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Part 9 of 10 in my "What I've Learned..." series
In the last two installments of this series, I've been examining something that's become increasingly difficult for me to ignore.
First, I watched experienced leaders make decisions that recreated the very problems they were trying to solve. They understood what wasn't working. They could explain why it wasn't working. But the assumptions underneath their decisions often remained largely unchanged.
Then I started exploring what happens when AI gives those same decisions unprecedented execution capacity. The technology can research faster. Prospect faster. Produce content faster. Coordinate activity faster. And increasingly, it can perform work that organizations have historically hired people to do.
That led me to a question I wasn't originally expecting to ask.
How many organizational problems have we been trying to solve by adding people when the underlying system was never designed to solve the problem in the first place? I've spent a lot of time thinking about that question. Because long before AI entered the conversation, I'd been watching organizations approach another problem in much the same way.
They hire experienced people to build capabilities the organization doesn't possess. Those people develop strategies. Introduce processes. Implement technology. Build teams. Create operating models. And, along the way, learn an extraordinary amount about what works and what doesn't. Then, somewhere down the road, the organization finds itself doing much of it all over again.
Different leader. Different plan. Different budget. Sometimes even the same technology. And I've started wondering whether the problem goes deeper than how organizations hire, structure, or manage their teams.
What if an organization can accumulate years of experience without ever becoming an organization that actually learns?
I've watched some version of this cycle play out across enough organizations that it's become difficult not to recognize. A company reaches a point where its commercial performance isn't meeting expectations.
Pipeline generation is inconsistent. Sales and Marketing aren't operating together. The CRM isn't producing reliable information. Forecasting is difficult. The organization needs stronger processes, better technology, or a more effective strategy.
Leadership decides it needs someone with the experience to fix it. That makes sense. So the company begins searching for a new executive.
The search can take months. Then comes the hiring process, onboarding, organizational assessment, stakeholder alignment, strategic planning, budgeting, and approvals.
Eventually, the new leader starts building. And depending on the organization, nine to twelve months may have passed before meaningful implementation is even underway. That's a substantial investment of time and money before the organization has begun realizing a return. Because building a commercial capability isn't the same thing as getting results from it.
The campaigns have to run. The processes have to stabilize. The team needs to adjust. The market needs to respond. The organization needs time to determine whether the strategy it invested in actually works.
Then something happens... The executive leaves.
Maybe they were recruited away. Maybe they became frustrated. Maybe leadership decided the results weren't coming quickly enough. Maybe the organization changed direction. Whatever the reason, another search begins. And with it, another assessment of what isn't working.
Another strategic plan. Another round of investment decisions. Sometimes the new leader reaches conclusions remarkably similar to those of the person who came before them. Sometimes they reject the previous strategy and start somewhere else entirely. Neither outcome necessarily tells us whether the original strategy was right or wrong. But I've found myself asking a different question.
What did the organization actually learn from everything it just went through?
Not what the departing executive learned. Not what individual members of the team remember. What does the organization now understand about its market, its customers, its commercial processes, and its own capabilities that it didn't understand before? And more importantly, how is that understanding influencing what happens next? Because if the next strategic planning cycle begins without a meaningful accounting of what the previous one taught us, I'm not sure we're improving the organization.
We may simply be changing the person responsible for trying.
For most of my career, I've thought of experience as something enormously valuable. I still do.
Experienced leaders recognize patterns. They understand why certain approaches succeed and others fail. They've made mistakes and developed judgment through the consequences of those mistakes. That kind of experience matters, especially when organizations are navigating uncertainty. But I've started questioning an assumption we rarely examine when we talk about hiring experienced people.
We tend to assume that bringing experience into an organization makes the organization more experienced. I'm not convinced the relationship is that simple. Think about how much knowledge accumulates inside a commercial organization during an ordinary operating cycle.
A salesperson discovers that a buyer segment responds to a problem the company hasn't emphasized in its positioning. An SDR learns that a particular conversation creates more interest than the messaging in the approved campaign. Marketing identifies engagement patterns that suggest the original audience assumptions may be incomplete. Revenue Operations notices that certain qualification rules are sending the wrong contacts into the wrong workflows. A sales leader recognizes that the deals everyone feels confident about aren't necessarily the deals making meaningful progress.
Each observation has value. Each person may be learning something. But what happens when those observations begin contradicting one another? Or when they contradict the strategy leadership has already approved?
