Weekly insights about GTM effectiveness, building brand reputation, and AI adoption.
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.
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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:
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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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Boards and C-suites are becoming more aware of this fact: Past is not prologue.
It’s also something I have covered with Mark Stouse, CEO of Proof Causal Advisory, on many occasions, and it was the key pillar in our last Causal CMO conversation.
Most board conversations about go-to-market still treat historical data as a guide to future probability. That model no longer works. And according to Mark’s 5-part GTM Effectiveness Report, the numbers show it: B2B GTM effectiveness fell from 78% in 2018 to 47% in 2025.
The gap between what boards are asking and what they actually need to know is getting expensive.
In this recap, Mark explains why and how to prepare.
Here’s something boards consistently get wrong.
“There’s been a default idea in boards and C-suites that if go-to-market is failing in some way, it’s their fault. As opposed to saying, ‘Huh, I wonder what’s going on out there in the marketplace that’s causing go-to-market to catch it first?’ Think of it as a virus. It’s go-to-market that’s gonna catch it before the rest of the business catches it. Go-to-market is sort of a canary in the coal mine.”
The data right now backs that up. CAC is climbing. Deal volume is down. Average deal size is down. Deal velocity is slowing. And around 73% of B2B tech deals are closing without a decision being made.
That’s a 13% increase in three years over research from The Jolt Effect.
A CFO Mark spoke to recently put it bluntly: “By that standard, my go-to-market effort is bankrupt.”
Mark’s response:
“That’s actually a really powerful way of putting it. If all you have is exploding costs and no countervailing exploding revenue, it’s only a matter of time you’re going to be out of business.”
The payback period on CAC becomes incalculable when deals die in indecision. Mark and I already covered the mechanics. You can check it out here.
Not to beat a dead horse, but the resistance to the reality of time lag is still an ongoing GTM debate. I’m not kidding.
People are motivated by risk. In volatile markets, awareness, confidence, and trust (aka ACT), the three pillars of brand and reputation, matter more, not less.
The 95:5 rule from LinkedIn’s B2B Institute and Ehrenberg-Bass research puts a number on it: most of your future buyers are out of market right now. Brand is how you reach them before the window opens. Which is also why cutting it feels safe. Until it isn’t.
For example, early in COVID, AirBnB’s CEO publicly cut nearly all marketing. There was no immediate revenue impact. But reality hit hard 12 months later.
“You’re gonna have that experience for approximately the next year. And then you’re going to suddenly come out from under the overhang of all that accumulated marketing, and all of a sudden your revenue is going to fall like a rock.”
Boards keep missing the same pattern.
CMOs get replaced just as the investment they made starts to compound. The next leader rides the wave, credits their own work, and then watches demand fall off a cliff (again!) when the accumulated effect runs out.
Two questions follow from this:
Skip those and you’ll misread momentum and punish the wrong people.
This is one of the first questions boards should be asking.
“CAC is usually a reflection of marketing costs of customer acquisition. But it is so much more than that. It’s almost like a mini P&L. You’ve got sales CAC, product CAC, customer success CAC. Anything that you do anywhere in your business that touches a customer in a way that makes them either want to buy or not buy, is CAC.”
Most companies don’t have that full picture. Without it, the return calculation is wrong.
And the problem compounds. CAC isn’t just a cost. It’s a loan of shareholder (or ownership) capital. When deal volume shrinks, deal values drop, and velocity slows, the GTM Debt accumulating inside the company grows while the ability to repay it deteriorates. Most boards have never seen that number laid out plainly.
The follow-on question isn’t just “what is CAC?” It’s: what is CAC buying us, how long is the payback period, and how much of that spend is dying in no-decision outcomes?
“Increasingly, if boards are not asking these questions and demanding the right answers, they’re in breach of their fiduciary duty if they’re talking about a Delaware corporation. If otherwise, based on new regulations around decision governance, that’s a lack of decision governance, which really is almost an identical idea to fiduciary duty. They’re increasingly going to be scrutinized very uncomfortably.”
The legal foundation is real. In January 2023, the Delaware Chancery Court ruled in the McDonald’s case that fiduciary duty of oversight extends to all corporate officers, not just the board and CEO. If an officer can’t outline a system by which their part of the business is managed on a risk-adjusted basis, they’re exposed.
The same pressure is moving through SEC priorities and decision governance expectations. Mark wrote an excellent piece about this here: The Key GTM Governance Questions.
If you don’t have a system to evaluate decisions and spend on a risk-adjusted basis, you are liable.
Every C-suite and GTM team should be prepared to answer these questions if and when they come up:
Most teams can’t answer them cleanly. That is the point.
For a full list of governance questions, Mark has assembled 12 of them here: The key GTM governance questions every company must move to address in 2026.
