The promise and
challenge of the age of artificial intelligence PART II
3.
Economies also stand to
benefit from AI, through increased productivity and innovation
Deployment of AI and automation technologies
can do much to lift the global economy and increase global prosperity. At a
time of aging and falling birth rates, productivity growth becomes critical for
long-term economic growth. Even in the near term, productivity growth has been sluggish in developed economies, dropping to an average of 0.5
percent in 2010–14 from 2.4 percent a decade earlier in the United States and
major European economies. Much like previous general-purpose technologies, AI
has the potential to contribute to productivity growth.
AI could contribute to economic impact
through a variety of channels
The largest economic
impacts of AI will likely be on productivity growth through labor market
effects including substitution, augmentation, and contributions to labor
productivity.
Our research suggests
that labor substitution could account for less than half of the total benefit.
AI will augment human capabilities, freeing up workers to engage in more
productive and higher-value tasks, and increase demand for jobs associated with
AI technologies.
AI can also boost
innovation, enabling companies to improve their top line by reaching
underserved markets more effectively with existing products, and over the
longer term, creating entirely new products and services. AI will also create
positive externalities, facilitating more efficient cross-border commerce and
enabling expanded use of valuable cross-border data flows. Such increases in
economic activity and incomes can be reinvested into the economy, contributing
to further growth.
The deployment of AI
will also bring some negative externalities that could lower, although not
eliminate, the positive economic impacts. On the economic front, these include
increased competition that shifts market share from nonadopters to
front-runners, the costs associated with managing labor market transitions, and
potential loss of consumption for citizens during periods of unemployment, as
well the transition and implementation costs of deploying AI systems.
All in all, these
various channels net out to significant positive economic growth, assuming
businesses and governments proactively manage the transition. One simulation we
conducted using McKinsey survey data suggests that AI adoption could raise global GDP by as much as $13 trillion by
2030, about 1.2 percent
additional GDP growth per year. This effect will build up only through time,
however, given that most of the implementation costs of AI may be ahead of the
revenue potential.
The AI readiness of countries varies
considerably
The leading enablers of
potential AI-driven economic growth, such as investment and research activity,
digital absorption, connectedness, and labor market structure and flexibility,
vary by country. Our research suggests that the ability to innovate and acquire
the necessary human capital skills will be among the most important
enablers—and that AI competitiveness will likely be an important factor
influencing future GDP growth.
Countries leading the
race to supply AI have unique strengths that set them apart. Scale effects
enable more significant investment, and network effects enable these economies
to attract the talent needed to make the most of AI. For now, China and the
United States are responsible for most AI-related research activities and
investment.
A second group of
countries that includes Germany, Japan, Canada, and the United Kingdom have a
history of driving innovation on a major scale and may accelerate the
commercialization of AI solutions. Smaller, globally connected economies such
as Belgium, Singapore, South Korea, and Sweden also score highly on their
ability to foster productive environments where novel business models thrive.
Countries in a third
group, including but not limited to Brazil, India, Italy, and Malaysia, are in
a relatively weaker starting position, but they exhibit comparative strengths
in specific areas on which they may be able to build. India, for instance,
produces around 1.7 million graduates a year with STEM degrees—more than the
total of STEM graduates produced by all G-7 countries. Other countries, with
relatively underdeveloped digital infrastructure, innovation and investment
capacity, and digital skills, risk falling behind their peers.
4.
AI and automation will have
a profound impact on work
Even as AI and
automation bring benefits to business and the economy, major disruptions to
work can be expected.
About half of current work activities (not
jobs) are technically automatable
Our analysis of
the impact of automation and AI on work shows that certain categories of activities are
technically more easily automatable than others. They include physical
activities in highly predictable and structured environments, as well as data
collection and data processing, which together account for roughly half of the
activities that people do across all sectors in most economies.
The least susceptible
categories include managing others, providing expertise, and interfacing with
stakeholders. The density of highly automatable activities varies across
occupations, sectors, and, to a lesser extent, countries. Our research finds
that about 30 percent of the activities in 60 percent of all occupations could
be automated—but that in only about 5 percent of occupations are nearly all
activities automatable. In other words, more occupations will be partially
automated than wholly automated.