I've seen organizations collect tremendous amounts of information without establishing a reliable way for that information to influence the decisions being made elsewhere in the business.
Sales learns something about the buyer that Marketing never incorporates into its messaging. Marketing identifies a pattern that never changes Sales qualification. Operations sees a recurring process failure but continues maintaining the workflow because that's the process the organization approved. Leadership reviews performance against assumptions that the people closest to the market have already begun questioning. Everyone is learning from their own part of the experience. Yet the organization continues behaving as though nothing fundamental has changed.
That distinction has become increasingly important to me. An organization can employ extraordinarily intelligent people without becoming an intelligent organization. And I don't think the problem is necessarily a lack of communication. We've examined that before in this series.
It's possible for different functions to communicate regularly, share reports, attend the same meetings, and still interpret the business through entirely different operating assumptions. But now I'm beginning to see another layer. Even when the information is shared, and even when people agree on what it means, the organization may not change what it does. That's a much harder problem.
One of the reasons I've been thinking about this so much is that we've been working through a commercial transformation where the relationship between execution and learning has become increasingly visible.
The engagement began with a relatively focused objective. Take an emerging go-to-market strategy, put it in front of the market, and determine what the response actually tells us. Which buyers engage? Which problems create conversations? What messaging resonates? Can those conversations become meaningful commercial opportunities?
It would have been relatively easy to evaluate that initial work entirely through activity and conversion metrics. Calls made. Emails delivered. Contacts engaged. Meetings booked. Those numbers matter. But as we began examining the results, we recognized that the next constraint wasn't necessarily our ability to generate more engagement.
It was the organization's ability to consistently prioritize and convert the engagement it already had.
The market was producing signals. The operating structure wasn't yet equipped to make full use of them. That changed what needed to happen next. Instead of simply increasing outreach volume, the next phase became about building more of the system around the activity.
Data operations. Contact qualification. Lead routing. Multichannel coordination. CRM infrastructure. Follow-up processes. Reporting. A more deliberate operating rhythm.
We weren't abandoning what the initial work had accomplished. We were responding to what it had exposed. Then, as that more connected system began operating, another layer of learning emerged.
Certain technology executives were responding to specific legacy-system modernization risks. Other potential buyers, particularly those closer to business operations, appeared to require a different conversation. We discovered limitations in the way certain audiences were being nurtured. Repeated outreach to the same cohorts wasn't necessarily producing better results. We also had to become more disciplined about distinguishing engagement from buying intent.
An open isn't a qualified opportunity. A click isn't evidence that a company has decided to solve a problem. Even a positive conversation needs to be understood in the context of what the buyer actually does next. Some of those distinctions should sound familiar. They've appeared throughout this series. But here's what I find interesting about this engagement.
We're now using what the previous phases taught us to shape a different kind of commercial strategy.
Rather than simply expanding the audience and increasing activity, the proposed next phase places greater emphasis on prioritized accounts, existing relationships, relevant executive events, buyer context, and the conversion of engagement into qualified commercial conversations. There are still assumptions to validate. The next strategy hasn't yet proven its outcomes. And I think that's important to acknowledge. Because organizational learning shouldn't mean that every decision informed by experience automatically becomes the right decision. It should mean that each decision becomes more informed by what the organization has actually discovered, while remaining open to being challenged by what happens next.
That is a very different way to think about commercial execution. The first phase helps us learn what the market does. The next phase responds to what we learned. Then the work produces new evidence that may require us to reconsider what we thought we understood.
The value isn't simply that the system becomes more sophisticated. It's that the organization has a reason to make different decisions because of what the system has taught it. And increasingly, that's what I've been looking for. Not just whether an organization can execute a strategy. Whether the organization can learn enough from that execution to improve the next one.
This is where I think a lot of organizations unintentionally confuse activity with progress. We've become extraordinarily good at collecting information. CRMs capture conversations. Marketing platforms track engagement. Sales tools record activity. Dashboards visualize performance. Forecasting systems assign probabilities. AI increasingly helps interpret and summarize everything happening across those environments. But capturing information isn't the same thing as learning from it. And learning something isn't necessarily the same thing as changing anything because of it.
I've watched organizations identify an issue in one reporting cycle, discuss it in the next, and continue operating around it several cycles later. Not because anyone was deliberately ignoring the evidence. Often, the information was available. The people involved understood the problem. But the process responsible for turning that understanding into a different decision either wasn't clear or wasn't part of the way the organization operated.
