What’s now and next
in analytics, AI, and automation
·
Innovations
in digitization, analytics, artificial intelligence, and automation are
creating performance and productivity opportunities for business and the
economy, even as they reshape employment and the future of work.
Rapid technological
advances in digitization and data and analytics have been reshaping the
business landscape, supercharging performance, and enabling the emergence of
new business innovations and new forms of competition. At the same time, the
technology itself continues to evolve, bringing new waves of advances in
robotics, analytics, and artificial intelligence (AI), and especially machine
learning. Together they amount to a step change in technical capabilities that
could have profound implications for business, for the economy, and more
broadly, for society.
Table of contents
1. The opportunity
available now
2. The next wave of
opportunity
3. What about
employment and work?
4. What should
leaders do?
The opportunity available now
Some companies are
gaining a competitive edge with their use of data and analytics,
which can enable faster and larger-scale evidence-based decision making,
insight generation, and process optimization. But there is room to catch up and
to excel. Harnessing digitization’s potential is similarly uneven.
Data and analytics are transformational, yet many
companies are capturing only a fraction of their value
Data and analytics have
been changing the basis of competition in the years since our first report on big data in 2011. Leading companies are using their
capabilities not only to improve their core operations but also to launch
entirely new business models. The network effects of digital platforms are
creating a winner-take-most dynamic in some markets. Yet while the volume of
available data has grown exponentially in recent years, most companies are
capturing only a fraction of the potential value in terms of revenue and profit
gains.
Effective data and
analytics transformations have several components:
·
Asking fundamental questions to shape the
strategic vision: What will data and analytics be used for? How will the
insights drive value? Which data sets are most useful for the insights needed?
·
Solving for the problems in the way data is
generated, collected, and organized. Many incumbents struggle to switch from
legacy data systems to a more nimble and flexible architecture that can get the
most out of big data and analytics. They may also need to digitize their
operations more fully in order to capture more data from their customer
interactions, supply chains, equipment, and internal processes.
·
Acquiring the skills needed to derive
insights from data; organizations may choose to add in-house capabilities or
outsource to specialists.
·
Changing business processes to incorporate
data insights into the actual workflow. This is a common stumbling block. It
requires getting the right data insights into the hands of decision makers—and
making sure that these executives and mid-level managers understand how to use
data-driven insights.
Putting all these
components in place is not easy. In a recent McKinsey survey of more than 500 executives representing companies
across the spectrum of industries, regions, and sizes, more than 85%
acknowledged that they were only somewhat effective at meeting goals they set
for their data and analytics initiatives.
Data and analytics are disrupting business models and
bringing performance benefits
Disruptive data-driven
models and capabilities are reshaping some industries, and could transform many more. Certain characteristics
of a given market open the door to disruption by those using new data-driven
approaches, including:
·
inefficient matching of supply and demand
·
prevalence of underutilized assets
·
dependence on large amounts of demographic
data when behavioral data is now available
·
human biases and errors in a data-rich
environment
In industries where
most incumbents have become used to relying on a certain kind of standardized
data to make decisions, bringing in fresh types of data sets (“orthogonal
data”) to supplement those already in use can change the basis of competition.
We see this playing out for example in property and casualty insurance, where new companies have entered the marketplace with
telematics data that provides insight into driving behavior, beyond the
demographic data that had previously been used for underwriting.
One of the most
powerful uses is micro-segmentation based on behavioral characteristics of
individuals. This is changing the fundamentals of competition in many sectors,
including education, travel and leisure, media, retail, and advertising.
Digitization, more broadly, is also progressing unevenly
among companies, sectors, and economies
The corporate world’s
broader embrace of digitization is similarly uneven. Our use of the term
digitization (and our measurement of it), encompasses:
·
Assets, including infrastructure, connected
machines, data, and data platforms, etc.,
·
Operations, including processes, payments and
business models, customer and supply chain interactions and
·
The workforce, including worker use of
digital tools, digitally-skilled workers, new digital jobs, and roles. In
measuring each of these various aspects of digitization, we find relatively
large disparities even
among big companies.
Our research finds that
companies with advanced digital capabilities across assets, operations, and
workforces grow revenue and market shares faster than peers. They improve
profit margins three times more rapidly than average and, more often than not,
have been the fastest innovators and the disruptors in their sectors—and in
some cases beyond them.
Many of these top
performers were “born digital,” but perhaps more impressive are the smaller set
of incumbent companies that have actively transformed themselves into digital
leaders and benefit doubly from their traditional strengths and their new
digital capabilities.
There are also disparities
between sectors in terms of degree of digitization:
·
In the United States, the information and
communications technology (ICT) sector, media, financial services, and
professional services are surging ahead, while utilities, mining, and
manufacturing, among others, are in the early stages of digitizing. In
labor-intensive industries such as retail and health care, substantial parts of
their large workforces do not use technology extensively.
