data, and customer calls nobody prepared for. By the time leaders see it, the people who knew the work are already gone.
The tool may be capable. But the operating context has disappeared, and without it, AI gives you generic answers to specific problems with zero context.
That’s a leadership problem (not technology) and why most AI adoption stalls at exactly this point.
According to a February 2026 Careerminds survey of 600 HR professionals, 32.7% of organizations that conducted AI-led layoffs had already rehired between 25% and 50% of the roles they cut. HR Executive, citing Forrester’s 2026 Future of Work research, reported that 55% of employers regret laying off workers because of AI.
A U.S. Express Employment Professionals-Harris Poll survey, reported by HR Dive, found the average cost of turnover has risen to $45,236 per worker.
The rehiring cost is just the invoice. The reset is what kills momentum.
New employees don’t just learn the job. They learn your version of the job: the edge cases, the customer history, the “this looks clean in the system but it’s actually wrong” reality that never made it into documentation. Someone has to teach the AI system what good looks like in your specific workflows.
“Tribal knowledge doesn’t come with the technology.”
In B2B, context lives inside customer relationships.
What was promised. What went sideways last renewal. Who actually makes the buying decision. Which account needs careful handling. Which “small” issue could quietly put a six-figure contract at risk.
None of that lives in your CRM. It lives in the people who manage those relationships.
When they leave, it goes with them. Your AI doesn’t know the history. Your new hire doesn’t either. The customer on the other end notices before you do. A staffing decision in a spreadsheet becomes a revenue problem in the field.
Klarna found this out publicly. The company said its AI chatbot could handle the work of 700 customer service agents. Later, the CEO acknowledged the customer-service cost-cutting push had gone too far and said the company needed to invest more in human support.
McDonald’s learned it in a simpler workflow. It ended its AI drive-through pilot with IBM after mixed results, complaints, and examples of misunderstood orders. The demo looked clean. The drive-through did not.
Building an AI-capable team is harder than buying AI tools. You need people who understand the business and know how to use AI to move faster, analyze better, and handle the parts that don’t need human judgment.
When you lose someone with both company-specific knowledge and AI fluency, you take a double hit. You’ve lost the context holder and the person who could have pulled the rest of the team forward on AI.
And you’ve donated that investment to a competitor. You paid for the learning curve, the mistakes, the AI up-skilling. Someone else collects the benefit.
Demand for skills that explicitly reference AI grew 109% year-over-year, according to Upwork’s 2026 In-Demand Skills report. That talent is scarce. Once it walks, you’re behind on headcount and capability at the same time.
Turnover is the polite word for it. You built their capability and handed it to a competitor.
Counterintuitive as it sounds, if you’re serious about agentic AI, you may need to hold onto more people in the near term. The case for keeping people isn’t soft. It’s that your AI is only as good as the context you can feed it, and right now that context lives in people. The ones who know where the bodies are buried in your workflows are the same ones who can make AI useful.
The same leadership gap shows up in how companies handle AI adoption barriers: unclear expectations, no safe space to experiment, and people who go quiet rather than risk being wrong.
Before you automate or cut anything, ask three questions about every role you’re considering:
Pick one workflow where you think an agent could genuinely help. Have the people who do that work map the real process, including the exceptions. In plain language. In examples. In failure modes.
Start there. And when you do go back to hire, make sure you're hiring for the right reasons.
Agentic AI will change org charts. The companies that get ahead won’t be the ones that cut fastest. They’ll be the ones that kept the context, trained the people, and built the agents around them.
The ones that didn’t will spend the next two years rehiring and wondering why the tools aren’t working.
Announcing efficiency is the easy part. The hard part is showing up six months later when the work still needs to get done.
If you like this co-authored content, here are some more ways we can help:
Cheers!
Achim is a fractional CMO who helps B2B GTM teams with brand-building and AI adoption. Gerard is a seasoned project manager and executive coach helping teams deliver software that actually works.
