DRIVING IMPACT AT SCALE FROM AUTOMATION AND AI - FEBRUARY 2019 - MCKINSEY
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Driving impact at scale from automation and AI February 2019
Contents Introduction 2 Part 1: Why automation and AI? Harnessing automation for a future that works 6 Notes from the AI frontier: Applications and value of deep learning 10 Artificial intelligence is getting ready for business, but are businesses ready for AI? 26 Part 2: How to make transformation successful Burned by the bots: Why robotic automation is stumbling 44 Ten red flags signaling your analytics program will fail 48 The automation imperative 56 How to avoid the three common execution pitfalls that derail automation programs 64 The new frontier: Agile automation at scale 72 Part 3: Understanding functional nuances How bots, algorithms, and artificial intelligence are reshaping the future of corporate 78 support functions A CIO plan for becoming a leader in intelligent process automation 86
Introduction
Automation, leveraging artificial intelligence (AI) and other technologies, has
opened up new possibilities. The pace of adoption has been rapid. Institutions
of all sizes globally are leveraging automation to drive value. According to the
McKinsey Automation Survey in 2018, 57 percent of 1,300 institutions have
already started on this journey, with another 18 percent planning to kick off
something within the next year.
When done right, automation has proven to deliver real benefits, including the
following:
• Distinctive insights: Hundreds of new factors to predict and improve drivers
of performance
• Faster service: Processing time reduced from days to minutes
• Increased flexibility and scalability: Ability to operate 24/7 and scale up or
down with demand
• Improved quality: From spot-checking to 100 percent quality control
through greater traceability
• Increased savings and productivity: Labor savings of 20 percent or more
However, success is far from guaranteed. According to our Automation Survey,
only 55 percent of institutions believe their automation program has been
successful to date. Moreover, a little over half of respondents also say that the
program has been much harder to implement than they expected.
In this collection of articles, we explore why automation and AI are so
important, how to transform, and what the functional nuances are that can
be the difference between success and failure. At a high level, these articles delve into the four most
important practices that are strongly correlated with success in automation:
• Understand the opportunity and move early: Start taking advantage of automation and AI by
assessing the opportunity, identifying the high-impact use cases, and laying out the capability and
governance groundwork.
• Balance quick tactical wins with long-term vision: Identify quick wins to automate activities
with the highest automation potential and radiate out, freeing up capital; in parallel, have a long-
term vision for comprehensive transformation, with automation at the core.
• Redefine processes and manage organizational change: Since 60 percent of all jobs have at least
30 percent technically automatable activities, redefining jobs and taking an end-to-end process
view are necessary to capture the value.
• Integrate technology into core business functions: Build AI and other advanced technologies
into the operating model to create transformative impact and lasting value, support a culture
of collecting and analyzing data to inform decisions, and build the muscle for continuous
improvement.
We hope this curated collection will be helpful to you in realizing the full value potential from your
own automation transformation.
Alex Edlich Greg Phalin Rahil Jogani Sanjay Kaniyar
Senior partner, New York Senior partner, New York Partner, Chicago Partner, Boston
We wish to thank Keith Gilson, Vishal Koul, and Christina Yum for their contributions to this collection.
Introduction 3
01
Part
Why automation and AI?
4 Making a secure transition to cloud
Photo credit/Getty
Oli Scarff/Getty Images
Images News
Harnessing automation for a future
that works
Jacques Bughin, Michael Chui, Martin Dewhurst, Katy George, James Manyika, Mehdi
Miremadi, and Paul Willmott
Automation is happening, and it will bring substantial benefits to
businesses and economies worldwide, but it won’t arrive overnight.
A new McKinsey Global Institute report finds realizing automation’s
full potential requires people and technology to work hand in hand.
Recent developments in robotics, artificial and CEOs. But how quickly will these automation
intelligence, and machine learning have put us technologies become a reality in the workplace?
on the cusp of a new automation age. Robots and And what will their impact be on employment and
computers can not only perform a range of routine productivity in the global economy?
physical work activities better and more cheaply
than humans, but they are also increasingly The McKinsey Global Institute has been conducting
capable of accomplishing activities that include an ongoing research program on automation
cognitive capabilities once considered too difficult technologies and their potential effects. A new MGI
to automate successfully, such as making tacit report, A future that works: Automation, employment,
judgments, sensing emotion, or even driving. and productivity, highlights several key findings.
Automation will change the daily work activities
The automation of activities can enable businesses
of everyone, from miners and landscapers to
to improve performance by reducing errors
commercial bankers, fashion designers, welders,
6 Digital/McKinsey
and improving quality and speed, and in some Still, automation will not happen overnight. Even
cases achieving outcomes that go beyond human when the technical potential exists, we estimate it
capabilities. Automation also contributes to will take years for automation’s effect on current
productivity, as it has done historically. At a time work activities to play out fully. The pace of
of lackluster productivity growth, this would give automation, and thus its impact on workers, will
a needed boost to economic growth and prosperity. vary across different activities, occupations, and
It would also help offset the impact of a declining wage and skill levels. Factors that will determine
share of the working-age population in many the pace and extent of automation include the
countries. Based on our scenario modeling, we ongoing development of technological capabilities,
estimate automation could raise productivity the cost of technology, competition with labor
growth globally by 0.8 to 1.4 percent annually. including skills and supply and demand dynamics,
performance benefits including and beyond labor
The right level of detail at which to analyze the cost savings, and social and regulatory acceptance.
potential impact of automation is that of individual Our scenarios suggest that half of today’s work
activities rather than entire occupations. Every activities could be automated by 2055, but this
occupation includes multiple types of activity, each could happen up to 20 years earlier or later
of which has different requirements for automation. depending on various factors, in addition to other
Given currently demonstrated technologies, economic conditions.
