At the Intel Corporation, in the days when the company still relied heavily on the production of DRAM memory chips, the company allowed engineers to work on what it called embryonic technologies. Highly skilled engineers were given individual R&D budgets and ample autonomy to decide what to work on. When top management observed more and more of these engineers abandoning projects centered on the old DRAM chips and flocking toward a new technology called microprocessors, they realized it was time to change their strategy.
For such new, innovative products, it was impossible to compute any reliable numbers (in terms of market size, demand growth, margin, or a variant of a net present value calculation), so then CEO Andy Grove relied on the collective insights of his engineers. Although Grove later abandoned this process, his successor, Craig Barrett, partly reinstated it. His “autonomous strategy processes,” though less extreme than Grove’s original, are still in use at Intel today. Companies like Google and Pixar have also adopted versions of these autonomous processes, to great success.
3. Objectivize the process. Various research and case examples have confirmed the risk of “escalation of commitment” in selection processes. This phenomenon occurs when decision makers hold on to a failing course of action because it provided success in the past or because someone’s reputation is tied to it or simply because they have “come this far already.” To combat escalation of commitment, companies need to objectivize the process and decouple it from individual decision makers’ personal interests and emotions.
Here Intel provides another case in point. When it was producing both DRAMs and microprocessors, it let these products compete for scarce production capacity on its manufacturing line. But the company had to ensure that decisions would be made on the basis of hard facts, rather than feelings or preferences that the engineers in charge of production may have had about one product or the other. Years earlier, top management had designed a formula called the production capacity allocation rule. Using a variety of input numbers (such as efficiency, demand growth, and margins), it would compute which product would get what amount of production capacity. When it came time to make decisions about what to produce, the engineers followed the formula to a T. Even when the outcome of the formula seemed to run counter to the company’s focus—which until that time had centered on DRAMs—Grove would urge engineers to follow the formula, and with it the objective process. When microprocessors won out, it was because the data supported it.
4. Let the evidence match the investment. Data also plays a key role in the next step. Executives often rely on just one or two selection moments. But the most successful innovators view selection as an ongoing process. As a project progresses and begins to demand increased investment, more and more data becomes available. The information revealed at one decision point should guide the next.
Consider the case of the Sadler’s Wells dance group, which operates three theaters in central London. It has an explicit mission to be the center of innovation in dance. It starts out by scouting a large variety of dancers whose work might be suitable for its theaters. It then invites a limited number of these artists to come together to develop rough ideas, in an informal way, for potential new productions. Sadler’s Wells provides studio space and a small budget to those collaborating artists who come up with a concrete and innovative idea, in order to test it. If the various people involved—artists, producers, and theater managers—believe that it has strong potential after viewing the raw idea in action in the studio, the company adds more investment to develop it into a show. Subsequently, if the show’s scale permits, it will premiere in the group’s smallest theater. If it becomes a box office success, organizers will schedule it later for a longer period in the main theater.