AlphaGo caused a stir by defeating 18-time world champion Lee Sedol in Go, a game the AI thought was impenetrable for another 10 years. AlphaGo’s success is emblematic of a larger trend: An explosion of data and advancements in algorithms have made technology smarter than ever. Machines can now perform tasks ranging from recommending movies to diagnosing cancer – independently and in many cases better than humans. In addition to performing well-defined tasks, technology begins to deal with larger and more ambiguous problems. It is not improbable to imagine that one day a “strategist in a box” could independently develop and execute a business strategy. We’ve spoken to executives who express such a vision – and companies like Amazon and Alibaba are already starting to make it a reality.
But it is dangerous and naive to assume that better technology and more data guarantees better results. Remember long-term capital management? LTCM was founded in 1994 by some of the best minds in financial theory, including two Nobel Prize winners. He printed money while his financial models, based on advanced option theory, worked, with annualized after-fee returns of over 40% in his second and third years. Nonetheless, the over-reliance on models was its downfall. LTCM’s model continued to predict that it was properly hedged against a potential Russian default; the idea that it really needed – that it was under-hedged and exposed to liquidity risk – could only come from outside the model. After the Russian financial crisis in 1998, LTCM imploded and lost $ 4.6 billion.
No matter how advanced the technology is, it needs human partners to strengthen its competitive advantage. It must be anchored in what we call the integrated strategy machine.
An integrated strategy machine is the set of resources, both technological and human, that work together to develop and execute business strategies. It includes a range of conceptual and analytical operations, including problem definition, signal processing, pattern recognition, abstraction and conceptualization, analysis and prediction. One of its core functions is cropping, which repeatedly redefines the problem to allow for more in-depth analysis. Within this machine, people and technology must each play their particular role in an integrated manner.
Amazon represents the state of the art in the deployment of an integrated strategy machine. It has at least 21 data science systems, which include multiple supply chain optimization systems, inventory forecasting system, sales forecasting system, profit optimization system, recommendation engine and many more. These systems are intertwined with each other and with human strategists to create an integrated, well-oiled machine. If the sales forecasting system detects that an item’s popularity is increasing, it triggers a cascade of changes throughout the system: the inventory forecast is updated, which brings the chain system up to date. supply to optimize the inventory in its warehouses; the recommendation engine pushes the item further, increasing sales forecasts; the profit optimization system adjusts the prices, again updating the sales forecast. Other second and third order interactions occur downstream. While many of these operations happen automatically, humans play a critical role in designing experiments and examining traces of data to continue to learn and evolve machine design.
Or consider the integrated strategy machine of Correlation Ventures, a venture capital firm that thrives on the explosive amount of data on startups, including data on funding, investors, business segments, founding teams. and other relevant business characteristics. Like many venture capital firms, Correlation finds many of its trading opportunities through its human relationships. But while conventional due diligence for a deal involves in-depth market research and repeated rounds of interviews with founders and key clients, the correlation focuses on documentary information. To assess investment opportunities, it runs the data through its predictive analytics algorithm, and then humans perform a more holistic review of the opportunities that pass the algorithmic screen. Thus, machines and humans each bring their unique strengths to make precise investment decisions possible. Beyond its predictive power, this approach also makes it possible to obtain speed, scalability and scalability. Correlation’s strategic machine enables him to make an investment decision in two weeks, examine a large number of opportunities with limited human input, and reliably improve his investment decisions over time by accumulating data and experience.
To design such an integrated strategy machine, we believe there are six requirements:
Relevant and specific strategic objective. Don’t let technological capabilities dictate the problems you solve. If all you have is a hammer, then everything will look like a nail. Humans must formulate the central question and thereby define the initial glimpse of where the opportunity lies.
Appropriate design for the purpose. Just as different environments call for fundamentally different approaches to strategy and execution, different strategies also call for different designs for the strategy machine. For example, strategies in a classic predictable environment require “analyze, plan, execute” logic. On the other hand, adaptive, unpredictable environments require a process that can be characterized as ‘vary, select, scale’. Form must follow function.
Correct distribution of man-machine work. Human beings are always unique in their ability to think outside the immediate scope of a task or problem and deal with ambiguity. Machines are good at performing a well-defined task or solving a well-defined problem, but they cannot think beyond the specified context (at least not currently). Nor can they ask new questions, invent answers beyond what is asked, or reframe or relate the problem to a different challenge they have already faced.
Integrated solution. The proper division of labor is essential, but the human and technological components must nonetheless work together seamlessly. Humans, with our unique ability to understand broad contexts and connect information from disparate spheres, must design and optimize the flow of information and information in the strategic machine to ensure that it is optimized for the future. overall objective rather than for individual operations.
An interface that allows detailed analysis. Architects of the strategic machine must avoid the temptation to rely on reductive visualizations. People need to be able to see inside the black box, probe into “messy” data and results, and reframe for richer information.
Unique tools, data or processes. The ultimate function of the integrated strategy machine is to generate competitive advantage. Certain aspects of the machine must be impervious to imitation by competitors, whether it is the tool, the data, the people or the design. The strategy machine itself must be able to evolve and, like any successful conventional strategy, must continue to function so that it does not even stay in the same place.
Business leaders can begin to design an integrated strategy machine by asking themselves these questions:
What strategic goals do I want to achieve through a technology-enhanced process? The initial set of questions should always come from human beings. Only people can set goals and use the necessary holistic judgment. As BCG founder Bruce Henderson once said, “The first definition of a problem is inevitably intuitive. It must be to be recognized as a problem.
What technologies, people and design do I need to achieve these goals? Different questions come with different required abilities, which are often expensive and difficult to obtain. Tech giants who have developed effective strategy machines, like Amazon and Google, have done so by continually investing in technology and paying a premium to access top talent. Companies that don’t have such advantages need to be realistic about what it takes to build a competitive strategy machine.
How can people and machines interact in such a way that they are mutually reinforcing? The goal of the Integrated Strategy Machine is to improve rather than marginalize or inhibit human thinking. To do this, the machine must stimulate people’s ability to create new ideas, question their own thinking, and continually reframe their understanding.
How can the machine evolve and update itself? A successful strategy machine must be able to improve over time. It needs a mechanism to learn from its experience and its feedback. Those who oversee the machine must have the courage and discipline to question and re-evaluate the design of the machine.
How can the organization at large adopt the strategy machine? Ultimately, a strategy is only valuable to the extent that it is adopted and exploited by the organization. Business leaders need to pay attention to what can be achieved within organizational constraints – or have a clear path to eliminate them. Otherwise, the machine may not be relevant or even be actively bypassed by the organization.
General purpose technologies, such as the steam engine, electricity, and information technology, still take decades to unlock their full potential, as companies need to learn and organize themselves to get the most out of their business. Powerful. When electricity initially replaced the steam engine, engineers simply placed the electric motors where the steam engine was located, with limited productivity gains. Electricity only led to huge productivity gains when the plant layout was revisited and optimized for new technology.
We believe the Integrated Strategy Machine is the AI analogue of what new factory designs were for electricity. In other words, the growing intelligence of machines could be wasted unless companies reshape the way they develop and execute their strategies.