very few occupations—less than 5 percent—are
candidates for full automation. However, almost The effects of automation might be slow at a macro
every occupation has partial automation potential, level, within entire sectors or economies, for
as a proportion of its activities could be automated. example, but they could be quite fast at a micro
We estimate that about half of all the activities level, for individual workers whose activities are
people are paid to do in the world’s workforce could automated or for companies whose industries are
potentially be automated by adapting currently disrupted by competitors using automation.
demonstrated technologies. That amounts to
almost $15 trillion in wages. While much of the current debate about
automation has focused on the potential for mass
The activities most susceptible to automation are unemployment, people will need to continue
physical ones in highly structured and predictable working alongside machines to produce the growth
environments, as well as data collection and in per capita GDP to which countries around the
processing. In the United States, these activities world aspire. Thus, our productivity estimates
make up 51 percent of activities in the economy, assume that people displaced by automation will
accounting for almost $2.7 trillion in wages. find other employment. Many workers will have
They are most prevalent in manufacturing, to change, and we expect business processes to be
accommodation and food service, and retail trade. transformed. However, the scale of shifts in the
And it’s not just low-skill, low-wage work that could labor force over many decades that automation
be automated; middle-skill and high-paying, high- technologies can unleash is not without precedent.
skill occupations, too, have a degree of automation It is of a similar order of magnitude to the long-
potential. As processes are transformed by the term technology-enabled shifts away from
automation of individual activities, people will agriculture in developed countries’ workforces
perform activities that complement the work that in the 20th century. Those shifts did not result
machines do, and vice versa. in long-term mass unemployment, because they
Harnessing automation for a future that works 7
were accompanied by the creation of new types of come about if people work alongside machines. That
work. We cannot definitively say whether things in turn will fundamentally alter the workplace,
will be different this time. But our analysis shows requiring a new degree of cooperation between
that humans will still be needed in the workforce: workers and technology.
the total productivity gains we estimate will only
Jacques Bughin and James Manyika are directors of the McKinsey Global Institute, and Michael Chui is
an MGI partner; Martin Dewhurst and Paul Willmott are senior partners in McKinsey’s London office; Katy
George is a senior partner in the New Jersey office; and Mehdi Miremadi is a partner in the Chicago office.
Copyright © 2017 McKinsey & Company. All rights reserved.
8 Digital/McKinseyA global force that will transform economies and the workforce
Technical automation potential by adapting currently demonstrated technologies
Wages associated with technically
automatable activities
While few occupations are fully automatable, 60 percent of all occupations $ trillion
have at least 30 percent technically automatable activities China
Remaining
Share of roles countries 4.7 3.6
ACTIVITIES WITH HIGHEST 100% = 820 roles
AUTOMATION POTENTIAL: About 60% of occupations 100% =
Predictable physical activities 81%
have at least 30% of 100 $14.6T 2.3
their activities that 91 1.0 United
Processing data 69% are automatable Japan States
1.1 1.9
Collecting data 64% 73 India
Big 5 in Europe1
62
Labor associated with technically
51 automatable activities
90 >80 >70 >60 >50 >40 >30 >20 >10 >0 62 United
235 States
India Big 5 in Europe1
Technical automation potential, %
1
France, Germany, Italy, Spain, and the United Kingdom.
Five factors affecting pace and Automation will boost global
extent of adoption productivity and raise GDP
G19 plus Nigeria
1 2 3 4 5
TECHNICAL COST OF LABOR ECONOMIC REGULATORY Productivity growth, %
FEASIBILITY DEVELOPING MARKET BENEFITS AND SOCIAL Automation can help provide some of the productivity needed
Technology AND DYNAMICS Include higher ACCEPTANCE to achieve future economic growth
has to be DEPLOYING The supply, throughput Even when
invented, SOLUTIONS demand, and and increased automation Employment growth, %
integrated, Hardware costs of quality, makes will slow drastically because of aging
and adapted and software human labor alongside business
into solutions costs affect which labor cost sense,
for specific activities will savings adoption can
case use be automated take time Last 50 years Next 50 years Next 50 years
Growth aspiration Potential impact of automation
Scenarios around time spent on current work activities, %
Adoption, Adoption, Technical automation Technical automation 1.8
Early scenario Late scenario potential, Early scenario potential, Late scenario
100 2.8
Technical
automation
80 potential 1.7 1.4
must precede 0.8
60 adoption
0.1 0.1 0.1
40 Technical, 3.5 2.9 1.5 0.9
economic,
and social Historical Required to Early Late
20 factors affect achieve scenario scenario
pace of projected growth
0 adoption
2020 2030 2040 2050 2060 2065 in GDP per capita
Harnessing automation for a future that works 9Notes from the AI frontier:
Applications and value of deep
learning
Michael Chui, Rita Chung, Nicolaus Henke, Sankalp Malhotra, James Manyika, Mehdi Miremadi,
and Pieter Nel
An analysis of more than 400 use cases across 19 industries
and nine business functions highlights the broad use and
significant economic potential of advanced AI techniques.
Artificial intelligence (AI) stands out as a and the problems they can solve to more
transformational technology of our digital than 400 specific use cases in companies and
age—and its practical application throughout organizations.1 Drawing on McKinsey Global
the economy is growing apace. In our discussion Institute research and the applied experience
paper Notes from the AI frontier: Insights from with AI of McKinsey Analytics, we assess both the
hundreds of use cases, we mapped both traditional practical applications and the economic potential
analytics and newer “deep learning” techniques of advanced AI techniques across industries and
1 For the full McKinsey Global Institute discussion paper, see “Notes from the AI frontier: Applications and value of deep
learning,” April 2018, on McKinsey.com.
