Weekly insights about GTM effectiveness, building brand reputation, and AI adoption.
My good friend and AI adoption co-writer, Gerard Pietrykiewicz, asked me a question recently. Are you a sheep or a shepherd? Do you own the loops, or are you a piece of one?
My answer was neither.
Sheep and shepherd both assume you’re inside someone else’s structure. One takes direction, the other gives it.
Independent work doesn’t have that structure.
Nobody assigns my tasks. Nobody checks my drafts before they go out. Nobody tells me when the work is off, because nobody’s reading it before the client does. Run through what that actually removes: no performance review, no peer looking over your shoulder, no boss’s gut check before something ships. When you work for yourself, all of that lives in one person. You.
That’s not a complaint. I chose this. But it’s worth calling out because it changes what “adopting AI well” actually requires.
There are two things that can fill it. Your own judgment. Or whatever tool is fastest and easiest at 4pm on a deadline day.
AI is very good at quietly becoming the second one. Especially if you’re someone who’s used to trusting your own read on things. It’s easy to mistake “the AI didn’t flag a problem” for “there is no problem,” when what you actually needed was a second set of eyes (that you don’t have). It’s the same effectiveness-before-efficiency trap that shows up when teams optimize for output over judgment.
Teams have redundancy built in. A colleague catches what you miss, even by accident, even if nobody’s officially checking.
Independents don’t have that. If your own judgment slips, there’s no safety net until the client notices — and by then it’s not a quality problem anymore. It’s a relationship problem.
You’ve probably seen “human in the lead” this year. It’s been framed, repeatedly, as the shift enterprises need to make — humans directing AI instead of just supervising it, so frontline workers stop feeling like a checkpoint on their way out. Fair point, for an enterprise.
But that framing assumes there’s an enterprise to redefine. A team, a hierarchy to reassure. Strip that away and the phrase means something else entirely. There’s no one to lead but yourself. No org chart to redesign around the new relationship with the tool. It’s not human in the loop. It’s human in the lead, except there’s no one else in the loop. Just you, and whatever discipline you bring to checking your own work.
Here’s where it stops being just an independent’s problem.
A go-to-market team (Sales, Marketing, Product, CS) looks like it has the redundancy independents don’t. Someone signs off on the forecast. Someone reviews the campaign brief. Someone technically outranks the person who drafted it. That structure implies a check exists. That’s the same illusion of control that shows up whenever process gets mistaken for oversight.
Often it’s signing off on the output, not the judgment behind it. A VP approving a forecast built with AI assistance is rarely re-deriving the assumptions underneath it. They’re trusting that whoever built it did the thinking. If that person leaned on AI to fill a gap in their own judgment, the sign-off doesn’t catch it. It just adds a signature to it.
That’s not a hunch: a 2026 Grant Thornton survey of 950 executives found 78% lack strong confidence they could pass an independent AI governance audit within 90 days. The structure exists. The proof underneath it usually doesn’t.
Ask the same question, just inside a team: what's genuinely being checked here, and by whom, with the expertise to actually catch a bad call? “Someone outranks me” and “someone is checking this” are different claims. Most org charts only guarantee the first one.
It requires a few specific things, not more willpower.
Lovable used to be the same. Before its MCP server launch, it was cut-and-paste like everything else. Now Claude connects to Lovable directly and can build, iterate on, and deploy my prototypes without me relaying anything by hand. That upgrade also changed what kind of dependency it is. Without it, the prototype doesn’t exist. That’s binary.
Most people never sort their own work this way, and fewer still notice when a tool crosses from one category to the other. Workflow dependency means staying sharp enough to catch a bad output. Binary dependency means knowing exactly how exposed you are if the tool disappears — and that exposure can arrive quietly, the moment a tool gets more capable, not less.
Being your own shepherd is a specific job, independent or not. You have to build the checks a boss, or an org chart, would normally imply deliberately, or they don’t exist.
Start with the sort: go through what you actually use AI for and split it in two: what disappears completely without the tool, and what just gets slower. That should take less than ten minutes. Most people have never done it.
Then ask: who’s actually checking the slower stuff before it goes out? If the honest answer is nobody — whether you’re independent or three levels into an org chart — that’s not an AI problem. That’s the job nobody built.
Human in the lead. No one else in the loop. Nobody’s assigning you tasks, or nobody’s really checking them. Same job either way.
