Who’s on Your Team?

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 by Joel Cutcher-Gershenfeld, Alan Gershenfeld, and Neil Gershenfeld, demonstrate that this cooperatively minded community holds the potential to make supply chains more resilient. And beyond that, it may unleash what the authors call a third digital revolution — this time, one centered on production rather than computation and communication.

Certainly, the recent success of this nascent “self-sufficient production” movement is due in part to the power of crises to bring together more diverse teams, as Elsbeth Johnson and Fiona Murray explain in their article about why it’s actually easier to innovate in these exceptional conditions. Big, urgent problems often call for “all hands on deck,” pulling people in from across the organization — people who might not ordinarily work together, and who have a broad array of expertise. Such teams are more likely to surface new, creative ideas and are more effective at problem-solving, Johnson and Murray argue.

More heterogeneous teams can not only solve problems better but in some cases actually prevent problems from arising. That’s especially true in AI, where the limited perspective of insufficiently diverse teams can lead to the development and deployment of badly biased systems, say Ayanna Howard and Charles Isbell. We’ve already seen examples of AI-driven facial recognition technology failing to accurately identify Black faces. If people of color — and women — remain poorly represented on AI teams, we can expect a “cascade of crises” caused by biased AI, say Howard and Isbell.

Building more-diverse teams takes intentional work, of course, and those seeking to do so would do well to heed recommendations from Elizabeth J. Kennedy, who lays out a framework for using data and analytics to that end. It requires managers to collect the right data, track the right metrics, use advanced analytics to gain insight into underlying trends, and set quantifiable goals. Leaders who commit to improving equity and representation for women and people of color must apply the same data-driven mentality that they bring to other business problems — and start by asking, “Who’s on my team?”