10 Digital/McKinseybusiness functions. Our findings highlight the We analyzed the applications and value of three
substantial potential of applying deep learning neural network techniques:
techniques to use cases across the economy, but we
also see some continuing limitations and obstacles— Feed-forward neural networks: The
along with future opportunities as the technologies simplest type of artificial neural network.
continue their advance. Ultimately, the value of AI In this architecture, information moves
is not to be found in the models themselves, but in in only one direction, forward, from the
companies’ abilities to harness them. input layer, through the “hidden” layers, to
the output layer. There are no loops in the
It is important to highlight that, even as we see network. The first single-neuron network
economic potential in the use of AI techniques, the was proposed already in 1958 by AI pioneer
use of data must always take into account concerns Frank Rosenblatt. While the idea is not
including data security, privacy, and potential new, advances in computing power, training
issues of bias. algorithms, and available data led to higher
levels of performance than previously
Mapping AI techniques to problem possible.
types
As artificial intelligence technologies advance, so Recurrent neural networks (RNNs): Artificial
does the definition of which techniques constitute neural networks whose connections
AI.2 For the purposes of this article, we use AI as between neurons include loops; RNNs are
shorthand for deep learning techniques that use well suited for processing sequences of
artificial neural networks. We also examined inputs. In November 2016, Oxford University
other machine learning techniques and traditional researchers reported that a system based on
analytics techniques (Exhibit 1). recurrent neural networks (and convolutional
neural networks) had achieved 95 percent
Neural networks are a subset of machine learning accuracy in reading lips, outperforming
techniques. Essentially, they are AI systems based experienced human lip readers, who tested at
on simulating connected “neural units,” loosely 52 percent accuracy.
modeling the way that neurons interact in the
brain. Computational models inspired by neural Convolutional neural networks (CNNs):
connections have been studied since the 1940s Artificial neural networks in which the
and have returned to prominence as computer connections between neural layers are
processing power has increased and large training inspired by the organization of the animal
data sets have been used to successfully analyze visual cortex, the portion of the brain that
input data such as images, video, and speech. AI processes images; CNNs are well suited for
practitioners refer to these techniques as “deep perceptual tasks.
learning,” since neural networks have many
(“deep”) layers of simulated interconnected For our use cases, we also considered two other
neurons. techniques—generative adversarial networks and
reinforcement learning—but did not include them
2 For more on AI techniques, including definitions and use cases, see “An executive’s guide to AI,” February 2018,
McKinsey.com.
Notes from the AI frontier: Applications and value of deep learning 11Web
Exhibit
Exhibit 1 of
We examined artificial intelligence (AI), machine learning, and other
We examined
analytics artificialfor
techniques intelligence (AI), machine learning, and other
our research.
analytics techniques for our research.
Considered AI for our research
MORE
Transfer
learning
Reinforcement
Deep learning learning
(feed-forward networks,
CNNs1, RNNs2, GANs3)
Dimensionality
reduction
Likelihood to Ensemble
be used in AI Instance-based Decision-tree learning
applications learning learning
Monte Linear Clustering
Carlo classifiers
methods
Statistical Markov Regression
inference process analysis
Descriptive Naive Bayes
statistics classifiers
LESS
TRADITIONAL Complexity of technique ADVANCED
1
Convolutional neural networks.
2
Recurrent neural networks.
3
Generative adversarial networks.
Source: McKinsey Global Institute analysis
12 Digital/McKinseyin our potential value assessment of AI, since they Following are examples of where AI can be used to
remain nascent techniques that are not yet widely improve the performance of existing use cases:
applied:
Predictive maintenance: The power of
Generative adversarial networks (GANs) machine learning to detect anomalies. Deep
use two neural networks contesting one learning’s capacity to analyze very large
another in a zero-sum game framework (thus amounts of high-dimensional data can take
“adversarial”). GANs can learn to mimic existing preventive maintenance systems to
various distributions of data (for example, a new level. Layering in additional data, such
text, speech, and images) and are therefore as audio and image data, from other sensors—
valuable in generating test data sets when including relatively cheap ones such as
these are not readily available. microphones and cameras—neural networks
can enhance and possibly replace more
Reinforcement learning is a subfield of traditional methods. AI’s ability to predict
machine learning in which systems are failures and allow planned interventions
trained by receiving virtual “rewards” or can be used to reduce downtime and
“punishments,” essentially learning by trial operating costs while improving production
and error. Google’s DeepMind has used yield. For example, AI can extend the life
reinforcement learning to develop systems of a cargo plane beyond what is possible
that can play games, including video games using traditional analytics techniques by
and board games such as Go, better than combining plane model data, maintenance
human champions. history, and Internet of Things (IoT) sensor
data such as anomaly detection on engine-
In a business setting, these analytic techniques vibration data, and images and video of
can be applied to solve real-life problems. The engine condition.
most prevalent problem types are classification,
continuous estimation, and clustering (see sidebar, AI-driven logistics optimization can reduce
“Problem types and their definitions”). costs through real-time forecasts and
behavioral coaching. Application of AI
Insights from use cases techniques such as continuous estimation
We collated and analyzed more than 400 use cases to logistics can add substantial value across
across 19 industries and nine business functions. sectors. AI can optimize routing of delivery
They provided insight into the areas within traffic, thereby improving fuel efficiency
specific sectors where deep neural networks can and reducing delivery times. One European
potentially create the most value, the incremental trucking company has reduced fuel costs by
lift that these neural networks can generate 15 percent, for example, by using sensors
compared with traditional analytics (Exhibit 2), that monitor both vehicle performance and
and the voracious data requirements—in terms of driver behavior; drivers receive real-time
volume, variety, and velocity—that must be met coaching, including when to speed up or slow
for this potential to be realized. Our library of use down, optimizing fuel consumption and
cases, while extensive, is not exhaustive and may reducing maintenance costs.
overstate or understate the potential for certain
sectors. We will continue refining and adding to it.
