AI, automation, and the future of
work: Ten things to solve for PART I
As machines
increasingly complement human labor in the workplace, we will all need to
adjust to reap the benefits.
Automation and artificial intelligence (AI) are transforming businesses and will contribute to
economic growth via contributions to productivity. They will also help address
“moonshot” societal challenges in areas from health to climate change.
At the same time, these
technologies will transform the nature of work and the workplace itself.
Machines will be able to carry out more of the tasks done by humans, complement
the work that humans do, and even perform some tasks that go beyond what humans
can do. As a result, some occupations will decline, others will grow, and many
more will change.
While we believe there
will be enough work to go around (barring extreme scenarios), society will need
to grapple with significant workforce transitions and dislocation. Workers will
need to acquire new skills and adapt to the increasingly capable machines
alongside them in the workplace. They may have to move from declining
occupations to growing and, in some cases, new occupations.
This executive
briefing, which draws on the latest research from the McKinsey Global
Institute, examines both the promise and the challenge of automation and AI in
the workplace and outlines some of the critical issues that policy makers,
companies, and individuals will need to solve for.
1.
Accelerating progress in AI and automation is creating opportunities for
businesses, the economy, and society
2.
How AI and automation will affect work
3.
Key workforce transitions and challenges
4.
Ten things to solve for
1. Accelerating progress in AI and automation is creating
opportunities for businesses, the economy, and society
Automation and AI are
not new, but recent technological progress is pushing the frontier of what
machines can do. Our research suggests that
society needs these improvements to provide value for businesses, contribute to
economic growth, and make once unimaginable progress on some of our most
difficult societal challenges. In summary:
Rapid technological progress
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.
Potential to transform businesses and
contribute to economic growth
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 latest generation of AI advances, including
techniques that address classification, estimation, and clustering problems,
promises significantly more value still. An analysis we conducted of several hundred AI use cases found that the most advanced
deep learning techniques deploying artificial neural networks could account for
as much as $3.5 trillion to $5.8 trillion in annual value, or 40 percent of the
value created by all analytics techniques.
Deployment of AI and
automation technologies can do much to lift the global economy and increase
global prosperity, at a time when aging and falling birth rates are acting as a
drag on growth. Labor productivity growth, a key driver of economic growth, has
slowed in many economies, dropping to an average of 0.5 percent in 2010–2014
from 2.4 percent a decade earlier in the United States and major European
economies, in the aftermath of the 2008 financial crisis after a previous
productivity boom had waned. AI and automation have the potential to reverse
that decline: productivity growthcould
potentially reach 2 percent annually over the next decade, with 60 percent of
this increase from digital opportunities.
Potential to help tackle several societal
moonshot challenges
AI is also being used
in areas ranging from material science to
medical research and climate science. Application of the technologies in these
and other disciplines could help tackle societal moonshot challenges. For
example, researchers at Geisinger have developed an algorithm that could reduce diagnostic times for intracranial hemorrhaging by up to 96 percent.
Researchers at George Washington University, meanwhile, are using machine
learning to more accurately weight the climate models used by the Intergovernmental Panel on Climate
Change.
Challenges remain before these technologies
can live up to their potential for the good of the economy and society everywhere
AI and automation still
face challenges. The limitations are partly technical, such as the need for
massive training data and difficulties “generalizing” algorithms across use
cases. Recent innovations are just starting to address these issues. Other
challenges are in the use of AI techniques. For example, explaining decisions
made by machine learning algorithms is technically challenging, which
particularly matters for use cases involving financial lending or legal
applications. Potential bias in the training data and algorithms, as well as
data privacy, malicious use, and security are all issues that must be addressed. Europe is leading with the new General Data Protection
Regulation, which codifies more rights for users over data collection and
usage.
