The Work Before the Work
Why the decisions made before execution matter more than ever.
Over the past two decades, I've watched product organizations become remarkably good at execution. We've adopted Agile, refined discovery practices, accelerated delivery, and built increasingly sophisticated systems for turning ideas into software. More recently, AI has begun reshaping that work, compressing activities that once required weeks into hours.
Collectively, these advances have transformed how product organizations build.
Yet across every organization in my twenty years as a design leader, one pattern has remained constant. The quality of product outcomes has rarely been constrained by our ability to execute. More often, it has been constrained by the decisions made before execution ever began.
The challenge wasn't a lack of talented people, customer research, or engineering capability. It was that different parts of the organization were making decisions from different interpretations of the customer, the market, and the opportunity.
Execution didn't create those differences.
It exposed them.
That observation fundamentally changed how I think about leadership.
Today, I believe the highest-leverage work begins long before a roadmap is approved or a backlog is prioritized. It begins by helping an organization develop a common understanding of reality before it commits significant time, talent, and capital.
Agreement isn't alignment
One assumption I've encountered repeatedly is that alignment comes from agreement.
I've sat in enough roadmap reviews to know the pattern. The strategy is thoughtful. Priorities get debated. The roadmap is approved. Everyone leaves the room believing they're aligned.
I've rarely found that to be true.
Agreement is a commitment to a plan.
Alignment is a common understanding of why that plan exists in the first place.
Those are fundamentally different things.
Every strategic artifact we rely on—roadmaps, PRDs, strategy decks, OKRs—communicates decisions. None ensures those decisions will be interpreted the same way. Every function fills those gaps through the lens of its own expertise, experience, and incentives.
Take something as simple as a roadmap line that says 'AI advisor.' Engineering starts thinking about technical architecture. Product thinks about sequencing and priority. Marketing imagines positioning. Sales imagines the conversation they'll have with a customer next quarter. Everyone leaves the room believing they understand the strategy. What they actually understand is their own version of it.
No one is intentionally ignoring the strategy. They’re simply interpreting it differently.
Over time, I've come to believe that many debates we attribute to prioritization are actually debates about interpretation. Teams aren't arguing about what should happen next as much as they're working from different understandings of the customer, the business, and the problem they're trying to solve.
Execution simply makes those differences impossible to ignore.
The Work of Making Sense
Modern product organizations don't suffer from a lack of information. They are surrounded by customer interviews, analytics, competitive research, market trends, financial models, sales feedback, and product data. The challenge isn't collecting more information. It's determining what matters, what has changed, and what deserves to shape the organization's next investment.
That's the work of making sense.
When organizations invest in this work, the effects become surprisingly visible. Teams make more decisions without escalation. Priorities remain durable even as conditions change. Tradeoffs become faster because the criteria are already shared. Executives spend less time arbitrating disagreements and more time shaping strategy.
Doing this well requires synthesizing evidence, challenging assumptions, and making strategy tangible enough for people to collectively examine and improve. Research, analysis, storytelling, and visualization each contribute by helping organizations move beyond isolated observations toward a more coherent understanding of reality.
This is one of my favorite capabilities of Design. At its best, Design is far more than the creation of interfaces. It's a discipline for understanding complexity, exploring possibilities, and making ideas tangible enough for organizations to examine, challenge, and strengthen together.
That distinction matters.
Organizations make better decisions when they create opportunities for people to reason about the same customer, the same opportunity, and the same future before significant investments are made.
To me, that's the work before the work.
From Making Sense to Better Judgment
Making sense of complexity is valuable for one reason.
It improves the quality of an organization's judgment.
Organizations don't invest based on information alone. They invest based on how they interpret it. The same customer research can support different product strategies. The same market shift can lead to different investment priorities. Even a well-crafted strategy presentation can leave every function believing it understands the direction while carrying a different interpretation of what the organization is actually trying to achieve.
That's why I've come to think of this less as a communication challenge than an interpretation challenge.
The organizations I've admired most aren't necessarily those with the best ideas. They're the ones that develop a common understanding of the customer, the market, and the opportunity before they commit significant resources. Once that understanding exists, many decisions become surprisingly straightforward.
I saw this most clearly during a period when the market beneath one of our businesses shifted dramatically.
Customers were consolidating. Budgets were shrinking. Assumptions that had shaped our strategy for years were becoming less reliable. As uncertainty increased, so did the number of explanations for what was happening—and the opinions about what we should do next.