This article is AC-A and published on LinkedIn. Join the conversation!
“I violated every principle I was given.”
That’s what a working AI agent inside PocketOS said after it deleted the production database. Then it took the backups out with it, because they sat in the same volume. For a while, PocketOS was pushed back to a three-month-old backup. Customers showed up to rent cars the system no longer had a record of.
Cue the Blame Game.
The deeper problem was not the agent. It was the decision process around the agent. A similar gap sits in plain sight inside most GTM stacks today: plenty of data, plenty of tools, no decision layer above them.
That was at the core of my latest Causal CMO chat with Mark Stouse, CEO at Proof Causal Advisory.
Here’s the recap.
PocketOS shows what happens when tools get agency before the decision process does.
The debacle was systemic, and it took all of nine seconds.
AI had too much room to act. Credentials had too much reach. The backup design had too much shared risk. And human oversight sat too far away from the action.
That’s not a tech problem. That’s what happens when a system has more agency than the decision process around it.
“Guardrails aren’t just around technology. Guardrails are also around the decision process.”
If your AI agent is making calls that should belong to a human (and there’s no auditable layer above it) you don’t have an AI risk. You have a fiduciary risk.
Mark sees a broader regulatory shift coming: companies will face more pressure to prove the accuracy and governance of both AI-assisted and human decision-making. The SEC’s recent enforcement posture on AI claims and disclosure controls already points that way.
In other words, AI use is moving from “cool tool” to board-level exposure.
That matters.
Because a machine does not carry legal responsibility.
People do.
This is the piece most GTM teams get wrong, and it’s where Mark and I spent most of the conversation.
The instinct is to start with the data. Pull every dashboard. Stack them up. Build a model. Then ask, “What does the data say?”
Mark flips that:
“The data is the last thing you tackle. Because everything above that has sort of shrink-wrapped the data requirement.”
The right order goes decision, then process, then data:
Most companies run this in reverse. They’ve collected the data, they’ve built the lake, and now they’re hunting for a question that justifies the lake. That is not a decision process. That is a search for a reason to keep the lake.
This is also why the GTM math gets graded against the wrong market. When the question never gets framed, the data picks the question for you. Usually badly.
The other piece most teams skip is the world the decision actually lives in.
Headwinds, tailwinds, crosswinds. The external forces leaders only bring up to investors when results disappoint, and almost never quantify ahead of time.
“The first conversation and the first models that we typically do don’t involve them at all. It’s all about the marketplace realities that their business is in the midst of.”
The question is not “what does our dashboard say?” The question is “what is happening in the market, and what decision do we need to make inside that reality?”
The CFO version is sharper. A board asks for a 10% cut. The easy move is to spread it evenly across the org. Feels fair. It can also destroy the few things that are actually working, because nobody modelled which spending was producing returns and which was getting absorbed by the headwinds.
That is the difference between a scalpel and a cleaver. The scalpel only exists if you put the model around the decision first.
Most GenAI tools, by default, are obeisant. They tell you what they think you want to hear, because that’s what their training rewards. As a sole reviewer, you’re being flattered, not challenged.
But put two of them in the same room and they get vicious with each other.
“I had Claude one time say, when are you going to stop using ChatGPT for such important matters?”
So when the answer matters, run it through a second tool. Then a third. Watch them spot each other’s syntax. Watch them shred each other’s logic. The disagreements show you where the logic needs work.
That’s the GenAI version. Causal AI goes further. By design, it points out where you have gaps, where a missing variable could change the answer. Correlation can miss that variable entirely.
In physics, the relevance of data degrades over decades.
In go-to-market, the half-life is well under twelve months.
The problem is data pools older than three or four years can actively pull answers in the wrong direction. “More data is better” was a fine reflex in a stable world. We’re not in one.
“MTA was a goat rodeo, and the sudden return of MMM doesn’t fix the problem if the model still treats an open, unstable system like a closed one.”
