New initiatives are everywhere at work right now. Maybe it’s an AI rollout or a new sales operating model. Maybe it’s another new technology in your tech stack. They all seem to have budget behind them and high expectations attached to them. Whatever the focus is, the message is clear: Success isn’t optional. So you lean in.
But more often than not, these programs follow a familiar pattern. Early signals look promising. Rollout and training go off without a hitch. Success metrics, like completions or adoption, are trending the right way. It feels like a win.
But is it?
Once the in-depth support and deliberate change management of the early days starts to fade, things may start to slip. New behaviors don’t stick and usage drops off. The productivity gains that justified the investment don’t materialize.
It’s not clear why.
Only 32% of business leaders report achieving healthy change adoption by employees, and less than half of employees say they actually achieved the change goals their organization set.
So, what’s the secret to navigating change programs successfully? When your data tells you the program is slipping, don’t rush to deploy more communications, more content, or more programs. Those may be part of the problem. Focus on a single question: Why?
Knowing Why Transformation Programs Work (and Why They Fail) Makes Success Repeatable
When programs fail, understanding why is obviously valuable. But understanding why programs succeed is just as important.
Most organizations only go looking for « why » when something breaks. When a program succeeds, they declare it a win and move on. But without understanding what drove that success, it’s almost impossible to replicate it intentionally. Success becomes something that happens rather than something you can engineer.
The « why » isn’t soft. It’s not a nice-to-have. It’s what separates organizations that focus resources where they matter most from organizations that guess at what will work. Right now, most organizations are guessing.
A Harvard Business Review study put a number on that guessing problem: 85% of executives believe their organizations are bad at diagnosing problems in the first place, and 87% think that failure carries real costs.
Recognize the Transformation Data Gaps
A good doctor doesn’t prescribe medicine based on symptoms alone. The process is: symptoms, diagnosis, then treatment. Organizations navigating transformation are skipping the middle step and going straight from ‘something isn’t working’ to ‘here’s the fix,’ without ever understanding why.
Harvard Business School has a name for this exact discipline: root cause analysis, or RCA. It’s the process of uncovering a problem’s real cause instead of treating whatever symptom is loudest. As HBS Professor Michael Tushman puts it, « Instinctually, most leaders go straight to solutions. You must do a thorough diagnosis first, determine the root causes of your gap, and only then can you move to an integrated intervention. »
Knowing something isn’t working and understanding why are two very different things with two different outcomes.
The nuance that actually determines why change sticks or not (confidence, clarity, specific blockers, unspoken concerns) is hard to capture at scale. Organizations default to measuring output and outcome data instead. That outcome data (completion, usage, productivity metrics) acts as a rearview mirror. It confirms what happened. It doesn’t show where friction is building or where to intervene.
In theory, that “why” surfaces in manager one-on-ones or consultant interviews. In practice, it often doesn’t surface at all. Or at least not in a way that’s measurable.
That’s a data gap.
Managers don’t always know the right questions to ask to fill that gap, and often aren’t equipped to. McKinsey’s research describes a « frozen middle »: Messages about the change get stuck or garbled at the manager layer because many middle managers aren’t set up for success in leading people through change in the first place.
Surveys help, but they’re built for structured responses. Sentiment and nuance don’t translate to a rating scale, and in large enterprises, regulated survey processes and fatigue thin out participation. Friction compounds quietly, and leaders end up making decisions based from incomplete data.
When the Data Gap Goes Unnoticed
Here’s why that matters: If your AI tools aren’t getting adopted, and a handful of vocal employees tell you they don’t have access to the right tools, you might start evaluating new purchases. But what if that’s only true for a small segment of the workforce? What if most employees simply don’t know where to start? What if others are quietly worried about job displacement, and no one has addressed it directly?
When your business operates on outcomes and minimal feedback, the proposed solution is usually set up in response to the loudest voices. Many blockers don’t get addressed and transformation initiatives stall out.
That’s the insight gap you’re facing: the space between what your systems can measure and what your employees are actually experiencing.
Close the Insight Gap Between Outcomes and Experience
Most organizations already have the right tools to address transformation blockers: training programs, content libraries, and communications strategies. But without knowing where friction arises, they don’t address the right problems. You end up deploying the right interventions to the wrong people, in the wrong sequence, for the wrong reasons.
Organizations need to do three things to close their insight gap and reignite their change initiative:
- Listen at scale: Leadership needs to know what employees are actually experiencing. Completions and skills acquired matter, but so does whether they feel confident and clear on what’s expected of them. Without that, signals never make it into the data.
- Get specific: Insights need to be meaningful. You need to know which areas of the business are experiencing friction, what the concerns are, and where confidence is breaking down. Broad insights tell you something is wrong, but specific insights tell you exactly what and where.
- Act on it: Without a clear follow-through plan, the data is just a diagnosis without a prescription. You decide which bottleneck to address first, which teams need a different kind of support, and what intervention will actually move the needle.
The Truth About Change Management
Go back to where this started: the budget, the expectations, the pressure that makes success feel non-negotiable. The real prize was never the adoption number. It’s understanding why people changed, or didn’t.
That understanding keeps paying off long after adoption metrics plateau. McKinsey found that transformation success rates have been stuck around 30% for years, but organizations that took a rigorous, fact-based approach to diagnosing what was actually driving results saw success rates as high as 58%. That number climbed higher still among those that saw the work through to completion.
That’s the opportunity for every team running a change program right now. Build the habit of asking why at every stage, whether the numbers are climbing or stalling.
Do that, and you stop reacting to whatever the latest dashboard tells you and start anticipating it. You know which teams need a different kind of support before they ask for it. You walk into the next budget conversation with a real answer for why the program works, not a guess.
That’s the version of transformation worth building toward: one you understand well enough to repeat on purpose.
Restons en contact
Je souhaite m’inscrire à la newsletter mensuelle pour me tenir informé(e) des prochains événements, bénéficier d’éclairages exclusifs et recevoir l’actualité des solutions Degreed.