Four fundamentals of workplace automation
As the
automation of physical and knowledge work advances, many jobs will be redefined
rather than eliminated—at least in the short term.
The potential of artificial
intelligence and advanced robotics to
perform tasks once reserved for humans is no longer reserved for spectacular
demonstrations by the likes of IBM’s Watson, Rethink Robotics’ Baxter,
DeepMind, or Google’s driverless car. Just head to an airport: automated check-in
kiosks now dominate many airlines’ ticketing areas. Pilots actively steer
aircraft for just three to seven minutes of many flights, with autopilot
guiding the rest of the journey. Passport-control processes at some airports
can place more emphasis on scanning document bar codes than on observing
incoming passengers.
What will be the impact of
automation efforts like these, multiplied many times across different sectors
of the economy? Can we look forward to vast improvements in productivity,
freedom from boring work, and improved quality of life? Should we fear threats
to jobs, disruptions to organizations, and strains on the social fabric?
Earlier this year, we launched
research to explore these questions and investigate the potential that
automation technologies hold for jobs, organizations, and the future of
work.Our results to date suggest, first and foremost, that a focus on occupations is
misleading. Very few occupations will be automated in their entirety in the
near or medium term. Rather, certain activities are more
likely to be automated, requiring entire business processes to be transformed,
and jobs performed by people to be redefined, much like the bank teller’s job
was redefined with the advent of ATMs.
More specifically, our research
suggests that as many as 45 percent of the activities individuals are paid to
perform can be automated by adapting currently demonstrated technologies.4 In the United States, these activities represent
about $2 trillion in annual wages. Although we often think of automation
primarily affecting low-skill, low-wage roles, we discovered that even the
highest-paid occupations in the economy, such as financial managers,
physicians, and senior executives, including CEOs, have a significant amount of
activity that can be automated.
The organizational and leadership
implications are enormous: leaders from the C-suite to the front line will need
to redefine jobs and processes so that their organizations can take advantage
of the automation potential that is distributed across them. And the
opportunities extend far beyond labor savings. When we modeled the potential of
automation to transform business processes across several industries, we found
that the benefits (ranging from increased output to higher quality and improved
reliability, as well as the potential to perform some tasks at superhuman
levels) typically are between three and ten times the cost. The magnitude of
those benefits suggests that the ability to staff, manage, and lead
increasingly automated organizations will become an important competitive
differentiator.
Our research is ongoing, and in
2016, we will release a detailed report. What follows here are four interim
findings elaborating on the core insight that the road ahead is less about
automating individual jobs wholesale, than it is about automating the
activities within occupations and redefining roles and processes.
1.
The automation of activities
These preliminary findings are
based on data for the US labor market. We structured our analysis around roughly
2,000 individual work activities, and assessed the requirements for each
of these activities against 18 different capabilities that potentially could be
automated. Those capabilities range from fine motor skills and navigating in
the physical world, to sensing human emotion and producing natural language. We
then assessed the “automatability” of those capabilities through the use of
current, leading-edge technology, adjusting the level of capability required
for occupations where work occurs in unpredictable settings.
The bottom line is that 45
percent of work activities could be automated using already demonstrated
technology. If the technologies that process and “understand” natural language
were to reach the median level of human performance, an additional 13 percent
of work activities in the US economy could be automated. The magnitude of
automation potential reflects the speed with which advances in artificial
intelligence and its variants, such as machine learning, are challenging our
assumptions about what is automatable. It’s no longer the case that only
routine, codifiable activities are candidates for automation and that
activities requiring “tacit” knowledge or experience that is difficult to
translate into task specifications are immune to automation.
In many cases, automation
technology can already match, or even exceed, the median level of human
performance required. For instance, Narrative Science’s artificial-intelligence
system, Quill, analyzes raw data and generates natural language, writing
reports in seconds that readers would assume were written by a human author.
Amazon’s fleet of Kiva robots is equipped with automation technologies that
plan, navigate, and coordinate among individual robots to fulfill warehouse
orders roughly four times faster than the company’s previous system. IBM’s
Watson can suggest available treatments for specific ailments, drawing on the
body of medical research for those diseases.
2.
The redefinition of jobs and business processes
According to our analysis, fewer
than 5 percent of occupations can be entirely automated using current
technology. However, about 60 percent of occupations could have 30 percent or
more of their constituent activities automated. In other words, automation is
likely to change the vast majority of occupations—at least to some degree—which
will necessitate significant job redefinition and a transformation of business
processes. Mortgage-loan officers, for instance, will spend much less time
inspecting and processing rote paperwork and more time reviewing exceptions,
which will allow them to process more loans and spend more time advising
clients. Similarly, in a world where the diagnosis of many health issues could
be effectively automated, an emergency room could combine triage and diagnosis
and leave doctors to focus on the most acute or unusual cases while improving
accuracy for the most common issues.
