Three Ways Machine Learning Will Help Leaders Become Better Decision Makers
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Nov 20, 2015

Three Ways Machine Learning Will Help Leaders Become Better Decision Makers

By Alanna Lazarowich | Based on insights from Brian Uzzi

Curious about machine learning and its impact on business?

Once the stuff of science fiction novels, machine learning—where computers improve automatically through experience—is now attracting the attention of a wide range of industries.

As with many recent advances in tech, machine learning’s growth has been largely fueled by the development of new learning algorithms and theory, and by the ongoing explosion in the availability of online data and low-cost computation. Machines are better equipped than ever to capture and analyze large quantities of multisourced, ever-changing data.

But analyzing data is not the same thing as using it to make decisions—and here is where humans come in. Humans are still needed to innovate, to put ideas in an appropriate context, and to suss out an action’s wide-ranging implications. Thus human and machine thinking are complementary and additive.  This may be illustrated as:

  Human + Machine > Human or Machine

So how will human-machine partnerships help leaders make better, faster, and more innovative decisions?

1. Leaders will make better decisions

Machine learning allows for decision making that is more accurate and less biased. In the words of David Ferrucci, lead scientist in the development of IBM’s Watson supercomputer, machine learning will “improve our peripheral vision.”

With so much data available, it is impossible for any one leader to be able to capture and process all relevant information pertinent to a decision, much less respond neutrally to such information.  But machines can tackle some of the heavy lifting—preventing leaders from falling prey to poor judgment, and freeing them up to focus on the bigger picture.

“Through human-machine partnerships, leaders will be able to strip away latent biases and make more empirical decisions, leading to more creative and insightful decisions,” says Brian Uzzi, a Kellogg School professor and faculty director of the Kellogg Architectures of Collaboration Initiative.

Consider the way that the sports analytics company Second Spectrum is developing tools to analyze player performance during games—providing coaches with the information they need to win. Or how startup Invino applies algorithms to best ascertain pricing for fine wine—which investors can use to decide when to buy or sell the wines in their portfolios.

2. Leaders will make faster decisions

Machine learning techniques are already digesting data and making predictions faster than any human could. And the benefits of speed are starting to trickle down to the humans who use these machines.

In the insurance sector, KPMG predicts that relying on machine learning to partly automate the claims process could “cut claims processing time down from a number of months to just a matter of minutes.” In the oil industry, “what could take eight weeks” using human inspectors takes only five days using SkyFuture’s oil rig inspecting drones, as well as a single drone operator and engineer.

Travelers also stand to benefit from machine learning. Airbnb uses host-guest interactions, local market history, and current events to propose data-driven, real-time price recommendations—which travelers can choose to accept or not. Hotels can benefit too: a Nigerian hackathon team recently developed algorithms to predict visitors likely to no-show on their reservations. With this information, hoteliers can “double” their efforts to forecast who will arrive and free up otherwise unused space.

3. Leaders will make more innovative decisions

Access to this peripheral vision and speed makes further innovations possible.

Consider analysis conducted and actions taken by the Chicago Police Department. Combining network analysis and other information, the department derived a heat list of about 420 individuals who “in the worst cases were 500 times more likely than average to be involved in violence.”

Police officers not only had new insights—they were able to take action. They met with individuals on the heat list, advised them of their probability to be involved in a future crime, and offered social services like job training and substance-abuse assistance to help reduce the likelihood of that outcome. The interaction also gave the police an opportunity to highlight the consequences of any future criminal activity.

Or consider the positive social impact from biotech company Mitra’s ability to predict how patients respond to anticancer drugs. Using a machine-learning algorithm, a team of scientists built a model that was able to accurately predict whether a given patient would respond, partially respond, or not respond to treatment.

With all this mounting evidence, it seems that now is the time to embrace machine learning’s potential while recognizing new complexities. In Ferruci’s words, “If a machine can [process information] faster and more completely, we’re all better off. It can help me see what the alternatives are and what the available evidence is for those alternatives, help me to weigh them, help me to think clearly about them. We can all use the help.”

Alanna Lazarowich is Senior Director of the Kellogg Architectures of Collaboration Initiative.

Image credit: Wildpixel.

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