AlphaGo caused a stir by defeating 18-time world champion Lee Sedol in Go, a game thought to be impenetrable by AI for another 10 years. AlphaGo’s success is emblematic of a broader trend: An explosion of data and advances in algorithms have made technology smarter than ever before. Machines can now carry out tasks ranging from recommending movies to diagnosing cancer — independently of, and in many cases better than, humans. In addition to executing well-defined tasks, technology is starting to address broader, more ambiguous problems. It’s not implausible to imagine that one day a “strategist in a box” could autonomously develop and execute a business strategy. We’ve spoken to leaders who express such a vision — and companies such as Amazon and Alibaba are already beginning to make it a reality.
But it’s dangerous and naïve to assume that better technology and more data guarantee better outcomes. Remember Long-Term Capital Management? LTCM was founded, in 1994, by some of the best minds in finance theory, including two Nobel Prize winners. It printed money while its financial models, based on cutting-edge option theory, worked, with annualized returns after fees of over 40% in its second and third years. Nevertheless, overreliance on models was its downfall. LTCM’s model continued to predict that it was properly hedged against a potential Russian default; the insight that it actually needed — that it was under-hedged and exposed to liquidity risk — could only have come from outside of the model. After the Russian financial crisis in 1998, LTCM imploded and lost $4.6 billion.
No matter how advanced technology is, it needs human partners to enhance competitive advantage. It must be embedded in what we call the integrated strategy machine.
An integrated strategy machine is the collection of resources, both technological and human, that act in concert to develop and execute business strategies. It comprises a range of conceptual and analytical operations, including problem definition, signal processing, pattern recognition, abstraction and conceptualization, analysis, and prediction. One of its critical functions is reframing, which is repeatedly redefining the problem to enable deeper insights. Within this machine, people and technology must each play their particular roles in an integrated fashion.
Amazon represents the state-of-the-art in deploying an integrated strategy machine. It has at least 21 data science systems, which include several supply chain optimization systems, an inventory forecasting system, a sales forecasting system, a profit optimization system, a recommendation engine, and many others. These systems are closely intertwined with each other and with human strategists to create an integrated, well-oiled machine. If the sales forecasting system detects that the popularity of an item is increasing, it starts a cascade of changes throughout the system: The inventory forecast is updated, causing the supply chain system to optimize inventory across its warehouses; the recommendation engine pushes the item more, causing sales forecasts to increase; the profit optimization system adjusts pricing, again updating the sales forecast. Further second- and third-order interactions occur downstream. While many of these operations happen automatically, human beings play a vital role in designing experiments and reviewing data traces to continue to learn and evolve the design of the machine.
Or consider the integrated strategy machine of Correlation Ventures, a venture capital firm that thrives on the exploding amount of data around startups, including data on financing, investors, business segments, founding teams, and other relevant business characteristics. Like many venture capital firms, Correlation sources many of its deal opportunities through its human connections.
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