MIT Sloan Management Review Winter 2021

We’ve spent most of 2020 coming to terms with relative isolation as we’ve tried to dodge infection by the relentless pathogen that has come to define this year. But those who have thrived over the past 10 months are not necessarily the go-it-alone types. Rather, they’re the connectors and the collaborators, who have always shown us that humans achieve the most when they find common purpose and work as a team — whether the task is raising a barn, a family, or another round of funding. Some teams are fueled by a shared commitment to an altruistic goal. Consider the networks of grassroots digital fabricators who have joined up with other experts in informal consortia to design and manufacture personal protective equipment for front-line health care workers. Their successes, described in…

Whether you turn to news outlets, tech magazines, or academic sources for insight, you’re likely to hear that the COVID-19 pandemic is going to drive massive growth in automation, especially via robots.1 The arguments in favor of this view seem reasonable: Main Street might look dead, but companies that provide shippable goods have been facing double, triple, or even 10 times their previous demand. Robots, the thinking goes, should be able to reliably do that repetitive physical work when many workers aren’t safely able or willing to set foot in the building. What’s more, access to the technology is getting less expensive, with “robots as a service” models allowing companies to pay per touch rather than dipping into precious capital reserves. And robots are becoming more capable. In just the past…

The summer of 2020 saw an unprecedented number of corporate leaders publicly acknowledge and condemn institutional and structural racism, as part of a national reckoning on racial injustice. Issued in response to widespread protests against police violence, these statements called for an end to racial disparities across all systems, including health care, education, housing, criminal justice, and employment. This movement emerged at a moment when people of color were more likely to be working on the dangerous front lines of a pandemic, for low wages and with scant protective gear, while higher-wage earners sheltered at home. Businesses claiming to support racial equity are, in reality, committing to change facts such as these: Black workers are more likely to work in low-wage industries with high turnover, earn less than their white counterparts,…

The economic disruptions caused by the ongoing pandemic are forcing myriad decisions on CEOs of B2B companies. Often, the most pressing decisions are whether and how to cut costs. As in business downturns past, some CEOs are implementing across-the-board salary cuts and widespread furloughs, while others are taking a more piecemeal approach — renegotiating vendor contracts; trimming underperforming products, regions, and divisions; and shifting to lower-cost sales channels. Our research shows that both of these approaches can be misguided. A more effective cost-cutting strategy should begin — and end — with customer focus. Customer focus tends to be overlooked during cost cutting because it is usually seen as a revenue enhancement strategy. This is a mistake: B2B companies that ignore what customers value when they are cutting costs leave a lot of money…

We can safely defer the discussion about whether artificial intelligence will eventually take over board functions. We cannot, however, defer the discussion about how boards will oversee AI — a discussion that’s relevant whether organizations are developing AI systems or buying AI-powered software. With the technology in increasingly widespread use, it’s time for every board to develop a proactive approach for overseeing how AI operates within the context of an organization’s overall mission and risk management. According to McKinsey’s 2019 global AI survey, although AI adoption is increasing rapidly, overseeing and mitigating its risks remain unresolved and urgent tasks: Just 41% of respondents said that their organizations “comprehensively identify and prioritize” the risks associated with AI deployment. Board members recognize that this task is on their agendas: According to the 2019 National…

Wikipedia defines recommendation engines (and platforms and systems) as “a subclass of information filtering system that seeks to predict the ‘rating’ or ‘preference’ a user would give to an item.”1 But as a tool, technology, and digital platform, recommendation engines are far more intriguing and important than this definition suggests. In data-driven markets, the most effective competitors reliably offer the most effective advice. When predictive analytics are repackaged and repurposed as recommendations, they transform how people perceive, experience, and exercise choice. The most powerful — and empowering — engines of commerce are recommendation engines. Recommendation engines have been essential to the success of digital platforms Alibaba, Amazon, Netflix, and Spotify, according to their founders and CEOs. For companies such as these, recommendation engines aren’t merely marketing or sales tools but drivers of…