Co-authored by Gerard Pietrykiewicz and Achim Klor. Follow us on LinkedIn, schedule a call with Achim, or contact Gerard if you need help with AI adoption. Subscribe below for more.
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A company cuts its corporate officer layer. AI is doing the work now, the thinking goes, “We don’t need as many decision-makers.” Costs come down. Shareholders cheer.
Then the legal letters arrive.
The people are gone. Accountability isn’t. Under Delaware law and the EU’s upcoming AI Act, fiduciary duty doesn’t disappear when the org chart gets flatter. It gets concentrated in whoever’s still around.
Mark Stouse and I got into this during our recent chat on The Causal GTM Leader. We came in planning to talk about what AI actually costs when companies treat it as free labor. We ended up somewhere thornier: what it costs when companies mistake efficiency for effectiveness. And why that mistake is starting to have legal consequences.
When OpenAI launched publicly, Mark did a word cloud of their launch materials. Efficiency was front and center. In business, efficiency has one meaning: cut costs.
The problem is that efficiency is a derivative metric. It tells you how cheaply you’re doing something. It says nothing about whether that something is worth doing.
You have to be effective first for any cost burden to be relevant, to be acceptable.
That’s not a philosophical point. It’s a decision-making sequence. If you don’t know whether a function, a role, or a workflow is producing commercial outcomes, cutting its cost doesn’t save you anything. It just makes a broken thing run cheaper.
There’s an economic frame that fits here: the Jevons Paradox. When you make something cheaper, and there’s already demand for it, consumption expands until total cost rises. You make content generation cheaper, so you generate more. Your total marketing cost goes up while output quality diffuses. More activity. No more effectiveness.
Gerard Pietrykiewicz and I made the same case from a different angle in AI Agents Are Cheaper Than Unmanaged Work, Not People. The per-prompt cost may be low, but the workflow cost is where it gets away from you.
This is the pattern GTM teams are running into right now. The tool can produce more, so the focus becomes MORE, rather than using the tool to make better decisions. The same argument is at the centre of The Decision Layer: effectiveness has to come before scale, or you’re just running the wrong system faster.
Large companies installed automated applicant tracking systems (ATS) to reduce recruiting costs. Fewer recruiters, faster screening, lower cost-per-hire. Reasonable on the surface.
The problem is the human configuring the system’s keyword filters and screening criteria. Research compiled by Select Software Reviews found that 88% of employers believe they are losing highly qualified candidates who are screened out because they didn’t submit ATS-friendly resumes. In other words, the system’s filters weren’t calibrated to what the role actually required.
Mark made it even more concrete. His wife, an ex-Accenture change-management specialist, applied for a role at a major consulting firm. Her CV was rejected by the ATS. When she connected with someone inside the firm later, they told her, “Oh, that happens all the time, and you’re perfect for this job.”
The talent you didn’t hire is invisible. The savings are on the ledger. That asymmetry shapes every one of these decisions.
If you are spending less money and it’s not effective, it doesn’t matter. You should be spending zero money if it’s not effective.
The ATS example is about recruiting. But the logic applies wherever AI is substituting for human judgment without a validated effectiveness baseline.
Automate what’s proven. Don’t automate the question of whether it’s working.
This is where our conversation went somewhere most AI-and-headcount pieces don’t.
Mark referenced a company (which shall remain unnamed) that significantly cut its corporate officer layer based on the AI efficiency argument. Shareholders have since brought legal action. Those officers held fiduciary duties. When they left, those duties didn’t leave with them.
The more you replace people with bots, the more you’re concentrating liability in the hands of a fewer and fewer number of people.
This is grounded in real law. Delaware’s 2022 amendment to the General Corporation Law affirmed that corporate officers have a duty of oversight and that officers of Delaware-domiciled companies can be held personally liable for negligence, not just bad faith. That covers roughly two-thirds of the Fortune 1000 and 90% of venture-backed companies in the US. As Mark put it in an earlier session: Saying “I didn’t know” won’t protect you.
The EU is moving in the same direction. Under Article 26 of the EU AI Act, deployers of high-risk AI systems — which explicitly includes employment and recruiting tools — are responsible for human oversight, monitoring, and audit logs. That obligation doesn’t sit with the vendor who built the system. It sits with the organization deploying it. Full enforcement kicks in August 2026.