Three simultaneous effects on work: Jobs
lost, jobs gained, jobs changed
The pace at and extent
to which automation will be adopted and impact actual jobs will depend on
several factors besides technical feasibility. Among these are the cost of
deployment and adoption, and the labor market dynamics, including labor supply
quantity, quality, and associated wages. The labor factor leads to wide
differences across developed and developing economies. The business benefits
beyond labor substitution—often involving use of AI for beyond-human
capabilities—which contribute to business cases for adoption are another factor.
Social norms, social
acceptance, and various regulatory factors will also determine the timing. How
all these factors play out across sectors and countries will vary, and for
countries will largely be driven by labor market dynamics. For example, in
advanced economies with relatively high wage levels, such as France, Japan, and
the United States, jobs affected by automation could be more than double that in India, as a percentage of the total.
Given the interplay of
all these factors, it is difficult to make predictions but possible to develop various
scenarios. First, on jobs lost:
our midpoint adoption scenario for 2016 to 2030 suggests that about 15 percent
of the global workforce (400 million workers) could be displaced by automation.
Second, jobs gained: we
developed scenarios for labor demand to 2030 based on anticipated economic
growth through productivity and by considering several drivers of demand for
work. These included rising incomes, especially in emerging economies, as well
as increased spending on healthcare for aging populations, investment in
infrastructure and buildings, energy transition spending, and spending on
technology development and deployment.
The number of jobs
gained through these and other catalysts could range from 555 million to 890
million, or 21 to 33 percent of the global workforce. This suggests that the
growth in demand for work, barring extreme scenarios, would more than offset
the number of jobs lost to automation. However, it is important to note that in
many emerging economies with young populations, there will already be a
challenging need to provide jobs to workers entering the workforce and that, in
developed economies, the approximate balance between jobs lost and those
created in our scenarios is also a consequence of aging, and thus fewer people
entering the workforce.
No less significant are
the jobs that will change as machines increasingly complement human labor in
the workplace. Jobs will change as a result of the partial automation described
above, and jobs changed will affect many more occupations than jobs lost.
Skills for workers complemented by machines, as well as work design, will need
to adapt to keep up with rapidly evolving and increasingly capable machines.
Four workforce transitions will be
significant
Even if there will be
enough work for people in 2030, as most of our scenarios suggest, the
transitions that will accompany automation and AI adoption will be significant.
First, millions of
workers will likely need to change occupations. Some of these shifts will
happen within companies and sectors, but many will occur across sectors and
even geographies. While occupations requiring physical activities in highly
structured environments and in data processing will decline, others that are
difficult to automate will grow. These could include managers, teachers,
nursing aides, and tech and other professionals, but also gardeners and
plumbers, who work in unpredictable physical environments. These changes may
not be smooth and could lead to temporary spikes in unemployment.
Second, workers will
need different skills to thrive in the workplace of the future. Demand for
social and emotional skills such as communication and empathy will grow almost
as fast as demand for many advanced technological skills. Basic digital skills have been increasing in all jobs. Automation will also spur growth
in the need for higher cognitive skills, particularly critical thinking,
creativity, and complex information processing. Demand for physical and manual
skills will decline, but these will remain the single largest category of
workforce skills in 2030 in many countries. The pace of skill shifts has been
accelerating, and it may lead to excess demand for some skills and excess
supply for others.
Third, workplaces and
workflows will change as more people work alongside machines. As self-checkout
machines are introduced in stores, for example, cashiers will shift from
scanning merchandise themselves to helping answer questions or troubleshoot the
machines.
Finally, automation
will likely put pressure on average wages in advanced economies. Many of the
current middle-wage jobs in advanced economies are dominated by highly
automatable activities, in fields such as manufacturing and accounting, which
are likely to decline. High-wage jobs will grow significantly, especially for
high-skill medical and tech or other professionals. However, a large portion of
jobs expected to be created, such as teachers and nursing aides, typically have
lower wage structures.
In tackling these
transitions, many economies, especially in the OECD, start in a hole, given the
existing skill shortages and challenged educational systems, as well as the
trends toward declining expenditures on on-the-job training and worker
transition support. Many economies are already experiencing income inequality
and wage polarization.
CONTINUES IN PART III
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