How Big Things Get Done: The Surprising Factors Behind Every Successful Project, from Home Renovations to Space Exploration

by Professor Bent Flyvbjerg

One driver is psychology. In any big project—meaning a project that is considered big, complex, ambitious, and risky by those in charge—people think, make judgments, and make decisions. And where there are thinking, judgment, and decisions, psychology is at play; for instance, in the guise of optimism.

Successful projects, by contrast, tend to follow the opposite pattern and advance quickly to the finish line. That’s how the Nepal schools project unfolded.

There are some recent and important wrinkles in the numbers, which I’ll discuss later, but the general story remains the same: In total, only 8.5 percent of projects hit the mark on both cost and time.

But then you come across my research and discover that the actual mean cost overrun of a major building project is 62 percent.

My database revealed that information technology projects have fat tails. To illustrate, 18 percent of IT projects have cost overruns above 50 percent in real terms. And for those projects the average overrun is 447 percent!

Projects that fail tend to drag on, while those that succeed zip along and finish.

People say that projects “go wrong,” which they all too often do. But phrasing it that way is misleading; projects don’t go wrong so much as they start wrong.

My key heuristic for managing optimism on projects is “You want the flight attendant, not the pilot, to be an optimist.”

We typically don’t engage in such careful calculation even when we are deciding whether to take a job offer or make some other highly consequential decision.16 Instead, as Klein demonstrated, people ordinarily take the first option that occurs to them and quickly run it through a mental simulation. If it seems to work, they go with that option. If it doesn’t, they search for another and repeat the process.

In one study, researchers asked students to estimate how long it would take them to finish various academic and personal tasks and then asked them to break down their estimates by confidence level—meaning that someone might say she was 50 percent confident of finishing in a week, 60 percent confident of being done in two weeks, and so on, all the way up to 99 percent confidence. Incredibly, when people said there was a 99 percent probability that they would be done—that it was virtually certain—only 45 percent actually finished by that time.19 For us to be so consistently wrong, we must consistently ignore experience.

Planning is working on the project. Progress in planning is progress on the project, often the most cost-effective progress you can achieve.

Projects are often started by jumping straight to a solution, even a specific technology. That’s the wrong place to begin. You want to start by asking questions and considering alternatives. At the outset, always assume that there is more to learn. Start with the most basic question of all: Why?

In contrast, good planning explores, imagines, analyzes, tests, and iterates.

Unfortunately, it’s not. Projects routinely start with answers, not questions.

At the beginning of a project, we need to disrupt the psychology-driven dash to a premature conclusion by disentangling means and ends and thinking carefully about what exactly we want to accomplish.

backcasting starts by developing a detailed description of a desirable future state; then you work backwards to tease out what needs to happen for that imagined future to become reality.

What sets good planning apart from the rest is something completely different. It is captured by a Latin verb, experiri. Experiri means “to try,” “to test,” or “to prove.” It is the origin of two wonderful words in English: experiment and experience.

A bad plan is one that applies neither experimentation nor experience. The plan for the Sydney Opera House was very bad.

Third, an iterative process such as Pixar’s corrects for a basic cognitive bias that psychologists call the “illusion of explanatory depth.” Do you know how a bicycle works?

When a minimum viable product approach isn’t possible, try a “maximum virtual product”—a hyperrealistic, exquisitely detailed model

But it is necessary to say it—loudly and insistently—because, as we shall see, big projects routinely do not make maximum use of experience.

Planners don’t value experience to the extent they should because they commonly suffer yet another behavioral bias, “uniqueness bias,” which means they tend to see their projects as unique, one-off ventures that have little or nothing to learn from earlier projects.5 And so they commonly don’t.

The Olympics are forever planned and delivered by beginners—a crippling deficiency I call “Eternal Beginner Syndrome.”11

A good plan, as I said, is one that maximizes experience or experimentation; a great plan is one that does both.

But basing forecasting on anchoring and adjustment is tricky. As psychologists have shown in countless experiments, final estimates made this way are biased toward the anchor, so a low anchor produces a lower estimate than a high anchor does. That means the quality of the anchor is critical. Use a good anchor, and you greatly improve your chance of making a good forecast; use a bad anchor, get a bad forecast.

Once we frame the problem as one of time and money overruns, it may never occur to us to consider that the real source of the problem is not overruns at all; it is underestimation. This project was doomed by a large underestimate. And the underestimate was caused by a bad anchor. To create a successful project estimate, you must get the anchor right.

people not only tend to naturally look at their projects this way, they tend to exaggerate just how unusual their specific project really is.

But there’s a way around them. You just need to start over with a different perspective: See your project as one in a class of similar projects already done, as “one of those.” Use data from that class—about cost, time, benefits, or whatever else you want to forecast—as your anchor. Then adjust up or down, if necessary, to reflect how your specific project differs from the mean in the class. That’s it. It couldn’t be simpler.

My data put the odds of having a benefit overrun that exceeds the cost overrun, even if by only a little, at 20 percent. Contrast that with the 80 percent probability of failure. It’s a dangerous bet—and an unnecessary one.

The value of experienced teams cannot be overstated, yet it is routinely disregarded.

Only five project types—solar power, wind power, fossil thermal power, electricity transmission, and roads—are not fat-tailed, meaning that they, unlike all the rest, do not have a considerable risk of going disastrously wrong. So what sets the fortunate five apart? They are all modular to a considerable degree, some extremely so.