Leadership development is about developing leaders for new realities
One of the most startling of the new realities is artificial intelligence. Even if many predictions are only partially correct, the leaders of the next twenty years will be working in an environment very different from when they started.
In this post, I want to jog your thinking by pointing you to some recent articles about artificial intelligence, leadership, and leadership development. Start with “Artificial intelligence meets the C-suite†from the good people at McKinsey. Here’s the money quote.
“Many of the jobs that had once seemed the sole province of humans—including those of pathologists, petroleum geologists, and law clerks—are now being performed by computers.â€
Then, let’s look at two important aspects of developing leaders.
Leadership development is about identifying possible leaders
Leadership development begins with hiring and the selection of people with the potential and desire to become leaders. Check out “Wall Street wants to use artificial intelligence to aid the hiring process†from Olivia Oran. Here’s a sample.
“Several banks are in the early stages of adding artificial intelligence software to complement in-person interviews and other traditional hiring processes. The banks hope that the technology can help predict which employees will succeed at a given job by creating patterns around large amounts of data that the tests produce.â€
Leadership development is about helping leaders develop decision making skills
Decision making skills are critical to the success of any leader. Read how researchers from Kellogg School of Management think computers and people may make better decisions together. The title of the article is “Three Ways Machine Learning Will Help Leaders Become Better Decision Makers.†Here’s a key quote.
“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.â€