Notes from the AI frontier: Applications and value of deep learning 13Exhibit 2
Web
Advanced
Exhibit of deep learning artificial intelligence techniques can be
applied
Advancedacross industries,
deep learning alongsidetechniques
artificial intelligence more traditional analytics.
can be applied across industries,
alongside more traditional analytics.
Technique relevance1 heatmap by industry Frequency of use Low High
Focus of report Traditional analytics techniques
Feed- Recurrent Convolutional Generative Tree-based
forward neural neural adversarial Reinforcement ensemble Regression Statistical
networks networks networks networks learning learning Classifiers Clustering analysis inference
Advanced electronics/
semiconductors
Aerospace and
defense
Agriculture
Automotive and
assembly
Banking
Basic materials
Chemicals
Consumer
packaged goods
Healthcare systems
and services
High tech
Insurance
Media and
entertainment
Oil and gas
Pharmaceuticals and
medical products
Public and
social sectors
Retail
Telecommunications
Transport and
logistics
Travel
1
Relevance refers to frequency of use in our use case library, with the most frequently found cases marked as high
relevance and the least frequently found as low relevance. Absence of circles indicates no or statistically insignificant
number of use cases.
Note: List of techniques is not exhaustive.
Source: McKinsey Global Institute analysis
14 Digital/McKinseyProblem types and their definitions
Classification: Based on a set of training data, categorize new inputs as belonging to one of a set of categories. An example of
classification is identifying whether an image contains a specific type of object, such as a cat or a dog, or a product of acceptable
quality coming from a manufacturing line.
Continuous estimation: Based on a set of training data, estimate the next numeric value in a sequence. This type of problem
is sometimes described as “prediction,” particularly when it is applied to time-series data. One example of continuous estimation
is forecasting the sales demand for a product, based on a set of input data such as previous sales figures, consumer sentiment,
and weather.
Clustering: These problems require a system to create a set of categories, for which individual data instances have a set of
common or similar characteristics. An example of clustering is creating a set of consumer segments, based on a set of data
about individual consumers, including demographics, preferences, and buyer behavior.
All other optimization: These problems require a system to generate a set of outputs that optimize outcomes for a specific
objective function (some of the other problem types can be considered types of optimization, so we describe these as “all other”
optimization). Generating a route for a vehicle that creates the optimum combination of time and fuel utilization is an example of
optimization.
Anomaly detection: Given a training set of data, determine whether specific inputs are out of the ordinary. For instance, a
system could be trained on a set of historical vibration data associated with the performance of an operating piece of machinery,
and then determine whether a new vibration reading suggests that the machine is not operating normally. Anomaly detection can
be considered a subcategory of classification.
Ranking: Ranking algorithms are used most often in information-retrieval problems where the results of a query or request
needs to be ordered by some criterion. Recommendation systems suggesting next product to buy use these types of algorithms
as a final step, sorting suggestions by relevance, before presenting the results to the user.
Recommendations: These systems provide recommendations based on a set of training data. A common example of
recommendations are systems that suggest “next product to buy” for an individual buyer, based on the buying patterns of similar
individuals and the observed behavior of the specific person.
Data generation: These problems require a system to generate appropriately novel data based on training data. For instance,
a music composition system might be used to generate new pieces of music in a particular style, after having been trained on
Notes from the AI frontier: Applications and value of deep learning 15 AI can be a valuable tool for customer Greenfield AI solutions are prevalent in business
service management and personalization areas such as customer-service management, as
challenges. Improved speech recognition in well as among some industries where the data are
call center management and call routing as rich and voluminous and at times integrate human
a result of the application of AI techniques reactions. Among industries, we found many
allows a more seamless experience for greenfield use cases in healthcare, in particular.
customers—and more efficient processing. Some of these cases involve disease diagnosis
The capabilities go beyond words alone. For and improved care and rely on rich data sets
example, deep learning analysis of audio incorporating image and video inputs, including
allows systems to assess a customer’s from MRIs.
emotional tone; in the event a customer is
responding badly to the system, the call On average, our use cases suggest that modern
can be rerouted automatically to human deep learning AI techniques have the potential
operators and managers. In other areas to provide a boost in additional value above and
of marketing and sales, AI techniques can beyond traditional analytics techniques—ranging
also have a significant impact. Combining from 30 percent to 128 percent, depending on
customer demographic and past transaction industry.
data with social media monitoring can
help generate individualized product In many of our use cases, however, traditional
recommendations. Next-product-to-buy analytics and machine learning techniques
recommendations that target individual continue to underpin a large percentage of the
customers—as companies such as Amazon value-creation potential in industries including
and Netflix have successfully been doing— insurance, pharmaceuticals and medical products,
can lead to a twofold increase in the rate of and telecommunications, with the potential of AI
sales conversions. limited in certain contexts. In part this is due to
the way data are used by these industries and to
Two-thirds of the opportunities to use AI are regulatory issues.
in improving the performance of existing
analytics use cases Data requirements for deep learning
In 69 percent of the use cases we studied, deep are substantially greater than for other
neural networks can be used to improve analytics
performance beyond that provided by other Making effective use of neural networks in most
analytics techniques. Cases in which only neural applications requires large labeled training data
networks can be used, which we refer to here as sets alongside access to sufficient computing
“greenfield” cases, constituted just 16 percent of infrastructure. Furthermore, these deep learning
the total. For the remaining 15 percent, artificial techniques are particularly powerful in extracting
neural networks provided limited additional patterns from complex, multidimensional data
performance over other analytics techniques, types such as images, video, and audio or speech.
because, among other reasons, of data limitations
that made these cases unsuitable for deep learning Deep learning methods require thousands of
(Exhibit 3). data records for models to become relatively
16 Digital/McKinseyWeb
Exhibit
Exhibit of
In more than two-thirds of our use cases, artificial intelligence (AI)
In more
can than performance
improve two-thirds of our use cases,
beyond artificial by
that provided intelligence (AI) can
other analytics
improve performance beyond that provided by other analytics techniques.
techniques.
