Where machines could replace humans—and where they can’t (yet)
The technical potential for automation
differs dramatically across sectors and activities.
As automation technologies such as machine learning and robotics play an increasingly
great role in everyday life, their potential effect on the workplace has,
unsurprisingly, become a major focus of research and public concern. The
discussion tends toward a Manichean guessing game: which jobs will or won’t be
replaced by machines?
In fact, as our research has begun to
show, the story is more nuanced. While automation will eliminate very few
occupations entirely in the next decade, it will affect portions of almost all
jobs to a greater or lesser degree, depending on the type of work they entail.
Automation, now going beyond routine manufacturing activities, has the
potential, as least with regard to its technical feasibility, to transform
sectors such as healthcare and finance, which involve a substantial share of
knowledge work.
These conclusions rest on our detailed
analysis of 2,000-plus work activities for more than 800 occupations. Using
data from the US Bureau of Labor Statistics and O*Net, we’ve quantified both
the amount of time spent on these activities across the economy of the United
States and the technical feasibility of automating each of them. The full
results, forthcoming in early 2017, will include several other countries, but
we released some initial findings late last year and are following up now with
additional interim results.
Last year, we showed that currently
demonstrated technologies could automate 45 percent of the activities people
are paid to perform and that about 60 percent of all occupations could see 30
percent or more of their constituent activities automated, again with
technologies available today. In this article, we examine the technical
feasibility, using currently demonstrated technologies, of automating three
groups of occupational activities: those that are highly susceptible, less
susceptible, and least susceptible to automation. Within each category, we
discuss the sectors and occupations where robots and other machines are
most—and least—likely to serve as substitutes in activities humans currently
perform. Toward the end of this article, we discuss how evolving technologies,
such as natural-language generation, could change the outlook, as well as some
implications for senior executives who lead increasingly automated enterprises.
Understanding
automation potential
In discussing automation, we refer to the
potential that a given activity could be automated by adopting currently
demonstrated technologies, that is to say, whether or not the automation of
that activity is technically feasible. Each whole occupation is
made up of multiple types of activities, each with varying degrees of technical
feasibility. There are seven top-level groupings of activities we have
identified. Occupations in retailing, for example, involve activities such as
collecting or processing data, interacting with customers, and setting up
merchandise displays (which we classify as physical movement in a predictable
environment). Since all of these constituent activities have a different
automation potential, we arrive at an overall estimate for the sector by
examining the time workers spend on each of them during the workweek.
Technical feasibility is a necessary
precondition for automation, but not a complete predictor that an activity will
be automated. A second factor to consider is the cost of developing and
deploying both the hardware and the software for automation. The cost of labor
and related supply-and-demand dynamics represent a third factor: if workers are
in abundant supply and significantly less expensive than automation, this could
be a decisive argument against it. A fourth factor to consider is the benefits
beyond labor substitution, including higher levels of output, better quality,
and fewer errors. These are often larger than those of reducing labor costs.
Regulatory and social-acceptance issues, such as the degree to which machines
are acceptable in any particular setting, must also be weighed. A robot may, in
theory, be able to replace some of the functions of a nurse, for example. But
for now, the prospect that this might actually happen in a highly visible way
could prove unpalatable for many patients, who expect human contact. The
potential for automation to take hold in a sector or occupation reflects a
subtle interplay between these factors and the trade-offs among them.
Even when machines do take over some human
activities in an occupation, this does not necessarily spell the end of the
jobs in that line of work. On the contrary, their number at times increases in
occupations that have been partly automated, because overall demand for their
remaining activities has continued to grow. For example, the large-scale
deployment of bar-code scanners and associated point-of-sale systems in the
United States in the 1980s reduced labor costs per store by an estimated 4.5
percent and the cost of the groceries consumers bought by 1.4 percent.3It
also enabled a number of innovations, including increased promotions. But
cashiers were still needed; in fact, their employment grew at an average rate
of more than 2 percent between 1980 and 2013.
