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Read the following sections of McKinsey Quarterly
Page 30 – 41 Strategy to Beat the Odds.
Page 43 – 54 Organizing for the age of urgency
Page 55 – 59 Data as jet fuel: An interview with Boeing’s CIO
Page 61 – 75 Why digital strategies fail
Page 76 – 81 Why digital transformation is now on the CEO’s shoulders
Write your summary of each section and submit.
2018 Number 1
Games in the strategy room
Why people play them—and how
to beat the real odds they mask
Copyright © 2018
McKinsey & Company.
All rights reserved.
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2018 Number 1
The pace of change across the business landscape is unrelenting. Technological,
economic, and political disruptions are requiring a rethink by most
companies of where and how they compete, what organizational model they
need to keep up, and where they must build capabilities. This issue of the
Quarterly provides a road map for navigating many of these challenges.
The cover story, “Strategy to beat the odds,” is the culmination of a multiyear
research effort by our Strategy Practice. In a nutshell, my colleagues Chris
Bradley, Martin Hirt, and Sven Smit broke from the usual best-practice
examples and frameworks that often characterize writing on strategy and
instead developed a new set of strategic tools, based on data from thousands
of companies.
The authors’ research shows how to boost the odds of achieving strategic
breakthroughs by capitalizing on your endowment, riding the right trends,
and making a few big moves. They also believe that when leaders have an
empirically backed view of strategy, they stand a much better chance of
overcoming the social dynamics that frequently conspire to produce inertia,
gamesmanship, and risk aversion in the strategy room.
The article is drawn from their new book, Strategy Beyond the Hockey Stick,
and it’s a must-read for any leader trying to formulate strategy that stays
ahead of rapid change. One of the biggest strategic questions facing many
companies is how to harness, rather than get blindsided by, digitization,
an incredibly disruptive economic force. Another article in this issue,
THIS QUARTER
“Why digital strategies fail,” lays out five pitfalls that many leaders are
stumbling into, and suggests how to sidestep them.
Strategy and organizational structure are inextricably related. In the 1962
classic Strategy and Structure, Professor Alfred Chandler argued that
structure follows from strategy. Today’s environment appears to be inverting
that logic. Aaron De Smet and Chris Gagnon assert in “Organizing for the
age of urgency” that competing at the speed of digital calls for adaptive,
fast-moving organizations that can respond quickly and flexibly to new
opportunities and challenges as they arise. Often, that means moving
decision making to the front lines, rather than capturing data, moving it up
a hierarchical chain, centrally analyzing it, and sending guidance back.
In a related article, Boeing’s senior vice president and CIO describes how his
company is trying to do exactly that.
Leaders hoping to create the tech- and data-enabled organization of the
future need more than data. They also must understand how increasingly
powerful tools, particularly those enabled by artificial intelligence, are
shaking up what companies can do with that data. In “What AI can and
can’t do (yet) for your business,” Michael Chui, James Manyika, and Mehdi
Miremadi provide a field guide on several promising developments poised to
bend the trajectory of AI, enabling it to generate sharper insights, sometimes
with less data than is necessary today.
As these articles suggest, the nature of functional business knowledge is
changing: evergreen topics such as strategy and organization are colliding in
unexpected ways with the forces of digital, big data, and artificial intelligence.
Those collisions are creating new business opportunities, and they are also
necessitating new organizational capabilities—starting at the top and moving
all the way to the front lines. I hope this issue of the Quarterly helps you build
the muscle you and your organization need.
Robert Sternfels
Senior partner,
San Francisco office
McKinsey & Company
Organizing for the age of urgency
To compete at the speed of digital, you need to unleash your
strategy, your structure, and your people.
Aaron De Smet and Chris Gagnon
Why digital strategies fail
Most digital strategies don’t reflect how digital is changing
economic fundamentals, industry dynamics, or what it means to
compete. Companies should watch out for five pitfalls.
Jacques Bughin, Tanguy Catlin, Martin Hirt, and Paul Willmott
THE TECH- AND DATA-ENABLED ORGANIZATION
OF THE FUTURE
REACHING FOR THE DIGITAL PRIZE
Data as jet fuel: An interview with Boeing’s CIO
It isn’t always comfortable, but data analytics is helping
Boeing reach new heights.
Why digital transformation is now on the
CEO’s shoulders
Big data, the Internet of Things, and artificial intelligence
hold such disruptive power that they have inverted the
dynamics of technology leadership.
Thomas M. Siebel
43
61
55
76
Strategy to beat the odds
If you internalize the real odds of strategy, you can tame
its social side and make big moves.
Chris Bradley, Martin Hirt, and Sven Smit
30
Features
What AI can and can’t do (yet) for your business
Artificial intelligence is a moving target. Here’s how to take
better aim.
Michael Chui, James Manyika, and Mehdi Miremadi
The four questions to ask when serving on a
nonprofit board
Directors need to probe, nudge, and prod to make sure the
organization achieves its full potential.
William F. Meehan III and Kim Starkey Jonker
96
109
The automotive ecosystem shifts into gear
Matthias Kässer, Thibaut Müller, and Andreas Tschiesner
Working across many cultures at Western Union
The CEO of the global money-transfer company explains
how it brings in the multicultural voice of the consumer
through a broadly diverse team of top executives.
Banking needs an ecosystem play
Miklós Dietz, Joydeep Sengupta, and Nicole Zhou
Pulp and paper: Where digital help far outweighs the hurt
Peter Berg and Oskar Lingqvist
A digital upgrade for engineering and construction
Jose Luis Blanco, Andrew Mullin, and Mukund Sridhar
116
82
REACHING FOR THE DIGITAL PRIZE (CONTINUED)
Digital snapshots: Four industries in transition
Features
Extra Point
128
The most dangerous strategy?
Make no bold moves
Leading Edge
8
Should assessing financial similarity be
part of your corporate portfolio strategy?
Businesses with different financial profiles
can tax managers and put performance at risk.
When divesting isn’t an option, here’s how to
manage the conflicts.
Tim Koller, Dan Lovallo, and Zane Williams
11
A closer look at impact investing
The mistaken rap on this kind of “social”
investment is that returns are weak and
realizing them takes too long.
Vivek Pandit and Toshan Tamhane
15
Accelerating the diffusion of technologyenabled
business practices
New research highlights some of the most
important actions available to executives.
Tera Allas and Vivian Hunt
19
Shaking up the leadership model in
higher education
Economic pressures, digital disruption,
and rising job complexity are prompting
universities to seek more “outsider” leaders
for their top jobs.
Scott C. Beardsley
Industry Dynamics
Insights from selected sectors
24
Maximizing industrial revenues—
after the sale
Markus Forsgren, Florent Kervazo,
and Hugues Lavandier
26
Will batteries disrupt the utilities industry?
David Frankel and Amy Wagner
China Pulse
Snapshots of China’s digital economy
28
How China’s shift to consumer-led growth
is changing industry dynamics
Elisabeth Hirschbichler, Nathan Liu,
and Ulrich Weihe
125
How companies can guard against
gender fatigue
Show you are serious about basics such as
mentoring and work–life flexibility—then
hold yourself accountable.
Dominic Barton and Lareina Yee
Closing View
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8 McKinsey Quarterly 2018 Number 1
SHOULD ASSESSING FINANCIAL
SIMILARITY BE PART OF YOUR
CORPORATE PORTFOLIO STRATEGY?
Strategic connections among, for example,
a company’s suppliers, customers, skills,
and technology have long been the sine
qua non of corporate portfolio decisions.
Businesses that are strategically similar—
or related, in the parlance of portfolio
theory—belong in the same company.
Those that aren’t, the theory posits,
would be better owned by someone else.
What we are calling financial similarity may
be just as relevant. In a recent survey
of more than 1,200 executives,1 we found
that those managing portfolios of financially
similar businesses are 20 percent more
likely than those managing financially
dissimilar portfolios to describe themselves
as more profitable and faster growing
than their peers (exhibit). Financial similarity
is not an issue addressed in discussions of
portfolio theory, and (other than among
executives at complex conglomerates)
we frequently find that it’s a subconscious
issue for many executive teams. As a
result, they underestimate the difficulty of
managing businesses with fundamentally
different economic characteristics—
including revenues, margins, capital
intensity, and revenue growth.
How does financial dissimilarity affect performance?
In part, it’s a cognitive challenge
for managers to make comparisons
across businesses with dissimilar business
models, growth rates, and maturity.2
Using different metrics to evaluate and
capture the complexity of the portfolio
complicates comparisons, while turning
to coarser metrics or crude rules of
thumb leads to worse decisions.
Businesses with different financial profiles can tax managers and put
performance at risk. When divesting isn’t an option, here’s how to manage
the conflicts.
by Tim Koller, Dan Lovallo, and Zane Williams
Leading Edge
8 Should assessing financial
similarity be part of
your corporate portfolio
strategy?
11 A closer look at
impact investing
15 Accelerating the
diffusion of technologyenabled
business
practices
19 Shaking up the
leadership model in
higher education
Industry Dynamics:
24 Aftermarket sales
26 Electricity storage
China Pulse:
28 Chemicals growth
Research, trends, and emerging thinking
9
Managers of financially dissimilar businesses
also often face greater internal
political challenges. Performance goals and
resource allocation necessarily vary
across units that differ in business model,
scale, or maturity, and that variability
can generate conflict. This is especially
true when some units are given a budget
to invest and grow while others are asked
to cut costs, or when one unit’s goals
seem easier to hit than do another’s. As
a result, large, established units often end
up with more of a company’s resources
than their performance warrants—at the
expense of small, faster-growing businesses.
Large, powerful business units are often
not cash cows but rather just fat cows.
When strategic linkages among
businesses are limited or nonexistent,
often the most value-creating solution
is just to divest or spin off those with
significantly different financial characteristics
from the core business. But in
many cases, the strategic advantages of
keeping financially dissimilar businesses
in the same portfolio may outweigh
the inevitable challenges. For example,
consider a company that serves the
same customers with two businesses:
one that supports a legacy, analog
technology and another that supports
a transition to an emerging digital one.
Or consider companies with units that
offer complementary goods to common
customers, such as the manufacturing,
servicing, and financing of equipment
or combinations of products and an
advisory/data business.
Exhibit
Financially similar companies are more likely to outperform peers.
Q1 2018
Financial Similarity
Exhibit 1 of 1
1
Financial similarity defined as companies with business units that have similar size, margins, returns on capital, and
revenue growth.
Source: McKinsey online investment-performance survey of 1,271 executives, 2016
~20% ~20% Difference in likelihood of
outperformance
Percentage-point difference in survey responses relative to the mean
Difference between the share of high- and low-similarity1
companies reporting …
… faster growth
than competitors
… greater profitability
than competitors
7
−3
7
High −2
similarity
Low
similarity
High
similarity
Low
similarity
Mean = 50%
10 McKinsey Quarterly 2018 Number 1
In these cases, a company must make
an extra effort to ensure that all units are
managed to maximize value. This might
entail combining financially dissimilar
businesses into a separate unit with distinct
and specialized management—much as
Google did when it renamed itself Alphabet.
Managers there left the core business
in a central Google division and designated
smaller, newer businesses as separate
units—which it reports collectively
to investors as “Other Bets”—under
Alphabet’s CEO.3
A company might also implement a flat
accounting structure, eliminating most
intermediate reporting units. With unit results
reported at a highly detailed level, for
as many as 50 or more units, managers
could more easily identify smaller,
faster-growing businesses, protect their
resources, and foster their development.
Both approaches protect the budgets
and other resources of small units
embedded in larger ones from cuts to their
product development or advertising
spending to meet the larger unit’s budget.
A company might also consider more
structural protection for smaller-unit budgets,
commonly known as ring-fencing.
Similarly, a company’s planning processes
must differentiate performance targets
for different units, rather than applying
broad corporate programs to all units.
For example, some units may need to be
exempt from a broad general and
administrative cost-reduction program.
For very new fast-growing units, more
emphasis might be shifted to revenue
targets rather than profit targets, or even to
meeting specific nonfinancial objectives,
such as launching a product by a certain
date. Targets for more mature units might
put more weight on margins and return
on capital.
Financial similarity is an issue that’s seldom
a part of corporate portfolio discussions.
Our research suggests that companies
will benefit if more leaders become more
aware of the challenge and look for
opportunities to address it.
1 The online survey was in the field from April 12 to April
22, 2016, and received responses from 1,271 executives.
Analysis controlled for strategic linkages as well as industry,
region, company size, and functional specialties.
2 See, for example, Robert L. Goldstone, “Similarity,
interactive activation, and mapping,” Journal of
Experimental Psychology: Learning, Memory, and
Cognition, January 1994, Volume 20, Number 1, pp. 3–28;
Arthur B. Markman and Dedre Gentner, “Structural
alignment during similarity comparisons,” Cognitive
Psychology, October 1993, Volume 25, Number 4,
pp. 431–67.
3 Alphabet Inc. Form 10-K, US Securities and Exchange
Commission, December 31, 2016, sec.gov.
Tim Koller is a partner in McKinsey’s New York
office, where Zane Williams is a senior expert.
Dan Lovallo is a professor at the University
of Sydney Business School and an adviser to
McKinsey.
Copyright © 2018 McKinsey & Company. All rights reserved.
11
A CLOSER LOOK AT IMPACT INVESTING
With the fraying contract between society
and business an urgent priority, many
companies and banks are eager to find
investments that generate business
and social returns. One avenue is “impact
investing”—directing capital to enterprises
that generate social or environmental
benefits, in projects from affordable housing
to sustainable timberland and eye-care
clinics, that traditional business models
often sidestep.
Mainstream investors often fear to tread on
this terrain, leaving the field to adventurous
venture capitalists and nongovernmental
organizations (NGOs) who act as “first
institutional investors.” While they see a
clear upside in new customers and
satisfied employees, they accept the conventional
view that these investments
can’t be scaled adequately to create
attractive returns, carry higher risk overall,
and are less liquid and thus tougher to
exit. Impact investing may be forecast to
grow to more than $300 billion by 2020,
but even that would be a small fraction of
the $2.9 trillion or so that will likely be
managed by private-equity (PE) firms
worldwide in 2020.
Our research in India, a testbed of new
impact-investment ideas where some
50 investors have poured $5.2 billion
into projects since 2010 and investment
is growing at a 14 percent annual clip,
presents a different perspective. We tested
four notions that have made mainstream
investors shy. The findings suggest that
as more companies and larger investors
become acquainted with the true state
of play, in India and elsewhere, they’ll find
investment opportunities that align with
their social and business aims.
