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Discovering mysteries  starts  within universities, government-funded labs, and other not-for-profit entities. Research professors spend their entire careers staring into the mysteries of their particular field. Most of their work is not economically viable. It doesn’t conform to any particular schedule, budget cycle, or planning document.

Design thinkers have to look at everything in a mystery, because they don’t yet know what to leave out. The danger is that what’s omitted might be the key to the mystery. With little to go on, the design thinker employs abductive reasoning to discern a pattern in what to others is still an amorphous whole. Of course, the search for patterns is typically marked by repeated false starts and blind alleys; many abductive inferences to the best explanation will be wrong. But with experience, design thinkers learn to spot handholds where others see only a sheer cliff face.

Mysteries, then, are expensive, time consuming, and risky; they are worth tackling only because of the potential benefits of discovering a path out of the mystery to a revenue-generating heuristic. The benefits of moving knowledge to the heuristic stage derive from the process of omission. Instead of having to consider every facet of a mystery, the creator of a heuristic need consider only a subset, which yields results more quickly.

But a heuristic takes advanced skill and judgment to operate. The operators of the heuristic form a cognitive elite in their organization, highly valued for their skill, training, and experience in applying the heuristic. But the organization pays a high cost for their elite capabilities. Driving down those costs provides the motivation for applying abductive reasoning once again, toward moving knowledge to the next stage—the algorithm.

The algorithm generates savings by turning judgment—a general way of getting toward the desired solution—into a formula or a set of rules that, if followed, will produce the desired solution. Having removed further variables and variation from the equation, an algorithm is even more efficient than a heuristic. Algorithms can be run by less experienced and less expensive personnel than can heuristics.

Computer code—the digital end point of the algorithm stage—is the most efficient expression of an algorithm. The unit cost of data entry by a modestly trained clerk in Bangalore is already low, but the cost of a computer to scan an invoice and enter the data into the appropriate cells on a spreadsheet is essentially zero. All that’s needed is for someone to operate and monitor the computer. At the code stage, knowledge has been narrowed to the extreme. But with it comes lightning speed and infinitesimal costs, the ultimate efficiency. Code takes the cost dynamic of knowledge to its logical limit.

The analysis of cost dynamics seems to imply that the most profitable course for any company that solves a mystery is to drive it to a heuristic and then to an algorithm so tight it turns to code. Then it should give up on design thinking and run that code forever, making heaps of money for shareholders. But that approach is shortsighted, because, as I discussed in chapter 1, it fails to capitalize on the option that the company created—at sizable human and economic cost—as it pushed knowledge quickly through the knowledge funnel. To exploit that opportunity, a company can choose to redeploy its design thinkers. By putting them to work on new mysteries, the company both defends its current position and goes on the offensive, like RIM’s Lazaridis, who has continually reinvented both products and strategy by tackling new mysteries and revisiting heuristics and algorithms that grew out of answers to older mysteries.

 

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