·
This unevenness can also be observed across
countries; all have significant room to increase their digitization:
·
The US economy as a whole is reaching only
18% of its digital potential;
·
France has achieved 12% of its digital
potential, the European Union
average, while Germany and Italy are at 10%;
·
Emerging economies are even further behind,
with countries in the Middle East and Brazil capturing less than 10% of their
digital potential.
Digitization is transforming globalization, creating
opportunities now for companies and economies
The world is more
connected than ever, but the nature of its connections has changed in a
fundamental way. The amount of cross-border data flows has grown 45 times larger since just 2005. It is
projected to increase by an additional nine times over the next five years as
flows of information, searches, communication, video, transactions, and
intracompany traffic continue to surge.
In addition to
transmitting valuable streams of information and ideas in their own right, data
flows enable the movement of goods, services, finance, and people. Virtually
every type of cross-border transaction now has a digital component.
Approximately 12% of
the global goods trade is conducted via international e-commerce, with much of
it driven by platforms such as Alibaba, Amazon, eBay, Flipkart, and Rakuten.
Beyond e-commerce, digital platforms for both traditional employment and freelance
assignments are beginning to
create a more global labor market. Some 50% of the world’s traded services are
already digitized. These transformations enable small and medium-sized
enterprises around the world to compete head to head with larger industry incumbents.
The next wave of opportunity
Coming over the horizon
is a new wave of opportunity related to the use of robotics, machine learning,
and AI. Companies that deploy automation technologies can realize substantial
performance gains and take the lead in their industries, even as their efforts
contribute to economy-level increases in productivity.
Advances in robotics, AI, and machine learning herald a
new era of breakthrough innovation and opportunity
Recent advances in robotics, machine learning, and AI are pushing the frontier of
what machines are capable of doing in all facets of business and the economy.
Physical robots have
been around for a long time in manufacturing, but more capable, more flexible,
safer, and less expensive robots are now engaging in ever expanding activities
and combining both mechanization, cognitive and learning capabilities—and
improving over time as they are trained by their human coworkers on the shop
floor, or increasingly learn by themselves.
The idea of AI is not
new, but the pace of recent breakthroughs is. Three factors are driving this
acceleration:
·
Machine-learning algorithms have progressed
in recent years, especially through the development of deep learning and
reinforcement-learning techniques based on neural networks.
·
Computing capacity has become available to
train larger and more complex models much faster. Graphics processing units
(GPUs), originally designed to render the computer graphics in video games,
have been repurposed to execute the data and algorithm crunching required for
machine learning at speeds many times faster than traditional processor chips.
More silicon-level advances beyond the current generation of GPUs are already
emerging, such as Tensor Units. This compute capacity has been aggregated
in hyper-scalable data centers and is accessible to users through the cloud.
·
Massive amounts of data that can be used to
train machine learning models are being generated, for example through daily
creation of billions of images, online click streams, voice and video, mobile
locations, and sensors embedded in the Internet of
Things.
The combination of
these breakthroughs has led to spectacular demonstrations like DeepMind’s
AlphaGo, which defeated a human champion of the complex board game
Go in March 2016.
Google’s DeepMind and the University of Oxford applied deep learning to a huge
data set of BBC programs in 2016 to create a lip-reading system that is more accurate than a professional lip
reader.
Formidable
technological challenges must still be overcome before machines can match human
performance across the range of cognitive activities. One of the biggest
technical challenges is for machines to acquire the capability to understand
and generate natural language—capabilities that are indispensable for a
multitude of work activities. Digital personal assistants such as Apple’s Siri,
Amazon’s Alexa, and Google Assistant, are still in development—and often
imperfect—even though their progress is palpable for millions of smartphone
users.
Harnessing these evolving technologies will unlock
multiple benefits for companies
For companies,
successful adoption of these evolving technologies will significantly enhance
performance. Some of the gains will come from labor substitution, but
automation also has the potential to enhance productivity, raise throughput,
improve predictions, outcomes, accuracy, and optimization, as well expand the
discovery of new solutions in massively complex areas such as synthetic biology
and material science.
Already today, a range
of automation technologies is generating real value. For example:
·
Rio Tinto has deployed automated
haul trucks and drilling machines at its mines in
Pilbara, Australia, and says it is seeing 10–20% increases in utilization
there.
·
Google has applied artificial intelligence
from its DeepMind machine learning to its own data centers, cutting
the amount of energy they use by 40%.
·
In financial services, automation in the form
of “straight-through processing,” where transaction workflows are digitized
end-to-end, can increase the scalability of transaction throughput by 80%,
while reducing errors by half.
Furthermore, a plethora
of machine learning business use cases are emerging across sectors .
