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In the last installment of this series, What I've Learned About Sales and Marketing Alignment After Watching Two Teams Optimize for Different Truths, I ended with an observation I thought I understood.
Even when organizations establish a shared understanding of buyer progress, opportunities still slow down.
Not because Marketing stopped generating demand. Not because Sales forgot how to sell. Because somewhere between buyer interest and commercial commitment, momentum quietly begins to disappear.
At the time, I thought I knew where that observation was taking me. Deal velocity. I started thinking about stalled opportunities, unclear next steps, inconsistent follow-up, and all the ways good opportunities quietly lose momentum after entering the pipeline. Then something happened.
Over the next several weeks, I watched three very different organizations make three very different decisions. And I started wondering whether I had skipped a step. Because these opportunities did not stall because nobody followed up. They did not stall because the problem wasn't understood. They did not stall because leadership lacked information. In fact, the opposite was true.
The organizations understood their problems surprisingly well. They could describe what wasn't working. They could explain why it wasn't working. They could identify what needed to change. And then...
They made decisions that recreated the very conditions they had just identified as the problem.
That got my attention. Because I've spent enough time around executives to know these weren't unintelligent people. They were experienced leaders making decisions they believed were entirely rational. The more I thought about it, the more uncomfortable the pattern became.
Maybe one of the biggest obstacles to organizational change isn't recognizing that something is broken. Maybe it's recognizing the problem while remaining unable to see how your own assumptions are helping keep it alive.
One organization came to us with a problem that leadership had already diagnosed remarkably well.
Their salespeople were responsible for both prospecting and closing. When pipeline was light, they prospected. When opportunities entered the pipeline, those same people naturally shifted their attention toward active deals.
Prospecting slowed. Pipeline eventually thinned. Then everyone turned their attention back toward prospecting. The cycle repeated.
Leadership understood the problem. They even recognized that outbound needed dedicated capacity so prospecting would continue regardless of what was happening elsewhere in the pipeline. That seemed like meaningful clarity. Then they decided what to do...
They hired another salesperson with the same competing responsibilities. I remember thinking about that decision afterward. The organization had correctly identified a structural problem. Then it attempted to solve the problem by adding capacity back into essentially the same structure.
Maybe it works. Maybe the new person behaves differently. Maybe leadership manages the role differently. But none of those possibilities change the underlying question:
If the structure helped create the problem, why are we so confident that adding another person to the structure will solve it?
That's when I started realizing this wasn't really a sales problem. It was a decision-making problem. And a few weeks later, I watched another version of it happen.
This organization was different. Leadership wasn't struggling to understand what was missing. They had already identified it.
Their commercial data needed work. Their CRM needed more structure. Relationship intelligence needed to become more visible. Demand generation needed to become more repeatable. They needed a clearer system for turning all of those pieces into an operating motion.
We spent considerable time working through those gaps together. Eventually, the organization was presented with a process specifically designed to help build, integrate, test, and validate those capabilities. Leadership agreed with much of the diagnosis. Then came the decision...
They weren't ready.
Before moving forward, they wanted to improve the data. Strengthen the CRM. Develop more relationship intelligence. Generate more MQLs. Build more of the commercial foundation internally.
Read that again. The organization decided it needed to become more commercially ready before engaging the process designed to help it become commercially ready.
That's a fascinating decision.
Not because any individual action was irrational. Improving data makes sense. Strengthening CRM discipline makes sense. Building relationship intelligence makes sense. Generating demand makes sense. Every decision can be defended independently.
It's only when you put the diagnosis and the decision next to each other that the contradiction becomes visible. And that's something I've started noticing more often. Organizations don't always make bad decisions because individual decisions are obviously bad. Sometimes they make bad decisions because a collection of individually reasonable choices recreates the exact system they're trying to escape.
The third situation made the pattern impossible for me to ignore. This organization was rebuilding its commercial motion. Leadership openly acknowledged that its historical approach wasn't working.
Its sales organization was still oriented toward a market leadership wanted to move away from. Its positioning was changing. Its messaging was changing. Its market presence was weak.
Leadership acknowledged that the organization struggled to communicate the economic value of what it sold. And its emerging commercial strategy had not yet been validated against the market.