The myth of lower returns
Impact investments in India have demonstrated
how capital can be employed
sustainably as well as meet the financial
expectations of investors. We looked
at 48 investor exits between 2010 and
2015 and found that they produced
a median internal rate of return (IRR) of
about 10 percent. The top one-third
of deals yielded a median IRR of 34 percent,
clearly indicating that it is possible to
achieve profitable exits in social enterprises.
We sorted the exiting deals by sector:
agriculture, clean energy, education,
microfinance firms and others that work
to increase financial inclusion, and
healthcare. Nearly 80 percent of the exits
in financial inclusion were in the top twothirds
of performance. Half the deals in
clean energy and agriculture generated a
The mistaken rap on this kind of “social” investment is that returns are weak and
realizing them takes too long.
by Vivek Pandit and Toshan Tamhane
12 McKinsey Quarterly 2018 Number 1
similar financial performance, while those
in healthcare and education have
lagged. With a limited sample of only 17 exits
outside financial inclusion, however,
it is too early to be definitive about the
performance of the other sectors.
Exhibit 1 shows some evident relationships
between deal size and volatility of turns
as well as overall performance. The larger
deals produced a much narrower range
of returns, while smaller deals generally
produced better results. The smallest
deals had the worst returns and the greatest
volatility. These findings suggest that
investors (particularly those that have been
hesitant) can pick and choose their
opportunities, according to their expertise
in seeding, growing, and scaling social
enterprises.
Capital doesn’t need as much
patience as you think
Our analysis shows both the mean and the
median holding periods when investors
exit have been about five years, no different
than the holding periods for conventional
PE and venture-capital (VC) firms. Deals
yielded a wide range of returns no matter
the holding period. Viewed another way,
this also implies that social enterprises with
strong business models do not need
long holding periods to generate value
for shareholders.
Conventional funds are joining in
Social investment requires a wide range
of investors to maximize social welfare;
companies receiving investment need
Exhibit 1
Midsize deals produce better results on average, while the smallest generated
the greatest volatility.
Q1 2018
Impact Investing
Exhibit 1 of 3
1
Number of exited deals = 48.
Source: Impact Investors Council (IIC) survey covering investments over the years 2010–16; VCCEdge; McKinsey analysis
Internal rate
of return (IRR),
median, %
Largest
Smallest
≥5.0 160
160
160
160
–50
0 18
–39
–28
–46
49
84
153
–50
–50
–50
1.0 to 5.0
0.1 to 1.0
<0.1
13.06
2.38
0.45
0.02
Average
investment,
$ million
Size of
deal,1
$ million IRR range, %
8
16
12
2
13
different skills as they evolve. Stage-one
companies need investors with expertise
in developing and establishing a viable
business model, basic operations, and
capital discipline. For example, one
investment in a dairy farm needed a round
of riskier seed investment before becoming
suitable to conventional investors.
Stage two calls for skills in balancing
economic returns with social impact and
the stamina to commit to and measure
the dual bottom line. And stage three
requires expertise in scaling up, refining
processes, developing talent, and
systematic expansion.
Core impact investors were the first investors
in 56 percent of all deals (Exhibit 2), and
in eight of the top ten microfinance institutions
in India. Significantly, we found
that this led to interest from conventional
PE and VC funds, even as the business
models of the underlying industries began
to mature. Conventional PE and VC funds
brought larger pools of capital, which
accounted for about 70 percent of initial
institutional funding by value.1 This is
particularly important for capital-intensive
and asset-heavy sectors such as clean
energy and microfinance. Overall, mainstream
funds contributed 48 percent
of the capital across sectors (Exhibit 3).
Club deals that combine impact investors
and conventional PE and VC funds
contributed 32 percent of capital, and
highlight the complementary role of
both kinds of investors. As enterprises
mature and impact investors remain
involved, they are able to pull in funding
from mainstream funds. Nonprofit
Exhibit 2
Core impact investors play a critical role in seeding and de-risking
social enterprises.
Q1 2018
Impact Investing
Exhibit 2 of 3
1
Based on data for 248 first institutional deals; figures may not sum to 100%, because of rounding.
Source: Impact Investors Council (IIC) survey covering investments over the years 2010–16; VCCEdge; McKinsey analysis
Deals as first institutional investor,¹ %
Total
Conventional
private equity and
venture capital
Core-impact
investors
Agriculture Financial
inclusion
Other Education Healthcare Clean
energy
28
72
26
66
44
56
31
53
46
46
56
40
39
56
16
4
8
8
Club deals 6
14 McKinsey Quarterly 2018 Number 1
organizations also play a complementary
role, by providing highly effective bootson-the-ground
capabilities. Nonprofits have
typically been active longer than impact
companies, and have developed costeffective
mechanisms for delivering products
and services and implementing business
plans. Impact investors could be seen
as strategic investors in nonprofits, which
in turn play a role in scale-up, talent
attraction, and the delivery of financial
and operating leverage. One impact
investor, for instance, build a sister organization
to coach microfinance founders
as they set out, and help them build skills.
The social impact is significant
Impact investments touched the lives
of 60 million to 80 million people in India.
That’s equivalent to the population of
France, a figure that is much greater than
the proverbial drop in the ocean many
imagine impact investment to be—more
like a small sea. To be sure, India has
vast populations of people in need. But
then again, as social enterprises scale, so
will their impact, reaching a critical number
of at-risk people in smaller populations.
As investors, reexamine their understanding
of impact investing, the capital commitments
they make are sure to expand. That
will undoubtedly provide new challenges.
But our research suggests that this nascent
asset class can meet the financial challenges
as well as achieve the social returns
sought by providers of capital globally.
Exhibit 3
1 VCCEdge, McKinsey analysis.
Vivek Pandit is a senior partner in McKinsey’s
Mumbai office, and Toshan Tamhane is a senior
partner in the Jakarta office.
Copyright © 2018 McKinsey & Company. All rights reserved.
Overall, mainstream funds contributed nearly half the capital across sectors.
Q1 2018
Impact Investing
Exhibit 3 of 3
1
Private equity and venture capital.
Source: Impact Investors Council (IIC) survey covering investments over the years 2010–16; VCCEdge; McKinsey analysis
Club deals
Conventional
PE and VC1
Impact
investors
Share of investment value by type of investor, %
100% = $5.2 billion in cumulative investments
32
20
48
15
ACCELERATING THE DIFFUSION
OF TECHNOLOGY-ENABLED BUSINESS
PRACTICES
McKinsey research has long demonstrated
the wide gap between productivity levels
in different countries. Research in 2015,
for example, suggested that if the degree
of productivity dispersion among the
bottom 75 percent of UK firms matched
that of Germany, the United Kingdom
would be more than £100 billion better off
annually as measured by incremental
gross value added (GVA).1 This analysis
also showed that a major reason for
that discrepancy is the United Kingdom’s
relatively slower diffusion of digital
technologies and proven business practices
among the bulk of its business population.
We set out recently to investigate what
drives, and holds back, the diffusion
of technology-enabled business practices,
using a mix of academic literature,
studies from multinational organizations
such as the Organisation for Economic
Co-operation and Development (OECD)
and the World Economic Forum, and
in-depth interviews with business leaders
and other experts. We identified 13 levers,
or “characteristics,” that appear to accelerate
the adoption of technologies and
practices that have been implemented by
innovation leaders but are new to less
advanced firms.2 Six of those 13 levers
can be influenced directly by the actions
of businesses themselves, largely
independent of broader factors such as
competition, education, regulation,
and infrastructure quality.
The application of these six levers varies
widely among firms within countries and
across different geographies (exhibit).
For example, professional management
practices that drive diffusion have been
more widely adopted, on average, in
German and US firms than in firms in other
countries. On the other hand, Japanese
firms tend to benefit more than others from
access to plentiful science and technology
talent. UK firms, in turn, stand out
for their external collaborations with
the strong local-science base and for
their embrace of value chains that
are advanced, global, or both.
Given the importance of, and wide disparity
in performance across, these six levers,
they form a useful checklist for companies
anywhere seeking ways to accelerate
their uptake of productivity-enhancing,
technology-enabled business practices:
New research highlights some of the most important actions available
to executives.
by Tera Allas and Vivian Hunt
16 McKinsey Quarterly 2018 Number 1
Exhibit
Six levers help companies to accelerate the adoption of technology and
innovative business practices.
Q4 2017
Innovation Diffusion
Exhibit 1 of 1
1
Average of z-scores for 2–4 selected metrics per lever, where each metric is given equal weight. Z-scores represent standard
deviations from the mean value of each metric across the G-7 countries.
Source: European Innovation Scoreboard 2016; Eurostat; Global Entrepreneurship Monitor; Organisation for Economic
Co-operation and Development (OECD); The Global Competitiveness Index 2016–17, World Economic Forum; The Global
Innovation Index 2016; World Management Survey; Organizational Health Index by McKinsey; McKinsey analysis
Performance scores1
Application of these levers varies across countries
Attract top managers with
vision and desire to drive
adoption of new ideas
Recruit people with skills to turn
external innovation into internal
business practices
Collaborate externally to
stay updated and to absorb
best practices
Cultivate mind-sets and culture
to take considered risks
Prioritize training and development
to build employee skills and to
improve understanding of technology
Integrate business with
advanced or global value chains
–1.5 –1.0 –0.5 0 0.5 1.0 1.5
Canada UK Japan
France
Italy
Germany
US
17
1. Attract top managers with the vision
and desire to drive adoption of new ideas.
It makes sense to ensure that at least some
C-suite executives have a track record
of advocating and implementing new business
approaches or technologies such as
artificial intelligence (AI), big data analytics,
or robotics. Sixty percent of companies
identified as early adopters of artificial
intelligence in a recent MGI study,3 for
example, reported significant support from
their C-suite; only 33 percent of those
conducting more limited experiments with
AI reported this sort of support.
2. Cultivate the mind-sets and culture to
take considered risks. This can happen
through embedding the outside perspective
in company values and through creating
opportunities for managed experimentation
and quick wins (emphasizing that it’s
not essential to get it right the first time).
McKinsey innovation analysis shows
that 55 percent of top-quartile innovators
set concrete targets and aspirations for
innovation and growth, compared with
just 38 percent of second-quartile innovators
and 20 percent and 10 percent, respectively,
of third- and fourth-quartile innovators.4
3. Collaborate externally. Business and
professional hubs and networks, as well as
exchanges or joint research activities
between universities and business, are key.
As Corning’s Silicon Valley technology
chief Dr. Waguih Ishak pointed out in a recent
McKinsey Quarterly article,5 such
relationships constantly renew how a firm
operates. Indeed, academics estimate
that around 40 percent of a company’s
success in adopting new ideas is explained
by the quality of its internal and external
networks.6 Associations among business,
government, research institutions,
and trade unions have been behind the
adoption of Industrie 4.0 in Germany.
4. Integrate the business with advanced
or global value chains to expose it to
the maximum number of best practices.
This can mean looking beyond the usual
supplier suspects to more innovative
up-and-coming companies, or seeking
experimental partnerships with leadingedge
potential customers (even if not
initially profitable). Surveys consistently
show that suppliers and customers
are among the most important sources
of encouragement for the adoption
of advanced business practices.7
5. Prioritize training and development
to build better employee skills. Such
efforts may include initiatives to improve
top management’s understanding of
technology but may also be targeted at
ways of working. In an experiment in
India, textile firms were split into two groups,
with one set receiving training (a key
mechanism for diffusing knowledge) to
build up its management skills, while
the other did not. The group with training
was 11 percent more productive and
$230,000 a year more profitable.8
6. Recruit people with the skills to turn
external innovation into concrete business
practices and competitive advantage.
The United Kingdom’s Innovation Survey
shows that companies that both invent
new ideas and adopt those of others
employ almost twice as many degreelevel
graduates and two and a half times
as many science and engineering
18 McKinsey Quarterly 2018 Number 1
graduates as noninnovative ones. Highly
educated talent not only tends to be
more externally oriented9 but also enhances
“absorptive capacity”: the ability of
companies to observe, learn from, and
implement ideas from the outside.10
These levers sound fairly intuitive, but
our research suggests they’re too often
overlooked. Leaders worried about
staying at the leading edge can’t afford
to ignore them.
1 See Jonathan Dimson, Vivian Hunt, Daniel Mikkelsen, Jay
Scanlan, and James Solyom, “Productivity: The route to
Brexit success,” December 2016, McKinsey.com.
2 For a full list of the 13 characteristics, see Exhibit 8 in From
ostrich to magpie: Increasing business take-up of proven
ideas and technologies, CBI, November 2017, cbi.org.uk.
3 See “How artificial intelligence can deliver real value
to companies,” McKinsey Global Institute, June 2017,
McKinsey.com.
4 See Marc de Jong, Nathan Marston, and Erik Roth, “The
eight essentials of innovation,” McKinsey Quarterly, April
2015, McKinsey.com.
5 See Dr. Waguih Ishak, “Creating an innovation culture,”
McKinsey Quarterly, September 2017, McKinsey.com.
6 See Hans Georg Gemünden and Thomas Ritter, “Network
competence: Its impact on innovation success and its
antecedents,” Journal of Business Research, September
2003, Volume 56, Number 9, pp. 745–55.
7 See, for example, “UK innovation survey 2012 to 2014:
Statistical annex,” Department for Business, Energy &
Industrial Strategy, October 2016, gov.uk.
Tera Allas is a senior fellow with the McKinsey
Center for Government and is based in
McKinsey’s London office, where Vivian Hunt is
a senior partner.
The authors wish to thank Kimberley Moran for her
contributions to this article.
Copyright © 2018 McKinsey & Company. All rights reserved.
8 See Nicholas Bloom et al., “Does management matter?
Evidence from India,” Quarterly Journal of Economics,
February 2013, Volume 128, Number 1, pp. 1–51,
academic.oup.com.
9 See Stefanie Schurer, Sonja C. Kassenboehmer,
and Felix Leung, Do universities shape their students’
personality?, IZA Institute of Labour Economics
discussion paper, number 8873, February 2015, iza.org.
10 See Rachel Griffith, Stephen Redding, and John Van
Reenen, “Mapping the two faces of R&D: Productivity
growth in a panel of OECD industries,” Review of
Economics and Statistics, November 2004, Volume 86,
Number 4, pp. 883–95, mitpressjournals.org.
19
Higher education in the United States
is a big industry—more than $500 billion
in annual expenditures—and it’s under
some big-time pressure as well. Colleges
and universities are being squeezed
by rising costs, buffeted by increasingly
activist stakeholders, struggling to keep
up with the effects of digitization on
traditional educational models, and facing
off against new competitors, such as
MOOCs (massive open online courses).