The
most automatable activities
Almost one-fifth of the time spent in US
workplaces involves performing physical activities or operating machinery in a
predictable environment: workers carry out specific actions in well-known
settings where changes are relatively easy to anticipate. Through the adaptation
and adoption of currently available technologies, we estimate the technical
feasibility of automating such activities at 78 percent, the highest of our
seven top-level categories. Since predictable physical activities figure
prominently in sectors such as manufacturing, food service and accommodations,
and retailing, these are the most susceptible to automation based on technical
considerations alone.
In manufacturing, for example, performing
physical activities or operating machinery in a predictable environment
represents one-third of the workers’ overall time. The activities range from
packaging products to loading materials on production equipment to welding to
maintaining equipment. Because of the prevalence of such predictable physical
work, some 59 percent of all manufacturing activities could be automated, given
technical considerations. The overall technical feasibility, however, masks
considerable variance. Within manufacturing, 90 percent of what welders,
cutters, solderers, and brazers do, for example, has the technical potential
for automation, but for customer-service representatives that feasibility is
below 30 percent. The potential varies among companies as well. Our work with
manufacturers reveals a wide range of adoption levels—from companies with
inconsistent or little use of automation all the way to quite sophisticated
users.
Manufacturing, for all its technical
potential, is only the second most readily automatable sector in the US
economy. A service sector occupies the top spot: accommodations and food
service, where almost half of all labor time involves predictable physical
activities and the operation of machinery—including preparing, cooking, or
serving food; cleaning food-preparation areas; preparing hot and cold beverages;
and collecting dirty dishes. According to our analysis, 73 percent of the
activities workers perform in food service and accommodations have the
potential for automation, based on technical considerations.
Some of this potential is familiar.
Automats, or automated cafeterias, for example, have long been in use. Now
restaurants are testing new, more sophisticated concepts, like self-service
ordering or even robotic servers. Solutions such as Momentum Machines’
hamburger-cooking robot, which can reportedly assemble and cook 360 burgers an
hour, could automate a number of cooking and food-preparation activities. But
while the technical potential for automating them might be high, the business
case must take into account both the benefits and the costs of automation, as
well as the labor-supply dynamics discussed earlier. For some of these
activities, current wage rates are among the lowest in the United States,
reflecting both the skills required and the size of the available labor supply.
Since restaurant employees who cook earn an average of about $10 an hour, a
business case based solely on reducing labor costs may be unconvincing.
Retailing is another sector with a high
technical potential for automation. We estimate that 53 percent of its
activities are automatable, though, as in manufacturing, much depends on the
specific occupation within the sector. Retailers can take advantage of
efficient, technology-driven stock management and logistics, for example.
Packaging objects for shipping and stocking merchandise are among the most
frequent physical activities in retailing, and they have a high technical
potential for automation. So do maintaining records of sales, gathering
customer or product information, and other data-collection activities. But
retailing also requires cognitive and social skills. Advising customers which
cuts of meat or what color shoes to buy requires judgment and emotional
intelligence. We calculate that 47 percent of a retail salesperson’s activities
have the technical potential to be automated—far less than the 86 percent
possible for the sector’s bookkeepers, accountants, and auditing clerks.
As we noted above, however, just because
an activity can be automated doesn’t mean that it will be—broader economic
factors are at play. The jobs of bookkeepers, accountants, and auditing clerks,
for example, require skills and training, so they are scarcer than basic cooks.
But the activities they perform cost less to automate, requiring mostly
software and a basic computer.
Considerations such as these have led to
an observed tendency for higher rates of automation for activities common in
some middle-skill jobs—for example, in data collection and data processing. As
automation advances in capability, jobs involving higher skills will probably
be automated at increasingly high rates.