Some believed we needed to expand into adjacent markets.
Others argued for doubling down on existing customers.
Some focused on pricing.
Others believed new product capabilities were the answer.
None of those perspectives were unreasonable.
They were simply being formed from different interpretations of the same reality.
Rather than rushing toward solutions, we returned to understanding. We immersed ourselves in customer conversations, analyzed product usage, engaged our customer advisory board, and looked for patterns instead of opinions. That work didn't eliminate uncertainty, but it gave us a more credible understanding of how our customers' businesses—and the market around them—were changing.
The next challenge wasn't deciding what to build.
It was helping the rest of the organization make sense of what we had learned.
We synthesized our research into a narrative connecting customer needs, business strategy, and product direction. We used visual models and interactive concepts to explore possible futures, allowing people to challenge assumptions before decisions were made. The objective wasn't to persuade people to adopt a particular point of view. It was to create enough clarity for the organization to reason about the same problem together.
That changed the conversation.
Discussions became less about defending competing viewpoints and more about improving a common understanding of the problem. Tradeoffs became easier because teams evaluated them through the same lens. Externally, customers gained confidence—not because we claimed to have all the answers, but because they could see that we understood the challenges they were facing and had a thoughtful strategy for responding to them.
We couldn't control the market.
We couldn't guarantee the outcome.
But we could improve the quality of our judgment.
That experience reinforced something I've observed repeatedly over the past two decades.
Leadership isn't about eliminating uncertainty.
It's about helping organizations make better decisions while uncertainty still exists.
Why AI changes the equation
The patterns I've described aren't new.
What's changing is their importance.
For most of the software industry's history, execution was the primary constraint. Building software required specialized expertise, significant investment, and time. Every roadmap represented a series of difficult tradeoffs because organizations simply couldn't pursue every opportunity they identified.
So we optimized execution.
Across Product, Design, Engineering, and every function involved in bringing products to market, we invested in better tools, better processes, and better ways to turn ideas into reality.
Those investments transformed how product organizations operate.
They still matter.
But AI is changing the economics of knowledge work.
It's reducing the cost of generating ideas, conducting research, exploring concepts, creating software, analyzing information, and communicating strategy. Activities that once consumed weeks of specialized effort increasingly take hours.
Execution isn't disappearing.
It's becoming more accessible.
As the cost of execution falls, the number of possible investments grows. Every organization will have more opportunities than it can responsibly pursue, more customer requests than it can satisfy, and more ideas than it can execute. Leadership's constraint will shift from execution capacity to investment judgment.
The question is no longer simply, Can we build it?
Increasingly, it's Should we?
The organizations that answer that question well won't necessarily have better technology.
They'll have better judgment.
An operating model for better judgment
Looking back across my career, I've come to think about this work as three connected capabilities that consistently appear in organizations that navigate uncertainty well.
1. Understand - Develop an accurate understanding of reality
Organizations need a rich understanding of their customers, their business, their market, and the outcomes they're trying to achieve. No single discipline owns that understanding. It emerges by integrating perspectives from across the organization into a more complete understanding of reality.
2. Align - Create shared understanding
Information alone rarely changes decisions.
Organizations create leverage by synthesizing evidence, challenging assumptions, connecting insights to strategy, and making complex ideas tangible enough for people to examine and improve together. This is the work that transforms individual observations into organizational judgment.
3. Commit - Invest time and resources with conviction
Organizations commit resources, prioritize investments, and begin building.
Execution isn't the beginning of strategy.
It's evidence of the quality of the thinking that came before it.
Organizations that consistently invest in these capabilities don't eliminate uncertainty.
They improve their ability to navigate it.
The next competitive advantage
For most of the last two decades, product organizations have competed by becoming better builders.
That investment transformed our industry.
I don't believe the next decade will be defined by who builds the fastest.
As AI continues to reduce the cost of execution, building will become less of a competitive advantage and better judgment will become more of one.
Every product organization will have more opportunities than it can pursue.
More customer problems it could solve.
More experiments it could run.
More ideas than it could ever reasonably invest in.
The organizations that succeed won't simply execute faster than their competitors. They'll understand their customers more deeply, make better sense of increasingly complex markets, and invest with greater discipline because they're operating from a richer understanding of reality before committing resources.
Their last twenty years learning how to build will be important.
How well they understand, align, and commit before they build may determine their success in the next twenty years.
To me, that's the work before the work.