Competition for students is so fierce
that many universities must rely heavily
on student-aid “discounts” to keep
dorms and classrooms filled. Demographic
change, meantime, demands the
continuous reassessment of student–
customers and their needs.
This litany of disruption should sound
familiar to people in private industry, where
corporate boards often respond by
seeking nontraditional leaders—those outside
a company’s industry—who have
different sets of skills and who can bring
fresh approaches to problems.
Do business leaders have any business
leading universities? Anecdotally, at least,
it seems that colleges and universities
are turning to the for-profit sector for an
injection of nontraditional leadership. Just
to name three recent examples: Janet
Napolitano, former secretary of homeland
security, was named president of the
University of California system in 2013.
Clayton Rose, a former vice chairman
at JPMorgan Chase was appointed
president of Bowdoin College in 2015.
And in 2016, South Carolina State
University appointed James Clark, a
retired AT&T executive, as president.
Yet research on the scope of these leadership
changes and the reasons behind
them remains spotty. I’ve had the opportunity
to observe the phenomenon from
both sides of the desk, as it were—first as
a McKinsey senior partner and now
as the dean of the University of Virginia’s
Darden School of Business. To gain
additional insights into higher education’s
leadership transition, I dug into the data
and conducted interviews with leading
search firms, which have become ubiquitous
in presidential-succession processes.
More outsiders than ever
My research1 reveals that there is discord
on the definition2 of a nontraditional leader
and that, no matter what the definition,
Economic pressures, digital disruption, and rising job complexity are prompting
universities to seek more “outsider” leaders for their top jobs.
by Scott C. Beardsley
SHAKING UP THE LEADERSHIP MODEL
IN HIGHER EDUCATION
20 McKinsey Quarterly 2018 Number 1
the sheer number of nontraditional leaders
is significant and growing (Exhibit 1).
Nontraditional leaders by my definition—
those who have not, at some point in
their careers, come through the full-time
tenured-faculty track—now represent fully
a third of the presidential population.
They could become the majority of leaders
of liberal-arts colleges within another
decade or so, if present trends hold.
Nontraditional leaders are not
uniformly distributed
It is also clear that the proportion of
nontraditional presidents is not uniform
across universities. Search-firm
executives interviewed indicated that
institutions facing a crisis or with less
risk-averse boards tend to look for
nontraditional leaders. The data further
Exhibit 1
The typical profile of a higher-education leader has been trending
toward nontraditional.
Q1 2018
CEOs in Higher Ed
Exhibit 1 of 1
1
Estimates vary across studies because definitions of nontraditional leaders and types of universities in samples vary.
2Michael D. Cohen and James G. March, Leadership and Ambiguity: The American College President (Harvard Business Review
Press, 1986); data from large public and independent colleges and universities. Typical promotional hierarchy for academic
administrators defined as proceeding from professor to department chair to dean to provost to president.
3Robert Birnbaum and Paul D. Umbach, “Scholar, steward, spanner, stranger: The four career paths of college presidents,” The
Review of Higher Education, spring 2001; data from baccalaureate colleges in 1995. 4On the Pathway to the Presidency, American Council on Education, 2013; data from US colleges and universities in 2012. 5Scott C. Beardsley, Higher Calling: The Rise of Nontraditional Leaders in Academia (University of Virginia Press, 2017); data
from US News & World Report on 2014 liberal-arts colleges and Internet searches. 6Using Cohen and March’s definition (ie, % of presidents whose prior job was not president, provost, or chief academic ošcer)
and data from 2014 liberal-arts-college presidents; Scott C. Beardsley, Higher Calling.
Estimated share of presidents with nontraditional backgrounds,1 %
Nontraditional
defined as
Cohen and
March2 American
Council on
Education (ACE)4
Birnbaum and
Umbach3 Beardsley6 Beardsley5
Did not have
prior academic
administrative
experience
Did not have
prior academic
administrative
experience
Immediate prior
two jobs were
not in higher
education
First-time
presidents from
outside higher
education
Was not on
tenure track
at any point
in career
10
14
23
33
62
1986 1995 2012 2014
21
suggest that schools with a higher-thanaverage
proportion of nontraditional leaders
tend to be smaller (in students and staff),
less well-resourced (in endowment
per student), on the East Coast of the
United States, and religiously affiliated.
Institutions at the top of popular lists,
such as US News & World Report’s Best
Colleges ranking, are far less likely to
appoint nontraditional leaders than lowerranked
institutions—16 percent nontraditional
presidents for the top quintile of
colleges against 44 percent for the bottom
two quintiles (Exhibit 2). That said, there are
still significant numbers of nontraditional
presidents in the least likely segments:
those that include the highest ranked, most
selective, and richly endowed schools.
Among them are stalwarts such as Bates,
Bowdoin, Carleton, and Colby colleges.
Exhibit 2
Institutions at the top of popular college-ranking lists are far less likely
to appoint nontraditional leaders than lower-ranked institutions.
Q1 2018
CEOs in Higher Ed
Exhibit 2 of 3
Source: Scott C. Beardsley, Higher Calling: The Rise of Nontraditional Leaders in Academia (University of Virginia Press, 2017);
Internet searches; Integrated Postsecondary Education Data System; 2014 college rankings from US News & World Report
Presidents of liberal-arts colleges by background, %
School ranking
Top quintile
(n = 50)
3rd quintile
(n = 50)
Nontraditional
Traditional
2nd quintile
(n = 50)
Bottom 2 quintiles
(n = 98)
84 74 62 56
16 26 38 44
22 McKinsey Quarterly 2018 Number 1
Looking ahead
Are nontraditional leaders more
successful? The data fall silent on this
question because answering it requires
defining and measuring success. A
few markers, however, suggest that
nontraditional leaders are holding their
own. For example, institutions are more
likely to hire a nontraditional president
following a traditional president than the
reverse. Nontraditional presidents
also tend to have longer tenures: their
median is 6.9 years versus 4.6 years
for traditional presidents.
Executive-search professionals had
much to say about the trends underlying
the growing number and apparent
success of nontraditional leaders. On the
leadership “supply side,” there has been
a dramatic decline, over the past few
decades, in the number of tenure-track
professors in the United States (Exhibit 3).
Then there’s the job itself: just as in the
corporate world, it has changed, with
leaders now required to take on many
external-facing duties that extend beyond
fund-raising and maintaining good town–
gown relations. Understanding academic
norms and culture remains essential, but
Exhibit 3
The pipeline for traditional college presidents is thinning.
Q1 2018
CEOs in Higher Ed
Exhibit 3 of 3
Source: William G. Bowen and Eugene M. Tobin, Locus of Authority: The Evolution of Faculty Roles in the Governance of
Higher Education (Princeton University Press, 2015); Jack H. Schuster and Martin J. Finkelstein, The American Faculty
(Johns Hopkins University Press, 2006); National Center for Education Statistics’s Integrated Postsecondary Education
Data System, 2009
1969 2009
78
67
22
33
Not on
tenure track
Tenured or on
tenure track
Faculty composition in US higher-education institutions, %
23
intense public scrutiny brought on by
24/7 social media, shifting government
regulations, and declining state funding
for public universities are all placing a
premium on better management, so many
talented traditional leaders no longer
want the job. Universities have become
much more complex businesses, as
well. Many large research institutions,
for example, have hospital systems
that account for as much as half of their
revenue and employment.
While these trends show no signs of
reversing, they won’t stop talented tenuretrack
professors from continuing to reach
the top. The forces at work do mean,
though, that colleges and universities
will need to be managed and led more
like the large, complex organizations
they are. The debate will rightfully shift
from whether the next president should
be traditional or nontraditional to what
challenges the leader needs to address.
Over time, search committees will
increasingly consider outsiders, many of
them from business. And to the extent
that they are successful, the door will
open wider for more of them.
Scott C. Beardsley is the dean and Charles C.
Abbott Professor of Business Administration at the
University of Virginia’s Darden School of Business.
He is an alumnus of McKinsey’s Brussels office,
where he was a senior partner until 2015.
Copyright © 2018 McKinsey & Company. All rights reserved.
1 The quantitative data set studied the 248 liberal-arts
colleges identified by US News & World Report.
2 Search-firm executives’ and academic definitions of a
nontraditional leader vary widely, from anyone who hasn’t
climbed the tenure-track ranks to the provost office
to anyone whose last two jobs were not at a university.
Colleges and universities will need to be
managed and led more like the large, complex
organizations they are.
This article is based on
research that appears in
the author’s recent book,
Higher Calling: The Rise
of Nontraditional Leaders
in Academia (University
of Virginia Press,
September 2017).
24 McKinsey Quarterly 2018 Number 1
MAXIMIZING INDUSTRIAL REVENUES—
AFTER THE SALE
New equipment sales are declining for
many original-equipment manufacturers
(OEMs) in industries from agriculture to
oil and gas. To boost the bottom line,
many are looking to postsales services,
where our analysis has shown that typical
earnings-before-interest-and-taxes
margins can be 25 percent or higher,
compared with roughly 10 percent for
new equipment.
To capture those potential gains, we find
industrial OEMs often are tempted to
prioritize data-driven advanced services,
such as e-commerce platforms and
remote monitoring. In doing so, however,
they may overlook core aftermarket
services—the provision of parts, repair,
and maintenance. To identify the best
opportunities, OEMs first need to
undertake a detailed examination of
aftermarket lifetime value—the total
amount of service revenue they could
capture across their customer base.1
Our research showed striking
performance variations in aftermarket
lifetime value at more than 40 Fortune
500 companies. Companies in the
top-performing industries captured five
times as much aftermarket lifetime value
per customer than those in the lowestperforming
industries. The differences
within industries were equally significant,
with the best performers realizing three
times more value than the lowest.
Lagging OEMs should identify the
aftermarket lifetime value of each
individual product and then select levers
tailored to performance improvement
(exhibit). For instance, they might be able
to increase product lifetime effectively by
remarketing used equipment or increase
average annual service revenue by
repricing spare parts more dynamically.
As companies evaluate improvement
levers, they should take care to balance
opportunities related to digital offerings
with those of core services.
Strengthening OEMs’ core service businesses in parts, repair, and
maintenance could give performance a big lift.
by Markus Forsgren, Florent Kervazo, and Hugues Lavandier
Industry Dynamics
Markus Forsgren is a partner in McKinsey’s
Stockholm office, and Florent Kervazo and
Hugues Lavandier are partners in the New
York office.
The authors would like to thank Aditya Ambadipudi,
Alex Brotschi, and James Xing for their contributions
to this article.
For the full article, see “Industrial
aftermarket services: Growing the core,”
on McKinsey.com.
1 Aftermarket lifetime value is the product of three variables:
product lifetime, lifetime penetration (the percent of an
OEM’s installed base for which it provides services during a
product’s lifetime), and average annual services revenue.
25
Copyright © 2018 McKinsey & Company. All rights reserved.
Exhibit
Companies can apply a broad set of improvement levers to boost the
aftermarket lifetime value of their products.
Q1 2018
Aftermarket
Exhibit 1 of 1
45–59
Average
improvement
Address
late-cycle
equipment;
upgrade
technology
continuously
Control sales
channels;
bundle servicecontract
coverage
Maximize value
from parts pricing;
expand into new
service offerings
5–9
10–16
2–4
Improvement in aftermarket lifetime value across industries,1
% of product’s initial sales price
EBIT impact,3
in percentage
points (pp)
For example:
Typical improvement Maximum observed
25
Starting
value
0–2 pp 1–5 pp 0–5 pp
Lifetime
penetration:
Share of
lifetime under
OEM service
Lifetime
penetration:
Attach rate2
Average
annual
services
revenue
3–5
Offer overhaul
and modernization;
remarket used
equipment
Product
lifetime
Start:
EBIT margin
22%
End:
EBIT margin
22–37%
–1 to 3 pp
1
Analysis of 40 OEMs.
2 Attach rate = % of new equipment sold with warranty or service contracts.
3 EBIT = earnings before interest and taxes; impact is average achieved when companies apply various improvement levers to
elements of aftermarket lifetime value.
26 McKinsey Quarterly 2018 Number 1
Industry Dynamics
WILL BATTERIES DISRUPT THE
UTILITIES INDUSTRY?
Cheap solar energy is already a challenge
to utilities. But cheap storage will be even
more disruptive, raising the prospect that
individual and business customers will
bypass traditional suppliers for greater
parts of their consumption.
Storage prices are dropping much faster
than anyone expected—battery costs in
2016 were one-quarter of what they were
in 2010. In this new world of low-cost
storage, solar users can stay connected
to the grid in order to have 24/7 access
but rarely have to use or pay for energy,
instead using stored energy, which helps
dramatically reduce their utility bills.
So-called partial grid defection reduces
demand for power provided by utilities
(because consumers are making their
own energy) and likely increases rates for
those who remain (because there is less
consumption to cover fixed grid costs).
This is already happening in places where
electricity is expensive and solar is widely
available, such as Australia and Hawaii. On
the horizon are other solar-friendly
markets such as Arizona, California,
Nevada, and New York (exhibit).
Storage, though, can also benefit utilities
in markets where loads are expected to
be flat or falling. In some US states, for
example, utilities can earn returns by
providing contracts for distributed energy
resources. This would, among other
things, allow them to defer expensive
new investments.
The future of storage is a matter of balance.
The ideal would be a regulatory system
that strives to balance the desire for
a healthy storage market and greater
freedom for customers to manage
their own energy requirements against
the need to ensure the economic
sustainability of the utilities and access
to electricity service for all customers.
Getting this right will be tricky, and no
doubt there will be missteps along the
way. But there is also no doubt that
storage’s time is coming.
A rapid decline in storage prices encourages customers to produce a greater
share of their own power, partially “defecting” from the grid.
by David Frankel and Amy Wagner
David Frankel is a partner in McKinsey’s Southern
California office, and Amy Wagner is a senior
expert in the San Francisco office.
The authors wish to thank Jesse Noffsinger and
Matt Rogers for their contributions to this article.
For the full article, see “Battery storage:
The next disruptive technology in the power
sector,” on McKinsey.com.
27
Exhibit
Copyright © 2018 McKinsey & Company. All rights reserved.
Partial grid defection likely makes economic sense within a few years; full
defection will take longer.
Q1 2018
Battery Storage
Exhibit 1 of 1
1
Levelized based on upfront capital cost and annual operations over total energy production.
2 Grid-defection economics are estimated based on solar power and storage for a hypothetical Arizona residential customer.
Partial grid defection assumes that 10% of power needs will be supplied by the utility grid. Full defection assumes addition of
a small generator for backup power.