Activities
and sectors in the middle range for automation
Across all occupations in the US economy,
one-third of the time spent in the workplace involves collecting and processing
data. Both activities have a technical potential for automation exceeding 60
percent. Long ago, many companies automated activities such as administering
procurement, processing payrolls, calculating material-resource needs,
generating invoices, and using bar codes to track flows of materials. But as
technology progresses, computers are helping to increase the scale and quality
of these activities. For example, a number of companies now offer solutions
that automate entering paper and PDF invoices into computer systems or even
processing loan applications. And it’s not just entry-level workers or low-wage
clerks who collect and process data; people whose annual incomes exceed
$200,000 spend some 31 percent of their time doing those things, as well.
Financial services and insurance provide
one example of this phenomenon. The world of finance relies on professional
expertise: stock traders and investment bankers live off their wits. Yet about
50 percent of the overall time of the workforce in finance and insurance is
devoted to collecting and processing data, where the technical potential for
automation is high. Insurance sales agents gather customer or product
information and underwriters verify the accuracy of records. Securities and
financial sales agents prepare sales or other contracts. Bank tellers verify
the accuracy of financial data.
As a result, the financial sector has the
technical potential to automate activities taking up 43 percent of its workers’
time. Once again, the potential is far higher for some occupations than for
others. For example, we estimate that mortgage brokers spend as much as 90
percent of their time processing applications. Putting in place more
sophisticated verification processes for documents and credit applications
could reduce that proportion to just more than 60 percent. This would free up
mortgage advisers to focus more of their time on advising clients rather than
routine processing. Both the customer and the mortgage institution get greater
value.
Other activities in the middle range of
the technical potential for automation involve large amounts of physical
activity or the operation of machinery in unpredictable environments.
These types of activities make up a high proportion of the work in sectors such
as farming, forestry, and construction and can be found in many other sectors
as well.
Examples include operating a crane on a
construction site, providing medical care as a first responder, collecting
trash in public areas, setting up classroom materials and equipment, and making
beds in hotel rooms. The latter two activities are unpredictable largely
because the environment keeps changing. Schoolchildren leave bags, books, and
coats in a seemingly random manner. Likewise, in a hotel room, different guests
throw pillows in different places, may or may not leave clothing on their beds,
and clutter up the floor space in different ways.
These activities, requiring greater
flexibility than those in a predictable environment, are for now more difficult
to automate with currently demonstrated technologies: their automation
potential is 25 percent. Should technology advance to handle unpredictable
environments with the same ease as predictable ones, the potential for
automation would jump to 67 percent. Already, some activities in less
predictable settings in farming and construction (such as evaluating the
quality of crops, measuring materials, or translating blueprints into work
requirements) are more susceptible to automation.
Activities
with low technical potential for automation
The hardest activities to automate with
currently available technologies are those that involve managing and developing
people (9 percent automation potential) or that apply expertise to decision
making, planning, or creative work (18 percent). These activities, often
characterized as knowledge work, can be as varied as coding software, creating
menus, or writing promotional materials. For now, computers do an excellent job
with very well-defined activities, such as optimizing trucking routes, but
humans still need to determine the proper goals, interpret results, or provide
commonsense checks for solutions. The importance of human interaction is
evident in two sectors that, so far, have a relatively low technical potential
for automation: healthcare and education.
Overall, healthcare has a technical
potential for automation of about 36 percent, but the potential is lower for
health professionals whose daily activities require expertise and direct
contact with patients. For example, we estimate that less than 30 percent of a
registered nurse’s activities could be automated, based on technical
considerations alone. For dental hygienists, that proportion drops to 13
percent.
Nonetheless, some healthcare activities,
including preparing food in hospitals and administering non-intravenous
medications, could be automated if currently demonstrated technologies were
adapted. Data collection, which also accounts for a significant amount of
working time in the sector, could become more automated as well. Nursing
assistants, for example, spend about two-thirds of their time collecting health
information. Even some of the more complex activities that doctors perform,
such as administering anesthesia during simple procedures or reading
radiological scans, have the technical potential for automation.
Of all the sectors we have examined, the
technical feasibility of automation is lowest in education, at least for now.
To be sure, digital technology is transforming the field, as can be seen from
the myriad classes and learning vehicles available online. Yet the essence of
teaching is deep expertise and complex interactions with other people.
