What does it take to translate technological advances into strategic advantage?
We believe that technology-enhanced strategy can be realized only in the context of an integrated strategy machine: a collection of resources—both technological and human—that act in concert to develop and execute business strategy. It comprises a range of conceptual and analytical operations—including problem definition, signal processing, pattern recognition, abstraction and conceptualization, analysis, and prediction—that connect into a seamless whole. This alignment of individual operations toward the overall aim makes the strategy machine integrated.
Effective business strategy development, with or without technology, must accommodate reframing, the process of redefinition and reanalysis of the problem that BCG founder Bruce Henderson considered to be at the heart of effective business thinking. To enable reframing, the strategy machine must span the end-to-end process of strategy development and implementation. Rather than formulating strategy in a vacuum, the strategy machine must continuously update and improve strategy by analyzing feedback and execution data. There needs to be a constant interplay between upstream and downstream elements of the strategy machine. (See the exhibit.)
Although machines and algorithms can play increasingly large and important roles in strategy making and execution, the integrated strategy machine must, at least for now, be designed by human beings: people must assemble the machine and direct it toward a strategic aim.
It is important to understand why. Human beings are still unique in their capacity to “go meta”—that is, to think outside the immediate scope of a task or problem. Machines can’t yet do that well; they are good at executing a well-defined task or solving a well-defined problem, but they can’t pose new questions or connect a problem to a different one they previously faced.
In other words, artificial intelligence is still far from being general. Of course, this is not to say that machines are incapable of learning these higher-order skills—we anticipate that technology will play a larger and larger role within the strategy machine.
Amazon provides an excellent example of an integrated strategy machine. The company has at least 21 data science systems, including several for supply chain optimization, an inventory forecasting system, a sales forecasting system, a profit optimization system, a recommendation engine, and many others. These systems are intertwined with one another and with human strategists to create an integrated, well-oiled machine. For example, if the sales forecasting system detects that the popularity of an item is increasing, it triggers a cascade of changes: the inventory forecast is updated, causing the supply chain system to optimize inventory across warehouses; the recommendation engine pushes the item more, and the profit optimization system adjusts pricing; these changes in turn update the sales forecast. These are only some of the first-order effects, and further interactions occur downstream. While many of the operations happen automatically, human beings design experiments and review data traces to continue to learn and evolve the machine design. Humans also extract higher-order insights from anomalies and patterns, captured by machines, that inform their next strategic moves.
Or consider how the integrated strategy machine works in the venture capital industry. Correlation Ventures thrives on the exploding amount of data around start-ups, including data on financing, investors, business segments, founding teams, and other relevant parameters. Like many venture capital firms, Correlation sources most of its deal opportunities through its human connections. However, whereas the conventional approach to deal due diligence involves repeated rounds of interviews with founders and key customers as well as deep market research, Correlation focuses on documentary information. To evaluate investment opportunities, the firm runs the data through its predictive analytics algorithm; humans then perform a more holistic review of the opportunities that pass the algorithmic screen. Thus, machines and people each contribute their unique strengths to make accurate investment decisions. Beyond predictive power, this approach also achieves speed, scalability, and evolvability: Correlation’s strategy machine allows the firm to make an investment decision in two weeks, review a large number of opportunities with limited human resources, and reliably improve investment decisions over time through the accumulation of data and experience.