A machine cannot bear legal accountability for a bad outcome. That’s not a limitation of current AI. It’s a structural fact. Someone always owns the decision. Remove the people, and the accountability doesn’t disappear. It redistributes upward to whoever’s left.
Removing the people does not actually remove the responsibility.
The efficiency argument for AI is easy to make. The numbers are visible. The savings are immediate. What’s harder to see is the cost of automating something that wasn’t working in the first place, and the exposure created when the accountability chain has a gap in it.
Before any AI-driven headcount or workflow decision, two questions are worth writing down.
1. Can you prove what’s effective here?
Not what the dashboard shows — what you can causally connect to a commercial outcome. If the answer is no, you’re not ready to automate. Keep investigating.
2. Who owns the decision if it fails?
For every workflow or role you’re considering automating, name the person who remains accountable if the system produces a bad result. If that name isn’t clear before the change, the change isn’t ready.
The decision layer doesn’t transfer to the tool. It can’t. Someone always owns the outcome.
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Co-authored by Gerard Pietrykiewicz and Achim Klor
A story has been making the rounds about an unnamed company that allegedly ran up a $500 million Claude bill in a single month because nobody set usage limits. Every account traces back to the same Axios report, sourced from an unnamed AI consultant — no named company, no invoice, no primary confirmation.
Gerard and I are not treating the $500M as fact.
But we are treating it as a warning.
Whether the $500M number is real is neither here nor there. The recognisable pattern is more important. AI spend can behave less like traditional software and more like cloud spend, where it grows quietly, in places leadership doesn’t see until the bill arrives.
The surface pricing doesn’t help either.
Anthropic publishes Claude pricing in dollars per million tokens, which makes individual usage feel small. What it doesn’t show is how fast the meter runs when there are no guardrails on the workflow.
AI agents retry behind the scenes . They loop. They re-read long context windows. They call tools, spawn more work, generate multiple versions, critique them mulitple times, then generate many more. None of that feels dramatic while it’s happening. It just feels like the agent is working.
Until you ask what the work was for.
This is where the conversation can get lazy.
Someone looks at token costs, compares them with a salary, and concludes agents are obviously cheaper than junior employees. In the narrowest possible sense, that can be true. A bounded task can cost pennies in model usage while a human costs considerably more per hour. The Government of Canada Job Bank puts the median for a computer software engineer at CAD $56.49/hour nationally, CAD $62.50 in British Columbia.
But that comparison ignores what a junior person actually is.
A junior developer — or a junior analyst, or a junior content manager — is someone learning your systems, your customers, your standards, your trade-offs. They ask questions. They make mistakes that require review. They also build context, and over time that context turns into someone who can own work, mentor others, and make better decisions because they understand how things actually fit together.
An agent has its own costs that the tokens-versus-salary math skips over. It also makes mistakes. It can produce output that looks right but is subtly wrong. It can generate work faster than the team can evaluate it. And if the task isn’t clearly defined, it tends to resolve ambiguity by generating volume.
That’s the hidden cost. The agent may be cheap per prompt. The workflow may still be expensive.
If AI is cheaper, cheaper than what, exactly?
Cheaper than a salary line? Cheaper than a task taking three days? Cheaper than a senior person getting interrupted ten times to answer questions a clear brief would have prevented? Cheaper than work not getting done because nobody had time to start it?
Those are different questions. The metric that matters isn’t cost per prompt, tokens consumed, or drafts produced. Not even “hours saved” unless those hours convert into something useful.
The better question is this: What did it cost to produce a completed, reviewed, useful outcome?
If an agent drafts a campaign brief in ten minutes but a marketer spends two hours correcting hallucinated positioning and wrong product names, the cheap part wasn’t the whole cost.
If an agent writes documentation nobody trusts, the output isn’t an asset. If it generates twenty prospect summaries and the team acts on two, the activity looked productive but the value was thin.
On the other hand, if an agent takes a messy sales call transcript, extracts the objections and action items, and gives a rep a better starting point for follow-up, that’s real value. The agent didn’t replace the rep. It removed the worst part of the work and made the human part easier to begin.
That’s where agents are most useful. As a way to make difficult work more startable, more reviewable, and less blocked by repetitive setup.
This is why the $500M story, accurate or not, resonates.
Anyone who’s been through enterprise software adoption recognises the pattern:
Then finance or security eventually asks the questions that should have been asked earlier:
Those questions aren’t bureaucratic overhead. They’re how you tell adoption from a “free-for-all”.
