The US labor market looks markedly different today than it did two decades ago. It has been reshaped by dramatic events like the Great Recession but also by a quieter ongoing evolution in the mix and location of jobs. In the decade ahead, the next wave of automation technologies may accelerate the pace of change. Millions of jobs could be phased out even as new ones are created. More broadly, the day-to-day nature of work could change for nearly everyone as intelligent machines become fixtures in the American workplace. Until recently, most research on the potential effects of automation, including our own, has focused on the national-level effects. Our previous work ran multiple scenarios regarding the pace and extent of adoption. In the midpoint case, our modeling shows some jobs being phased out but sufficient numbers being added at the same time to produce net positive job growth for the United States as a whole through 2030.
Economic intersectionality can refer to the compounded effects of any combination of characteristics associated with economic disadvantage. In this article, we focus on differing levels of automation-based challenges for African American men and women of various ages and education levels in rural and urban America. We project that African Americans in the 13 community archetypes we analyzed may have a higher rate of job displacement than workers in other segments of the US population due to rising automation and gaining a smaller share of the net projected job growth between 2017 and 2030. By 2030, the employment outlook for African Americans--particularly men, younger workers (ages 18–35), and those without a college degree--may worsen dramatically. Additionally, we find that African Americans are geographically removed from future job growth centers and more likely to be concentrated in areas of job decline.
Beyond traditional industrial automation and advanced robots, new generations of more capable autonomous systems are appearing in environments ranging from autonomous vehicles on roads to automated check-outs in grocery stores. Much of this progress has been driven by improvements in systems and components, including mechanics, sensors and software. AI has made especially large strides in recent years, as machine-learning algorithms have become more sophisticated and made use of huge increases in computing power and of the exponential growth in data available to train them. Spectacular breakthroughs are making headlines, many involving beyond-human capabilities in computer vision, natural language processing, and complex games such as Go. These technologies are already generating value in various products and services, and companies across sectors use them in an array of processes to personalize product recommendations, find anomalies in production, identify fraudulent transactions, and more.
The age of automation, and on the near horizon, artificial intelligence (AI) technologies offer new job opportunities and avenues for economic advancement, but women face new challenges overlaid on long-established ones. Between 40 million and 160 million women globally may need to transition between occupations by 2030, often into higher-skilled roles. To weather this disruption, women (and men) need to be skilled, mobile, and tech-savvy, but women face pervasive barriers on each, and will need targeted support to move forward in the world of work. A new McKinsey Global Institute (MGI) report, The future of women at work: Transitions in the age of automation (PDF–2MB), finds that if women make these transitions, they could be on the path to more productive, better-paid work. If they cannot, they could face a growing wage gap or be left further behind when progress toward gender parity in work is already slow. This new research explores potential patterns in "jobs lost" (jobs displaced by automation), "jobs gained" (job creation driven by economic growth, investment, demographic changes, and technological innovation), and "jobs changed" (jobs whose activities and skill requirements change from partial automation) for women by exploring several scenarios of how automation adoption and job creation trends could play out by 2030 for men and women given current gender patterns in the global workforce. These scenarios are not meant to predict the future; rather, they serve as a tool to understand a range of possible outcomes and identify interventions needed.