An executive's guide to machine learning – McKinsey

AI in the C-suite

AI in the C-suite

“Any organisation that is not a math house now or is unable to become one soon is already a legacy company.” – Ram Charan


It came into its own as a scientific discipline in the late 1990s as steady advances in digitisation and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so.

The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning-and the need for it.

In 2007 Fei-Fei Li, the head of Stanford’s Artificial Intelligence Lab, gave up trying to programme computers to recognise objects and began labeling the millions of raw images that a child might encounter by age three and feeding them to computers.



Dazzling as such feats are, machine learning is nothing like learning in the human sense (yet). But what it already does extraordinarily well-and will get better at-is relentlessly chewing through any amount of data and every combination of variables.


Indeed, management author Ram Charan suggests that “any organisation that is not a math house now or is unable to become one soon is already a legacy company.”


Well, let’s start with sports. This past spring, contenders for the US National Basketball Association championship relied on the analytics of Second Spectrum, a California machine-learning start-up. By digitising the past few seasons’ games, it has created predictive models that allow a coach to distinguish between, as CEO Rajiv Maheswaran puts it, “a bad shooter who takes good shots and a good shooter who takes bad shots”-and to adjust his decisions accordingly.

You can’t get more venerable or traditional than General Electric, the only member of the original Dow Jones Industrial Average still around after 119 years. GE already makes hundreds of millions of dollars by crunching the data it collects from deep-sea oil wells or jet engines to optimise performance, anticipate breakdowns, and streamline maintenance.


What about outside North America?



Closer to home, as a recent article in McKinsey Quarterly notes, our colleagues have been applying hard analytics to the soft stuff of talent management. Last fall, they tested the ability of three algorithms developed by external vendors and one built internally to forecast, solely by examining scanned résumés, which of more than 10,000 potential recruits the firm would have accepted.


As ever more of the analog world gets digitised, our ability to learn from data by developing and testing algorithms will only become more important for what are now seen as traditional businesses.

Google chief economist Hal Varian calls this “computerkaizen.” For “just as mass production changed the way products were assembled and continuous improvement changed how manufacturing was done,” he says, “so continuous [and often automatic] experimentation will improve the way we optimise business processes in our organisations.”

Read the rest of the article at McKinsey Quarterly

Source: An executive's guide to machine learning – McKinsey

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