Consider a familiar example. A campaign produces strong engagement, but the resulting opportunities don't advance. Marketing sees evidence that its messaging is attracting attention. Sales sees evidence that the prospects aren't ready to buy. Revenue Operations sees a conversion problem. Leadership sees a gap between investment and expected return.
We've already explored how those different interpretations can create competing versions of reality. But suppose everyone eventually agrees. Suppose the evidence shows that engagement is real, but the qualification assumptions are wrong. What happens next?
Does Marketing change how success is measured? Does Sales change the definition of a qualified opportunity? Does Revenue Operations change the routing and scoring logic? Does leadership reconsider the assumptions behind the pipeline forecast? Or does everyone acknowledge the finding and return to the same operating model?
Because that's the point where I think organizational learning becomes observable. Not when the dashboard reveals something. Not when the meeting reaches agreement. When the organization starts behaving differently because of what it now understands. And that takes more than access to information. It takes an operating model that allows evidence to challenge assumptions, cross functional boundaries, and influence decisions. Which brings me back to organizational design.
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When most people talk about organizational design, they tend to begin with structure.
Who reports to whom? Which functions need to exist? Who owns the budget? How many people does each team need? Where should responsibility sit?
Those are necessary questions. Companies need structure to operate, especially as they grow. But after spending years working across commercial organizations, I've started wondering whether we're overlooking another set of questions.
How does what Sales learns change what Marketing does? How does what Marketing discovers influence how Sales engages buyers? How do the problems Operations identifies change the assumptions leadership uses to make investment decisions? And who is responsible for making sure those lessons actually influence what happens next?
Because assigning responsibility for execution doesn't automatically establish responsibility for learning. A team can be accountable for delivering campaigns without being responsible for validating the assumptions behind those campaigns. Another can be accountable for pipeline without having the authority to change the process that creates it. Operations can own the technology while the decisions determining how that technology should behave are made somewhere else. Leadership can approve a strategy without establishing how evidence from the market should cause that strategy to evolve.
Each responsibility makes sense independently. Together, they can create an organization that's exceptionally well structured to carry out decisions, but poorly equipped to reconsider them. And there's an uncomfortable connection here to Part 7. We watched organizations accurately diagnose their problems and then make decisions that recreated them. At the time, I focused heavily on the assumptions underneath those decisions. But what if the problem isn't just that leaders have assumptions they need to question?
What if the organization itself doesn't have a reliable way to bring those assumptions into contact with what it's learning?
That would explain something I've observed repeatedly. An organization can recognize that its current approach isn't working, possess people with the experience to identify why, and still continue investing in essentially the same operating model. Not necessarily because anyone lacks intelligence. But because the organization has separated the work of learning from the authority to act on what has been learned. That separation may be one of the most consequential organizational design problems we overlook.
I've talked about feedback loops throughout this series. And I still believe they're fundamental to effective commercial systems. A buyer responds. The system captures the signal. Someone interprets it. The next action changes. New evidence emerges. But the more I've worked with those loops, the more I've realized that their value depends on what the organization is actually willing to do with the feedback.
A system can become very effective at capturing information without becoming effective at challenging the decisions responsible for its behavior. We can automate the collection of campaign responses. We can improve how quickly information reaches Sales. We can build better workflows, cleaner dashboards, and more sophisticated reporting. We can even use AI to identify patterns across thousands of interactions. But if those patterns never influence the assumptions behind the strategy, we've made the organization better informed without necessarily making it better at learning.
That distinction matters more now than it did even a few years ago. Because AI is beginning to remove many of the capacity constraints that once limited how quickly organizations could act. And as I explored in Part 8, removing those constraints doesn't remove uncertainty. It may actually make our relationship with uncertainty more consequential.
If an organization can execute faster than it can reconsider its assumptions, the gap between what it's doing and what it understands can grow very quickly. The answer can't simply be to slow everything down. Nor can it be to require a human to approve every action.
I'm increasingly convinced the more important question is whether we've designed the organization so that learning can influence execution at the points where that influence actually matters. Sometimes that means changing a campaign. Sometimes it means redefining a qualification rule. Sometimes it means reconsidering where people spend their time. And sometimes it means leadership has to acknowledge that the strategy it approved no longer reflects what the organization has learned. That's not always comfortable.
