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the same strategy, executed the same way, under the same leadership all along. Remember that Cisco Systems went from the poster child of the Internet era to a cautionary tale in a matter of a few years. Likewise, for six years before its spectacular implosion in 2001, Enron was billed by Fortune magazine as “America’s most innovative company,” while Steve & Barry’s—a now-defunct low-cost clothing retailer—was heralded by the New York Times as a game-changing business only months before it declared bankruptcy. Rosenzweig’s conclusion is that in all these cases, the way firms are rated has more to do with whether they are perceived as succeeding than what they are actually doing.5

To be fair, Enron’s appearance of success was driven in part by outright deception. If more had been known about what was really going on, it’s possible that outsiders would have been more circumspect. Better information might also have tipped people off to lurking problems at Steve & Barry’s and maybe even at Cisco. But as Rosenzweig shows, better information is not on its own any defense against the Halo Effect. In one early experiment, for example, groups of participants were told to perform a financial analysis of a fictitious firm, after which they were rated on their performance and asked to evaluate how well their team had functioned on a variety of metrics like group cohesion, communication, and motivation. Sure enough, groups that received high performance scores consistently rated themselves as more cohesive, motivated, and so on than groups that received low scores. The only problem with these assessments was that the performance scores were assigned at random by the experimenter—there was no difference in performance between the high and low scorers. Rather than highly functioning teams delivering superior results, in other words, the appearance of superior results drove the illusion of high functionality. And remember, these were not assessments made by external observers who might have lacked inside information—they were by the very members of the teams themselves. The Halo Effect, in other words, turns conventional wisdom about performance on its head. Rather than the evaluation of the outcome being determined by the quality of the process that led to it, it is the observed nature of the outcome that determines how we evaluate the process.6

Negating the Halo Effect is difficult, because if one cannot rely on the outcome to evaluate a process then it is no longer clear what to use. The problem, in fact, is not that there is anything wrong with evaluating processes in terms of outcomes—just that it is unreliable to evaluate them in terms of any single outcome. If we’re lucky enough to get to try out different plans many times each, for example, then by keeping track of all their successes and failures, we can indeed hope to determine their quality directly. But in cases where we only get to try out a plan once, the best way to avoid the Halo Effect is to focus our energies on evaluating and improving what we are doing while we are doing it. Planning techniques like scenario analysis and strategic flexibility, which I discussed earlier, can help organizations expose questionable assumptions and avoid obvious mistakes, while prediction markets and polls can exploit the collective intelligence of their employees to evaluate the quality of plans before their outcome is known. Alternatively, crowdsourcing, field experiments, and bootstrapping—discussed in the last chapter—can help organizations learn what is working and what isn’t and then adjust on the fly. By improving the way we make plans and implement them, all these methods are designed to increase the likelihood of success. But they can’t, and should not, guarantee success. In any one instance, therefore, we need to bear in mind that a good plan can fail while a bad plan can succeed—just by random chance—and therefore try to judge the plan on its own merits as well as on the known outcome.7

TALENT VERSUS LUCK

Even when it comes to measuring individual performance, it’s easy to