5 New Principles Of Strategy For The Digital Age
We tend to see technology through advances in products. One company launches a new and improved version, only to be matched or overtaken by a competitor. The R&D treadmill continues and we all struggle to keep up.
While that is certainly an important aspect of today’s competitive environment, a far greater shift is happening with respect to strategy. Even foundational concepts, such as core competency, 5 forces and competitive advantage have lost their relevance.
The upshot is that technology is not only changing how we run our operations, but how we need to think about our enterprises. Speed now trumps intelligence. We need to break free of old assumptions and adapt to a new business environment in which everything is connected, information is cheap and resources are not owned, but accessed.
1. From Sustainable Competitive Advantage to Transient Competitive Advantage
In 1985, Michael Porter proposed that organizations pursue sustainable competitive advantage. By building key assets such as brand, culture, intellectual property and proprietary processes, firms could outperform the competition over a long period of time. That idea made him the most influential business thinker of his generation.
Since then, the central question of strategy has been how best to attain sustainable competitive advantage. For example, in Good to Great, author Jim Collins suggested that advantage works like a flywheel. Little advances combine over time to form an unbeatable model. Others had different solutions, but the problem always remained the same.
Yet, in The End of Competitive Advantage, Columbia professor Rita Gunther McGrath argues that sustainable competitive advantage is no longer viable or even desirable. She points out that, “it allows for inertia and power to build up along the lines of an existing business model,” which will soon be defunct. Business models no longer last.
Instead, she suggests that firms pursue transient competitive advantage, knowing full well that any advantage will be short lived. The evidence would seem to bear this out. Innosight reports that every two weeks a company is replaced on the S&P 500 and the average lifespan has fallen from over 60 years to less than 20.
2. From The Knowledge Economy to The Information Economy
When Peter Drucker coined the phrase “knowledge economy” in the late 1960’s, he used the term to signal a change in how value was being created. Rather than managing workers to perform well-understood tasks, firms would have to coordinate the work of highly trained specialists whose job it was to create new knowledge.
A turning point came in 2009 with the onset of the H1N1 flu virus. The Center for Disease Control (CDC) requested that highly trained specialists (i.e. doctors) report signs of flu in their area in order to track the spread of the flu. The data was accurate, but with a lag of two weeks, it was too slow to be very helpful.
At the same time, Google began its own flu tracking, using big data methods to correlate search terms with flu outbreaks. The data was just as accurate, but nearly instantaneous. The machine had no medical training, yet was able to outperform the work of thousands of specialists by identifying specific patterns in information.
Today, we’re creating new information where there was none before. From links in social networks to vibrations in car engines and the structure of chemical compounds, machines are learning to recognize patterns and turn them into actionable insights in areas ranging from genomics to marketing to just about everything else you can think of.
3. From The Scale Economy to The Semantic Economy
Traditional strategic thinking, from Coasian firms to Porter’s value chains assumes that knowledge increases with scale. You have more access to suppliers, customers and markets and can apply that wisdom to running your business more efficiently. Bigger was better, in most if not all cases.
Yet a fundamental difference between knowledge and information is that while knowledge is personal and gained over time, information is fungible. It can move from one place to another with negligible loss of fidelity or value.
That’s why we’re now moving from a scale economy to a semantic economy, where the knowledge you own is not nearly as important as that which you can access. We’re also in the midst of a new industrial revolution in which an entrepreneur can leverage manufacturing, finance, marketing and even supercomputing resources from the cloud.
So it is no longer clear that scale advantages outweigh the strategic rigidity that comes with large enterprises. Big firms are learning that they need to network their organizations in order to stay competitive with an onslaught of nimble startups.
4. From Industries to Missions
In the past, competition was usually seen through the lens of industry analysis. You succeeded by finding a viable strategic position within well established industry principles. Planning was mostly an exercise in identifying the right mix of assets and capabilities to maximize operational excellence.
However, as Walter Isaacson noted in his acclaimed biography of Steve Jobs, the Apple founder revolutionized no less than 7 industries. In a similar vein, Amazon, a retailer, has been competing in enterprise computing and entertainment while Google and Elon Musk are seeking to transform the automotive industry.
So rather than thinking in terms of traditional industries, we need to think in terms of adjacencies. Amazon developed its supercomputing capacity running its online stores, then began offering access to other businesses. Google learned to excel at pattern recognition through its search business before it applied that capability to driverless cars.
In the past, I’ve referred to this activity as questing (as one would do in a role playing game). Rita Gunther McGrath describes it as competing in “arenas” rather than industries. Whatever you want to call it, industries no longer serve as viable strategic units. Competition—and opportunity for that matter—can come from anywhere.
5. From Strategic Planning to Bayesian Strategy and the New Learning Organization
For a long time, corporate strategy has been synonymous with planning. Corporate chieftains acted very much like army generals—scouting out the terrain and matching objectives to tactics. Your formulated the plan and then you executed it.
Today, extensive strategic planning has become untenable. By the time you gather information, check your numbers, analyze the situation, make the plan, revise the plan and build a consensus, the facts on the ground have already rendered the plan useless. Success is becoming more a matter of time and place than of vision or insight.
So it’s crucial that we move from strategic planning to Bayesian strategy, where we’re not trying to “get it right” as much as we are striving to become less wrong over time. This will entail creating new learning organizations that are able to manage complexity through combining human driven ambitions with automated market intelligence.
Most of all, strategy is becoming less about assets and capabilities and more about connections and access. It’s not so important anymore what you have—or even what you know—but how you can forge networks of purpose which can adapt in real time.