Breakdown of Potential incremental value from AI over other analytics techniques, %
use cases by
applicable Travel 128
techniques, %
Transport and logistics 89
Full value can Retail 87
be captured
using non-AI 15 Automotive and assembly 85
techniques
High tech 85
AI necessary
to capture Oil and gas 79
16
value
Chemicals 67
(“greenfield”)
Media and entertainment 57
Basic materials 56
Agriculture 55
Consumer packaged goods 55
AI can
Banking 50
improve
performance Healthcare systems and services 44
over that
69
provided Public and social sectors 44
by other
analytics Telecommunications 44
techniques
Pharmaceuticals and medical products 39
Insurance 38
Advanced electronics/semiconductors 36
Aerospace and defense 30
Source: McKinsey Global Institute analysis
Notes from the AI frontier: Applications and value of deep learning 17good at classification tasks and, in some cases, Realizing AI’s full potential requires a
millions for them to perform at the level of diverse range of data types, including
humans. By one estimate, a supervised deep images, video, and audio
learning algorithm will generally achieve Neural AI techniques excel at analyzing image,
acceptable performance with around 5,000 video, and audio data types because of their
labeled examples per category and will match or complex, multidimensional nature, known
exceed human-level performance when trained by practitioners as “high dimensionality.”
with a data set containing at least ten million Neural networks are good at dealing with high
labeled examples. 3 In some cases where advanced dimensionality, as multiple layers in a network
analytics are currently used, so much data are can learn to represent the many different features
available—millions or even billions of rows per present in the data. Thus, for facial recognition,
data set—that AI usage is the most appropriate the first layer in the network could focus on raw
technique. However, if a threshold of data volume pixels, the next on edges and lines, another on
is not reached, AI may not add value to traditional generic facial features, and the final layer might
analytics techniques. identify the face. Unlike previous generations
of AI, which often required human expertise to
These massive data sets can be difficult to obtain do “feature engineering,” these neural network
or create for many business use cases, and labeling techniques are often able to learn to represent
remains a challenge. Most current AI models these features in their simulated neural networks
are trained through “supervised learning,” as part of the training process.
which requires humans to label and categorize
the underlying data. However, promising new Along with issues around the volume and variety of
techniques are emerging to overcome these data data, velocity is also a requirement: AI techniques
bottlenecks, such as reinforcement learning, require models to be retrained to match potential
generative adversarial networks, transfer learning, changing conditions, so the training data must
and “one-shot learning,” which allows a trained be refreshed frequently. In one-third of the cases,
AI model to learn about a subject based on a the model needs to be refreshed at least monthly,
small number of real-world demonstrations or and almost one in four cases requires a daily
examples—and sometimes just one. refresh; this is especially the case in marketing
and sales and in supply-chain management and
Organizations will have to adopt and implement manufacturing.
strategies that enable them to collect and integrate
data at scale. Even with large data sets, they will Sizing the potential value of AI
have to guard against “overfitting,” where a model We estimate that the AI techniques we cite in the
too tightly matches the “noisy” or random features discussion paper together have the potential to
of the training set, resulting in a corresponding create between $3.5 trillion and $5.8 trillion in
lack of accuracy in future performance, and value annually across nine business functions in
against “underfitting,” where the model fails to 19 industries. This constitutes about 40 percent
capture all of the relevant features. Linking data of the overall $9.5 trillion to $15.4 trillion annual
across customer segments and channels, rather impact that could potentially be enabled by all
than allowing the data to languish in silos, is analytical techniques (Exhibit 4).
especially important to create value.
3 Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, Cambridge, MA: MIT Press, 2016.
18 Digital/McKinseyWeb
Exhibit 4
Exhibit of
Artificial
intelligence (AI) has the potential to create value across
sectors.
Artificial intelligence (AI) has the potential to create value across sectors.
700
Retail
600
500
Healthcare systems Transport and logistics
and services
AI impact, 400 Travel
$ billion Consumer packaged goods
Public and social sectors
Automotive and assembly
300 Advanced electronics/
semiconductors Banking
Insurance Basic materials
High tech
200 Media and
entertainment Oil and gas
Telecommunications Chemicals
Agriculture
100
Pharmaceuticals
and medical Aerospace and defense
products
0
20 30 40 50 60
Share of AI impact in total impact derived from analytics, %
Source: McKinsey Global Institute analysis
Per industry, we estimate that AI’s potential These figures are not forecasts for a particular
value amounts to between 1 and 9 percent of 2016 period, but they are indicative of the considerable
revenue. The value as measured by percentage potential for the global economy that advanced
of industry revenue varies significantly among analytics represents.
industries, depending on the specific applicable
use cases, the availability of abundant and From the use cases we have examined, we find
complex data, as well as regulatory and other that the greatest potential value impact from
constraints. using AI are both in top-line-oriented functions,
Notes from the AI frontier: Applications and value of deep learning 19such as marketing and sales, and bottom-line- potential 5 percent reduction in inventory
oriented operational functions, including supply- costs and revenue increases of 2 to 3 percent.
chain management and manufacturing.