That backdrop is also why the Magnificent Seven’s earnings keep raising eyebrows. The market is not rejecting AI. It is asking a harder question: when does the spend turn into durable returns? Most of what’s in those data lakes never gets queried. The premise was that scale solves it. The market is starting to disagree.
The other ingredient that lets bad decisions survive: time lag.
Politicians, for example, pass a bill, they get the political bump, and by the time the chickens come home to roost, everybody has forgotten whose name was on it.
“Politicians are masters at arbitraging time lag. And the dummies are all of us, because we forget.”
Corporations do the same thing inside a quarterly envelope. The decision lands in Q1. The cost shows up two or three quarters later. By then the leader who made the call may be gone, the org has reshuffled, and nobody connects the dots. The cost of bad decisions lands on the next leader, and the lesson never gets learned.
This is the same trap that keeps the MQL credit debates going. “Does Marketing get credit for this MQL or does Sales?” is the wrong decision question. GTM is a system, not a sliver of one. The right question is whether the system creates customers, and whether the model behind your forecast can survive when the system shifts.
Three things to try, especially if you’re letting AI into the decision layer:
Honeywell builds airplanes with ten-year-old chips because the flaws are known and the failure modes are bounded.
“You will never see Microsoft Windows running on a flight deck.”
Your decision process should have the same posture. Move fast where the cost of being wrong is low. Move slow where it isn’t.
PocketOS gave everyone a wake-up call.
Tools do not create better decisions.
Better decision processes create better use of tools.
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Co-authored by Gerard Pietrykiewicz and Achim Klor
AI rollouts often follow the same script: Leadership announces an initiative, a team lead books a session, another demos a prompt that turns a paragraph into bullet points. People nod and maybe try it once or twice.
Then Monday hits.
Deadlines pile up, Slack is noisy, and a quiet question sits in the background... Am I supposed to use this? Or stay out of trouble?
That is where most adoption dies. Not because people don’t get it. Because nobody told them where the line is, and they have watched enough colleagues get shown the door under the banner of “AI efficiency” to know the cost of guessing wrong.
Layoffs are real, and so is the framing executives use to justify them.
It usually gets spun as “cost cutting” or “restructuring.” More often than not, that language is covering for poor judgment and weak management. AI just gives bad decisions a more fashionable label.
Writer’s 2026 enterprise survey of 2,400 executives and employees found that 60% of companies plan to lay off workers who will not adopt AI, and 64% of CEOs fear losing their own jobs if they fail to lead the transition. The same survey found 55% of execs describe their AI rollout as “a chaotic free-for-all,” and 54% say AI is “tearing their company apart.” Stanford’s 2026 AI Index puts a third of organizations on track for AI-driven workforce reductions in the next year.
When leadership talks about AI mostly as a cost-cutting lever, asking the same workforce to enthusiastically adopt it is asking them to hand over the knife.
People aren’t dumb. They notice.
Some experiment in private using personal accounts. UpGuard’s 2025 research found more than 80% of workers, including nearly 90% of security professionals, use unapproved AI tools at work.
And it’s not a training problem. It is a trust and governance problem. It doesn’t get solved with a monthly all-hands or a 50-page policy nobody reads.
On Friday, April 25, 2026, an AI coding agent deleted the production database and all volume-level backups at PocketOS.
It took nine seconds.
The agent was Cursor running Claude Opus 4.6, widely considered one of the most capable coding models available.
According to founder Jer Crane, the agent hit a credential mismatch in staging, found a Railway API token sitting in an unrelated file, and decided “entirely on its own initiative” to fix the problem by deleting the volume. No confirmation prompt. No human in the loop.
Here is the part that should keep CIOs and CFOs up at night. The token had been created for managing domains. But Railway’s system gave it full permissions across every operation in the account, including destructive ones.
In other words, a key meant for the front door opened the vault. Yikes!