Full grid defection2 (100%)
Projected cost of electricity1
Partial grid defection2 (90%)
2018 2022 2026 2030 2018 2022 2026 2030
10
20
30
40
10
20
30
40
Customer generated
Grid generated
Customer generated
Grid generated
¢ per kilowatt-hour ¢ per kilowatt-hour
28 McKinsey Quarterly 2018 Number 1
HOW CHINA’S SHIFT TO CONSUMERLED
GROWTH IS CHANGING INDUSTRY
DYNAMICS
China’s move from an investment-led to
a consumption-led economy is a familiar
theme. But the momentous shift is
changing the fortunes of manufacturing
industries in less visible ways as demand
for higher-value products expands. The
specialty-chemical industry is a case in
point (exhibit). In line with wider economic
trends, the fastest growers (and those
with higher earnings before interest, taxes,
depreciation, and amortization) include
the specialty chemicals used in the
manufacture of consumer goods such
as personal-care ingredients and
fragrances. Similarly, growth in advanced
industries such as autos, aerospace,
and electronics is supporting higher
demand for the likes of electronic chemicals
and high-performance plastics. On the
flip side, products used in traditional
industries are growing more slowly, their
margins squeezed as these markets
become more commoditized.
There may be lessons for other industries
in the way the changes are reshaping
the specialty-chemical sector. Chinese
players will benefit, to be sure, but the
new playing field should also allow
international players—which have been
losing share on their earlier, older-line
investments—scope to reposition
themselves to their advantage. The
demand for more sophisticated products,
after all, plays to the strengths of foreign
companies in specialty chemicals
and elsewhere.
With China’s economic turn likely to affect
the prospects for individual specialty
chemicals in different ways, executives
will need to carefully adapt product
strategies to fit these evolving patterns
of demand.
The experience of the specialty-chemical sector shows the groundlevel
impact.
by Elisabeth Hirschbichler, Nathan Liu, and Ulrich Weihe
Elisabeth Hirschbichler is an associate partner
in McKinsey’s Vienna office, Nathan Liu is a
partner in the Shanghai office, and Ulrich Weihe is
a partner in the Frankfurt office.
For a more complete set of findings,
see “A game plan for international
specialty-chemical companies in China,”
on McKinsey.com.
China Pulse
Exhibit
29
Copyright © 2018 McKinsey & Company. All rights reserved.
Many of the specialty-chemical industry’s advantaged segments are related to
the manufacture of consumer goods.
Q1 2018
China Chemicals
Exhibit 1 of 1
1
For selected specialty-chemical sectors. EBITDA = earnings before interest, taxes, depreciation, and amortization; margins
estimated based on EBIT margin + 5 percentage points; correlation derived from 50 publicly listed Chinese specialty-chemical
companies.
2 Excludes construction chemicals and polyurethanes.
3 Compound annual growth rate.
Source: CCID Consulting; Freedonia; IHS World Industry Survey; Marketline; McKinsey analysis
Profitability: 2014 average
EBITDA margin,1 %
Market-segment growth: CAGR,3 2014–19, %
20
4
8
12
16
4 6 8 10 12
Advantaged segments
Flavors, fragrances
Industrial
and institutional
cleaners
Battery materials
Specialty
films
Electronic
chemicals
Specialty surfactants
Compounded
engineering plastics
Construction
chemicals Plastics additives
Catalysts
Specialty coatings
Adhesives, sealants
Agrochemicals
Enzymes
Flame
retardants
Antioxidants
Oil-field chemicals
Nutritional ingredients,
food additives
High-performance
thermoplastics
Personal-care
chemicals
Polyurethanes
Specialty fertilizers
China 2015
revenues $1.5 billion
$18 billion
Advantaged segments
Weighted average2
30 EXCERPT FROM
Signed cartoons by Mike Shapiro;
all other cartoons by Jeremy Banks (Banx)
Strategy to beat the odds 31
Strategy to beat the odds
If you internalize the real odds of strategy, you can tame its social
side and make big moves.
by Chris Bradley, Martin Hirt, and Sven Smit
Several times a year, top management teams enter the strategy room with
lofty goals and the best of intentions: they hope to assess their situation
and prospects honestly, and mount a decisive, coordinated response toward a
common ambition.
Then reality intrudes. By the time they get to the strategy room, they find it is
already crowded with egos and competing agendas. Jobs—even careers—
are on the line, so caution reigns. The budget process intervenes, too. You may
be discussing a five-year strategy, but everyone knows that what really
matters is the first-year budget. So, many managers try to secure resources for
the coming year while deferring other tough choices as far as possible into
the future. One outcome of these dynamics is the hockey-stick projection, confidently
showing future success after the all-too-familiar dip in next year’s
budget. If we had to choose an emblem for strategic planning, this would be it.
In our book, Strategy Beyond the Hockey Stick (Wiley, February 2018), we set
out to help companies unlock the big moves needed to beat the odds. Another
strategy framework? No, we already have plenty of those. Rather, we need to
address the real problem: the “social side of strategy,” arising from corporate
politics, individual incentives, and human biases. How? With evidence.
We examined publicly available information on dozens of variables for
thousands of companies and found a manageable number of levers that
explain more than 80 percent of the up-drift and down-drift in corporate
32 McKinsey Quarterly 2018 Number 1
performance. That data can help you assess your strategy’s odds of success
before you leave the strategy room, much less start to execute the plan.
Such an assessment stands in stark contrast to the norms prevailing in most
strategy rooms, where discussion focuses on comparisons with last year,
on immediate competitors, and on expectations for the year ahead. There is
also precious little room foruncertainty, for exploration of the world beyond
the experience of the people in the room, or for bold strategies embracing big
moves that can deliver a strong performance jolt. The result? Incremental
improvements that leave companies merely playing along with the rest of
their industries.
Common as that outcome is, it isn’t a necessary one. If you understand the social
side of strategy, the odds of strategy revealed by our research, and the power
of making big moves, you will dramatically increase your chances of success.
THE SOCIAL SIDE OF STRATEGY
Nobel laureate Daniel Kahneman described in his book Thinking, Fast and
Slow the “inside view” that often emerges when we focus only on the case at
hand. This view leads people to extrapolate from their own experiences and
data, even when they are attempting something they’ve never done before.
The inside view also is vulnerable to contamination by overconfidence and
other cognitive biases, as well as by internal politics.
It’s well known by now that people are prone to a wide range of biases such
as anchoring, loss aversion, confirmation bias, and attribution error.
While these unintentional mental shortcuts help us filter information in our
daily lives, they distort the outcomes when we are forced to make big,
consequential decisions infrequently and under high uncertainty—exactly
the types of decisions we confront in the strategy room. When you bring
together people with shared experiences and goals, they wind up telling themselves
stories, generally favorable ones. A study found, for instance, that
80 percent of executives believe their product stands out against the competition—
but only 8 percent of customers agree.1
Then, add agency problems, and the strategy process creates a veritable petri
dish for all sorts of dysfunctions to grow.2
Presenters seeking to get that allimportant
“yes” to their plans may define market share so it excludes geo1
See Dominic Dodd and Ken Favaro, The Three Tensions: Winning the Struggle to Perform Without Compromise,
first edition, San Francisco, CA: Jossey-Bass, 2007.
2 Agency problems emerge when an agent is required to make decisions for another person or group, whose
information, preferences, and interests may not be aligned with the agent’s.
Strategy to beat the odds 33
graphies or segments where their business units are weak, or attribute weak
performance to one-off events such as weather, restructuring efforts, or a
regulatory change. Executives argue for a large resource allotment in the full
knowledge that they will get negotiated down to half of that. Egos, careers,
bonuses, and status in the organization all depend to a large extent on how
convincingly people present their strategies and the prospects of their business.
That’s why people often “sandbag” to avoid risky moves and make triple sure
they can hit their targets. Or they play the short game, focusing on performance
in the next couple of years in the knowledge that they likely won’t be running
their division afterward. Emblematic of these strategy-room dynamics is the
hockey-stick presentation. Hockey sticks recur with alarming frequency,
as the experience of a multinational company, whose disguised results appear
in Exhibit 1, demonstrates. The company planned for a breakout in 2011,
only to achieve flat results. Undeterred, the team drew another hockey stick
for 2012, then 2013, then 2014, then 2015, even as actual results stayed
roughly flat, then trailed off.
To move beyond hockey sticks and the social forces that cause them, the CEO
and the board need an objective, external benchmark.
Exhibit 1
One thing leads to another: Social dynamics and cognitive biases can lead to
successive hockey sticks.
Q1 2018
Strategy To Beat The Odds
Exhibit 1 of 3
1
Earnings before interest, taxes, depreciation, and amortization.
EBITDA,¹ disguised example, $ billion
3.0
2010
2.5
2014
2.0
1.5
1.0
0.5
2011 2012 2013 2015 2018
2016 2017
2012
plan
2013
plan
2014
plan
2015
plan
Actual performance
2011
plan
34 McKinsey Quarterly 2018 Number 1
THE ODDS OF STRATEGY
The starting point for developing such a benchmark is embracing the fact that
business strategy, at its heart, is about beating the market; that is, defying
the power of “perfect” markets to push economic surplus to zero. Economic
profit—the total profit after the cost of capital is subtracted—measures
the success of that defiance by showing what is left after the forces of competition
have played out. From 2010 to 2014, the average company in our
database of the world’s 2,393 largest corporations reported $920 million in
annual operating profit. To make this profit, they used $9,300 million
of invested capital,3 which earned a return of 9.9 percent. After investors
and lenders took 8 percent to compensate for use of their funds, that left
$180 million in economic profit.
Plotting each company’s average economic profit demonstrates a power
law—the tails of the curve rise and fall at exponential rates, with long
flatlands in the middle (Exhibit 2). The power curve reveals a number of
important insights:
• Market forces are pretty efficient. The average company in our sample
generates returns that exceed the cost of capital by almost two percentage
points, but the market is chipping away at those profits. That brutal
competition is why you struggle just to stay in place. For companies in the
middle of the power curve, the market takes a heavy toll. Companies in
those three quintiles delivered economic profits averaging just $47 million
a year.
• The curve is extremely steep at the bookends. Companies in the top quintile
capture nearly 90 percent of the economic profit created, averaging $1.4 billion
annually. In fact, those in the top quintile average some 30 times as much
economic profit as those in the middle three quintiles, while the bottom
20 percent suffer deep economic losses. That unevenness exists within
the top quintile, too. The top 2 percent together earn about as much as the
next 8 percent combined. At the other end of the curve, the undersea
canyon of negative economic profit is deep—though not quite as deep as the
mountain is high.
• The curve is getting steeper. Back in 2000–04, companies in the top
quintile captured a collective $186 billion in economic profit. Fast forward
3 We measure profit as NOPLAT—net operating profit less adjusted taxes. Invested capital comprises operating
invested capital of $6,660 million and goodwill and intangibles of $2,602 million. In other words, 28 percent of the
capital of a typical company represents additional value over book value paid in acquisitions.
Strategy to beat the odds 35
a decade and the top quintile earned $684 billion. A similar pattern
emerges in the bottom quintile. Since investors seek out companies that
offer market-beating returns, capital tends to flow to the top, no matter
the geographic or industry boundaries. Companies that started in the top
quintile ten years earlier soaked up 50 cents of every dollar of new capital
in the decade up to 2014.
• Size isn’t everything, but it isn’t nothing, either. Economic profit reflects the
strength of a strategy based not only on the power of its economic formula
(measured by the spread of its returns over its cost of capital) but also
on how scalable that formula is (measured by how much invested capital
it could deploy). Compare Walmart, with a moderate 12 percent return
on capital but a whopping $136 billion of invested capital, with Starbucks,
which has a huge 50 percent return on capital but is limited by being in a
much less scalable category, deploying only $2.6 billion of invested capital.
They both generated enormous value, but the difference in economic profit
is substantial: $5.3 billion for Walmart versus $1.1 billion for Starbucks.
Exhibit 2
The power curve of economic profit: The global distribution of economic profit
is radically uneven.
Q1 2018
Strategy To Beat The Odds
Exhibit 2 of 3
Average annual economic profit (EP) generated per company,
2010–14, $ million, n = 2,3931
Average EP
10,000
–10,000
5,000
–5,000
Cutoff for bottom quintile
EP average
for all
companies
Bottom Middle Top
Cutoff for top quintile
The value
exponentially
accrues
to the top
quintile
The ‘majority in the
middle’ make almost
no economic profit
296
1,428
–146
–670 47
180
1
Excluding 7 outliers (companies with economic profit above $10 billion or below –$10 billion).
Source: Corporate Performance Analytics by McKinsey
36 McKinsey Quarterly 2018 Number 1
• Industry matters, a lot. Our analysis shows that about 50 percent of your
position on the curve is driven by your industry—highlighting just how
critical the “where to play” choice is in strategy. Industry performance
also follows a power curve, with the same hanging tail and high leading
peak. There are 12 tobacco companies in our research, and 9 are in
the top quintile. Yet there are 20 paper companies, and none is in the top
quintile. The role of industry in a company’s position on the power curve
is so substantial that it’s better to be an average company in a great industry
than a great company in an average industry.
• Mobility is possible—but rare. Here is a number that’s worth mulling: the
odds of a company moving from the middle quintiles of the power curve
to the top quintile over a ten-year period are 8 percent (Exhibit 3). That
means just 1 in 12 companies makes such a leap. These odds are sobering,
but they also encourage you to set a high bar: Is your strategy better than
the 92 percent of other strategies?
THE POWER OF BIG MOVES
So what can you do to improve the odds that your company will move up the
power curve? The answer is lurking in our data. Consider this analogy:
To estimate a person’s income, we can start with the global average, or about
$15,000 per year. If we know that the person is American, our estimate
jumps to the average US per capita income, or $56,000. If we know that the
individual is a 55-year-old male, the estimate jumps to $64,500. If that
guy works in the IT industry, it jumps to $86,000. And if we know the person
is Bill Gates, well, it’s a lot more than that.
Adding ever more information similarly helps to zero in on the probabilities
of corporate success. Even if you know your overall odds, you need to understand
which of your attributes and actions can best help you raise them.
We identified ten performance levers and, importantly, how strongly you
have to pull them to make a real difference in your strategy’s success. We
divided these levers into three categories: endowment, trends, and moves.
Your endowment is what you start with, and the variables that matter
most are your revenue (size), debt level (leverage), and past investment in
R&D (innovation). Trends are the winds that are pushing you along, hitting
you in the face, or buffeting you from the side. The key variables there are
your industry trend and your exposure to growth geographies. In analyzing
the odds of moving on the power curve, we found that endowment determines
about 30 percent and trends another 25 percent.
