Three-quarters of business initiatives flounder—new manufacturing plants close pre-maturely, mergers and acquisitions don’t pay off, start-ups fail to gain market share. Why? Delusional optimism: We overemphasize projects’ potential benefits and underestimate likely costs, spinning success scenarios while ignoring the possibility of mistakes.

The culprits? Cognitive biases and organizational pressures to accentuate the positive. We can’t eradicate either, but we can take a more objective view of an initiative’s likely outcome. How? Reference forecasting: comparing a project’s potential out-comes with those of similar, past projects— to produce more accurate predictions.


We’re subject to numerous cognitive biases:

Competing for limited funding, we create project proposals accentuating the positive. These initial forecasts skew subsequent analyses of market and financial information toward overoptimism: We don’t adjust our original estimates enough to account for inevitable problems.

We ignore competitors’ capabilities and plans. Rushing to secure a new market, for example, we forget that rivals will follow suit. As competitors ramp up production and marketing, sup-ply outstrips demand—rendering the market unprofitable.

We take credit for positive outcomes while attributing negative outcomes to external factors and deny the role of chance in our plans’ out-comes. Result? We assume we can avoid or over-come all project problems.

We also fall victim to organizational pressures:
Since only the most promising proposals attract investment dollars, we make overoptimistic forecasts. Highly overoptimistic proposals are approved.

Reinforcing one another’s unrealistic views of the future, we undermine our company’s critical thinking.

How to counteract cognitive biases and organizational pressures? Awareness and a more objective forecasting method—especially with never-before-attempted initiatives. These steps can give us an “outside view” to augment our intuitive “inside view”:

A studio executive forecasting sales of a new film selects recent films in the same genre, featuring similar actors and comparable budgets.

Identify the average and extremes in the reference-class projects’ outcomes. The studio executive’s reference-class movies sold $40 million in tickets on average. But 10% sold less than $2 mil-lion and 5% sold more than $120 million.

Intuitively estimate where your project would fall along the reference class’s distribution. The studio executive predicted $95 million as his new film’s sales.

Counteract your biased prediction from Step 3. Based on how well your past predictions matched actual outcomes, estimate the correlation between your intuitive prediction and the actual outcome. Express your estimate as a coefficient between 0 and 1 (0 = no correlation; 1 = complete correlation). The studio executive expressed his correlation coefficient as 0.6.

Adjust your intuitive prediction based on your predictability analysis. The studio executive’s corrected estimate was $62 million: $95M + [0.6 ($40M – $95M)].

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