One executive gave perhaps the best description of the situation himself. The organization was, in his words: "Half-baked." Again, the diagnosis wasn't the problem. Leadership knew uncertainty existed.
The proposed next step was deliberately designed around that uncertainty. Take the strategy that already existed. Pressure-test the assumptions. Put the ICP, positioning, messaging, and qualification logic in front of actual buyers. Learn what survives contact with the market. Then scale what the evidence supports.
Leadership's concern? Time.
Would spending eight weeks validating the commercial strategy delay getting into what one executive described as "attack mode"? That one stayed with me. Because the organization wasn't choosing between moving and standing still.
It was choosing between two different kinds of speed. Execution speed and Learning speed. Those are not the same thing.
An organization can execute incredibly quickly while learning almost nothing. Worse, it can become extraordinarily efficient at scaling the wrong assumption. Validation can feel slower because learning becomes an explicit part of the process. But if eight weeks of evidence prevents eight months of misdirected execution, I'm not sure which option is actually slower.
That's when another thought occurred to me. If you already know the strategy is half-baked...
turning up the oven isn't a validation strategy.
After watching these situations unfold, I kept coming back to the same question.
How does this happen?
How can experienced leaders identify a problem accurately, understand many of the conditions creating it, and then make a decision that appears to preserve those same conditions? I don't think the answer is incompetence. I think it's something much more ordinary.
Organizations are incredibly difficult to see clearly from the inside. The longer you operate inside a system, the more its assumptions begin to feel like facts. Existing roles feel fixed. Existing processes feel necessary. Existing costs feel normal. Existing capabilities feel stronger because they're familiar. And existing ways of working gradually stop looking like choices at all.
They simply become: How we do things here.
That's where familiarity becomes dangerous. Because when leaders evaluate a new approach, they rarely compare two alternatives from a neutral starting point. The existing system gets the benefit of familiarity.
The alternative has to prove itself. Internal costs are already absorbed. External costs appear incremental. Internal capabilities are assumed. External capabilities are scrutinized.
The shortcomings of the existing model are familiar enough to be tolerated. The shortcomings of an alternative are reasons to reject it. The comparison looks rational. It isn't always fair. And sometimes, the very system creating the problem gets an enormous structural advantage in deciding whether it should be replaced.
There's another part of this that I find even more interesting. Diagnosis creates confidence.
When leadership can finally articulate what is wrong, something changes.
The problem feels smaller. More manageable. Maybe even obvious.
We need better data. We need stronger outbound. We need clearer positioning. We need more consistent follow-up. We need better qualification. We need stronger commercial infrastructure.
Once those pieces have names, they start looking like a checklist. And that's where understanding can quietly become overconfidence.
Because: Knowing what needs to exist is not the same thing as knowing how to build it.
This is especially dangerous with systems. Most executives can recognize the individual ingredients of a commercial system. CRM. Data. Messaging. Outbound. Qualification. Automation. Reporting. Sales process. None of those things are mysterious. But recognizing the ingredients doesn't mean you understand how they need to interact.
A recipe isn't valuable because flour, butter, eggs, and sugar are difficult to identify. The value is knowing what to do with them.
I've started thinking about organizational capability the same way. The fact that a leadership team understands the components of a solution does not necessarily mean the organization possesses the capability required to assemble those components into a working system.
Yet diagnosis can create exactly that illusion.
We understand the problem. Quietly becomes: We understand how to solve the problem. Those are very different statements.
This may be the most uncomfortable part. Executives are paid to make decisions. That's the job.
They operate with incomplete information constantly. They evaluate risk. Allocate capital. Set direction. Choose priorities. Good leadership requires judgment. But responsibility for making a decision can create an interesting psychological trap.
The person responsible for deciding what happens next can gradually begin to feel like the person most qualified to know what should happen next.
Those aren't always the same thing. Authority answers: Who makes the decision? Evidence answers: What should the decision be?
Experience matters. Instinct matters. Pattern recognition matters. But none of them eliminate uncertainty.
Strong leadership isn't knowing the answer to every question. Sometimes it's recognizing when the evidence isn't strong enough to justify the confidence behind the answer. That's especially important during transformation. Because organizations rarely seek help when everything is working perfectly.
They seek help because something isn't. A system isn't producing the expected result. Growth isn't predictable. Pipeline isn't converting. The market isn't responding. Execution isn't scaling. Somewhere, something the organization believes about itself has stopped matching reality.