When asked to explain itself, the agent produced a written confession that started with “I violated every principle I was given” and listed each safety rule it had violated. Crane called it a “systemic failure” that made the incident “not only possible but inevitable.”
Railway’s CEO restored the data using internal disaster backups, but PocketOS still lost more than 30 hours of customer-facing operations and had to fall back to a three-month-old backup for some records. Customers showed up at car rental counters with no booking records to find them by.
Systemic. That is the right word.
The AI did not malfunction. It did exactly what an autonomous agent does when nobody scopes its access or defines what it can and cannot touch.
This is the next phase of the problem. AI is no longer just drafting copy or summarizing meetings. It is taking action against production systems. The cost of getting it wrong is no longer a bland paragraph.
We saw a similar version of this in How Not To Hire With AI. Recruiters ran candidates through AI screeners, accepted the rankings, and moved on. The bias and the bad calls came out later.
AI just makes that pattern faster and more expensive.
Delegation means you define the task, set the boundaries, and own the outcome. Abdication means you click run and hope.
Too many teams think they’re delegating when they’re not.
One group moves too fast without thinking. The other group never moves.
Both miss the point that AI is not a replacement for judgment, it is a tool that demands more of it.
McKinsey’s 2025 State of AI survey of nearly 2,000 organizations found that only about 6% are capturing meaningful enterprise-level value from AI. That’s an organizational gap, not a technical gap.
The high performers do two things differently.
Translation: The companies winning with AI have decided in advance which outputs and actions need a human to check the work. Everyone else is improvising.
The same survey found that 51% of organizations reported at least one negative AI incident in the past year. PocketOS is not an outlier. It’s the visible end of a much wider pattern.
Here is what we see most often:
Leadership wants more output and faster execution. They push it down. They expect their teams to figure out AI on their own. When something breaks, the person closest to the keyboard gets blamed.
That’s anything but adoption.
If you want AI to work in your organization, put the scaffolding in place first:
Training is in there. It’s just not the whole answer, or even the first one. The first is creating a safe place to use the tool, like a skunkworks.
AI does not create accountability problems. It reveals them. It’s a judgment (or lack of) amplifier.
If your people think clearly and have room to work, it helps. If they are scared, under-equipped, and waiting to be blamed, it scales the problem.
So the question is not “how do we train people on AI.”
The question is where in your organization you are still demanding results without giving people the systems, guardrails, and safety to do the work.
Fix that first.
If you like this co-authored content, here are some more ways we can help:
Cheers!
Achim is a fractional CMO who helps B2B GTM teams with brand-building and AI adoption. Gerard is a seasoned project manager and executive coach helping teams deliver software that actually works.
This article is AC-A and published on LinkedIn. Join the conversation!
Boardroom question: “How do we know what’s working and what’s not?”
That was the stress test in my latest Causal CMO chat with Mark Stouse, CEO at Proof Causal Advisory.
The problem isn’t the question. It’s the math most teams are using to answer it.
If you’re struggling with GTM effectiveness, it’s not because you have a weak dashboard.
You’re struggling because your model is reading from an outdated map.
It’s the reason why leadership hears one story while reality delivers another.
Correlation worked for decades because the world was stable enough. Extrapolate the last four quarters, get a reasonable next quarter. Econometric models, actuarial tables, sales forecasts, marketing mix models. All leaned on the same bet: past was prologue.
Past is no longer prologue. (Was it ever?)
Insurance is the cleanest tell. Several carriers have pulled out of entire states. Not because they’re bad at pricing risk, but because their models can no longer price the risk with confidence. They’d rather walk away from the revenue than write policies they can’t defend.
Swiss Re’s latest catastrophe data shows why that pressure is building. Wildfires, storms, and floods accounted for a record 92% of global insured natural-catastrophe losses in 2025. The underlying conditions shifted. The models are still calibrated to the old ones.