Strategy to beat the odds 37
The moves that matter
However, it is your moves—what you do with your endowment and how you
respond to trends—that make the biggest difference. Our research found
that the following five moves, pursued persistently, can get you to where you
want to go:
• Programmatic M&A. You need a steady stream of deals every year, each
amounting to no more than 30 percent of your market cap but adding over
ten years to at least 30 percent of your market cap. Corning, which over
the course of a decade moved from the bottom to the top quintile of the power
curve, shows the value of disciplined M&A. Corning understands that
doing three deals a year means it must maintain a steady pipeline of potential
targets, conduct due diligence on 20 companies, and submit about five bids.
• Dynamic reallocation of resources. Winning companies reallocate capital
expenditures at a healthy clip, feeding the units that could produce a
major move up the power curve while starving those unlikely to surge. The
threshold here is reallocating at least 50 percent of capital expenditure
among business units over a decade. When Frans van Houten became
Philips’ CEO in 2011, the company began divesting itself of legacy assets,
including its TV and audio businesses. After this portfolio restructuring,
Exhibit 3
What are the odds? Companies have an 8 percent chance of jumping from the
middle to the top.
Q1 2018
Strategy To Beat The Odds
Exhibit 3 of 3
Source: Corporate Performance Analytics by McKinsey
% of companies staying in or moving out of
middle 3 quintiles, n = 1,435
10,000
–10,000
5,000
–5,000
Bottom Middle Top
stayed
moved up
fell down
0 78%
14%
8%
Average annual
economic profit,
$ million
38 McKinsey Quarterly 2018 Number 1
Philips succeeded at reinvigorating its growth engine by reallocating
resources to more promising businesses (oral care and healthcare were
two priorities) and geographies. Philips started, for example, managing
performance and resource allocations at the level of more than 340 businessmarket
combinations, such as power toothbrushes in China and
respiratory care in Germany. That led to an acceleration of growth, with
the consumer business moving from the company’s worst-performing
segment to its best-performing one within five years.
• Strong capital expenditure. You meet the bar on this lever if you are among
the top 20 percent in your industry in your ratio of capital spending to sales.
That typically means spending 1.7 times the industry median. Taiwanese
semiconductor manufacturer Taiwan Semiconductor Manufacturing
Company (TSMC) pulled this lever when the Internet bubble burst and
demand for semiconductors dropped sharply. The company bought missioncritical
equipment at the trough and was ready to meet the demand as soon
as it came back. TSMC had been in a head-to-head race before the downturn
but pulled clear of the competition after it ended because of its investment
strategy. That laid the foundation for TSMC to become one of the
largest and most successful semiconductor manufacturing pure plays in
the world.
• Strength of productivity program. This means improving productivity at
a rate sufficient to put you at least in the top 30 percent of your industry.
Global toy and entertainment company Hasbro successfully achieved the
top quintile of the power curve with a big move in productivity. Following
a series of performance shortfalls, Hasbro consolidated business units
and locations, invested in automated processing and customer self-service,
reduced head count, and exited loss-making business units. The company’s
selling, general, and administrative expenses as a proportion of sales
fell from an average of 42 percent to 29 percent within ten years. Sales
productivity lifted, too—by a lot. Over the decade, Hasbro shed more than
a quarter of its workforce yet still grew revenue by 33 percent.
• Improvements in differentiation. For business-model innovation and pricing
advantages to raise your chances of moving up the power curve, your
gross margin needs to reach the top 30 percent in your industry. German
broadcaster ProSieben moved to the top quintile of the power curve by
shifting its model for a new era of media. For example, it expanded its
addressable client base by using a “media for equity” offering for customers
whose business would significantly benefit from mass media but who
couldn’t afford to pay with cash. Some of ProSieben’s innovations were
Strategy to beat the odds 39
costly, sometimes even cannibalizing
existing businesses. But,
believing the industry would move
anyway, the company decided
that experimenting with change
was a matter of survival first and
profitability second. ProSieben’s
gross margin expanded from
16 percent to 53 percent during
our research period.
Greater than the sum
of the parts
Big moves are most effective
when done in combination—and
the worse your endowment or
trends, the more moves you need
to make. For companies in the
middle quintiles, pulling one or two of the five levers more than doubles their
odds of rising into the top quintile, from 8 percent to 17 percent. Three big
moves boost these odds to 47 percent.
To understand the cumulative power of big moves, consider the experience
of Precision Castparts Corp. (PCC). In 2004, the manufacturer of complex
metal components and products for the aerospace, power, and industrial
markets was lumbering along. Its endowment was unimpressive, with revenues
and debt levels in the middle of the pack, and the company had not invested
heavily in R&D. PCC’s geographic exposure was also limited, though the
aerospace industry experienced enormous tailwinds over the following ten
years, which helped a lot.
Most important, however, PCC made big moves that collectively shifted its
odds of reaching the top quintile significantly. The company did so by
surpassing the high-performance thresholds on four of the five levers. For
mergers, acquisitions, and divestments, it combined a high value and
large volume of deals between 2004 and 2014 through a deliberate and
regular program of transactions in the aerospace and power markets.
PCC also reallocated 61 percent of its capital spending among its three major
divisions, while managing the rare double feat of both productivity and
margin improvements—the only aerospace and defense company in our sample
to do so. While nearly doubling its labor productivity, PCC managed to
“. . . The third little pig wanted to build a wolf-proof brick
house. But the other two pigs thought that would take
away resources from their budgets, so they talked him
out of it right before the wolf killed all three of them.”
40 McKinsey Quarterly 2018 Number 1
reduce its overhead ratio by three percentage points. It lifted its gross profitto-sales
ratio from 27 to 35 percent.
The combination of a positive industry trend and successful execution of
multiple moves makes PCC a showcase of a “high odds” strategy and
perhaps explains why Berkshire Hathaway agreed in 2015 to buy PCC for
$37.2 billion. Could our model have predicted this outcome? Based on
the moves PCC made, its odds of rising to the top were 76 percent.
Patterns of movement
You should be mindful of several dynamics when undertaking major strategic
moves. First, our research shows that really big moves can “cancel out” the
impact of a poor inheritance. Making strong moves with a poor inheritance is
about as valuable as making poor moves with a strong inheritance. And
even small improvements in odds have a dramatic impact on the expected
payoff, owing to the extremely steep rise of the power curve. For example,
the probability-weighted expected value of a middle-tier company increasing
its odds to 27 percent from the average of 8 percent is $123 million—nearly
three times the total average economic profit for midtier companies.
Big moves are also nonlinear, meaning that just pulling a lever does not
help; you need to pull it hard enough to make a difference. For instance,
productivity improvements that are roughly in line with the improvement
rates of your industry won’t provide an upward boost. Even if you are
improving on all five measures, what matters is how you stack up against
your competitors.
And four of the five big moves are asymmetric. In other words, the upside
opportunity far outweighs the downside risk. While M&A is often touted as
high risk, for example, in reality
programmatic M&A not only
increases your odds of moving
up the curve but simultaneously
decreases your odds of sliding
down. Capital expenditures is
the one exception. By increasing
capital expenditures, your
chances of going up on the power
curve increase, but so do the
chances of dropping.
Strategy to beat the odds 41
In general, making no bold moves is probably the most dangerous strategy
of all. You not only risk stagnation on the power curve but also miss out on the
additional reward of growth capital, which mostly flows to the winners.
So how do you set up a strategy process that embraces a data-based outside
view in order to tame the social side of strategy and generate winning,
big moves? As we show in our book, there are several practical shifts you can
make to transform what happens in your strategy room, such as changing
the annual strategy-planning exercise into a continual strategy journey,
replacing base-case scenarios with momentum cases that extend the past
trajectory into the future, and making strong bets on a few breakout
opportunities rather than spreading resources across your divisions.
Adjustments such as these, combined with an empirical, objective benchmark
for the quality of a strategy that is independent from subjective judgments in
the strategy room, will change the conversation at the top of your company.
When you know, ahead of time, the chances of your strategy succeeding, and
you can see the levers that matter most to your own business, you can make
better choices and mitigate the impact of fear, ambition, rivalry, and bias. A
good strategy is still hard to shape, but you can at least navigate toward one
based on an accurate map.
Copyright © 2018 McKinsey & Company. All rights reserved.
Chris Bradley is a partner in McKinsey’s Sydney office, Martin Hirt is a senior partner in the
Greater China office, and Sven Smit is a senior partner in the Amsterdam office.
The authors would like to thank Nicholas Northcote for his contributions to this article and to
the accompanying body of research.
This article is adapted from the
authors’ new book, Strategy
Beyond the Hockey Stick: People,
Probabilities, and Big Moves to Beat
the Odds (Wiley, February 2018).
Illustrations by Emiliano Ponzi
THE TECH- AND DATA-ENABLED
ORGANIZATION OF THE FUTURE
42
43
Organizing for the age
of urgency
To compete at the speed of digital, you need to unleash your strategy,
your structure, and your people.
by Aaron De Smet and Chris Gagnon
Congratulations! Your organization is performing at or near the top of its
game, or it has been in the recent past. Perhaps even better, you have a strategy
to improve in the near future. Now for the bad news: the good news won’t last.
It can’t—at least without the right kind of organization. Across industries,
barely half of the top performers sustain their leadership position over the
course of a decade, according to research by our colleagues in McKinsey’s
Strategy Practice. The challenges in maintaining dominance are not new;
even sectors that digitization has not consigned to oblivion have seen
flagships such as Delta Airlines, General Motors, and Owens Corning move
from the top into Chapter 11 and then back into leadership positions again.
But of course, technology is changing everything. As digitization, advanced
analytics, and artificial intelligence (AI) sweep across industries and
geographies, they aren’t just reshaping the competitive landscape; they’re
redefining the organizational imperative: adapt or die. The average large
firm reorganizes every two to three years, and the average reorganization
takes more than 18 months to implement. Wait and see is not an option;
it’s a death sentence.
Organizing for the age of urgency
43 Organizing for the age
of urgency
55 Data as jet fuel:
An interview with Boeing’s CIO
In this package
44 McKinsey Quarterly 2018 Number 1
As a result, companies are beginning to experiment with increasingly radical
approaches. We’re struck by a commonality among those who get it right:
they create adaptive, fast-moving organizations that can respond quickly
and flexibly to new opportunities and challenges as they arise. In doing so,
they’re moving intelligent decision making to the front lines. That’s in sharp
contrast to the standard, “safer” modus operandi of capturing data, moving
it up a hierarchal chain, centrally analyzing it, and sending guidance back.
Several of these forward-thinking organizations now starkly describe their
decision making as being pushed to the “edges”—to and beyond employees,
past the organization’s four walls, and out to consumers and partners. The
process functions more like a network and less like a chain of command.
In this article, we’ll share these emerging elements of the organization of
the future. While there is no set formula for success, we’ve seen versions
of these elements at so many companies that we think they provide at least the
organizational outline to win (Exhibit 1). Along the way, we’ll try to dispel
some common misconceptions (too risky! too inefficient! too time consuming
to set up!) of what such an organization really means. We know you don’t
want your company to undergo yet another reorg—and another one a few
years after that. Consider this a road map out.
Exhibit 1
Organizing for urgency
Q1 2018
Organizing For Urgency
Exhibit 1 of 2
• Adopt a recipe
to run the place
• Cultivate purpose,
values, and social
connection
• Unleash decision
making
• Reimagine your
structure
• Personalize talent
programs
• Rethink your
leadership model
Identity
Agility Capability
Urgency
• Worship speed
• Shift to emergent
strategy
45
THE URGENCY IMPERATIVE
A good road map can come with callouts and suggestions, and here’s our
first: floor it. When you compete in a marketplace that moves so quickly, the
default outcome is to fall behind. If your organization is to have any hope
of keeping up, it will need to be reconceived as fast, quick to turn, and even
quicker to emerge from rapid pit stops and tune-ups. One could almost
analogize to a race car—almost, because race cars typically run on a fixed
track toward a clear finish line. Your organization’s race, by comparison,
is toward an unknowable destination. And that race doesn’t end.
Worship speed
At the highest-performing companies, speed is the objective function,
the operating model, and the cultural bias. And more: speed is an imperative.
Walk the halls of leading organizations, and you’ll repeatedly hear catchphrases
such as “energy,” “metabolic rate,” “bias for action,” and “clock speed.”
Jeff Bezos, in his April 2017 letter to Amazon shareholders, highlights
making not just “high-quality” decisions but “high-velocity” decisions. They
go hand in hand. “Most decisions,” writes Bezos, “should probably be made
with somewhere around 70 percent of the information you wish you had. If
you wait for 90 percent, in most cases you’re probably being slow.” Choosing
not to fail fast comes at a price. “If you’re good at course correcting,” Bezos
continues, “being wrong may be less costly than you think, whereas being
slow is going to be expensive for sure.”1
Shift to emergent strategy
Tacking and readjusting quickly are essential, even if the destination is
uncertain. In fact, because the destination is uncertain you need an “emergent
strategy,” which entails a relentless quest and not a defined end point. The
pursuit itself should be a firm’s North Star—a questioning of “how do we add
value” that’s unceasing but also unsolved, open to exactly how that manifests
in terms of specific opportunities and actions.
Too often, decisions about how to create value are made from on high and
tend to be “one and done.” They’re implemented by means of top-down
planning, frontline execution, frontline reporting back up the ladder, topdown
analysis of gaps, top-down replanning and pushing down mandates
to fill those gaps, frontline reexecution, and repeating it all again—a
process much too slow and mechanistic to keep up with real-world change.
That’s particularly the case in organizations with a number of “clay
Organizing for the age of urgency
1 Jeffrey P. Bezos, “2016 letter to shareholders,” April 12, 2017, Amazon, amazon.com.
46 McKinsey Quarterly 2018 Number 1
layers” of middle management, where officers feel compelled to add value
by refining, augmenting, synthesizing, piling on, micromanaging, and
adjusting information that passes their way—and where personal incentives
and cognitive biases inadvertently give rise to hockey-stick forecasts,
sandbagging, and poor decision making.
Our colleagues in McKinsey’s Strategy Practice have just written a book,
Strategy Beyond the Hockey Stick (Wiley, February 2018), about how to tame
this “social side” of strategy. By understanding the real odds (long) of
breaking out from the pack, by making a consistent series of big moves, and
by treating these steps as a journey that doesn’t end, they show that companies
can make strategic breakthroughs. (For more, see “Strategy to beat
the odds,” on page 30.)
