Artificial Intelligence

Though all of these advances are relatively recent, they hark back to the very beginnings of AI in the 1950s, when a number of researchers began to pursue top-down models for mimicking human intelligence. But when progress proved elusive and the rich potential for bottom-up machine learning methods became apparent, the top-down approach was largely abandoned. Today, however, through powerful new research and computational techniques, top-down AI has been reborn. As its great promise begins to be fulfilled, smart companies will put their money where the mind is.

TAKEAWAYS

As AI develops, it will rely less on bottom-up big data and more on top-down reasoning that resembles the way humans approach problems and tasks. This will enable us to apply AI more broadly

Humans routinely, and often effortlessly, sort through probabilities and act on the likeliest, even with relatively little prior experience. Machines are now being taught to mimic such reasoning through the application of Gaussian processes—probabilistic models that can deal with extensive uncertainty, act on sparse data, and learn from experience. Alphabet, Google’s parent company,

broadly applied than ever, creating opportunities for early adopters even in businesses and activities to which it previously seemed unsuited.

In the recent past, AI advanced through deep learning and machine learning, building up systems from the bottom by training them on mountains of data. For instance, driverless vehicles are trained on as many traffic situations as possible. But these data-hungry neural networks, as they are called, have serious limitations. They especially have trouble handling “edge” cases—situations where little data exists. A driverless car that can handle crosswalks, pedestrians, and traffic has trouble processing anomalies like children dressed in unusual Halloween costumes, weaving across the street after dusk.

Many systems are also easily stumped. The iPhone X’s facial recognition system doesn’t recognize “morning faces”—a user’s

Companies considering how to invest in AI capabilities should first understand that over the coming five years applications and machines will become less artificial and more intelligent. They will rely less on bottom-up big data and more on top-down reasoning that more closely resembles the way humans approach problems and tasks. This general reasoning ability will enable AI to be more

tions. Then they must try to imagine what new strategic options these predictions will create.
Better predictions will enable novel business models that will reshape the strategic playing field in many industries. Industrywide business model reinvention would bring with it strategic concerns such as seizing first-mover advantage and investing in different kinds of capabilities.
For example, an online retailer like Amazon could implement a prediction-based “ship-then-shop” model in which it delivers products to customers before they have selected them. Amazon might begin executing this model before it is profitable to be the first mover, and it might invest further in shipping and logistics infrastructure in order to accommodate the increase in customer returns such a model would generate.

AI will make prediction cheaper, faster, and more accurate. When the accuracy of predictions passes a certain threshold, it will have a profound impact on strategy.

Strategists must first invest in determining how fast and how much better predictions will become for their sectors and applica

Strategists face two challenges in light of all of this. First, they must invest in developing a better understanding of how fast and

By launching sooner, Amazon’s AI will get more data sooner, and improve faster. Amazon realizes that the sooner it gets started, the harder it will be for competitors to catch up. Better predictions will attract more shoppers, more shoppers will generate more data to train the AI, more data will lead to better predictions, and so on, creating a virtuous circle. In other words, there are increasing returns to AI, and thus the timing of adopting this kind of strategy matters. Adopting too early could be costly but adopting too late could be fatal.

value of complements to prediction—like human judgment—will rise. But what does all this mean for strategy?

AI is fundamentally a prediction technology. As advances in AI make prediction cheaper, economic theory dictates that we’ll use prediction more frequently and widely, and the