Why Product Transformations Stall: The Evidence-Based Path from Projects to Products

You’re two years into your product transformation. You’ve hired coaches. You’ve reorganized teams around value streams. You’ve trained everyone on product thinking. Yet somehow, the transformation feels stuck.

Leadership keeps asking: “Are we there yet?” Teams keep asking: “What exactly are we trying to achieve?” And you’re asking yourself: “How do I prove this is actually working?”

You’re not alone. Research from McKinsey shows that over half of large companies have attempted transformations, but many get bogged down, taking years with little concrete results to show for it.

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The Transformation Theater Problem

Most organizations treat transformation like a project. Set an end date. Hire consultants. Roll out the new structure. Declare victory.

But transformation isn’t a project with a finish line. It’s a fundamental shift in how your organization creates and delivers value. And without measurement systems, you have no idea if that shift is actually happening.

This creates what I call “transformation theater.” Everyone uses the right vocabulary. Teams have product roadmaps. Leadership talks about outcomes over outputs. But underneath, it’s the same old patterns dressed in new language.

The problem isn’t the framework. Whether you’re following Marty Cagan’s product operating model, using Scrum at scale, or implementing OKRs, the framework isn’t what makes transformation work. Evidence is.

Why Measurement Matters More Than Methodology

Without empirical evidence, transformation becomes faith-based. You believe you’re moving in the right direction because the coaches say so, because the books say so, because it feels like progress.

But belief doesn’t pay the bills. Value delivery does.

Evidence-Based Management, recently updated in 2024, provides a framework for “improving value delivery under conditions of uncertainty.” It identifies four key value areas every organization should measure:

Current Value: What value are you delivering to customers and stakeholders right now? This might include customer satisfaction scores, revenue per employee, or user engagement metrics.

Unrealized Value: What’s the gap between current value and potential value? Where are customers struggling? What needs aren’t being met? This is your opportunity space.

Ability to Innovate: Can you actually respond to what you’re learning? How quickly can you experiment with new ideas? How much technical debt is slowing you down?

Time to Market: How long does it take to go from idea to delivered value? What’s your cycle time? Where do things get stuck?

These aren’t vanity metrics. They’re strategic indicators that tell you whether your transformation is creating real capability or just rearranging the org chart.

The Data-Driven Difference

Organizations that embrace measurement transform differently. Research shows that companies using data-driven approaches are three times more likely to report significant improvements in decision-making.

That’s because data creates clarity. When you measure Current Value, you stop arguing about whether your product is successful. The customer satisfaction score tells you. When you measure Time to Market, you stop debating whether you’re moving fast enough. The cycle time data tells you.

Consider flow efficiency. Most teams operate at 15–25% flow efficiency, meaning work spends 75–85% of its time waiting, not being worked on. That’s not a guess. That’s measurable. And once you measure it, you can improve it.

Modern organizations are increasingly adopting DORA metrics and flow metrics to evaluate value delivery. These aren’t academic exercises. They’re practical tools that reveal where transformation is working and where it’s not.

From Big-Bang to Empirical Evolution

The traditional transformation approach is big-bang. Announce the change. Train everyone. Flip the switch. Hope it works.

The empirical approach is different. Start with measurement. Run small experiments. Learn what works in your context. Adapt. Repeat.

Here’s what that looks like in practice:

Before you reorganize around product teams, measure your baseline. What’s your current cycle time from idea to production? What’s your customer satisfaction? What percentage of your engineering capacity goes to new features versus keeping the lights on?

Then make a small change. Not a full reorganization. Maybe you align one team around a specific customer outcome. You give them autonomy to experiment. You measure what happens.

Did cycle time improve? Did customer satisfaction increase? Did the team’s ability to innovate go up? The data tells you whether to expand this approach or try something different.

This is how modern product leadership operates. Vision sets direction. Strategy defines the approach. But execution is guided by evidence, not predetermined plans.

Strategic Clarity Plus Operational Evidence

The magic happens when you connect strategic intent with measurable outcomes. Your strategy says you want to be more responsive to customer needs. Great. How do you know if you’re succeeding?

You measure Unrealized Value. You talk to customers about their biggest struggles. You quantify the gap between what you deliver and what they need. That number becomes your North Star.

Then you measure whether your transformation efforts are closing that gap. Are you moving faster? Are you learning faster? Are you delivering more of what customers actually value?

This creates a feedback loop. Evidence informs strategy. Strategy guides experiments. Experiments generate new evidence. The cycle continues.

It’s not about being “Agile” or “not Agile.” It’s about whether you’re delivering more value this quarter than last quarter. And whether you can prove it.

Start Measuring Before You Start Transforming

If you’re planning a transformation, or stuck in one, here’s my advice: Start with measurement, not methodology.

Establish baseline measurements in the four Evidence-Based Management value areas. You don’t need perfect data. You need directional data that tells you where you are today.

Create short learning loops. Thirty to ninety days. Each loop has a clear hypothesis: “We believe that if we do X, we’ll see Y improvement in Z metric.”

Use the evidence to guide your next experiment. Not a multi-year transformation roadmap. Not a consultant’s blueprint. Your own data, from your own context, showing what’s working.

For example, if you measure Time to Market and discover your average cycle time is six months, don’t immediately restructure everything. Ask: Where does work get stuck? Is it in requirements? In development? In testing? In deployment?

Measure the flow. Find the constraints. Remove one constraint. Measure again. See if it improved. That’s transformation grounded in evidence, not faith.

Ralph Jocham is Europe’s first Professional Scrum Trainers, co-author of “Professional Product Owner,” and contributor to the Scrum Guide Expansion Pack. As a ICF ACC certified coach, works with organizations to build Product Operating Models where strategic clarity, operational excellence, and adaptive learning create measurable competitive advantage. Learn more at effective agile.