Such an approach requires an organizational platform that allows for an
emergent mix of multiple strategies to be formulated and carried out in real
time. If the old world was a master composer like Mozart, planning every
detail for every instrument, the new world is improvisational jazz. But even
older cats can jam. One global chemical manufacturer, for example, had
originally been conceived, decades ago, to commercialize a singular scientific
breakthrough. When challenged, decades later, to dig deeper into how the
company had actually realized high returns after its founding period had passed,
leadership discovered that the business’s biggest moneymakers were
consistently the result of incremental, close-to-the-customer applications.
Many of those value-creating innovations had sprung from learning by doing,
improvising, and improving—and getting by on a shoestring. In fact, upon
further analysis, the company realized that it had been starving incremental
(but high-impact) innovation for the new New Thing, with poor returns
on investment too often the result. Grasping that insight, leadership decided
to flip its resource allocation almost completely. That fundamental shift,
hitching its star to emergent strategy, has since generated outperforming
value for more than a half dozen years.
AGILITY
The principles behind organizational agility have been around for decades.
In its current, most mainstream form, agility is a DevOps description of
how IT teams form to address problems, sprint toward solutions, and then
reconstitute to work on new challenges. These approaches have made
“agile” practical and concrete, and they’ve given rise to broader applications
yielding transformative impact across an entire enterprise. Much like
agile software development helps meet the challenge of producing an application
Organizing for the age of urgency 47
that is already obsolete when finally launched, enterprise agility helps
solve the problem of an organization’s strategies, resources, structures, and
capabilities being obsolete by the time they’re finally operational.
Organizing for urgency calls for organizing differently (Exhibit 2). The urgency
imperative places a premium on agility: it enables the shift to emergent
strategy, while unleashing your people so they can reshape your business in
real time. It’s also a powerful means of minimizing confusion and complexity
in our world of rapid-fire digital communications where everyone can
talk with everyone else—and will, gumming up the works if you don’t have
a sensible set of operating norms in place. Agility is also the ideal way to integrate
the power of machine-made decisions, which are going to become increasingly
important to your fundamental decision system.
Unleash decision making
In a competitive environment that’s changing so rapidly and so profoundly,
can any single individual keep up? Not in isolation, and certainly not
from the top down. But the right kind of organization—one that taps into a
network of individuals, recognizes the outperformance and resilience
that a diverse workforce will provide, and deploys technology aggressively
and purposefully—can.
To understand how, tap into your own decision system—the human brain—
and consider how people actually decide. While neuroscientists can identify
specific parts of the brain that are more active under certain circumstances, it’s
never the case that one discrete neuron, alone, is determinative. Rather,
intelligence is an emergent property of the whole system, and every person’s
“decision system” is a network of multiple, small, iterative processes honed
naturally over time.
That’s not to say all decisions are created equal; they are anything but, and a
failure to categorize often contributes to inefficient or ineffective decision
making. In our experience, the best way to understand decisions is to conceive
of them as part of a four-category taxonomy. The highest level of decision
making, we’d submit, comprises the decisions about how to decide. Call this
meta–decision making “greenhouse design.” It involves choosing the
foundational elements—the structures, governance arrangements, and
processes—that define how your organization operates and reflect its
core value proposition. This platform, in turn, supports looser, more dynamic
elements that can be adapted quickly in the face of new challenges and
opportunities. The CEO is absolutely essential for this organizational
48 McKinsey Quarterly 2018 Number 1
Exhibit 2
Q1 2018
Organizing For Urgency
Exhibit 1 of 2
Organizing for urgency calls for organizing differently.
From To
From To
Worship speed
Shift to emergent
strategy
Making a decision when you
have 90% of the information
Making a decision once you have
70% of the information
Setting your objective as a
predicted outcome
Realizing your objective is a
relentless, purposeful pursuit of
value creation
Unleash decision
making
Reimagine
your structure
Imposing decisions from the
top down
Encouraging real-time decisions at
the edges of your organization
Maintaining a hierarchical chain
of command, with decision-making
authority coupled to control
Creating a atter organization
and decoupling title or rank
from day-to-day control
From To
From To
Personalize
talent programs
Rethink your
leadership model
Offering generalized training
for the “average” employee
Customizing training for the individual,
in part by using advanced analytics
Elevating charismatic leaders
who get results by force
Recognizing that leadership can
come from anyone, regardless of
title, and is earned, not appointed
Adopt a recipe to
run the place
Cultivate purpose,
values, and social
connection
Sampling strategy à la carte
from a wide array of approaches
and methodologies
Sticking to a strategic prix xe to
select the one approach that best
matches how you create value
Conceiving of your
organization as a collection
of roles and processes
Aligning the individuals in your
enterprise around common principles
Identity
Agility
Capability
Urgency
49
“platform definition,” which is why some leading executives describe
themselves as “gardeners,” “city planners,” or “architects,” rather than
“operators” or even “strategists.”
The second category of decision making is “big-bet decisions.” These infrequent
and high-risk decisions have the potential to shape the future of your
company. Examples include major acquisitions and game-changing capital
investments—both high stakes and inherently risky. Organizations that
do well in this decision category focus not only on debiasing but also on
designating a single executive sponsor, atomizing decision components into
identifiable and more easily solvable parts, standardizing a decision-making
approach, and moving as fast as possible. Time, after all, is of the essence.
Less conspicuous but still high stakes are determinations in a third category:
“cross-cutting decisions.” These often look like big decisions but are actually
a series of smaller, interconnected choices made by different groups and
individuals as part of a collaborative, end-to-end decision process. Such
decisions include pricing, sales and operations planning (S&OP), new-product
launches, and portfolio management. These types of determinations are
necessarily cross-functional and often highly iterative. The challenge is to bring
together multiple parties who often have different priorities, so they can
provide the right input at the right time, without bureaucratic watering down.
The final category is made up of determinations that are pushed out to the
edges of your organization. These are the “delegated decisions” and “ad hoc
decisions.” Delegated decisions are high frequency and low risk (in other
words, even if long-term impact is high, bad decisions can be undone or corrected
long before significant consequences arise). They can be handled effectively
by an individual or a small natural working team, with limited input from
other parts of the organization. Such decisions also increasingly can be delegated
to algorithms (think instant recommendations on YouTube or route planning
at UPS). Ad hoc decisions are less frequent but still low stakes; they arise
unexpectedly, but frontline employee judgment should be supported by more
senior managers through an ethos that Jeff Bezos calls “disagree and
commit” and Zappos’s Tony Hsieh encourages as “safe enough to try.”
Reimagine your structure
The more interconnected your organization, and the more that decision
making can be diffused, the easier it will be to sustain high performance in
a world of uncertainty, speed, and disruption. Accelerating, unpredictable,
Organizing for the age of urgency
50 McKinsey Quarterly 2018 Number 1
and shifting currents of information are precisely not what a tall command
chain is designed to confront, especially in a turbulent external environment.
Those dynamics can render your firm’s advantages in numbers, tools, and
training irrelevant. That’s a key reason why even the most hierarchical chain
of command—the US military—moved to decentralize decision authority
to help beat back Al Qaeda’s Iraqi-based forces.2
Of course, hierarchies will continue to exist, and it’s right that certain functions
(think risk management, legal, treasury) should be centralized. In a world
growing more complex by the moment, there are compelling reasons for strata
of specializations and subspecializations—the very sort of dedicated
expertise that should be teamed for what we’ve described earlier as crosscutting
decisions. “Flattening,” without more, is not a comprehensive fix.
What does work is to free your initiatives and decisions from the constraining
hands of unnecessary hierarchy. While some level of prioritization and
resource allocation must be coordinated centrally, many actions and decisions
are best taken where the work is done at the front line, close to the customer.
To pull that off, eliminate superfluous management levels, decouple decisions
from control, and let go.
That calls for getting serious about letting your sensors, machine and human,
work their shared mojo as information providers and decision makers.
The human element is not a feel-good add-on. Winning organizations—from
the 2017 World Series Champion Houston Astros, who value player “heart”
and talent-evaluator intuition, to Zappos, whose passionate customer-service
agents have cultivated a passionately loyal customer base—are analytics
powerhouses, but they rely on inspired individuals to outpace the competition.
These organizations have also figured out that flatter makes it much easier to
operate in agile ways, to speed information along, and to integrate disparate
sources of it in ways that boost the odds of making decisions that serve the
interests of the company as a whole, not just of isolated, self-interested cells.
CAPABILITY
In order to operate with urgency and pursue the agility that makes high
performance possible, you’re likely going to have to fill some serious capability
gaps along the way. What’s more, many of the critical skills your people need—
as individuals, team members, and leaders—are changing rapidly as a result
of workplace automation and AI. As less complex work becomes increasingly
2 General Stanley McChrystal, Tantum Collins, David Silverman, and Chris Fussell, Team of Teams: New Rules of
Engagement for a Complex World, New York, NY: Portfolio, 2015.
51
automated, workers will need to be able not just to perform in concert with
machines but also to adapt to uncertainty. And the more that informationrich
tools are used (and the more effective they become), the harder it will
be to achieve the proper balance between person and machine—a challenge
that amplifies, in turn, the importance of continuous learning, employee
development, and consistent leadership.
Personalize talent programs
When direction comes primarily from “the boss,” your company will need
more bosses to keep on course. That’s one reason so many organizations
are too tall and bureaucratic. But if capabilities bubble up from within, and
learning is personalized for individuals and not the masses, employees
can act more urgently and, usually, more effectively.
Fortunately, organizations are gaining new tools—especially in people analytics—
that will enable them to manage and develop their people with greater
precision than ever before. Examples include a fast-food restaurant chain
that, after extensive testing, was able to identify and teach behaviors that
would inspire colleagues; rigorous research and statistical analyses used by
Alphabet to inform (but not replace) its engineers’ human judgment about
people decisions; and, in the case of one insurer, identifying which employees
would benefit most from which types of learning opportunities.
Rethink your leadership model
Central to talent development is a company’s leadership model. Leadership
can come from anyone, not just from those in positions of formal authority.
Think about your own firm: sometimes an employee can be a leader and sometimes
a follower, because while no one employee knows everything, many are
likely at the leading edge of something. What’s more, leaders in agile organizations
lead less by control than by influence. In one workshop we frequently
Organizing for the age of urgency
52 McKinsey Quarterly 2018 Number 1
conduct, we ask executives how they would solve a given issue. Most are
direct—they identify the problem and then fix it. A smaller group will drill
down to the problem’s root cause and fix that instead. Only a very few take
a more holistic approach; they consider how to create the conditions in which
an ecosystem can be largely self-managing, where individuals and tools can
learn and problems can be avoided before they manifest.
This, we believe, is what the urgency and uncertainty of the competitive future
will demand. The traditional model of a charismatic leader who gets results
by force of will has long proved expensive and is fast becoming outdated. Leaders
should strive, instead, to empower the organization as a whole, to be felt but
not seen, to be inspiring but not indispensable—and not to insist that everyone
else should be just like them. Such leadership rests on the ability to adapt
and on congruence with the essence of your organization.
IDENTITY
All of which leads into a fundamental challenge for urgency: If you build this
kind of “control light” organization, and it’s moving that fast—how do you keep
your bullet train from running off the rails? Our research shows that speed
needs to be channeled into stable processes, tasks, and roles if you’re going
to stay healthy as you move quickly. Realistically, lots of those sources of
stability are going to get upended by workplace automation, as we’ve noted
before. As well, operating with the urgency and agility we’re describing,
and overhauling organizational capabilities constantly to keep and exceed
competitive pace, can seem unsettling. And resource reallocation plainly
changes people’s lives. It’s hard, therefore, to keep your organization pulling
together when there’s so much ambiguity, so much shifting around, and too
little sense of why.
As digitization, advanced analytics, and
AI sweep across industries and geographies,
they aren’t just reshaping the competitive
landscape; they’re redefining the organizational
imperative: adapt or die.
53
Adopt a recipe to run the place
While there’s no pat answer to this uncertainty, following a clear recipe is an
effective way to start. By its very definition, a recipe is a defined set of conditions
and constraints. In siloed firms, one sees a wide array of processes and
practices, executed in dramatically different fashion across the organization
(and sometimes within the same silo). It makes for an incongruous hash,
with ingredients from management books over the last 20 years—a pinch of
this and a dash of that.
By contrast, the healthiest firms—those most capable of sustaining performance
and renewing over time—have a much simpler approach: they don’t sample à
la carte. Our research shows that four distinct recipes are particularly effective,
and having the discipline to stick with any one of them is critical. In fact,
organizational discipline is one of the foundations of both corporate health
and operational performance.
Nor are “health” and “operational results” binary choices. To keep from losing
their way, organizations must prioritize both at all times. That adds up to a
virtuous cycle that accelerates and enhances performance, even for fairly
mundane initiatives such as squeezing a bit more margin from better pricing
or lowering costs through more effective procurement. It also helps ground the
company and the people who comprise it, even in times of momentous change.
Cultivate purpose, values, and social connection
If you conceive of your organization as more than just a collection of roles
and processes, you’ll be far more prepared for the uncertainty ahead. Aligning
around common principles is a large part of what an organization of the
future is all about: participants making decisions under defined rules of
engagement, collaborating to create value, and earning the credibility to
lead rather than having “leadership” be imposed from on high.
Employees reach higher when their energies are channeled toward a higher
purpose. Because different people find inspiration from different sources,
it takes range to strike a chord that will resonate with almost everyone. Smart
organizations hit every note—and mean it. That calls for walking the talk
in, among other areas, race and gender diversity, social impact, and diversity
of political expression. Some employees are most inspired by personal
development (and, it must be said, monetary compensation); others find passion
in objectives geared more toward their working team, the company as a
whole, its customers, and even society at large. Cultivating purpose requires
Organizing for the age of urgency
54 McKinsey Quarterly 2018 Number 1
you to sharpen your organization’s sense of mission and strengthen your
employees’ social connection.
There’s an old quip that “everybody talks about the weather, but nobody does
anything about it.” With reorganizations, it’s too often the reverse: everybody
does a reorg, but nobody likes to talk about it. That’s because reorganizations
are hard to get right, distract everybody from senior leadership on down, and
have real consequences for meeting investor expectations. And even if you’re
game for continual top-down revisions, mantras such as “The only constant
around here is change!” run the risk of bewildering employees.
Ironically, shifting to urgency can stave off the ceaseless reorganization cycling.
In the face of today’s massive disruptions, an ethos of urgency actually serves
to smooth gyrations between “hurry up” and “settle in.” Of course, urgency
alone can also be a recipe for dysfunction. But combine urgency with agility,
capability, and identity, and you’ve got an organization that can play fast and
long. The future will be both.
Copyright © 2018 McKinsey & Company. All rights reserved.
Aaron De Smet is a senior partner in McKinsey’s Houston office, and Chris Gagnon is a senior
partner in the New Jersey office.