Together, those two categories—the least automatable of the seven identified in
the first exhibit—account for about one-half of the activities in the education
sector.
Even so, 27 percent of the activities in
education—primarily those that happen outside the classroom or on the
sidelines—have the potential to be automated with demonstrated technologies.
Janitors and cleaners, for example, clean and monitor building premises. Cooks
prepare and serve school food. Administrative assistants maintain inventory
records and personnel information. The automation of these data-collection and
processing activities may help to reduce the growth of the administrative
expenses of education and to lower its cost without affecting its quality.
Looking
ahead
As technology develops, robotics and
machine learning will make greater inroads into activities that today have only
a low technical potential for automation. New techniques, for example, are
enabling safer and more enhanced physical collaboration between robots and
humans in what are now considered unpredictable environments. These
developments could enable the automation of more activities in sectors such as
construction. Artificial intelligence can be used to design components in
engineer-heavy sectors.
One of the biggest technological breakthroughs
would come if machines were to develop an understanding of natural language on
par with median human performance—that is, if computers gained the ability to
recognize the concepts in everyday communication between people. In retailing,
such natural-language advances would increase the technical potential for
automation from 53 percent of all labor time to 60 percent. In finance and
insurance, the leap would be even greater, to 66 percent, from 43 percent. In
healthcare, too, while we don’t believe currently demonstrated technologies
could accomplish all of the activities needed to diagnose and treat patients,
technology will become more capable over time. Robots may not be cleaning your
teeth or teaching your children quite yet, but that doesn’t mean they won’t in
the future.
As stated at the outset, though, simply
considering the technical potential for automation is not enough to assess how
much of it will occur in particular activities. The actual level will reflect
the interplay of the technical potential, the benefits and costs (or the
business case), the supply-and-demand dynamics of labor, and various regulatory
and social factors related to acceptability.
Leading
more automated enterprises
Automation could transform the workplace
for everyone, including senior management. The rapid evolution of technology
can make harnessing its potential and avoiding its pitfalls especially complex.
In some industries, such as retailing, automation is already changing the
nature of competition. E-commerce players, for example, compete with
traditional retailers by using both physical automation (such as robots in
warehouses) and the automation of knowledge work (including algorithms that
alert shoppers to items they may want to buy). In mining, autonomous haulage
systems that transport ore inside mines more safely and efficiently than human
operators do could also deliver a step change in productivity.
Top executives will first and foremost
need to identify where automation could transform their own organizations and
then put a plan in place to migrate to new business processes enabled by
automation. A heat map of potential automation activities within companies can
help to guide, identify, and prioritize the potential processes and activities
that could be transformed. As we have noted, the key question will be where and
how to unlock value, given the cost of replacing human labor with machines. The
majority of the benefits may come not from reducing labor costs but from
raising productivity through fewer errors, higher output, and improved quality,
safety, and speed.
It is never too early to prepare for the
future. To get ready for automation’s advances tomorrow, executives must
challenge themselves to understand the data and automation technologies on the
horizon today. But more than data and technological savvy are required to
capture value from automation. The greater challenges are the workforce and
organizational changes that leaders will have to put in place as automation
upends entire business processes, as well as the culture of organizations,
which must learn to view automation as a reliable productivity lever. Senior
leaders, for their part, will need to “let go” in ways that run counter to a
century of organizational development.5
Understanding the activities that are most susceptible
to automation from a technical perspective could provide a unique opportunity
to rethink how workers engage with their jobs and how digital labor platforms
can better connect individuals, teams, and projects.6It
could also inspire top managers to think about how many of their own activities
could be better and more efficiently executed by machines, freeing up executive
time to focus on the core competencies that no robot or algorithm can
replace—as yet.
By Michael Chui, James Manyika, and Mehdi Miremadi
http://www.mckinsey.com/business-functions/business-technology/our-insights/where-machines-could-replace-humans-and-where-they-cant-yet?cid=other-eml-alt-mkq-mck-oth-1607
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