Around the world, automation is transforming work, business, and the economy. China is already the largest market for robots in the world, based on volume. All economies, from Brazil and Germany to India and Saudi Arabia, stand to gain from the hefty productivity boosts that robotics and artificial intelligence will bring. The pace and extent of adoption will vary from country to country, depending on factors including wage levels. But no geography and no sector will remain untouched.
In our research we took a detailed look at 46 countries, representing about 80% of the global workforce. We examined their automation potential today — what’s possible by adapting demonstrated technologies — as well as the potential similarities and differences in how automation could take hold in the future.
Today, about half the activities that people are paid to do in the global economy have the potential to be automated by adapting demonstrated technology. As we’ve described previously, our focus is on individual work activities, which we believe to be a more useful way to examine automation potential than looking at entire jobs, since most occupations consist of a number of activities with differing potential to be automated.
In all, 1.2 billion full-time equivalents and $14.6 trillion in wages are associated with activities that are automatable with current technology. This automation potential differs among countries, ranging from 40% to 55%.
The differences reflect variations in sector mix and, within sectors, the mix of jobs with larger or smaller automation potential. Sector differences among economies sometimes lead to striking variations, as is the case with Japan and the United States, two advanced economies. Japan has an overall automation potential of 55% of hours worked, compared with 46% in the United States. Much of the difference is due to Japan’s manufacturing sector, which has a particularly high automation potential, at 71% (versus 60% in the United States). Japanese manufacturing has a slightly larger concentration of work hours in production jobs (54% of hours versus the U.S.’s 50%) and office and administrative support jobs (16% versus 9%). Both of these job titles comprise activities with a relatively high automation potential. By comparison, the United States has a higher proportion of work hours in management, architecture, and engineering jobs, which have a lower automation potential since they require application of specific expertise such as high-value engineering, which computers and robots currently are not able to do.
On a global level, four economies — China, India, Japan, and the United States — dominate the total, accounting for just over half of the wages and almost two-thirds the number of employees associated with activities that are technically automatable by adapting demonstrated technologies. Together, China and India may account for the largest potential employment impact — more than 700 million workers between them — because of the relative size of their labor forces. Technical automation potential is also large in Europe: According to our analysis, more than 60 million full-time employee equivalents and more than $1.9 trillion in wages are associated with automatable activities in the five largest economies (France, Germany, Italy, Spain, and the United Kingdom).
We also expect to see large differences among countries in the pace and extent of automation adoption. Numerous factors will determine automation adoption, of which technical feasibility is only one. Many of the other factors are economic and social, and include the cost of hardware or software solutions needed to integrate technologies into the workplace, labor supply and demand dynamics, and regulatory and social acceptance. Some hardware solutions require significant capital expenditures and could be adopted faster in advanced economies than in emerging ones with lower wage levels, where it will be harder to make a business case for adoption because of low wages. But software solutions could be adopted rapidly around the world, particularly those deployed through the cloud, reducing the lag in adoption time. The pace of adoption will also depend on the benefits that countries expect automation to bring for things other than labor substitution, such as the potential to enhance productivity, raise throughput, and improve accuracy and regulatory and social acceptance.
Regardless of the timing, automation could be the shot in the arm that the global economy sorely needs in the decades ahead. Declining birthrates and the trend toward aging in countries from China to Germany mean that peak employment will occur in most countries within 50 years. The expected decline in the share of the working-age population will open an economic growth gap that automation could potentially fill. We estimate that automation could increase global GDP growth by 0.8% to 1.4% annually, assuming that people replaced by automation rejoin the workforce and remain as productive as they were in 2014. Considering the labor substitution effect alone, we calculate that, by 2065, the productivity growth that automation could add to the largest economies in the world (G19 plus Nigeria) is the equivalent of an additional 1.1 billion to 2.2 billion full-time workers.
The productivity growth enabled by automation can ensure continued prosperity in aging nations and could provide an additional boost to fast-growing ones. However, automation on its own will not be sufficient to achieve long-term economic growth aspirations across the world. For that, additional productivity-boosting measures will be needed, including reworking business processes or developing new products, services, and business models.
How could automation play out among countries? We have divided our 46 focus nations into three groups, each of which could use automation to further national economic growth objectives, depending on its demographic trends and growth aspirations. The three groups are:
- Advanced economies. These include Australia, Canada, France, Germany, Italy, Japan, South Korea, the United Kingdom, and the United States. They typically face an aging workforce, though the decline in working-age population growth is more immediate in some (Germany, Italy, and Japan) than in others. Automation can provide the productivity boost required to meet economic growth projections that they otherwise would struggle to attain. These economies thus have a major interest in pursuing rapid automation development and adoption.
- Emerging economies with aging populations. This category includes Argentina, Brazil, China, and Russia, which face economic growth gaps as a result of projected declines in the growth of their working population. For these economies, automation can provide the productivity injection needed to maintain current GDP per capita. To achieve a faster growth trajectory that is more commensurate with their developmental aspirations, these countries would need to supplement automation with additional sources of productivity, such as process transformations, and would benefit from rapid adoption of automation.
- Emerging economies with younger populations. These include India, Indonesia, Mexico, Nigeria, Saudi Arabia, South Africa, and Turkey. The continued growth of the working-age population in these countries could support maintaining current GDP per capita. However, given their high growth aspirations, and in order to remain competitive globally, automation plus additional productivity-raising measures will be necessary to sustain their economic development.
For all the differences between countries, many of automation’s challenges are universal. For business, the performance benefits are relatively clear, but the issues are more complicated for policy makers. They will need to find ways to embrace the opportunity for their economies to benefit from the productivity growth potential that automation offers, putting in place policies to encourage investment and market incentives to encourage innovation. At the same time, all countries will need to evolve and create policies that help workers and institutions adapt to the impact on employment.