BCG Henderson Institute

How can executives leverage the unique learning capabilities of A.I. to create new and possibly unexpected value for their business? John Deere, an agricultural equipment maker, provides an example.

The company initially leveraged A.I. to automate its farming equipment. However, over time new benefits emerged: A.I. was learning how to improve equipment performance while also enabling customers to learn how to best utilize them and make better decisions about their plants. This created a learning loop between users and A.I., enabling the company to redesign its business model as a precision agriculture solutions provider.

On the other hand, what happens when an A.I. algorithm learns a wrong model and evolves in unpredictable directions, or adjusts slowly to a fast-moving environment? Microsoft and Tay, its radicalized chatbot which was shut down less than 16 hours after its launch, of course offer a bleak and well-known illustration. But more recently, Zillow, the online real estate company, also found a costly answer to this question. Within two years its A.I. models contributed to a loss of more than $500 million as they failed to adjust to major changes in the housing market due to the pandemic.

In all these situations the outcome of the A.I. investments played out different than planned, largely due to the unique capabilities of A.I. to learn, successfully or not, or support the decision-making and learning of its users. They indicate the importance for businesses to reconsider their A.I. investment processes keeping these unique features of A.I. in mind—or else face unexpected risks and miss good investments when potential benefits are overlooked.

Understanding the new risks and benefits that come with the learning capabilities of A.I.—which, among other things, enable predictions and support decision-making—is key. We describe the two main types of each as a starting point. By nature, as learning is happening over the lifetime of an A.I. system, so these risks and benefits may only materialize well after the technology is adopted.

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