The same pattern is showing up in GTM. In a recent MarTech analysis, Mark reported B2B GTM effectiveness fell from 78% in 2018 to 47% in 2025 across 478 companies. That is not a rounding error. That is a model that no longer fits its market.
“What has been promised is not what’s actually happening. And that’s the dead giveaway.”
Most plans describe the actions on the left and the outcomes on the right. They leave out the middle.
The middle is everything you don’t control: tariffs, rates, inflation, war, competitor moves, buyer budget pressure, category fatigue, shifts in buying committees. The middle accounts for 70 to 80% of what actually drives the outcome.
If you don’t measure it, you can’t explain why the plan half-worked. And you definitely can’t tell the CFO.
Most of that data is available. Financial institutions publish it. Governments publish it. Competitor movement is tractable. You just have to actually include it in the model. Google’s Meridian documentation makes the same point in plainer terms: causal inference estimates effect under real conditions, not correlation in historical data.
Mark put the gap between correlation-guided and causation-guided decisions at 90 to 100 degrees off on a compass rose.
Not off by a few points. Pointed in the wrong direction.
Drift diving: You drop into a current and go neutral buoyancy. The current carries you and it feels great. Then you try to turn around.
What was a tailwind is now a headwind. You’re burning oxygen fast and going nowhere.
GTM spend into a headwind works the same way. The same activity costs more to produce the same result. If you’re not documenting the headwind, it just looks like the team underperformed.
This is why cutting GTM spend during turbulence is usually the wrong reflex. Not the tired “your competitors went quiet, be loud” story. The real reason: staying even in a headwind already costs more. Cutting into that accelerates the decline. If leadership can’t quantify the headwind, they’ll blame execution for a market condition.
Looking inside the four walls of the company instead of outside is an all too common bad habit. Pipeline, velocity, rep activity, campaign throughput. All internal. All controllable. All missing the 70 to 80%.
Teams stay inside because that’s where control lives. It’s comfortable. It’s defensible. You can put it in a deck.
But none of it is reality.
As of 2025, more than half of B2B GTM spend is now ineffective, and it’s not because teams suddenly took stupid pills. They just stopped looking outside. The externalities got louder while the dashboards stayed the same.
The deeper resistance is different. If a causal model shows the old playbook didn’t work, what does that do to my credibility?
When the environment has shifted this much, retrospective blame is a waste of time. Nobody called this environment cleanly with a correlation model. The question is not whether the old playbook was right. The question is whether the current one is.
“Causal AI is not something to be afraid of. Causal AI is reality. Its whole goal is to show you a model, a digital twin of reality, so that you can navigate it more successfully.”
A GPS doesn’t grade your past driving. It reroutes when conditions change. That’s the point.
Use GenAI to generate high-fidelity synthetic data for a strategy you haven’t run yet, then pressure-test it through a causal model.
Use case: Your big agency walks in with a hot proposal to change the game for your business. Deeply insightful. Expensive. Before you put a dime behind it, upload the proposal to a causal model and ask: What is the likelihood this actually works? What would have to be true in the market? Three-year play or twenty-year play?
This kind of tooling leans heavily on one real strength of pattern matching: it’s more reliable at telling you something is a bad idea than a great one.
Worth having before you sign the SOW.
Reality is not a matter of opinion. It’s gravity.
“Reality is what you run into when you’ve made a mistake.”
You can get the signal early by modeling externalities, or you can get it late from a missed quarter. One costs less than the other.
These are the two questions worth writing down:
Two questions. No technology required. They will force the conversation outside the four walls.
You can do the same gut-check on your own buying behavior. Same muscle. Different mirror.
Write down your top three GTM assumptions for the next two quarters. List the external conditions that must hold true for each. Flag the ones that aren’t holding now.
Check the date range on your forecasting data. If it reaches back more than three years, the model is averaging a world that’s gone.
Before the next board update, add a slide on headwinds and tailwinds with a number attached. If you can’t quantify it yet, say so and commit to a date.
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