In banking, particularly retail banking, AI
Consumer industries such as retail and high tech has significant value potential in marketing
will tend to see more potential from marketing and and sales, much as it does in retail. However,
sales AI applications because frequent and digital because of the importance of assessing and
interactions between the business and customers managing risk in banking—for example, for
generate larger data sets for AI techniques to tap loan underwriting and fraud detection—AI
into. E-commerce platforms, in particular, stand has much higher value potential to improve
to benefit. This is because of the ease with which performance in risk in the banking sector
these platforms collect customer information such than in many other industries.
as click data or time spent on a web page. These
platforms can then customize promotions, prices, The road to impact and value
and products for each customer dynamically and Artificial intelligence is attracting growing
in real time. amounts of corporate investment, and as the
technologies develop, the potential value that
Here is a snapshot of three sectors where we have can be unlocked is likely to grow. So far, however,
seen AI’s impact (Exhibit 5): only about 20 percent of AI-aware companies are
currently using one or more of its technologies in a
In retail, marketing and sales is the area core business process or at scale.4
with the most significant potential value
from AI, and within that function, pricing For all their promise, AI technologies have plenty
and promotion and customer-service of limitations that will need to be overcome. They
management are the main value areas. Our include the onerous data requirements listed
use cases show that using customer data previously, but also five other limitations:
to personalize promotions, for example,
including tailoring individual offers every First is the challenge of labeling training
day, can lead to a 1 to 2 percent increase in data, which often must be done manually
incremental sales for brick-and-mortar and is necessary for supervised learning.
retailers alone. Promising new techniques are emerging to
address this challenge, such as reinforcement
In consumer goods, supply-chain learning and in-stream supervision, in which
management is the key function that could data can be labeled in the course of natural
benefit from AI deployment. Among the usage.
examples in our use cases, we see how
forecasting based on underlying causal Second is the difficulty of obtaining data sets
drivers of demand rather than prior that are sufficiently large and comprehensive
outcomes can improve forecasting accuracy to be used for training; for many business
by 10 to 20 percent, which translates into a use cases, creating or obtaining such massive
4 See “How artificial intelligence can deliver real value to companies,” McKinsey Global Institute, June 2017, on
McKinsey.com.
20 Digital/McKinseyWeb
Exhibit of
Exhibit 5
Artificial
Artificial intelligence’s impactisislikely
intelligence’s impact likelytotobe
bemost
mostsubstantial
substantialininmarketing
marketing
and sales as and sales
well as well as supply-chain
as supply-chain management management and
and manufacturing,
manufacturing,
based on our use based on our use cases.
cases.
Value unlocked, $ trillion
By advanced analytics 9.5–15.4 By artificial intelligence 3.5–5.8
3.3–6.0 3.6–5.6
1.4–2.6
1.2–2.0
Marketing and sales Supply-chain management and manufacturing
0.9–1.3
0.5–0.9
0.6
0.3 0.3 0.2–0.4
0.2 0.2 0.2 0.2
0.1the automotive and aerospace industries, for Organizational challenges around
example, can be an obstacle; among other technology, processes, and people can
constraints, regulators often want rules and slow or impede AI adoption
choice criteria to be clearly explainable. Organizations planning to adopt significant
deep learning efforts will need to consider a
Fourth is the generalizability of learning: spectrum of options about how to do so. The range
AI models continue to have difficulties in of options includes building a complete in-house
carrying their experiences from one set AI capability, outsourcing these capabilities, or
of circumstances to another. That means leveraging AI-as-a-service offerings.
that companies must commit resources to
train new models even for use cases that are Based on the use cases they plan to build,
similar to previous ones. Transfer learning— companies will need to create a data plan that
in which an AI model is trained to accomplish produces results and predictions that can be
a certain task and then quickly applies that fed either into designed interfaces for humans
learning to a similar but distinct activity—is to act on or into transaction systems. Key data
one promising response to this challenge. engineering challenges include data creation or
acquisition, defining data ontology, and building
The fifth limitation concerns the risk of bias appropriate data “pipes.” Given the significant
in data and algorithms. This issue touches computational requirements of deep learning,
on concerns that are more social in nature some organizations will maintain their own
and which could require broader steps to data centers because of regulations or security
resolve, such as understanding how the concerns, but the capital expenditures could be
processes used to collect training data can considerable, particularly when using specialized
influence the behavior of the models they hardware. Cloud vendors offer another option.
are used to train. For example, unintended
biases can be introduced when training Process can also become an impediment to
data is not representative of the larger successful adoption unless organizations
population to which an AI model is applied. are digitally mature. On the technical side,
Thus, facial-recognition models trained organizations will have to develop robust data
on a population of faces corresponding to maintenance and governance processes and
the demographics of AI developers could implement modern software disciplines such
struggle when applied to populations with as Agile and DevOps. Even more challenging,
more diverse characteristics. 5 A recent in terms of scale, is overcoming the “last mile”
report on the malicious use of AI highlights a problem of making sure the superior insights
range of security threats, from sophisticated provided by AI are instantiated in the behavior of
automation of hacking to hyperpersonalized the people and processes of an enterprise.
political disinformation campaigns.6
5 See Joy Buolamwini and Timnit Gebru, “Gender shades: Intersectional accuracy disparities in commercial gender
classification,” Proceedings of Machine Learning Research, 2018, Volume 81, pp. 1–15, proceedings.mlr.press.
6 Peter Eckersley, “The malicious use of artificial intelligence: Forecasting, prevention, and mitigation,” Electronic Frontier
Foundation, February 20, 2018, eff.org.
22 Digital/McKinseyOn the people front, much of the construction and Societal concerns and regulations can also
optimization of deep neural networks remains constrain AI use. Regulatory constraints are
something of an art, requiring real experts to especially prevalent in use cases related to
deliver step-change performance increases. personally identifiable information. This is
Demand for these skills far outstrips supply at particularly relevant at a time of growing public
present; according to some estimates, fewer than debate about the use and commercialization of
10,000 people have the skills necessary to tackle individual data on some online platforms. Use
serious AI problems, and competition for them is and storage of personal information is especially
fierce among the tech giants.7 sensitive in sectors such as banking, healthcare,
and pharmaceuticals and medical products, as
AI can seem an elusive business case well as in the public and social sector. In addition
Where AI techniques and data are available and to addressing these issues, businesses and other
the value is clearly proven, organizations can users of data for AI will need to continue to evolve
already pursue the opportunity. In some areas, business models related to data use in order
the techniques today may be mature and the data to address societies’ concerns. Furthermore,
available, but the cost and complexity of deploying regulatory requirements and restrictions can
AI may simply not be worthwhile, given the value differ from country to country, as well from sector
that could be generated. For example, an airline to sector.
