The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Register now!
If you squint just right, the immediate future of work looks bright. Despite widespread unemployment during the pandemic — up to 16.3 million in the U.S. as of July, according to the Bureau of Labor Statistics — new jobs, and new types of jobs, are surfing the oncoming wave of robots and automation. These positions are showing up in industries ranging from shipping to trucking, construction, transportation, delivery, health care, and manufacturing.
But viewing automation solely through the lens of techno-optimism is, at best, myopic. Some people will be left behind, and it’s important to understand who will be most affected — based on race, age, gender, or other factors — and what can be done about it.
A wealth of opportunity
Broadly speaking, AI and automation promise new job opportunities and trillions of dollars in economic growth. While some of these jobs are highly technical, many won’t require an advanced degree or even a background in technology. Some roles will be filled by upskilling or reskilling workers with existing expertise, like retraining truck drivers to remotely pilot autonomous trucks.
Jobs involving remote operators may create unprecedented opportunities for people with physical limitations or sensory issues that prevent them from participating in traditional workplaces. And teleoperation is ideal for the throngs of workers eager to move away from densely populated cities, whether to reduce their potential COVID-19 exposure or to find affordable housing and a better quality of life.
Many jobs can be partially automated, with technological advancements sparing workers tedium and physical labor. In work environments like those found in the meatpacking industry, automation can augment productivity while reducing the number of people needed on the line. This allows workers to maintain safe distances from each other and helps keep businesses running — and food on our table — during tough economic times.
But these examples cut both ways. A machine that reduces manual labor is likely to displace the person who performed that work. Even if it’s necessary to reduce the number of people standing shoulder to shoulder for health purposes, the net result is fewer people working. Those jobs become casualties of both the pandemic and automation — and are unlikely to return.
A 2019 report from McKinsey details who is most at risk of being left behind by automation, which tends to come down to how automatable their job is. A similar report from the Brookings Institution on automation and AI frames the issue by parsing tasks from skills. Citing earlier work from economists David Autor, Frank Levy, and Richard Murnane, the Brookings report says, “A job is a bundle of tasks, to which workers apply skill endowments in exchange for wages. Some of these tasks may become automated. Others may not. Skills belong to workers, which can be ported to other jobs — even those with a different task composition.” In other words, automation cannot replace people — just some of the tasks they do in the course of performing their job. Of course, that distinction is of little comfort to those who find themselves out of work.
The McKinsey report found that the types of jobs most susceptible to automation by 2030 include cashier, food server, retail salesperson, customer service rep, office clerk, janitor, housekeeper, stock clerk, and order filler. Jobs at least risk of displacement include those in education, creative roles, health professions, business and legal professions, and jobs in property management and agriculture.
When the report’s authors mapped demographics like race and gender onto the job types, they were able to quantify which groups of people were most at risk. They found that Latinx and Black workers faced the greatest risk of displacement, at rates of 25.5% and 23.1%, respectively. Meanwhile, white workers face a displacement rate of 22.4% and Asian American workers of 21.7%.
There’s a great deal of nuance behind those numbers. Breaking things down by gender shows that the situation is more dire for Black men than for Black women, who are more likely to work in less automatable positions, including as health aides and nursing assistants. (Black people, generally, are overrepresented in the jobs most at risk of automation and underrepresented in those that are less vulnerable.) But women also face significant risks. Another McKinsey study found that globally, 40 million to 160 million women will need to shift into different occupations — some requiring additional skills — to keep pace with automation. And the types of jobs women are more likely to hold are at a greater risk of partial automation.
Location also matters because new automation jobs tend to cluster in geographic hotbeds (remote operator roles notwithstanding). People who live outside growth areas are less likely to grab those jobs — or may need to relocate. Younger workers are more vulnerable than those in the middle of their careers, and those without a college degree are more vulnerable still.
There’s also the matter of pay. “Only half of the top 10 occupations that African Americans typically hold pay above the federal poverty guidelines for a family of four ($25,750), and all 10 of those occupations fall below the median salary for a U.S. worker ($52,000),” according to the McKinsey report.
As a survey from the Joint Center for Political and Economic Studies noted, people of color will make up the majority of the U.S. population in the next 20 to 30 years. That’s soon — within roughly a generation — and reinforces the need for equitable access to the new jobs promised by automation. The survey also found that “Asian Americans, African Americans, and Latinos were all more likely than whites to be interested in obtaining education or training from all the provided options, including a college degree program, online college, community college, online training, a trade union, and a GED.”
Advice for a new world
Researchers are busy exploring ways to prepare workers for the coming wave of automation. Much of their advice centers on education — in traditional higher education institutions but also through professional certificate programs and two-year associate degrees, as well as general reskilling and retraining. These same researchers urge policymakers, educators, and companies to make reskilling attainable for more workers.
The McKinsey report presses higher education institutions to improve retention and completion rates for Black students and advocates decreased enrollment in for-profit schools. It advises companies to avoid imposing degree requirements that are higher than necessary and urges them to consider hiring skilled workers rather than only those with a university degree. It also suggests public and private sectors work together on targeted programs to increase awareness of shifting job requirements, offer support for higher education, and provide a path to transitioning into higher-paying and more future-resistant jobs.
The Brookings report arrives at similar conclusions, suggesting the need for public/private coordination to ease workers’ transitions and reduce hardships (potentially through targeted programs). The report advises measures to “future-proof” local and regional economies and communities from the negative impacts of job displacement and loss.
Like so many things, these studies address a very different world from the one we currently inhabit. Although research-based updates for 2020 are still in the works, VentureBeat spoke with one of the 2019 McKinsey report’s authors, Shelley Stewart III, to understand what, if anything, has changed during the pandemic.
Stewart said much has remained the same in terms of the team’s findings and suggestions, despite the relative economic chaos of the past few months. “I don’t think any of those things have changed,” he said. “The folks who are getting furloughed — not all of them, but the majority of them that do these jobs — were already at risk of [being displaced by] automation.” But he noted that the pandemic has accelerated the pace of those changes.
The number of jobs vulnerable to automation certainly appears to have increased in recent months. Stewart said that in April, McKinsey estimated 53 million U.S. jobs were vulnerable, but around four months later that number has ballooned to 57 million. To address accelerated job vulnerability, we need accelerated interventions. “This is not going to be solved by any one industry group, or even only by the private sector,” he said.
People working in private and social sectors will have to work in tandem, and governmental leaders will need to make job displacement a priority. “That’s the thing that we keep trying to push, is [that] this is going to require coordinated effort and it should be a top agenda item for whoever the next [presidential] administration is,” Stewart said.
Stewart also believes we need to shift our cultural orientation to embrace continued learning. Rather than seeing job training or a degree as terminal, people need to be constantly adding to their knowledge and skill sets. “But it requires a completely different way of thinking,” he said. “This notion of moving from ‘You get some formal education, then stop, and then you go to work’ versus ‘You’re on a perpetual learning journey, reskilling yourself based on where the puck is going.'” Authors of the Brookings report agree, citing the need to promote a “constant learning mindset” in workers, as well as in the educational system and within companies.
Keeping up with the times is always a challenge, and it may feel unfair that these changes are happening at greater speeds because of the pandemic. Unfortunately, this acceleration may leave many people out in the cold, at least in the short term. But a cultural shift toward perpetual learning could go a long way toward ensuring more people partake in the spoils of automation. Meanwhile, a concerted, multi-stakeholder effort to identify the jobs and workers most at risk and work aggressively to reduce barriers to their success could make all the difference.