55
Data as jet fuel:
An interview with
Boeing’s CIO
It isn’t always comfortable, but data analytics is helping Boeing
reach new heights.
Boeing CIO Ted Colbert is something of an evangelist for the power of data
analytics. He recently spoke with McKinsey’s Aamer Baig about how
he has been spreading the word within Boeing, and why even companies
overflowing with analytical talent sometimes have to work hard to reap
its full rewards.
The Quarterly:Does a company like Boeing, renowned for its engineering
prowess, have a head start when it comes to harnessing the power of data
analytics?
Ted Colbert: To some extent, yes. We have a company full of engineers,
mathematicians, scientists, and statisticians who achieve amazing things. And
data analytics is certainly not a new field to the company. When I first
started to raise its growing importance, we probably had about 800 people
we could classify as data scientists, which was a great start. But when we
started to ask how data driven our decisions were, whether we really used the
insights we had to drive productivity and the capabilities of the company,
we quickly discovered there was much more we could be doing.
Data as jet fuel: An interview with Boeing’s CIO
56 McKinsey Quarterly 2018 Number 1
For example, we’d been using data-science capabilities to improve maintenance
decisions for a decade. But we hadn’t been pulling data from the factory
floor to understand how well Boeing’s production system was working. Take
the 787. I visited our factory in Everett [Washington] at a time when we
were under pressure to improve productivity. I wanted to better understand
how the mechanics worked. I was told, quite reasonably, that they followed
processes that are documented in a procedures manual, and everything anyone
did was logged in a system, as required for certification. We took a more
concerted effort to find improvements for factory-floor disruption, such as
mechanics spending a quarter of their time identifying parts, plans, and
tools to start their jobs.
At first, many people told me there was nothing new in what I was saying
about data analytics. “We already do that,” was the common response. It’s
only when you can produce these kind of proof points in areas that matter
that the light comes on for people—when they are under pressure to drive
margins, for example, but realize that the playbook they’ve been using for
years just doesn’t deliver anymore. It changes the mind-set. People come to
understand that there is a ton of richness trapped below all the capability
that already exists in the company.
Getting to that understanding isn’t always a comfortable journey. For example,
we wove together about 13 systems to show how much inventory was sitting
in our systems that didn’t have a demand pull. In a company our size, you
might expect it to be worth tens of millions of dollars. But we found it added
up to hundreds of millions of dollars. That made a few people very uneasy,
and their first instinct was to dispute the data. Let’s face it, when you highlight
this kind of stuff, you are highlighting the need for cultural change. But
Boeing is a 100-year-old company, and I don’t see my role as one of simply
reinforcing how great it is. Rather, it’s to figure out where truth lies in data
that will help us flourish for the next 100 years.
The Quarterly: How do you move from demonstrating data analytics’ power in
a handful of projects, to embedding it across a company the size of Boeing?
Ted Colbert: Demand for data-analytics resources mushrooms as you
demonstrate its value. At one time, we had over 100 data-analytics projects
in the queue related to improving productivity, be it in design, engineering,
manufacturing, or product support. But you have to be very strategic
and deliberate about how to scale up. On the one hand, you have to build
Data as jet fuel: An interview with Boeing’s CIO 57
momentum with a portfolio of projects—some small, some medium-size,
and a few in bigger, important areas. At the same time, you have to think long
term. The portfolio might yield tens of millions of dollars here, and maybe
a couple hundred million there—and you still could be only scratching the
surface. Analytics will take billions off the bottom line if you figure out
how people across the entire organization can grasp the opportunity—and
how to democratize the capability.
That can be tricky, because what you don’t want is people trying to go create
their own data platforms all over the place. It’s that fragmentation that went
wrong in the IT world 20 years ago and that makes it so hard today to get
at data. So you need to keep working on projects that prove the power of data
analytics and at the same time, in the background, plan the foundational
architecture and work toward a common platform. That platform will eventually
allow you to stratify data-analytics work. You can still put the most expensive,
smartest data scientists on the biggest problems, but you have unleashed the
power of the platform to one and all.
The Quarterly:A high-performing digital culture is one that is agile, that can
move quickly to embrace technological developments, all the while testing
new ideas and products and services, and learning in the process. How do you
square that with the way of working at a company like Boeing, whose products
take decades to develop?
Ted Colbert: It’s a fundamental issue. Boeing’s DNA is built around a long
business cycle and one that puts safety first. So whether you are developing
airplanes, fighter jets, or satellites, progress can be barely perceptible, like
a giant cog rotating. Digital developments, on the other hand, are tiny cogs,
moving 100 times faster. My job is to make sure both function together—
that the smaller cogs don’t break the big one. Often that means isolating our
“fail fast” activities.
Boeing’s services business is essentially a digital business, and it’s often
a better place to learn than our commercial and defense businesses. If we
give our engineers and other people an opportunity to work there, it will
help move the culture forward. Ultimately you can introduce agile ways of
working and speed up processes even for products that are as complex
and important as ours—and the result will be a better product. But it helps
to begin with things that are far away from that big cog and work our way
toward it over time.
58 McKinsey Quarterly 2018 Number 1
There is another level of complication for us, too. At Boeing, we start designing
new products decades in advance. We don’t continuously roll out new ones
that can be tweaked with our latest know-how. Let’s say we’re looking ahead
to a new plane we’re likely to build in two or three decades’ time. The engineers
would want to know, today, the efficiency-enhancing tools that would be
available in order to build their business case for the plane. I can’t just say,
“Trust me, we’ll be using machine learning in the design process.” No one
can sign up to big productivity gains if there is any doubt they will materialize.
It would destroy the whole cost and sales model.
We can’t completely solve this. It comes back to proof points. So we are setting
up a series of what we call pathfinders that will demonstrate data analytics’
worth. These bring data-analytic capabilities and agile ways of working
to bear on mature production programs such as the 737, where we need to
raise the rate of production, and the 787, where there’s an opportunity for
additional margin expansion. This is the only way we are going to get buyin
to future programs.
TED COLBERT
Vital statistics
Born in 1974
Married, with 2 children
Education
Completed the Dual Degree
Engineering Program at the
Georgia Institute of
Technology and Morehouse
College with degrees in
industrial and systems
engineering and
interdisciplinary science
Career highlights
Boeing
(2009–present)
Senior vice president
and CIO
(2016–present)
Chief information officer
(2013–present)
Citigroup
(2007–09)
Senior vice president of
enterprise architecture
Ford Motor Company
Spent 11 years with the
company’s informationtechnology
organization
Fast facts
Serves on the board of
directors for the Thurgood
Marshall College Fund
Received the 2017
Morehouse College Bennie
Service Award for
Excellence in Business and
the 2016 National Society
of Black Engineers Golden
Torch Legacy Award
59
The Quarterly: Has Boeing’s hiring culture changed? Traditionally, Boeing’s
senior managers have been internal promotions—people who have been with the
company throughout their careers. Is that model still tenable?
Ted Colbert: What keeps me awake at night is whether we have the right
talent. On one of our projects, I simply couldn’t find someone on the business
side who understood all the end-to-end processes well enough to deliver. So
you absolutely have to build the skills of the people who know Boeing well,
who have so much expertise. And if you want them to work differently, you
also have to build credibility with them. Many have been around for 20 or
30 years. That can be hard for people like myself from outside the industry—
I came via the car industry and banking. We do the usual things like trips to
Silicon Valley to demonstrate different working environments. But fundamentally,
the only way to change minds is to prove that there’s value in doing
things differently.
The Quarterly:What would success look like for you in a couple of years?
Ted Colbert: Reaching escape velocity! By that I mean that I don’t want to
find myself pushing as hard as I’ve been pushing the last couple years for
changing the way we work. If that were the case, gravity would still be pulling
us back toward the status quo. I want to be a catalyst for change. I want to
have established the foundational capabilities that will help senior business
leaders harness the power of digital analytics to better deliver on their
objectives. Then I can step back and watch take-off.
Copyright © 2018 McKinsey & Company. All rights reserved.
Ted Colbert is the CIO of Boeing. This interview was conducted by Aamer Baig, a senior partner
in McKinsey’s Chicago office.
Data as jet fuel: An interview with Boeing’s CIO
Illustrations by Davide Bonazzi
REACHING FOR THE DIGITAL PRIZE
60
61
Why digital strategies fail
Most digital strategies don’t reflect how digital is changing economic
fundamentals, industry dynamics, or what it means to compete.
Companies should watch out for five pitfalls.
by Jacques Bughin, Tanguy Catlin, Martin Hirt, and Paul Willmott
The processing power of today’s smartphones are several thousand times
greater than that of the computers that landed a man on the moon in 1969.
These devices connect the majority of the human population, and they’re only
ten years old.1
In that short period, smartphones have become intertwined with our lives in
countless ways. Few of us get around without the help of ridesharing and
navigation apps such as Lyft and Waze. On vacation, novel marine-transport
apps enable us to hitch a ride from local boat owners to reach an island. While
we’re away, we can also read our email, connect with friends back home, check
to make sure we turned the heat down, make some changes to our investment
portfolio, and buy travel insurance for the return trip. Maybe we’ll browse
the Internet for personalized movie recommendations or for help choosing
a birthday gift that we forgot to buy before leaving. We also can create
and continually update a vacation photo gallery—and even make a few oldfashioned
phone calls.
Then we go back to work—where the recognition and embrace of digital
is far less complete. Our work involves advising the leaders of large
Why digital strategies fail
61 Why digital
strategies fail
76 Why digital
transformation is
now on the CEO’s
shoulders
82 Digital snapshots:
Four industries
in transition
1 Early versions of the smartphone date to the mid-1990s, but today’s powerful, multipurpose devices originated
with the iPhone’s launch, in 2007.
In this package
62 McKinsey Quarterly 2018 Number 1
organizations. And as we look at this small device and all the digital change
and revolutionary potential within it, we feel the urge to send every CEO we
know a wake-up call. Many think that having a few digital initiatives in the
air constitutes a digital strategy—it does not. Going forward, digital strategy
needs to be a heck of a lot different from what they have today, or they’re not
going to make it.
We find that a surprisingly large number underestimate the increasing
momentum of digitization, the behavioral changes and technology driving
it, and, perhaps most of all, the scale of the disruption bearing down on
them. Many companies are still locked into strategy-development processes
that churn along on annual cycles. Only 8 percent of companies we surveyed
recently said their current business model would remain economically
viable if their industry keeps digitizing at its current course and speed.
How can this be, at a moment when virtually every company in the world is
worried about its digital future? In other words, why are so many digital
strategies failing? The answer has to do with the magnitude of the disruptive
economic force digital has become and its incompatibility with traditional
economic, strategic, and operating models. This article unpacks five issues
that, in our experience, are particularly problematic. We hope they will
awaken a sense of urgency and point toward how to do better.
PITFALL 1: FUZZY DEFINITIONS
When we talk with leaders about what they mean by digital, some view it as
the upgraded term for what their IT function does. Others focus on digital
marketing or sales. But very few have a broad, holistic view of what digital
really means. We view digital as the nearly instant, free, and flawless ability
to connect people, devices, and physical objects anywhere. By 2025, some
20 billion devices will be connected, nearly three times the world population.
Over the past two years, such devices have churned out 90 percent of the
data ever produced. Mining this data greatly enhances the power of analytics,
which leads directly to dramatically higher levels of automation—both of
processes and, ultimately, of decisions. All this gives birth to brand-new
business models.2 Think about the opportunities that telematics have created
for the insurance industry. Connected cars collect real-time information about
a customer’s driving behavior. The data allow insurers to price the risk associated
with a driver automatically and more accurately, creating an opportunity to
offer direct, pay-as-you-go coverage and bypassing today’s agents.
2 See Andrew McAfee and Erik Brynjolfsson, Machine, Platform, Crowd: Harnessing Our Digital Future, New York,
NY: W. W. Norton & Company, 2017.
63
Lacking a clear definition of digital, companies struggle to connect digital
strategy to their business, leaving them adrift in the fast-churning waters of
digital adoption and change. What’s happened with the smartphone over the
past ten years should haunt you—and no industry will be immune.
PITFALL 2: MISUNDERSTANDING THE ECONOMICS OF DIGITAL
Many of us learned a set of core economic principles years ago and saw the
power of their application early and often in our careers. (For more on the
changing economics of digital competition, see the infographic on pages
66–67.) This built intuition—which often clashes with the new economic
realities of digital competition. Consider these three:
Digital is destroying economic rent
One of the first concepts we learned in microeconomics was economic rent—
profit earned in excess of a company’s cost of capital. Digital is confounding
the best-laid plans to capture surplus by creating—on average—more value
for customers than for firms. This is big and scary news for companies
and industries hoping to convert digital forces into economic advantage.
Instead, they find digital unbundling profitable product and service
offerings, freeing customers to buy only what they need. Digital also renders
distribution intermediaries obsolete (how healthy is your nearest big-box
store?), with limitless choice and price transparency. And digital offerings
can be reproduced almost freely, instantly, and perfectly, shifting value to
hyperscale players while driving marginal costs to zero and compressing prices.
Competition of this nature already has siphoned off 40 percent of incumbents’
revenue growth and 25 percent of their growth in earnings before interest
and taxes (EBIT), as they cut prices to defend what they still have or redouble
their innovation investment in a scramble to catch up. “In-the-moment”
metrics, meanwhile, can be a mirage: a company that tracks and maintains
its performance relative to its usual competitors seems to be keeping pace,
even as overall economic performance deteriorates.
There are myriad examples where these dynamics have already played out.
In the travel industry, airlines and other providers once paid travel agents to
source customers. That all changed with the Internet, and consumers now
get the same free services that they once received from travel agents anytime,
anyplace, at the swipe of a finger—not to mention recommendations for
hotels and destinations that bubble up from the “crowd” rather than experts.
In enterprise hardware, companies once maintained servers, storage,
application services, and databases at physical data centers. Cloud-service
offerings from Amazon, Google, and Microsoft, among others, have made
Why digital strategies fail
64 McKinsey Quarterly 2018 Number 1
it possible to forgo those capital investments. Corporate buyers, especially
smaller ones, won because the scale economies enjoyed by these giants in the
cloud mean that the all-in costs of buying storage and computing power
from them can be less than those incurred running a data center. Some hardware
makers lost.
The lesson from these cases: Customers were the biggest winners, and the
companies that captured the value that was left were often from a completely
different sector than the one where the original value pool had resided. So
executives need to learn quickly how to compete, create value for customers,
and keep some for themselves in a world of shrinking profit pools.