In fact, I suspect it's one reason organizations struggle with it. Learning can require us to revisit decisions we've already defended, investments we've already justified, and assumptions that helped us become successful in the first place. It can challenge the judgment of the very people responsible for making the next decision. And that may be why collecting feedback is so much easier than building an organization capable of learning from it.
Information can reinforce what we already believe. Learning sometimes requires us to change our minds.
When I started this series, I was looking closely at the technology organizations use to support their commercial operations. How the tools fit together. Why demand systems break. When automation becomes necessary. Then the questions started moving.
What happens when execution accelerates faster than visibility?
How do we distinguish confidence from evidence?
How do different functions develop a shared understanding of the same buyer?
Why do organizations continue recreating problems they already understand?
The more I've followed those questions, the more I've started to believe they point toward something larger. We've spent an enormous amount of time designing systems that help organizations do more. I'm not sure we've devoted the same attention to designing organizations that become more capable of understanding what they're doing. And there's a difference.
A company can have a sophisticated technology stack and still operate on outdated assumptions. It can have experienced leadership and still repeatedly make the same mistakes. It can have detailed reporting and still struggle to distinguish meaningful progress from activity. It can have well-defined departments and still fail to connect what one function learns to the decisions being made by another. And it can introduce AI into all of those environments without fundamentally changing any of them.
I've started thinking about organizational intelligence less as the amount of knowledge an organization possesses and more as its ability to use experience to improve its understanding and decisions. Can it recognize when an assumption no longer holds? Can it connect evidence from different parts of the business? Can it allow that evidence to influence priorities, processes, and investments? Can it change how it operates without requiring every lesson to become a crisis before someone acts?
Those aren't simply technology questions. They're questions about how the organization itself is designed to function. And the more I think about them, the less comfortable I am treating organizational learning as an accidental byproduct of employing experienced people.
Experience accumulates naturally. Organizational learning may have to be designed.
I've worked with organizations that had talented people, sophisticated technology, substantial budgets, and a clear understanding of the results they wanted. I've also watched those organizations struggle to make meaningful progress because the experience accumulating across the business wasn't changing how the business operated.
At the same time, I've seen what begins to happen when the learning becomes part of the work. The market challenges an assumption. The team examines what happened. Different functions contribute what they've observed. The organization changes its next decision. And the next round of execution provides another opportunity to learn.
It isn't always clean. It certainly isn't always fast. Sometimes the evidence is incomplete, and sometimes the next decision still turns out to be wrong. But at least the organization has begun doing something different.
It's no longer treating experience merely as something individual people possess. It's using that experience to improve how the organization thinks and acts. Maybe that's the distinction I've been working toward throughout this series.
We've learned how to connect technology so systems can execute. We've learned why connecting activity to evidence matters. We've examined what happens when teams fail to share the same understanding, when leaders make decisions around unchallenged assumptions, and when AI accelerates those decisions. But none of those capabilities, on their own, guarantee that the organization is becoming more intelligent.
That happens when the experience produced by the system changes the system itself. And I'm beginning to think that's one of the most important things organizational design should make possible.
There's something I keep coming back to from that leadership cycle I described earlier. A company hires someone experienced to build a capability it doesn't have. That person learns the business, develops a strategy, makes investments, builds processes, and begins figuring out what works. Along the way, the organization has an opportunity to become more intelligent. But whether it actually does depends on more than the quality of the person it hired.
It depends on whether what that person learns changes how the organization understands its business and makes decisions. That's the organizational learning problem. And I think it's one we've underestimated. But even if we solve it, there's another problem waiting. Because an organization can change how it operates based on what it learns and still remain dependent on the people who helped it get there.
The processes might have improved. The strategy might be more informed. The technology might reflect a deeper understanding of the business. But what happens when those people leave?
Can the next leader understand which assumptions were tested, which decisions were made, and why the system works the way it does? Can a new salesperson build on what previous salespeople learned? Can the organization keep improving without having to rediscover the same lessons every time the people change?
I've spent much of this series examining what it takes to build systems that work. Now I'm beginning to wonder whether the ultimate test is something different. Can the organization continue getting better when the people who built those systems are no longer there? Because learning from experience is valuable. But making that learning something the organization can build on, regardless of who happens to be in the room, may be an entirely different capability. And that's where I want to take the final installment.
