MIT Sloan Management Review Summer 2019

When my family went on vacation this April, we all needed the break — kind of desperately. The kids were tightly wound from school and activities. Everyone was having trouble sleeping. I was even starting to worry about short-term memory loss. On our way out the door, I realized I had forgotten my daughter’s medicine. After 10 minutes of retracing my steps, I discovered that I had inadvertently stashed it in our kitchen junk drawer, maybe while fishing for a pen. It’s hard to say. We’re familiar with the costs of burnout: Energy, motivation, productivity, engagement, and commitment can all take a hit, at work and at home. And many of the fixes are fairly intuitive: Regularly unplug. Reduce unnecessary meetings. Exercise. Schedule small breaks during the day. Take vacations even…

The Digital Future of Strawberries The mechanization of agriculture is an old story. In the United States, bigger and more automated tractors are planting and harvesting thousands of acres of megacrops like corn, wheat, and soybeans, using less and less human labor. Producing specialty crops like fruits and vegetables, however, is a lot trickier — involving careful handling and selective harvesting, activities beyond the grasp of machines. But, as John Seabrook writes in The New Yorker (“The Age of Robot Farmers,” April 8, 2019), we may be on the cusp of a new era of farming, with robots able to replace humans in picking delicate crops such as strawberries. Seabrook examines the efforts of a Florida strawberry farmer to apply technologies such as artificial intelligence, robotics, big data, GPS, and machine vision…

Depending on which forecasts you believe, we should be either moderately concerned1 or extremely concerned2 about robots taking our jobs in the near future. From truck drivers to lawyers to those designing the robots themselves, nobody is safe from being replaced by software, algorithms, and machines. Now that we are face-to-face (or face-to-screen) with that threat, an entire cottage industry has emerged around dispensing advice on how to prepare for it. Much of this advice centers on mastering skills that robots ostensibly cannot. What skills are needed to avoid being automated out of a job? One article3 suggests the answer is all of them: “The more skills, knowledge, and experience you have, the less likely you are to be replaced or automated, so acquire whatever you can, as fast as you…

Senior executives in the agile age know they have to recruit the best technical talent they can afford. Unfortunately, some of the best practices of former years have actually made it more difficult to hire the right people and increase the agility of the IT shop. For example, as companies began to realize that their IT managers did not always see problems through a business lens, they brought in new ones with more business savvy. The new hires were good communicators, understood the connection between business priorities and technology, and managed relationships well throughout the rest of the organization. The problem was that many of them were not deep technologists. As these managers hired more like themselves, the technological skills of many companies suffered. The unchecked use of external contractors has also…

High-skilled immigration is dramatically transforming the tech sector in the United States.1 In 1975 immigrants accounted for one in 12 inventors in America. Today it’s one in 3.5. This surge is due to immigrant concentration in science and engineering fields, and factors that make the United States attractive, such as access to the latest technologies and high pay levels. The impact has been most evident in advanced technology sectors in areas such as Boston and Silicon Valley, but nontech companies including JPMorgan Chase, KPMG, and Walmart are also pursuing more global talent.2 Studies tout the benefits that skilled immigrants bring to the workforce, including their roles in facilitating global teams and contributions as taxpayers.3 But not everyone is happy with the current system, most notably older tech workers. In an effort…

Most companies still equate doing analysis with writing formulas in spreadsheets. But that’s an outdated approach. Now that organizations must cater to and engage with millions of individual customers, not just a handful of segments, they must create reusable solutions to avoid reengineering problems from the ground up again and again. And they want to benefit from the latest advances in machine learning and AI. They can’t do any of that if they simply throw regressions at whatever challenges they face. In short, companies need to retrain their people to think in code, not just formulas. This requires a significant shift in mindset. Many managers see code as the province of data scientists and the IT department. But organizations that make it the natural language for diffusing analysis across units, teams,…