The rise of artificial intelligence (AI) is one of the defining business opportunities for leaders today. Closely associated with it: the challenge of creating an organization that can rise to that opportunity and exploit the potential of AI at scale. Meeting this challenge requires organizations to prepare their leaders, business staff, analytics teams, and end users to work and think in new ways--not only by helping these cohorts understand how to tap into AI effectively, but also by teaching them to embrace data exploration, agile development, and interdisciplinary teamwork. Often, companies use an ad hoc approach to their talent-building efforts. They hire new workers equipped with these skills in spurts and rely on online-learning platforms, universities, and executive-level programs to train existing employees.
April 20, 2018Three of the top five "most promising" jobs in the United States this year are high-tech data positions. But not every role on an analytics team requires a math or science degree. "In fact, in the role of a translator, having a computer background can go against you," says Prudencio Pedrosa Grandes, a partner based in Madrid who holds degrees in economics and business. He should know--he was the first analytics translator at McKinsey and helped to define the role more than 3 years ago. "I don't have advanced programming skills.
One or more of these issues is likely what's holding your organization back. How confident are you that your analytics initiative is delivering the value it's supposed to? These days, it's the rare CEO who doesn't know that businesses must become analytics-driven. Many business leaders have, to their credit, been charging ahead with bold investments in analytics resources and artificial intelligence (AI). Many CEOs have dedicated a lot of their own time to implementing analytics programs, appointed chief analytics officers (CAOs) or chief data officers (CDOs), and hired all sorts of data specialists. However, too many executives have assumed that because they've made such big moves, the main challenges to becoming analytics-driven are behind them.
MassMutual, a $30 billion per year life insurance company, had a problem. It was 2013 and, along with the rest of the insurance industry, it was bedeviled by fraud. According to FBI estimates, fraud sets the U.S. insurance industry (and policyholders) back by $40 billion a year. "We had to get much better at detecting fraud in real time," says Sears Merritt, MassMutual's chief of technology strategy and data science. So MassMutual launched an innovative collaboration between the company's data scientists and its line managers.