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No enterprise can out-innovate all potential competitors, suppliers and external knowledge sources. Knowledge frontiers are moving too fast. In almost every major discipline, up to 90% of relevant knowledge has appeared in the last 15 years. Terabytes of data (each approximating Shakespeare’s collected works) pour into every discipline’s or industry’s database daily.
This requires a transformation of cherished ideas about, first, strategic analysis and positioning; and second, approaches to gathering and validating information for executives. The self-sufficient enterprise is becoming anachronistic. Each organization is part of a matrix of merging and evolving ideas and opportunities. To realize its own potential, a company must engage external knowledge centers through well-developed alliances. Leading companies focus less on positioning and more on patterns of people and institutions they work with — or against.
In science-based industries, puddles of knowledge bubble all around. Even the best scientists can’t tell what any individual bubble will do. Many look interesting. Some energetic ones interact with bubbles in other puddles to become major opportunities. Some evaporate in the chaos of development. How do companies spot and manage promising opportunities? They do it as surfers ride waves or scientists conduct research. They observe selected environments systematically and scan ripples of opportunity on multiple horizons. They learn to recognizepatterns of impending change, anomalies or promising interactions, then monitor, reinforce and exploit them.
They concentrate on managing relevant knowledge flows and connecting them to their own centers of expertise. Surprisingly, many companies myopically underdevelop a key element: a genuine software strategy to organize the data terabytes and exploit their relationships at best-in-world levels. Fortunately, new analytical tools and structures are emerging to help organizations navigate. New neural networks can define interrelationships more easily than traditional tools do. And other software concepts — like the emerging Semantic Web — are expanding the Internet’s usefulness through software structures focusing more on the conceptual interactions of search terms.
To exploit such interactions, leading companies develop flexible core-knowledge platforms and, equally important, the entrepreneurial skills to seize opportunity waves. They systematically disseminate and trade knowledge and, if necessary, share proprietary information, recognizing that larger, unexpected innovations may arise to increase their own innovations’ value by orders of magnitude. They know it’s impossible to foresee all combinations and profit opportunities.
As waves drive surfers, external forces often drive companies’ success. Managers must read the waves expertly. But that means adopting a humility unfamiliar to traditional titans of industry. Accomplishments depend on ensembles of others’ work and unfold in ways managers can’t anticipate. Because leaders can’t predict which combinations will succeed, they can’t drive their organizations toward predetermined positions.
Meanwhile, it’s essential to abandon a spent wave, as too many dot-coms learned the hard way. Companies can benefit from growth and knowledge combinations by adopting the structures of science — in all their richness and complexity. Scientists generate useful information many ways. Too often, managers heed advice based on only one type of research — the reproducible testing of pre-established hypotheses. Management strategists, like scientists, ignore other approaches at their peril. In large-scale systems (meteorological, ecological or corporate-strategy systems), it may be impossible to create the reproducible conditions that strict hypothesis testing demands. Most of what we know about such systems comes from systematic observation.
In science, a carefully proposed hypothesis can be useful before thorough testing. Elements of quantum theory are used in lasers, cryptography and microelectronics although the full theory is not yet testable. The first rigorous field verification of Darwin’s model for rapid evolution of visible traits (such as bill sizes in birds) was in the 1980s El Niño events. Einstein’s concept of gravity bending light wasn’t finally tested for decades, yet its implications were widely accepted because of other supporting evidence.
Managers, take note. Often technological use precedes full scientific explanation (bumblebee flight, steam engines, anesthetics, float glass, aspirin). The only way to find out if some things work is to try them.
Many behavioral, environmental, technical and economic observations were useful before undergoing strict laboratory tests. Systematic observation and rapid, interactive, inexpensive in-use testing — enabled by superior software and planned flexibility — will be key strategies as managers try to ride today’s turbulent opportunity waves without wiping out.