That should create curiosity. Instead, I've watched it create something else.
A determination to solve the new problem using the same assumptions that existed before the problem became visible. And that's where organizations begin outsmarting themselves.
Every organization accumulates beliefs.
We've always sold this way. Our customers expect this. Our team can handle that internally. This market is where the opportunity is. Our salespeople should be able to prospect. We just need more leads. We need to move faster.
Individually, each statement may be true. But over time, beliefs stop getting treated like hypotheses. They become operating constraints. And once that happens, leaders stop asking whether the assumption is correct. They start designing solutions around it.
That's how an organization can identify a structural conflict and reproduce the structure. It's how an organization can recognize missing capabilities and decide to build those capabilities before accepting help building them. It's how an organization can acknowledge that its strategy hasn't been validated and decide validation might take too long.
Each decision makes sense inside the assumptions that produced it. That's the problem.
Sometimes the purpose of outside perspective isn't to provide an organization with intelligence it doesn't possess. It's to question the assumptions everyone inside the organization has been living with for so long that they no longer recognize them as assumptions. And I've started to believe that's one of the hardest forms of business advice to accept.
Not: Here's something you don't know. But: Here's something you think you know that may no longer be true.
It's easy to read these examples and see the contradiction. It's much harder when you're sitting inside it.
That's probably the most important thing I've learned from watching these decisions unfold.
From the outside, the pattern looks obvious. A company identifies a structural problem and recreates the structure. Another identifies missing capabilities and decides to build them before accepting help designed to build them. Another acknowledges that its strategy is unvalidated and decides validation might slow it down.
Put the diagnosis next to the decision and the contradiction almost becomes uncomfortable to read. But that's the benefit of distance.
Inside the organization, the decision doesn't feel ridiculous. It feels responsible. It feels financially prudent. It feels faster. It feels less risky. It feels familiar. And that is precisely what makes organizational self-sabotage so difficult to recognize.
I've started wondering how many decisions I've made in my own career that would look equally contradictory if someone placed the problem I described on one side of a page and the solution I chose on the other.
Probably more than I'd like to admit. Because nobody is immune to this. Experience doesn't eliminate blind spots. Sometimes it gives us better arguments for defending them.
The more time I spend inside growing organizations, the less convinced I become that transformation fails because leaders cannot identify what's wrong. Often, they can.
They know pipeline isn't predictable. They know the sales model isn't working. They know their data isn't reliable. They know their positioning hasn't been validated. They know the organization isn't commercially ready.
The harder problem comes next.
Can leadership challenge the assumptions it used to build the current system before using those same assumptions to design the next one?
That's where I've watched otherwise intelligent organizations struggle. Because familiarity feels like knowledge. Experience feels like evidence. Authority feels like expertise. And motion feels like progress.
Eventually, the organization becomes incredibly good at explaining why the solution must conform to the same constraints that helped create the problem.
That's not transformation. That's preservation with a new label.
One of the things I've learned while writing this series is that the problems keep moving further upstream.
I started with technology. Then demand systems. Then automation. Then visibility. Then forecasting. Then alignment. And now I find myself looking at the decisions underneath all of them. Because systems don't create themselves.
Leaders create them.
Leaders decide what gets measured. What gets funded. What gets automated. What gets questioned. And what gets preserved.
That's why I've started believing one of the most dangerous moments in transformation isn't when leadership doesn't understand the problem. It's when leadership understands enough of the problem to become confident in its own solution, without recognizing which assumptions still haven't been challenged.
Good leaders don't need to know every answer. They need to know when they don't know the answer. And increasingly, that distinction matters because organizations now have access to something they have never had before.
The ability to operationalize their assumptions almost instantly.
AI can research faster. Write faster. Prospect faster. Prioritize faster. Personalize faster. Follow up faster. Make decisions faster.
That's extraordinary leverage. But leverage has never cared whether the underlying decision was intelligent. And if organizations already have a tendency to recreate the problems they're trying to solve, I think we're about to discover what happens when we give them the ability to automate those decisions at scale. Because the next generation of GTM problems may not come from companies failing to adopt AI.
It may come from companies adopting it successfully... at exactly the wrong layer.