A different sort of
challenge concerns the ability of organizations to adopt these technologies,
where people, data availability, technology, and process readiness often make
it difficult. Adoption is already uneven across sectors and countries. The finance,
automotive, and telecommunications sectors lead AI adoption. Among countries,
US investment in AI ranked first at $15 billion to $23 billion in 2016,
followed by Asia’s investments of $8 billion to $12 billion, with Europe
lagging behind at $3 billion to $4 billion.
2. How AI and automation will affect work
Even as AI and
automation bring benefits to business and society, we will need to prepare for
major disruptions to work.
About half of the activities (not jobs) carried
out by workers could be automated
Our analysis of more
than 2000 work activities across more than 800 occupations shows that certain
categories of activities are more easily automatable than others. They include
physical activities in highly predictable and structured environments, as well
as data collection and data processing. These account for roughly half of the
activities that people do across all sectors. The least susceptible categories
include managing others, providing expertise, and interfacing with
stakeholders.
Nearly all occupations
will be affected by automation, but only about 5 percent of occupations could
be fully automated by currently demonstrated technologies. Many more
occupations have portions of their constituent activities that are automatable:
we find that about 30 percent of the activities in 60 percent of all
occupations could be automated. This means that most workers—from welders to
mortgage brokers to CEOs—will work alongside rapidly evolving machines. The
nature of these occupations will likely change as a result.
Jobs lost: Some occupations will see
significant declines by 2030
Automation will
displace some workers. We have found that around 15 percent of the global workforce, or about 400 million workers,
could be displaced by automation in the period 2016–2030. This reflects our
midpoint scenario in projecting the pace and scope of adoption. Under the
fastest scenario we have modeled, that figure rises to 30 percent, or 800
million workers. In our slowest adoption scenario, only about 10 million people
would be displaced, close to zero percent of the global workforce.
The wide range
underscores the multiple factors that will impact the pace and scope of AI and
automation adoption. Technical feasibility of automation is only the first
influencing factor. Other factors include the cost of deployment; labor-market
dynamics, including labor-supply quantity, quality, and the associated wages;
the benefits beyond labor substitution that contribute to business cases for
adoption; and, finally, social norms and acceptance. Adoption will continue to
vary significantly across countries and sectors because of differences in the
above factors, especially labor-market dynamics: in advanced economies with
relatively high wage levels, such as France, Japan, and the United States,
automation could displace 20 to 25 percent of the workforce by 2030, in a
midpoint adoption scenario, more than double the rate in India.
Jobs gained: In the same period, jobs will
also be created
Even as workers are
displaced, there will be growth in demand for work and consequently jobs. We developed scenarios for labor demand to 2030 from several catalysts of demand for work,
including rising incomes, increased spending on healthcare, and continuing or
stepped-up investment in infrastructure, energy, and technology development and
deployment. These scenarios showed a range of additional labor demand of
between 21 percent to 33 percent of the global workforce (555 million and 890
million jobs) to 2030, more than offsetting the numbers of jobs lost. Some of
the largest gains will be in emerging economies such as India, where the
working-age population is already growing rapidly.
Additional economic
growth, including from business dynamism and rising productivity growth, will
also continue to create jobs. Many other new occupations that we cannot
currently imagine will also emerge and may account for as much as 10 percent of
jobs created by 2030, if history is a guide.
Moreover, technology itself has historically been a net job creator. For
example, the introduction of the personal computer in the 1970s and 1980s
created millions of jobs not just for semiconductor makers, but also for
software and app developers of all types, customer-service representatives, and
information analysts.
Jobs changed: More jobs than those lost or
gained will be changed as machines complement human labor in the workplace
Partial automation will
become more prevalent as machines complement human labor. For example, AI
algorithms that can read diagnostic scans with a high degree of accuracy will
help doctors diagnose patient cases and identify suitable treatment. In other
fields, jobs with repetitive tasks could shift toward a model of managing and
troubleshooting automated systems. At retailer Amazon, employees who previously lifted and stacked objects are becoming robot operators,
monitoring the automated arms and resolving issues such as an interruption in
the flow of objects.
CONTINUES IN PART II
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