Microsoft’s Cloud Adoption Framework guidance on AI agents makes the point plainly: agent observability, governance, and security aren’t optional features — they’re requirements. The framework treats every agent as something that must be auditable and controlled throughout its lifecycle.
Anthropic moved in a similar direction when it released enterprise controls for Claude Code — spend limits at the organisation and user level, usage analytics, managed policy settings, and a Compliance API for programmatic access to usage data. Once agents do real work at scale, governance stops being a side feature.
The leadership question isn’t “how many people can we replace?”
It’s “which parts of the workflow should no human be wasting their best attention on?”
Those lead to very different decisions.
Let agents handle work that is bounded, repeatable, and easy to verify. Scaffold code from a clear spec. Summarise calls. Draft the first version of a brief, a ticket, a proposal. Produce the boring first pass that often prevents people from starting at all.
Keep humans responsible for the work that requires ownership: defining the problem, setting the constraints, judging quality, deciding what ships.
A good junior role isn’t a queue of small tasks. It’s how people learn what good looks like. Remove the repetitive setup with agents — that’s useful. Confuse that with removing the development path, and you’ve cut your pipeline of future senior talent.
More AI output doesn’t automatically mean more business value. Sometimes it’s more material to review, more edge cases to catch, more misplaced confidence to walk back.
Before scaling agents across a team, start with one workflow where the task is bounded and the output can be verified. Put an owner on it. Define what the agent can do alone and what requires human sign-off. Track whether the work gets accepted, reused, shipped, or acted on.
The measure is simple: Did this produce a better outcome, or just more output?
If the answer is better outcomes, expand it. If it’s more output, fix the workflow before adding more usage.
AI isn’t the problem. Unmanaged work is.
Co-authored by Gerard Pietrykiewicz and Achim Klor. Follow us on LinkedIn, schedule a call with Achim, or contact Gerard to see if there’s a fit. Subscribe below for more.
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Mark Stouse went to a baseball game. The Diamondbacks were hosting the Dodgers.
Five minutes after the final out, a guy walked back to his seat, looked up at the scoreboard, and said, “Wow! We won! How did that happen?”
That’s what a dashboard gives you: the final score.
Useful, sure. But not enough.
It won’t tell you how the team got there, who carried the play, who blew it, or almost did.
This is what Mark and I got into during our latest Causal GTM Leader session, the one that picked up right where AI needs human logic left off.
Here’s the recap.
Mark’s read on this concept stuck with me: data is the past by definition. Something has to happen before it can be measured, and by the time it’s measured, it’s already history. A dashboard is just a polished way of looking at history.
That wouldn’t matter much if markets held still long enough for last quarter’s numbers to still apply. They don’t, and never have.
Mark and I have made a related case before about why a forecast that starts with the past is already behind. Most of us spent the better part of the last 30 years in something close to a steady state, and we built our instincts, and our dashboards, around that. The steady state is gone. The dashboard hasn’t caught up.
The truth is nobody’s actually being held accountable for what their volumetric numbers mean to the business downstream. They’re being paid for hitting a KPI, not for understanding its ripple effect. So the dashboard becomes a kind of moat — the one story you can tell about yourself that you fully control, especially when the board or CFO starts asking harder questions.
I made a version of this case already, calling it process theatre. Dashboards do the exact same job, just with pretty charts instead of boring meetings.
This is where the conversation with a CEO or CFO actually gets hard. And in that context, the KPI itself is rarely the issue. What matters more is whether it still explains the market you’re selling into.
A lot of teams miss the headwinds, the tailwinds, and the crosswinds in the marketplace because they’re so focused on getting the deal across the line that they never look up. Buyer fear, budget pressure, trust, timing — most dashboards don’t show those forces unless you deliberately factor them in.
Mark put it about as plainly as it can be put:
There is a capital R reality out there that doesn’t negotiate with any of us, that doesn’t care how we feel. It just is.
You can disagree with gravity. You can resent it. But it doesn’t care if you step off a 30 storey building. The market runs the same way. It doesn’t lower the bar because your forecast says it should hold steady, and it doesn’t wait for your dashboard to catch up before it moves.
Most teams already sense the gap between the dashboard and the market. They just don’t want to be the one in the room who calls it out. Because doing that changes the conversation from reporting to accountability.