could use facial recognition and other biometric
scanning technology to streamline aircraft Implications for stakeholders
boarding, but the value of doing so may not justify As we have seen, it is a company’s ability to execute
the cost and issues around privacy and personal against AI models that creates value, rather than
identification. the models themselves. In this final section, we
sketch out some of the high-level implications
Similarly, we can see potential cases where the of our study of AI use cases for providers of AI
data and the techniques are maturing, but the technology, appliers of AI technology, and policy
value is not yet clear. The most unpredictable makers, who set the context for both.
scenario is where either the data (both the types
and volume) or the techniques are simply too new Many companies that develop or provide
and untested to know how much value they could AI to others have considerable strength in
unlock. For example, in healthcare, if AI were the technology itself and the data scientists
able to build on the superhuman precision we are needed to make it work, but they can lack
already starting to see with X-ray analysis and to a deep understanding of end markets.
broaden that to more accurate diagnoses and even Understanding the value potential of AI
automated medical procedures, the economic across sectors and functions can help
value could be very significant. At the same time, shape the portfolios of these AI technology
the complexities and costs of arriving at this companies. That said, they shouldn’t
frontier are also daunting. Among other issues, it necessarily prioritize only the areas of
would require flawless technical execution and highest potential value. Instead, they can
resolving issues of malpractice insurance and combine that data with complementary
other legal concerns. analyses of the competitor landscape
7 Cade Metz, “Tech giants are paying huge salaries for scarce AI talent,” New York Times, October 22, 2017, nytimes.com.
Notes from the AI frontier: Applications and value of deep learning 23and their own existing strengths, sector Policy makers will need to strike a balance
or function knowledge, and customer between supporting the development of
relationships to shape their investment AI technologies and managing any risks
portfolios. On the technical side, the from bad actors. They have an interest in
mapping of problem types and techniques supporting broad adoption, since AI can
to sectors and functions of potential value lead to higher labor productivity, economic
can guide a company with specific areas of growth, and societal prosperity. Their tools
expertise on where to focus. include public investments in research
and development as well as support for a
Many companies seeking to adopt AI in their variety of training programs, which can
operations have started machine learning help nurture AI talent. On the issue of data,
and AI experiments across their business. governments can spur the development of
Before launching more pilots or testing training data directly through open-data
solutions, it is useful to step back and take initiatives. Opening up public-sector data
a holistic approach to the issue, moving to can spur private-sector innovation. Setting
create a prioritized portfolio of initiatives common data standards can also help. AI
across the enterprise, including AI and is also raising new questions for policy
the wider analytics and digital techniques makers to grapple with, for which historical
available. For a business leader to create tools and frameworks may not be adequate.
an appropriate portfolio, it is important to Therefore, some policy innovations will
develop an understanding about which use likely be needed to cope with these rapidly
cases and domains have the potential to evolving technologies. But given the scale
drive the most value for a company, as well of the beneficial impact on business, the
as which AI and other analytical techniques economy, and society, the goal should not be
will need to be deployed to capture that to constrain the adoption and application of
value. This portfolio ought to be informed AI but rather to encourage its beneficial and
not only by where the theoretical value can safe use.
be captured but also by the question of how
the techniques can be deployed at scale
across the enterprise. The question of how
analytical techniques are scaling is driven
less by the techniques themselves and more
by a company’s skills, capabilities, and data.
Companies will need to consider efforts on
the “first mile,” that is, how to acquire and
organize data and efforts, as well as on the
“last mile,” or how to integrate the output of
AI models into frontline workflows, ranging
from those of clincial-trial managers and
sales-force managers to procurement
officers. Previous McKinsey Global Institute
research suggests that AI leaders invest
heavily in these first- and last-mile efforts.
24 Digital/McKinseyMichael Chui is a partner of the McKinsey Global Institute, where James Manyika is chairman and a director; Rita Chung is a consultant in McKinsey’s Silicon Valley office; Nicolaus Henke is a senior partner in the London office; Sankalp Malhotra is a consultant in the New York office, where Pieter Nel is a specialist; Mehdi Miremadi is a partner in the Chicago office. Copyright © 2018 McKinsey & Company. All rights reserved. Notes from the AI frontier: Applications and value of deep learning 25
REPLACE IMAGE
Photo credit: Getty Images
Artificial intelligence is getting
ready for business, but are
businesses ready for AI?
Terra Allas, Jacques Bughin, Michael Chui, Peter Dahlström, Eric Hazan, Nicolaus Henke,
Sree Ramaswamy, and Monica Trench
Companies new to the space can learn a great deal from early
adopters who have invested billions in AI and are now beginning
to reap a range of benefits.
Claims about the promise and peril of artificial Twice as many articles mentioned AI in 2016 as
intelligence (AI) are abundant—and growing. in 2015, and nearly four times as many as in 2014.1
AI, which enables machines to exhibit humanlike Expectations are high.
cognition, can drive our cars or steal our privacy,
stoke corporate productivity or empower corporate AI has been here before. Its history abounds with
spies. It can relieve workers of repetitive or booms and busts, extravagant promises, and
dangerous tasks or strip them of their livelihoods. frustrating disappointments. Is it different this
1 Factiva.
26 Digital/McKinseytime? New analysis suggests yes: AI is finally Our business experience with AI suggests that
starting to deliver real-life business benefits. this bust scenario is unlikely. In order to provide
The ingredients for a breakthrough are in a more informed view, we decided to perform
place. Computer power is growing significantly, our own research into how users are adopting
algorithms are becoming more sophisticated, AI technologies. Our research offers a snapshot
and, perhaps most important of all, the world of the current state of the rapidly changing AI
is generating vast quantities of the fuel that industry. To begin, we examine the investment
powers AI—data. Billions of gigabytes of it landscape, including firms’ internal investment
every day. in R&D and deployment, large corporate M&A,
and funding from venture capital (VC) and
Companies at the digital frontier—online private equity (PE) firms. We then combine
firms and digital natives such as Google and use-case analyses and our AI adoption and use
Baidu—are betting vast amounts of money survey of C-level executives at more than 3,000
on AI. We estimate between $20 billion and companies to understand how companies use AI
$30 billion in 2016, including significant M&A technologies today.
activity. Private investors are jumping in, too.