Scenarios we developed for several settings, including a hospital emergency
department, aircraft maintenance, oil and gas operations, a grocery store, and
mortgage brokering, show that the value of the potential benefits of
automation—calculated as a percentage of operating costs—could range from
between 10–15% for a hospital emergency department to 25% for aircraft
maintenance, and to more than 90% for mortgage origination.
AI and Automation will provide a much-needed boost to
global productivity and may help some ‘moonshot’ challenges
The application of AI
and the automation of activities can enable productivity growth and other
benefits not just for businesses, but also for entire economies. At a
macroeconomic level, based on our scenario modeling, we estimate automation
alone could raise productivity growth on a global basis by 0.8% to 1.4%
annually.
AI and other
technologies can also be broadly beneficial for society by helping tackle some
“moonshot” challenges, including climate change or curing disease. AI is
already being deployed in synthetic biology, cancer research, climate science,
and material science. For example, researchers at McMaster and Vanderbilt University have used computers to exceed
the human standard in predicting the most effective treatment for major
depressive disorders and eventual outcomes of breast cancer patients.
What about employment and work?
The advent of a new
automation age is raising public concerns about the effect on employment and
the future of work. For most occupations, partial automation is more likely
than full automation in the medium term, and the technologies will provide new
opportunities for job creation.
About half the activities carried out by workers today
have the potential to be automated
To assess the employment
implications of automation, we focused on work activities rather than whole
occupations as a starting point. We consider work activities to be a useful
measure since occupations are aggregations of different activities, where each
discrete activity has a different potential for automation. For example, a
retail salesperson will spend some time interacting with customers, stocking
shelves, or ringing up sales.
Activities that are
more easily automatable include physical activities in highly predictable and
structured environments, as well as data collection and data processing. These
activities exist across the entire spectrum of sectors, as this data visualization of the automation potential of individual sectors
shows.
Our analysis of the automation potential extends to 46 countries
representing about 80% of the global workforce. Overall, we estimate that about
half of the activities that people are paid almost $15 trillion to do in the
global economy have the potential to be automated by adapting currently
demonstrated technology. This data visualization of global automation potential shows sizable differences
between countries, based mainly on the structure of their economies, the
relative level of wages, and the size and dynamics of the workforce.
All occupations will be
affected. Only a small proportion of all occupations, about 5%, consist of 100%
of activities that are fully automatable using currently demonstrated technologies. However, we find that about 30%
of the activities in 60% of all occupations could be automated. This means that
many workers will work alongside rapidly evolving machines, which will require
worker skills also evolve. This rapid evolution in the nature of work will
affect everyone from welders to landscape gardeners, mortgage brokers—and CEOs;
we estimate about 25% of CEOs’ time is currently spent on activities that
machines could do, such as analyzing reports and data to inform decisions.
Our
interactive data
visualization of global automation potential shows
sizable differences between countries.
Several key factors
will influence the pace and extent of automation. These include:
·
Technical feasibility of automation, a critical
first step that will depend on sustained breakthrough innovation, but alone is
not sufficient;
·
Cost of developing and deploying solutions;
·
Labor market dynamics, including supply and
demand, and costs of human labor as an alternative to automation;
·
Business and economic benefits, not merely
labor substitution benefits but also benefits from new capabilities that go
beyond human capabilities;
·
Regulatory, user and social acceptance, which
can affect the rate of adoption even when deployment makes business and
economic sense.
A useful analogy to
consider is that electric vehicles were demonstrated to be technically feasible
several decades ago, but it was not until some of these other factors became
realistic that they showed up on the road.
Technology will also help create new jobs and new
opportunities for generating income, and will help labor markets function
better
The scale of shifts in
the labor force over many decades that automation technologies will likely
unleash is of a similar order of magnitude to the long-term technology-enabled
shifts in the developed countries’ workforces as they moved most workers from
farms to factories and service jobs. Those shifts did not result in long-term
mass unemployment because they were accompanied by the creation of new types of
work not foreseen at the time. We cannot definitively say whether historical
precedent will be repeated this time. But our analysis shows that humans will
still be needed in the workforce.
So even while
technologies replace some jobs, they are creating new work in industries that
most of us cannot even imagine, as well as new ways to generate income and
match talent to jobs.
One third of new jobs
created in the United States in the past 25 years were types that did not
previously exist, or barely existed, in areas including IT development,
hardware manufacturing, app creation, and IT systems management. The growing
role of big data in the economy and business will create a significant need for
statisticians and data analysts, for example; we estimate a shortfall of up to
250,000 data scientists in the US in a decade.
Technology helps work
in other ways. Digital talent platforms such as LinkedIn have already begun to improve
matching of workers with jobs, creating transparency and efficiency in labor
markets, and thereby raising GDP. While it is early days, there is already
evidence that such platforms can raise labor participation and working hours.
