When Good Enough Isn't

What Your Success Rate Is Actually Telling You

An application of the Why Change Fails series.

Between 65% and 75% of new products or services miss their revenue or profit goals.[1] In consumer packaged goods alone, roughly 40% of new SKUs have disappeared from shelves within two years of launch.[2] The product team shipped. The launch event happened. The press release went out. The dashboard showed green. The outcome didn’t follow.

The same pattern shows up in large-scale organizational change. McKinsey’s research found that 80% of executives described their transformation investments as successful, while only 38% reported meaningful ROI from the same investments.[3] The Standish Group has tracked project failure rates for thirty years.[4] The number hasn’t moved. Organizations announce transformations, stand up steering committees, publish progress updates, and close initiatives as complete. The outcome often doesn’t follow there either.

And in technology: Gartner’s 2024 survey of more than 3,100 CIOs found that fewer than half of all digital initiatives meet or exceed their intended business outcome targets, after all the planning, the investment, the rollout and the go-live celebration.[5]

Three different domains. Three different sets of researchers. Three different definitions of success. And in each one, the same structural gap: the moment of completion is rewarded as the goal, while the intended outcome remains largely unmeasured, or measured too late to act on.

This paper is about why that gap exists and what it actually costs.

The strategy no one names

Every organization running a structural gap between activity and outcome is making a choice, even when it doesn’t feel like one. The choice: launch more initiatives to cover the gap, rather than ask what’s creating the gap. Add more resources to cover an adoption rate that hasn’t moved. Run more programs to maintain the appearance of transformation momentum.

That is “good enough” as a strategy. It optimizes for short-term measurable output at the cost of long-term structural performance. And the moment it becomes visible as a choice is always the same: it’s the planning conversation where someone asks whether to add more activity or investigate why the activity isn’t producing results, and the room decides, usually without saying so out loud, that more activity is safer than the question.

It succeeds at the wrong thing while accumulating a cost those metrics were never designed to find.

This isn’t an irrational choice. It’s the rational output of a system designed to reward it.

Wald’s problem

In 1943, Abraham Wald was asked by the U.S. military to analyze bullet hole patterns on aircraft returning from combat. The military’s instinct was to reinforce the areas showing the most damage. Wald’s contribution was pointing out the error: the planes they were analyzing had returned. The holes showed where a plane could be hit and survive. The signal they needed was in the planes that never came back. Those planes weren’t in the sample.

This is the precise structure of the “good enough” problem.

Organizations that accept a structural gap between activity and outcome analyze individual failures. They have retrospectives, they identify why specific efforts fell short and they adjust the next one accordingly. What they often don’t do, and what Wald’s insight names precisely, is ask what structural conditions are generating most of their efforts as failures in the first place. The military wasn’t ignoring the bullet holes. They were studying the wrong planes. The roughly 80% of AI and machine learning projects that never reach meaningful production are not generating post-mortems on the structural decisions made at launch.[6] The transformation initiatives that stalled quietly are recorded as “execution challenges,” not as evidence that the conditions were wrong from the start.

The losses carry the signal. The returning planes confirm what already works. The planes that didn’t come back are telling you where the system is actually broken. And they are the one data source the organization was built to not require.

Where the cost accumulates

The costs don’t appear in the dashboard showing green because of a structural timing problem: the feedback gap between the decision that causes failure and the moment that failure becomes visible often can run for years.

The people on the receiving end of initiatives that didn’t deliver remember. Employees who were asked to change how they work, given inadequate support, and then watched the initiative quietly dissolve remember. Partners, customers and stakeholders who were promised outcomes that didn’t materialize remember. That memory doesn’t show up in the program dashboard. It shows up eighteen months later as a change initiative that can’t get traction because trust was spent on the last one, or a talented person who stops raising their hand because nothing came of it the last time they did.

More activity layered on top of structural conditions that haven’t changed is not a growth strategy. It is a treadmill. And the pace accelerates as the organization’s capacity for genuine change erodes under the weight of everything that looked like change but wasn’t.

The organizations that figure this out do not do it by finding more efficient ways to run the same motion. They do it by asking a different question: what are the losses telling us that the wins cannot?

Four questions

Before reading the next section, answer these honestly:

1. What is your current success rate, and when did you last ask why it is that number instead of higher?

2. Of your last ten major deployments or initiatives, how many produced measurable outcomes twelve months after launch?

3. When your team says “we’re being practical,” what specifically are they protecting themselves from having to change?

4. Can you name what your failures are telling you about the conditions generating them, or have you built a system that doesn’t require you to know?

An organization running a genuine “good enough” strategy will feel the absence of an answer to that fourth question more than any of the others.

The compounding alternative

This is not an argument about individual capability. It is an argument about what the system asks of the people inside it. The same person performs differently depending on what the structure around them is designed to reward and designed to ignore.

In 2005, Salesforce was growing and losing customers. Not dramatically, but at a rate quietly undermining the economics of a subscription business still proving out its model. The leadership team asked a question most software companies weren’t asking: what happens after a customer commits, and who in the organization owns what they actually experience? The answer was to create the customer success function: a named, staffed, measured capability dedicated to ensuring customers achieved the outcomes they purchased. The measure of success shifted from transactions completed to results delivered.

That structural decision compounded over years. Retention rates climbed well above industry averages. Existing customers expanded faster than others churned. The organization’s relationship with its customers changed because the structure changed, not because the people improved, but because what the system asked of them did. By 2010, the model had become a design standard the rest of the industry was trying to replicate.

Salesforce did not hire better people than its competitors. It built a different structure, anchored to a different definition of success, and had the discipline to ask what the organizations that weren’t coming back were trying to tell them.

The question is not whether your team is working hard. The question is whether the system they’re working inside is designed to learn from what isn’t working, or only from what is.

“Good enough” isn’t the floor of acceptable performance. It’s a ceiling. The organizations that sustain change, develop talent and build real capability at scale are not the ones that ran more volume. They are the ones that treated their loss rate as a diagnostic signal rather than a fixed cost, built the structural capability to address what that signal said and redesigned the conditions generating losses rather than covering them.

Notes

[1] Madhavan Ramanujam and Georg Tacke. “Your New Hit Product Might Be Underpriced.” Harvard Business Review, May 2016. Between 65% and 75% of new products or services miss revenue or profit goals.

[2] Kupor, D., Tormala, Z. L., and Norton, M. I. “How Common Is New Product Failure and When Does It Vary?” Marketing Letters, 2021. Analysis of 83,719 SKUs across 31 U.S. consumer packaged goods categories.

[3] McKinsey Global Survey on organizational transformations. 1,546 executives surveyed; 38% reported transformations completely or mostly successful in delivering ROI; 80% described same investments as successful. McKinsey & Company, 2008.

[4] Standish Group. CHAOS Report series, 1994-present. Project success, challenge, and failure rates tracked annually across thousands of software and technology projects.

[5] Gartner. “Gartner Survey Reveals That Only 48% of Digital Initiatives Meet or Exceed Their Business Outcome Targets.” Press release, October 22, 2024. Survey of more than 3,100 CIOs and technology executives and more than 1,100 executive leaders outside IT.

[6] RAND Corporation. AI project failure rate research, 2024. Consistent with Gartner data showing fewer than half of AI projects advance from prototype to production.