Validate before during and after

When Playing It Safe Is the Riskiest Move

Most teams agree that learning is key to reducing risk. But not all learning happens during implementation. And not all approaches to product discovery are as iterative as we like to think.

In many orgs, there’s a tendency to treat product discovery and design work as a way to front-load certainty. Research gets packaged into polished prototypes. User flows are vetted, click-through demos reviewed, copy finalized. Boxes have been checked, sign offs are obtained, success feels inevitable. By the time development begins, there’s a sense that the hard part—the learning—is done.

From there, teams move into delivery mode. They build incrementally, they iterate on implementation, they follow agile rhythms. But the product thinking has already solidified. What gets built may be sliced small, but the underlying decisions are no longer being questioned.

It creates the comfort of “knowing” while giving the appearance of agility.

But it’s not agility. It’s choreography.

And when things shift—as they inevitably do—it becomes clear how little room we left ourselves to adapt.

Certainty Feels Safe. Learning Is Safer.

When we invest heavily in up-front discovery, it can feel like we’ve reduced risk. We’ve anticipated edge cases, validated with users, aligned on a vision. But that confidence is only real if we keep validating as we go.

No matter how thorough your discovery process is, the most reliable learning still happens where software meets customer. Early. Often. In context.

The further discovery is from that point of contact, the more assumptions sneak in. The tighter the prototype, the harder it is to change course. And the longer we go without user feedback on the live experience, the more fragile our work becomes—no matter how good it looked in design reviews.

Treat Discovery Like Delivery

The healthiest product teams don’t just iterate on implementation. They iterate on understanding.

They approach product discovery the same way they approach development: in small, testable steps. They release early prototypes not just to stakeholders, but to real users. They fold learning from production back into their product thinking. They augment initial discovery learnings with live product analytics, customer feedback, and satisfaction surveys (to name a few) as input to adjust and improve on what they've delivered. And they keep discovery going through delivery—not just before it.

This is what continuous discovery looks like. Not a separate phase, but an ongoing practice.

Instead of trying to eliminate risk through “getting it right,” they reduce risk by learning faster and adjusting sooner.

Make the Learning Loop Short

The teams that adapt best are the ones with the shortest feedback loops. They don’t pretend to be certain—they design their work so they don’t have to be.

They test assumptions quickly. They validate throughout the development and delivery cycle, not just after launch. They expect their early thinking to evolve. And because they’ve built that expectation into how they work, change doesn’t feel like failure—it feels like progress.

What’s Next

If your product process feels stuck—if discovery happens all at once and delivery happens in isolation—it’s worth asking: when was the last time we changed our minds?

Not because we were wrong. But because we learned something new.

Why ‘Best Practices’ Don’t Exist

Why ‘Best Practices’ Don’t Exist

The problem with the term best practice is not always the practice itself—it’s the perspective the phrase invites. Best is steeped in finality. It implies that nothing better exists, or ever will. That the thinking is over. And while we may not mean it that way, language shapes thought. When we call something “best,” we stop asking “is it still working?”

Composition Isn’t Just About Code

Composition Isn’t Just About Code

The way your teams are structured shapes the software you build—and vice versa. In this article, we explore the profound connection between team and software composition through the lens of Conway’s Law, uncovering how misalignment leads to chaos and inefficiency, while thoughtful alignment drives collaboration, modularity, and adaptability. Learn why your true architecture isn’t in diagrams but in the code itself, and discover actionable steps to align your teams and software for better outcomes.

Small Deployments; Big Impact

Small Deployments; Big Impact

When teams hear about small, frequent deployments, they often picture chaos: code breaking, users complaining, and developers scrambling to fix issues. But in reality, small deployments do the opposite. They reduce complexity, amplify feedback, and create a smoother experience for both users and developers.

Opportunity Solution Trees

Opportunity Solution Trees

In this article, I want to go more in depth on Opportunity Solution Trees; what they are, how they are used, how to create one, and how I think about them slightly differently (but only slightly) than folks like Teresa Torres who really introduced them broadly to the Product community in her book “Continuous Discovery Habits”. This book, by the way, is a must have for anyone who works in software product development - not just folks in product-specific roles.

A large group, little time, and a gnarly problem to solve?

A large group, little time, and a gnarly problem to solve?

If you are not familiar with Liberating Structures, I suggest you take a look at them. I use a few of them now and then as circumstances warrant. In future articles, I will discuss some of the others, but today’s installation is dedicated to the Liberating Structure I most often use — 1–2–4-All.

The Behaviors

Over the past several years, as I’ve been helping teams and organizations improve their ability to deliver software products that are desirable, viable, and feasible, I have been experimenting with a Behavior Framework that has proven to be rather effective. And I’d like to share it with you in hopes that you find it useful and that you provide me feedback on your experiences with it.