While independent work
is nothing new (and self-employment is still the predominant form of work in
emerging economies), the digital enablement of it is. Our research finds that 20% to
30% of the working age population in the US and the European Union is engaged
in independent work. Just over half of these workers supplement their income
and have traditional jobs, or are students, retirees, or caregivers. While 70%
choose this type of work, 30% turn to it out of necessity because they cannot
find a traditional job at all, or one that meets their income and flexibility
needs. The proportion of independent work that is conducted on digital
platforms, while only about 15% of independent work overall, is growing
rapidly, driven by the scale, efficiency, and ease of use for workers and
customers that these platforms enable.
Those who pursue
independent work (digitally enabled or not) out of preference are generally
satisfied, although those who pursue it out of necessity are unsatisfied with
the income variability and the lack of benefits typically associated with
traditional work.
What should leaders do?
Business leaders and
policy makers have an imperative to find ways to harness the potential of these
technologies, even as they will have to address the significant challenges.
Business leaders
For businesses, the
opportunities are clear. Leaders should embrace the transformation and
performance opportunities already available to them (and their competitors)
from data, analytics, and digitization, as well as the rapidly evolving
opportunities in AI, robotics, and automation. To harness these benefits, business
leaders will not only have to invest in technology, but also in transforming
their organizations. Specific approaches will vary business by business,
however several new mindsets will be critical:
·
Testing, experimenting,
learning, and scaling fast: Beyond book
knowledge, business leaders will need to amass practical knowledge from
devoting resources to experiments applying technologies to real problems, and
then scaling those that show promise.
·
Reimagining business models
and business processes: To make full use of
the power of analytics, AI, and other digital technologies will require a
thorough reimagining of processes, with priorities for which processes to
transform. Similarly, leaders will need to reimagine how current business
models could be transformed and how new business models could be created based
on these capabilities.
·
Digital assets and
capabilities as the “new balance sheet”: These
assets and capabilities, both hard and soft, are increasingly becoming a
competitive differentiator and platforms for innovation and disruption. Each
business regardless of industry and sector will likely need to assess how
distinctive its digital assets and capabilities are vs. those of competitors.
·
Staying calibrated and
investing accordingly: When it comes to
digital capabilities and progress on digitization initiatives, all too often
business leaders are satisfied with progress vs. their own past. The most
relevant calibration will be relative to the scale of the opportunity and vs.
competitors and potential disruptors both from within their sectors and from
outside them.
·
A new focus on human
capital, including integrating workers and machines: Companies are likely to face gaps in skills they
need in a more technology-enabled workplace, and would benefit from playing a
more active role in education and training. Humans and machines will need to
work together much more closely. That will require retraining and often
redeploying workers.
Policy makers also have
a powerful incentive to embrace the productivity growth opportunity for their
economies that these technologies offer. This will help ensure future
prosperity, and create the surpluses that can be used to assist workers and
society adapt to these rapid changes. At the same time, policy makers must evolve
and innovate policies that help workers and institutions adapt to the impact on
employment:
·
Adopting policies to
encourage investment: Through tax benefits and other
incentives, policy makers can encourage companies to invest in human capital.
Policy makers could accelerate the creation of jobs in general through
stimulating investment, and accelerate creation of digital jobs in particular.
·
Encouraging new forms of
entrepreneurship and more rapid new business formation: Digitally enabled opportunities for individuals to
earn incomes. In addition, accelerating the rate of new business formation will
be critical. This will likely require simplifying regulations, creating tax and
other incentives.
·
Public–private partnerships
to stimulate infrastructure investment: The
lack of enabling digital infrastructure is holding back the digital benefits
for some emerging economies—and even underserved regions in developed
countries. Public–private partnerships could help address market failures.
·
Rethinking education,
training, and learning: Policy makers working
with education providers could do more to improve basic science, technology,
engineering, and math (STEM) skills through the school systems, and put a new
emphasis on creativity as well as critical and systems thinking.
·
Rethinking income support
and safety nets: If automation (full or partial) does
result in a significant reduction in employment and/or greater pressure on
wages, some ideas such as universal basic income, conditional transfers, and
adapted social safety nets may need to be considered and tested.
·
Incent investment in human
capital: A broad range of incentives exist for
businesses to make capital and R&D incentives. Something similar needed to
encourage investment in human capital.
http://www.mckinsey.com/global-themes/digital-disruption/whats-now-and-next-in-analytics-ai-and-automation?cid=other-eml-alt-mgi-mgi-oth-1705&hlkid=9a6016170fcb420f8a121a17c5a33c63&hctky=1627601&hdpid=8ce712f8-63e5-406f-bcdf-1cbc2c3f7d05
No comments:
Post a Comment