In the age of machine learning, what should managers know — and what must non-tech companies do to stay ahead?

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For data scientists and machine-learning experts, March 2016 was a momentous month. AlphaGo, a computer program developed by Google, beat world champion Lee Sedol at the ancient Chinese board game Go by a score of 4 to 1. In contrast to chess, where players might make about 40 moves per game, games of Go may have 200 moves.1 Whereas IBM’s Deep Blue was used to defeat chess grand master Garry Kasparov in 1997, computer scientists can’t calculate all the moves required to win at Go. Instead, Google had to create another kind of machine algorithm that could approximate humanlike qualities, playing the game by intuition and feel.

Significantly, programmers weren’t able to explain why and how AlphaGo made a certain move. Choices can’t be traced to the program’s source code any more than conscious decisions can be linked to a group of neural cells in our brains. AlphaGo’s latest triumph has therefore made clear that the rise of machines capable of self-learning is no longer just hypothetical.

We are past the point of debating whether human intuition can be replicated. Machine learning is already here. It will impact most companies over the next few decades and become part of everyday business life. Executives, regardless of which industries they are in, must quickly come to grip with how companies and industries will evolve. What should every manager know in the age of machine learning?

The Rise of Machine Intelligence

The idea of a thinking machine goes back at least as far as 1950, when British computer scientist Alan Turing wrote that if a machine was indistinguishable from a human during text-based conversations, then it was “thinking.”2 Computers, however, require programmers to write instructions. They don’t learn autonomously but follow rules.

The earliest iterations of so-called machine learning required heavy support and constant monitoring by computer scientists or statisticians. Data needed to be labeled, end goals explicitly set. In Amazon’s earliest days, it found that a machine algorithm called Amabot was more effective in generating customer recommendations to increase sales than humans who individually selected and promoted products. Amabot factored in customers’ previous purchases and their web searches.3

As capable as Amabot was, however, it couldn’t be used on different problems. Nor can algorithms be applied to unstructured data expressed in natural human language.

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About the Authors Howard Yu is a professor of strategy and innovation at IMD Business School in Lausanne, Switzerland. Thomas Malnight is a professor of strategy and general management at IMD.