As roles and processes get
redefined, the economic benefits of automation will extend far beyond labor
savings. Particularly in the highest-paid occupations, machines can augment
human capabilities to a high degree, and amplify the value of expertise by
increasing an individual’s work capacity and freeing the employee to focus on
work of higher value. Lawyers are already using text-mining techniques to read
through the thousands of documents collected during discovery, and to identify
the most relevant ones for deeper review by legal staff. Similarly, sales
organizations could use automation to generate leads and identify more likely
opportunities for cross-selling and upselling, increasing the time frontline
salespeople have for interacting with customers and improving the quality of
offers.
3.
The impact on high-wage occupations
Conventional wisdom suggests that
low-skill, low-wage activities on the front line are the ones most susceptible
to automation. We’re now able to scrutinize this view using the comprehensive
database of occupations we created as part of this research effort. It
encompasses not only occupations, work activities, capabilities, and their
automatability, but also the wages paid for each occupation.
Our work to date suggests that a
significant percentage of the activities performed by even those in the
highest-paid occupations (for example, financial planners, physicians, and
senior executives) can be automated by adapting current technology. For
example, we estimate that activities consuming more than 20 percent of a CEO’s
working time could be automated using current technologies. These include
analyzing reports and data to inform operational decisions, preparing staff
assignments, and reviewing status reports. Conversely, there are many
lower-wage occupations such as home health aides, landscapers, and maintenance
workers, where only a very small percentage of activities could be automated
with technology available today
4. The future of creativity and meaning
Capabilities such as creativity
and sensing emotions are core to the human experience and also difficult to
automate. The amount of time that workers spend on activities requiring these
capabilities, though, appears to be surprisingly low. Just 4 percent of the
work activities across the US economy require creativity at a median human
level of performance. Similarly, only 29 percent of work activities require a
median human level of performance in sensing emotion.
While these findings might be
lamented as reflecting the impoverished nature of our work lives, they also
suggest the potential to generate a greater amount of meaningful work. This
could occur as automation replaces more routine or repetitive tasks, allowing
employees to focus more on tasks that utilize creativity and emotion. Financial
advisors, for example, might spend less time analyzing clients’ financial
situations, and more time understanding their needs and explaining creative
options. Interior designers could spend less time taking measurements,
developing illustrations, and ordering materials, and more time developing
innovative design concepts based on clients’ desires.
These interim findings, emphasizing
the clarity brought by looking at automation through the lens of work
activities as opposed to jobs, are in no way intended to diminish the pressing
challenges and risks that must be understood and managed. Clearly,
organizations and governments will need new ways of mitigating the human costs,
including job losses and economic inequality, associated with the dislocation
that takes place as companies separate activities that can be automated from
the individuals who currently perform them. Other concerns center on privacy,
as automation increases the amount of data collected and dispersed. The quality
and safety risks arising from automated processes and offerings also are
largely undefined, while the legal and regulatory implications could be enormous.
To take one case: who is responsible if a driverless school bus has an
accident?
Nor do we yet have a definitive
perspective on the likely pace of transformation brought by workplace
automation. Critical factors include the speed with which automation technologies
are developed, adopted, and adapted, as well as the speed with which
organization leaders grapple with the tricky business of redefining processes
and roles. These factors may play out differently across industries. Those
where automation is mostly software based can expect to capture value much
faster and at a far lower cost. (The financial-services sector, where
technology can readily manage straight-through transactions and trade
processing, is a prime example.) On the other hand, businesses that are capital
or hardware intensive, or constrained by heavy safety regulation, will likely
see longer lags between initial investment and eventual benefits, and their
pace of automation may be slower as a result.
All this points to new
top-management imperatives: keep an eye on the speed and direction of
automation, for starters, and then determine where, when, and how much to
invest in automation. Making such determinations will require executives to
build their understanding of the economics of automation, the trade-offs
between augmenting versus replacing different types of activities with
intelligent machines, and the implications for human skill development in their
organizations. The degree to which executives embrace these priorities will
influence not only the pace of change within their companies, but also to what
extent those organizations sharpen or lose their competitive edge.
About the authors
Michael Chui is a principal at the McKinsey
Global Institute, where James Manyika is a director; Mehdi
Miremadi is a principal in McKinsey’s Chicago office.
The authors wish to thank McKinsey’s Rick Cavolo, Martin
Dewhurst, Katy George, Andrew Grant, Sean Kane, Bill Schaninger, Stefan Spang,
and Paul Willmott for their contributions to this article.
http://www.mckinsey.com/insights/business_technology/four_fundamentals_of_workplace_automation?cid=other-eml-nsl-mip-mck-oth-1512
No comments:
Post a Comment