“What is the reality right now? What is the reality, and what is the reality likely to be if we model it a year from now, two years from now, three years from now?”
If your board isn’t asking that question, someone else will ask it for them.
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Most forecasts fail before they’re even built.
Not because of bad data. Because of bad thinking… upstream.
That’s what Mark Stouse, CEO of Proof Causal Advisory, and I were chatting about even before we got into our latest Causal CMO chat.
Before models, cadence, or tooling, GTM teams need to deal with something harder: the thinking that makes a forecast worth defending.
Most GTM teams don’t want to hear this:
“The temptation is to try and follow every move that the graph makes with some sort of counter to it. The problem is that if you do that, you’re always behind your externalities. You’re never going to get ahead of them.”
In other words, stop reacting to every signal.
Pick investments that hold up across a wide range of conditions. In B2B right now, especially tech, that means brand and reputation. Not because it’s comfortable. Because it’s what’s working.
The catch is time lag.
Mark put it at 9 to 24 months before brand investment shows up in real results. I’ve seen similar timeframes (18 to 36 months). That’s why so many leaders cut it. They couldn’t connect the cause to the effect. So they called it worthless. The lag was hiding the impact.
We’ve covered time lag before. It's a core part of my End of MQLs series.
Right now, buyers aren’t shopping for the best pitch. They’re managing downside risk. They gravitate toward names they already trust, especially in times of crisis when headwinds are strongest. If you haven’t been building up your brand reputation before the pressure hits, you’re starting from zero when it matters most.
This connects to what we covered in our previous Causal CMO chat on the 95:5 rule. Only 5% of your market is in-market at any given time. The other 95% are forming impressions with or without you.
Brand is what stays in the room when your sales team isn’t.
“For 20 years in B2B, marketers and salespeople agreed on very few things. But one was that demand gen was real and you could somehow make somebody want to buy your product. The main reason why that appeared to work for so long is that money was cheap.”
When capital was abundant and risk tolerance was high, activity looked like causation. You pushed, things happened, you took credit.
But when capital tightened, the model broke. The activity stayed. The results didn’t follow.
“This whole idea of making the quarter based on what marketing is doing in that same quarter was always a fantasy. Always. The time lags are too long for that to be true.”
As Rohan Light shared, “cheap money equals lazy thinking”. The constraint we’re in now is forcing a more honest accounting.
These systems weren’t built by idiots. They were built for different conditions. Those conditions are gone.
We covered similar threads in The GTM Reality Gap and The Illusion of Control.
To be clear, this isn’t a tooling problem.
“This is a mindset issue. It’s not a technology issue. One of the things you have to confront is just how much we think in patterns. When we do that, we assume the past is prologue. It’s not.”
Patterns help… until the market changes and you’re still using the old map. Stack enough of them and you get a house of cards. Either very right or very wrong, with no middle ground.
Right now, reality is moving fast enough that the patterns expire before most teams notice.
“Your strategy is the most important thing. Do you have a durable strategy? Can it survive a lot of different changes, a lot of volatility? The planning, the ops, the tactics: that can easily change and should change and will change all the time. Strategy should not.”
Strategy is the what. Not the how.
Most companies don’t have a business strategy. If your org runs a marketing strategy, a sales strategy, a data strategy, and an IT strategy in parallel, that’s usually a sure giveaway there’s nothing unified sitting above them.
A recent HBR study of 500 organizations found that firms with stronger foresight capabilities — built around continuous signal detection and updating, not one-off planning exercises — report a meaningful performance edge. That finding matches what Mark said: durable strategy isn’t a static document. It’s a capability.
Without a durable strategy, you’re not forecasting. You’re decorating a guess.
Not the one rooted in the past. Conditions change. A model that can’t update isn’t a model.
“The definition of a trustworthy model is the one that gets closest to reality. You have to have a good model that represents reality, and then you have to be able to update it with whatever cadence is right for your business.”
The GPS analogy Mark often uses is the right frame. It doesn’t refuse to work when there’s an accident ahead. It recalculates. And it tells you why you’re going to be late.
That’s the test: not just accuracy, but explainability.
“If you have a gap between your projection and reality but can explain what caused it, you’re there. If you can’t explain the variance, you’re not done yet.”
One more thing: real-time data sounds valuable but often works against you. People slow down when flooded with signals. What you need is the right data at the cadence your decisions actually require, not a dashboard that keeps everyone busy and nobody clear.
The standard is not prettier, louder dashboards. Not more pipeline theater. Not false certainty dressed up as confidence.
It’s a model that updates. A strategy that holds. A variance you can explain.
That’s a forecast worth taking to your leadership team.
That’s a forecast everyone can trust.
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