There’s a single question that separates a useful dashboard conversation from a wasted one:
Am I trying to defend past performance, or am I trying to learn what the best performance going forward would look like? That’s the fault line.
Try it on your own numbers before your next leadership update. Pull up whatever you’re about to present and ask, line by line:
Is this here to defend what already happened, or to help someone decide what to do next?
If every line is in the first column, you don’t have a dashboard. You have an alibi.
This is also where most GTM frameworks fall apart, including ones I’ve pitched myself. We’ve gotten very good at showing the symptom and very bad at showing the cause.
Dashboards show the symptoms of what’s happened in the past. The market is creating the causes — causes we haven’t even planned for. The internal metrics arrive way too late to matter.
By the time the dashboard flags the problem, the window to do anything about it has already closed.
So no, dashboards aren’t useless.
Dashboards matter. But they’re just instruments. They’re not reality.
That’s the distinction I see GTM leaders forget every week. The mistake isn’t building a dashboard. It’s mistaking it for the market.
Before your next leadership update, sort every metric on the page into two piles: defending what happened, or deciding what’s next. If one pile is empty, you’ve found your actual problem.
In your next weekly GTM review, ask the fault-line question:
Are we defending past performance, or learning what good performance looks like tomorrow?
Then watch who flinches.
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I don’t use AI because I want it to think or write for me. I use it because it helps me stay with my own thinking long enough to turn it into something useful.
I have ADHD — mostly the inattentive kind — so my brain doesn’t always move in a straight line. Ideas arrive out of order. Connections show up before the structure does. I’ll know what I mean before I can explain it clearly.
I also deal with what I call delayed intelligence: I often understand what I actually meant five minutes, five hours, or five days after the conversation where I needed the thought.
That gap — between knowing something and being able to articulate it — used to cost me a lot of time and frustration.
AI narrowed that gap.
The blank page was never really the issue. The harder part was catching a half-formed thought, keeping it in view long enough to work with it, and turning it into something another person could follow.
For me, that’s got more to do with working memory than productivity. The scattered thoughts, the threads that disappear mid-thought, the idea that felt clear thirty seconds ago and is now somewhere behind three other ideas — that’s ADHD doing what it does.
AI helps with that. Not by generating content, but by reducing cognitive drag. It gives scattered thinking a place to land so I can actually do something with it.
The real value isn’t prompts. It’s conversation.
I bring a messy idea to ChatGPT or Claude and ask: what am I actually saying here? What’s missing? Where’s the stronger argument? The first answer is rarely the answer. But it gives me something to react to, and that reaction is where my actual thinking shows up. I push back, correct it, reject parts of it, sharpen others. Sometimes the AI finds something I hadn’t said clearly yet, something I’d forgotten, or an angle I hadn’t considered.
That back-and-forth forces structure. Not because the AI imposes it, but because explaining a half-formed idea to anything — even a language model — requires you to give it a shape.
They’re not interchangeable, and I don’t use them that way.
ChatGPT handles research, fact-checking, structure, and stress-testing. If I want a second opinion on an argument or need to pressure-test a claim before I commit to it, that’s where I go. It’s good at examining the idea from the outside.
Claude handles long-form drafting, article flow, tone, and tightening language once the idea has a shape — the iterative editorial work, multiple passes, voice calibration.
For visual mockups and video, pulling YouTube transcripts or summarising long-form content, I use Google’s AI tools.
I still decide what’s true, what sounds like me, what deserves to stay, and what needs to be properly designed.
AI can make unfinished thinking sound finished. That’s the trap.
A clean draft doesn’t mean the idea works. A better sentence doesn’t mean a better argument. AI will organise weak thinking just as readily as strong thinking, and the result looks credible enough to fool you into thinking you’re done.
So I still ask: is this true? Is this mine? Would I actually say this? Did we find the point, or just polish the fog?
That last question is the one that matters. If I can’t answer it, the draft isn’t ready — regardless of how good it reads.
For me, that’s the real value of AI. It gives my thinking somewhere to land, then helps me stay with it long enough to turn it into work I can stand behind.
PS: The em dashes are grammatically correct in case you’re wondering.
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Most GTM teams are using AI now.
That part is no longer interesting.
The real question is whether AI is helping teams make better decisions, or just helping them produce more stuff faster with weaker thinking behind it.
That was the core of the discussion Mark Stouse and I had during our Causal GTM Leader chat on LinkedIn last week (formerly The Causal CMO).