We estimate that venture capitalists invested AI generally refers to the ability of machines
$4 billion to $5 billion in AI in 2016, and private to exhibit humanlike intelligence—for
equity firms invested $1 billion to $3 billion. example, solving a problem without the use
That is more than three times as much as in of hand-coded software containing detailed
2013. An additional $1 billion of investment instructions. There are several ways to
came from grants and seed funding. categorize AI technologies, but it is difficult
to draft a list that is mutually exclusive and
For now, though, most of the news is coming collectively exhaustive, because people often
from the suppliers of AI technologies. And mix and match several technologies to create
many new uses are only in the experimental solutions for individual problems. These
phase. Few products are on the market or are creations sometimes are treated as independent
likely to arrive there soon to drive immediate technologies, sometimes as subgroups of other
and widespread adoption. As a result, analysts technologies, and sometimes as applications.
remain divided as to the potential of AI: Some frameworks group AI technologies by
some have formed a rosy consensus about basic functionality, such as text, speech, or
AI’s potential while others remain cautious image recognition, and some group them by
about its true economic benefit. This lack of business applications such as commerce or
agreement is visible in the large variance of cybersecurity.3
current market forecasts, which range from
$644 million to $126 billion by 2025.2 Given the Trying to pin down the term more precisely
size of investment being poured into AI, the low is fraught for several reasons: AI covers a
estimate would indicate that we are witnessing broad range of technologies and applications,
another phase in a boom-and-bust cycle. some of which are merely extensions of
2 Tractica; Transparency Market Research.
3 Gil Press, “Top 10 hot artificial intelligence (AI) technologies,” Forbes.com, January 23, 2017; “AI100: The artificial
intelligence start-ups redefining industries,” CBinsights.com, January 11, 2017.
Artificial intelligence is getting ready for business, but are businesses ready for AI? 27earlier techniques and others that are wholly with humans. Machine learning and a subfield
new. Also, there is no generally accepted called deep learning are at the heart of many
theory of “intelligence,” and the definition of recent advances in artificial intelligence
machine “intelligence” changes as people become applications and have attracted a lot of attention
accustomed to previous advances.4 Tesler’s and a significant share of the financing that
theorem, attributed to the computer scientist has been pouring into the AI universe—almost
Larry Tesler, asserts that “AI is whatever hasn’t 60 percent of all investment from outside the
been done yet.”5 industry in 2016.
The AI technologies we consider in this paper Artificial intelligence’s roller-coaster
are what is called “narrow” AI, which performs ride to today
one narrow task, as opposed to artificial general Artificial intelligence, as an idea, first appeared
intelligence, or AGI, which seeks to be able to soon after humans developed the electronic
perform any intellectual task that a human can do. digital computing that makes it possible. And, like
We focus on narrow AI because it has near-term digital technology, artificial intelligence, or AI,
business potential, while AGI has yet to arrive.6 has ridden waves of hype and gloom—with one
exception: AI has not yet experienced wide-scale
In this report, we focus on the set of AI technology commercial deployment (see sidebar, “Fits and
systems that solve business problems. We have starts: A history of artificial intelligence”).
categorized these into five technology systems
that are key areas of AI development: robotics and That may be changing. Machines powered
autonomous vehicles, computer vision, language, by AI can today perform many tasks—such as
virtual agents, and machine learning, which is recognizing complex patterns, synthesizing
based on algorithms that learn from data without information, drawing conclusions, and
relying on rules-based programming in order forecasting—that not long ago were assumed to
to draw conclusions or direct an action. Some require human cognition. And as AI’s capabilities
are related to processing information from the have dramatically expanded, so has its utility in
external world, such as computer vision and a growing number of fields. At the same time, it is
language (including natural-language processing, worth remembering that machine learning has
text analytics, speech recognition, and semantics limitations. For example, because the systems
technology); some are about learning from are trained on specific data sets, they can be
information, such as machine learning; and others susceptible to bias; to avoid this, users must be
are related to acting on information, such as sure to train them with comprehensive data sets.
robotics, autonomous vehicles, and virtual agents, Nevertheless, we are seeing significant progress.
which are computer programs that can converse
4 Marvin Minsky, “Steps toward artificial intelligence,” Proceedings of the IRE, volume 49, number 1, January 1961; Edward
A. Feigenbaum, The art of artificial intelligence: Themes and case studies of knowledge engineering, Stanford University
Computer Science Department report number STAN-CS-77–621, August 1977; Allen Newell, “Intellectual issues in the
history of artificial intelligence,” in The Study of Information: Interdisciplinary messages, Fritz Machlup and Una Mansfield,
eds., John Wiley and Sons, 1983.
5 Douglas R. Hofstadter, Gödel, Escher, Bach: An eternal golden braid, Basic Books, 1979. Hofstadter writes that he gave
the theorem its name after Tesler expressed the idea to him firsthand. However, Tesler writes in his online CV that he
actually said, “Intelligence is whatever machines haven’t done yet.”
6 William Vorhies, “Artificial general intelligence—the Holy Grail of AI,” DataScienceCentral.com, February 23, 2016.
28 Digital/McKinseyYou can also read