We renamed this series because GTM is a system. Marketing alone was too narrow a lens. Sales, CS, Product, Finance, and the C-suite all belong in the conversation.
Here’s the recap.
When pressure rises, cognitive load rises. When cognitive load rises, people look for the easier path.
That’s where AI adoption gets dangerous.
We are starting to see a lot of evidence... about people surrendering cognition. They’re basically saying, “That’s what AI said, and I’m just going to accept it, believe it, and hope that it’s true.”
Microsoft Research surveyed 319 knowledge workers and found that higher confidence in Gen AI is directly associated with less critical thinking. The more people trust the output, the less they question it.
There is a second problem underneath that one.
People also resist AI that challenges their assumptions. Teams complain about sycophantic AI, but every time developers try to stiffen the spine of these tools, users push back.
We say we want to be challenged. We usually don’t.
The old leadership rule applies: delegation is not abdication. You still own the output. You’re still on the hook for what you’re accepting.
You can’t say the software made me do it.
Gen AI is commonly used for asking things like, What should we do? What’s the best path? That is exactly what it cannot answer reliably. The algorithms inside Gen AI tools fundamentally can’t do that.
Why? Because success is not patterned.
Our outside environment controls 70 to 80% of whether it’s successful or not, and that is constantly changing. Full stop.
Failure, however, is heavily patterned. That is where Gen AI earns its place. Feed it a strategy document and you can punch holes in the logic, surface weak assumptions, and de-risk the plan before you execute.
In a recent example, Mark stress-tested a new strategy for a CMO and CRO. The findings made the boardroom uncomfortable and created pushback. But because the logic was legit, the CEO and CFO were okay with it.
De-risk first. Then execute.
Where Gen AI pattern-matches the past, Causal AI tracks what is actually driving outcomes now.
Mark uses a compass rose as an analogy:
Gen AI operates roughly 100 degrees off true north because it cannot model the external environment. Causal AI gets to around 5 degrees off, and keeps updating. It’s perpetual. Like a GPS. You still have to stay on top of it. You can't use either one as a static snapshot.
We went into the decision logic behind this previously in The Decision Layer.
A lot of AI adoption still rides on a cost-cutting premise: replace people, produce more, spend less.
The narrative around Gen AI has gone way off track.
This whole idea of efficiency and low cost and replacement of people... is just crap.
Why? Because the price end users pay today is heavily subsidized.
The fact that we can do whatever we do with ChatGPT for 20 bucks or even 200 bucks a month is astounding, but it is not real.
Reuters reported that OpenAI expects to spend $50 billion on computing power this year and is targeting roughly $600 billion through 2030. When that cost reaches users, the question changes. Not “how much can we automate?” but “which AI use cases are worth paying for?”
The answer is anything that is provably an improvement in effectiveness. Not output volume. Effective outcomes.
There is another cost in the headcount math.
When you cut the people who carry customer context and market judgment, and that knowledge was never captured, you do not get a leaner team. You get a team running AI on stale inputs with nobody left to question the output.
When the board says “do more,” GTM teams often hear: more activity, more channels, more volume.
That is not what the board had in mind.
The board means: I want more sales opportunities, a higher average selling price, a faster sales motion. I want those things for less money. They’re not talking about activity. And that difference is huge.
Activity metrics do not connect to the three things a CFO tracks: more deals, bigger deals, faster close. If your reporting cannot make that connection, you are speaking a different language.
More on this in GTM Reality Gap and The Causal Bridge.
If there was ever one thing every go-to-market leader needs to think about and take to next week, this is it:
Stop trying to make it all about channel optimization or delivery of the message. Start making it about THE message.
Markets are moving faster than GTM teams can adjust tactics. If you try to follow every shift, volatility turn times will defeat you. You end up fighting the last war.
The answer is evergreen customer strategy. Deep knowledge of what keeps your buyer up at night, what earns their confidence and trust over time.
FUD (Fear, Uncertainty, Doubt) has never been more prominent than now. Buyers want a reason to believe in you before they commit.
And THAT is a brand problem, not a demand problem.
The brand is demand.
They are converging fast. If you are cutting brand investment to fund more lead generation, that trade is working against you.
Write down and confirm what the board actually means by “do more”. More opportunities, higher ACV, faster close, lower CAC. Ask them.
Then check your current reporting against those outcomes.
If the connection is not clear, that is the conversation to have before the next planning cycle.
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