Digital is driving winner-takes-all economics
Just as sobering as the shift of profit pools to customers is the fact that when
scale and network effects dominate markets, economic value rises to the
top. It’s no longer distributed across the usual (large) number of participants.
(Think about how Amazon’s market capitalization towers above that of other
retailers, or how the iPhone regularly captures over 90 percent of smartphone
industry profits.) This means that a company whose strategic goal is
to maintain share relative to peers could be doomed—unless the company
is already the market leader.
A range of McKinsey research shows how these dynamics are playing out.
At the highest level, our colleagues’ research on economic profit distribution
highlights the existence of a power curve that has been getting steeper over
the past decade or so and is characterized by big winners and losers at the top
and bottom, respectively (see “Strategy to beat the odds,” on page 30). Our
research on digital revenue growth, meanwhile, shows it turning sharply
If you set a digital strategy without focusing squarely on the potential for customers
to reap massive gains, you are likely to be blindsided. Consider the insurance
sector, where digital competitors are poised to disintermediate agents and, at the
same time, intensify competition with lower prices and higher levels of service.
One major insurer is fighting back by writing and marketing its own digital policies.
This entails risks, starting with the alienation of agencies that have traditionally
distributed its products. But the insurer strongly believes that smart digital approaches
will enable better pricing and superior customer experience compared with that
currently received from agents, and it sees no reason to cede this battlefield to
someone else.
INSURANCE
GETTING A BETTER GRIP ON CONSUMER SURPLUS
65
Farming-equipment manufacturer John Deere is responding to the potential for
digital entrants to sweep up value as sensors, data analytics, and artificial intelligence
boost farming productivity beyond what has been feasible previously. That could
commoditize farming “hardware” such as tractors and harvesting equipment. John
Deere is trying to stay ahead of this shift by creating a data-driven service business
that collects soil samples and analyzes weather patterns to help farmers optimize
crop yields. Sensors in tractors and other machinery provide data for predictive
maintenance; automated sprinkler systems sync up with weather data; and an opensoftware
platform lets third parties build new service apps. As the company’s
chairman and chief executive officer, Samuel R. Allen, told shareholders recently,
“Precision agriculture may evolve to a point that farmers will be able to monitor,
manage, and measure the status of virtually every plant in the field.”
Although still in the early days, the company’s moves position it to lead in the new
business of data-enabled agriculture while differentiating its traditional products
and services.
INSURANCE
GETTING A BETTER GRIP ON CONSUMER SURPLUS
JOHN DEERE
STAYING AHEAD OF THE DIGITAL THREATS
negative for the bottom three quartiles of companies, while increasing for
the top quartile. The negative effects of digital competition on a company’s
growth in earnings before interest, taxes, depreciation, and amortization
(EBITDA), meanwhile, are twice as large for the bottom three-quarters of
companies as for those at the top.
A small number of winners—often in high tech and media—are actually doing
better in the digital era than they were before. They marshal huge volumes of
customer data drawn from their scale and network advantages. That triggers
a virtuous cycle in which information helps identify looming threats and
the best partners in defending value chains under digital pressure. In this
environment, incumbents often find themselves snared in some common
traps. They assume market share will remain stable, that profitable niches
will remain defendable, and that it’s possible to maintain leadership by
outgrowing traditional rivals rather than zeroing in on the digital models
that are winning share.
This phenomenon of major industry shakeouts isn’t new, of course. Well
before digital, we saw industry disruptions in automobiles, PC manufacturing,
tires, televisions, and penicillin. The number of producers typically peaked,
and then fell by 70 to 97 percent.3 The issue now is that digital is causing such
disruptions to happen faster and more frequently.
3 Boyan Jovanovic and Glenn M. MacDonald, “The life cycle of a competitive industry,” The Journal of Political
Economy, April 1994, Volume 102, Number 2, pp. 322–47.
Why digital strategies fail
66 McKinsey Quarterly 2018 Number 1
Disruption is always dangerous, but digital disruptions are happening
faster than ever.
Tipping point
Incumbents’
business
models are
threatened
Majority of incumbents do not
respond and ultimately fail
A few incumbents
partially transform
and/or find niche
markets
Bold movers (attackers
and agile incumbents)
survive and rise
Market share
Time
100
New digital
business models
Incumbent
business models
of companies believe their business
model will remain economically viable
through digitization
Digital competition shrinks value. Customers win, and companies lose.
Products/services become obsolete, and value pools consolidate.
A ridesharing service is 40%
cheaper than a regular cab for
a 5-mile trip into Los Angeles
$$$ Ridesharing
$$$$$ Taxi
When was the last time you used a travel agent,
bought a GPS device, or carried a point-and-shoot
camera separate from your phone?
Growth rates will plummet. To survive, companies must be first movers …
Percentage-point change in 3-year revenue growth
Respond at an
average level, and
you’ll barely cut
the drop in half
You’ve grown
comfortable with
a steady state
of revenue growth
… you’ll see a
precipitous drop
in growth
If you fail to
respond to the
current digital
challenge …
Full digitization
and continued
inaction = an even
steeper drop
–6.0 –6.7
+0.3
–12
Be bold (a rst mover or
among the fastest
followers), and you’ll
keep climbing
Winners will think
in terms of
ecosystems.
… and the payoff will
go to those who move
boldly.
Integrated
network
economy
By 2025, almost a third of total global sales
will come from ecosystems.
Invest in
digital to protect
your core
Play in new sectors
or compete in
new digital ways
6%
ROI
12%
ROI
68% Traditional economy
32%
Companies need to change where and how they
play—by creating their own network or by partnering
with companies within and beyond industry borders.
Don’t underestimate how digital disrupts the nature
of competition.
Source: McKinsey Digital Global Survey, 2016 and 2017; McKinsey analysis
Why digital strategies fail 67
Disruption is always dangerous, but digital disruptions are happening
faster than ever.
Tipping point
Incumbents’
business
models are
threatened
Majority of incumbents do not
respond and ultimately fail
A few incumbents
partially transform
and/or find niche
markets
Bold movers (attackers
and agile incumbents)
survive and rise
Market share
Time
100
New digital
business models
Incumbent
business models
of companies believe their business
model will remain economically viable
through digitization
Digital competition shrinks value. Customers win, and companies lose.
Products/services become obsolete, and value pools consolidate.
A ridesharing service is 40%
cheaper than a regular cab for
a 5-mile trip into Los Angeles
$$$ Ridesharing
$$$$$ Taxi
When was the last time you used a travel agent,
bought a GPS device, or carried a point-and-shoot
camera separate from your phone?
Growth rates will plummet. To survive, companies must be first movers …
Percentage-point change in 3-year revenue growth
Respond at an
average level, and
you’ll barely cut
the drop in half
You’ve grown
comfortable with
a steady state
of revenue growth
… you’ll see a
precipitous drop
in growth
If you fail to
respond to the
current digital
challenge …
Full digitization
and continued
inaction = an even
steeper drop
–6.0 –6.7
+0.3
–12
Be bold (a rst mover or
among the fastest
followers), and you’ll
keep climbing
Winners will think
in terms of
ecosystems.
… and the payoff will
go to those who move
boldly.
Integrated
network
economy
By 2025, almost a third of total global sales
will come from ecosystems.
Invest in
digital to protect
your core
Play in new sectors
or compete in
new digital ways
6%
ROI
12%
ROI
68% Traditional economy
32%
Companies need to change where and how they
play—by creating their own network or by partnering
with companies within and beyond industry borders.
Don’t underestimate how digital disrupts the nature
of competition.
Source: McKinsey Digital Global Survey, 2016 and 2017; McKinsey analysis
68 McKinsey Quarterly 2018 Number 1
Digital rewards first movers and some superfast followers
In the past, when companies witnessed rising levels of uncertainty and volatility
in their industry, a perfectly rational strategic response was to observe
for a little while, letting others incur the costs of experimentation and then
moving as the dust settled. Such an approach represented a bet on the
company’s ability to “outexecute” competitors. In digital scrums, though,
it is first movers and very fast followers that gain a huge advantage over
their competitors. We found that the three-year revenue growth (of over
12 percent) for the fleetest was nearly twice that of companies playing it
safe with average reactions to digital competition.
Why is that? First movers and the fastest followers develop a learning advantage.
They relentlessly test and learn, launch early prototypes, and refine results
in real time—cutting down the development time in some sectors from several
months to a few days. They also scale up platforms and generate information
networks powered by artificial intelligence at a pace that far outstrips the
capabilities of lower-pulsed organizations. As a result, they are often pushing
ahead on version 3.0 or 4.0 offerings before followers have launched their
“me too” version 1.0 models. Early movers embed information across their
business model, particularly in information-intensive functions such as R&D,
marketing and sales, and internal operations. They benefit, too, from word of
mouth from early adopters. In short, first movers gain an advantage because
they can skate to where the puck is headed.
How Tesla captured first-mover value in electric vehicles offers a lesson in
the discomfiting effects of a wait-and-see posture. Four years ago, incumbent
automakers could have purchased Tesla for about $4 billion. No one made
the move, and Tesla sped ahead. Since then, companies have poured money
into their own electric-vehicle efforts in a dash to compete with Tesla’s lead
in key dimensions. Over the past two years alone, competitors have spent
more than $20 billion on sensor technologies and R&D.
PITFALL 3: OVERLOOKING ECOSYSTEMS
Understanding the new economic rules will move you ahead, but only so far.
Digital means that strategies developed solely in the context of a company’s
industry are likely to face severe challenges. Traditional approaches such as
tracking rivals’ moves closely and using that knowledge to fine-tune overall
direction or optimize value chains are increasingly perilous.
Industries will soon be ecosystems
Platforms that allow digital players to move easily across industry and sector
borders are destroying the traditional model with its familiar lines of sight.
69
In an industry where long product life cycles have been the norm, BMW has moved
from an annual model cycle to one with continual improvements throughout the
year. This has helped it to learn and apply digital and other technology advances
at a faster pace than that of some competitors that have stayed with traditional
cycle times. “All aspects of our products—whether design, handling, or everyday
usage—will be modeled more closely than ever before on the customer’s needs,”
Klaus Fröhlich, BMW’s board of management member responsible for development,
noted recently.
Moving fast sometimes necessitates competing with oneself. Anticipating increased
cost pressures and a faster competitive landscape as the pace of digitization in
travel and tourism progressed, Qantas Airways launched its stand-alone lower-fare
Jetstar. Intensive use of digital technology in booking, app-based loyalty programs,
automated check-in, and baggage service, as well as digitization in other service and
operations arenas, prompted the creation of the Jetstar brand, which is differentiated
by lower fares and a better customer experience.
To speed up its response time and disrupt (rather than follow) the industry, Qantas
was open to cannibalizing its flagship brand. Today, Jetstar’s margins on its
earnings before interest and taxes (EBIT) exceed those of the Qantas brand.
BMW AND QANTAS
MEETING THE NEED FOR SPEED
Grocery stores in the United States, for example, now need to aim their
strategies toward the moves of Amazon’s platform, not just the chain down
the street, thanks to the Whole Foods acquisition. Apple Pay and other
platform-cum-banks are entering the competitive set of financial institutions.
In China, Tencent and Alibaba are expanding their ecosystems. They are
now platform enterprises that link traditional and digital companies (and
their suppliers) in the insurance, healthcare, real-estate, and other industries.
A big benefit: they can also aggregate millions of customers across these
industries.
How ecosystems enable improbable combinations of attributes
Can you imagine a competitor that offers the largest level of inventory, fastest
delivery time, greatest customer experience, and lower cost, all at once?
If you think back to your MBA strategy class, the answer would probably be
no. In the textbook case, the choice was between costlier products with
high-quality service and higher inventory levels or cheaper products with lower
service levels and thinner inventories. Digital-platform and -ecosystem
economics upend the fundamentals of supply and demand. In this terrain,
the best companies have the scale to reach a nearly limitless customer
Why digital strategies fail
70 McKinsey Quarterly 2018 Number 1
base, use artificial intelligence and other tools to engineer exquisite levels
of service, and benefit from often frictionless supply lines. Improbable
business models become a reality. Facebook is now a major media player
while (until recently) producing no content. Uber and Airbnb sell global
mobility and lodging without owning cars or hotels.
This will all accelerate. Our research shows that an emerging set of digital
ecosystems could account for more than $60 trillion in revenues by 2025, or
more than 30 percent of global corporate revenues. In a world of ecosystems,
as industry boundaries blur, strategy needs a much broader frame of
reference. CEOs need a wider lens when assessing would-be competitors—
or partners. Indeed, in an ecosystem environment, today’s competitor may
turn out to be a partner or “frenemy.” Failure to grasp this means that you will
miss opportunities and underplay threats.
While it’s true that not all businesses are able to operate in nearly frictionless
digital form, platforms are fast rewiring even physical markets, thus redefining
how traditional companies need to respond. Look around and you will see the
new digital structures collapsing industry barriers, opening avenues for
cross-functional products and services, and mashing up previously segregated
markets and value pools. With vast scale from placing customers at the
center of their digital activity, ecosystem leaders have captured value that
was difficult to imagine a decade ago. Seven of the top 12 largest companies
by market capitalization—Alibaba, Alphabet (Google), Amazon, Apple,
Facebook, Microsoft, and Tencent—are ecosystem players. What’s not
Intuit began taking an ecosystem view of its markets when a strategic review
showed that fintech start-ups had the potential to target its customers with digital
products. The review also showed ways the company could flex its financial power
and scale. Leadership decided to acquire new digital assets to expand beyond
its existing small-business and tax products, in an effort to reach digitally adept
consumers who were happy to use software apps to help manage their money
as well as to get a reading on their overall financial health.
Three offerings—Mint (for consumers), QuickBooks (for small businesses), and
TurboTax (for both)—have been integrated with one login, and the company
offers banks the ability to integrate customer accounts with its products, allowing
customers easier access to online bill paying.
INTUIT
BUILDING AN ECOSYSTEM BY ACQUISITION
71
encouraging is how far incumbents need to travel: our research shows that
only 3 percent of them have adopted an offensive platform strategy.
PITFALL 4: OVERINDEXING ON THE ‘USUAL SUSPECTS’
Most companies worry about the threats posed by digital natives, whose moves
get most of the attention—and the disruptive nature of their innovative
business models certainly merits some anxiety. Excessive focus on the usual
suspects is perilous, though, because incumbents, too, are digitizing and
shaking up competitive dynamics. And the consumer orientation of many
digital leaders makes it easy to overlook the growing importance of digital
in business-to-business (B2B) markets.
Digitizing incumbents are very dangerous
Incumbents are quite capable of self-cannibalizing and disrupting the status
quo. In many industries, especially regulated ones such as banking or insurance,
once an incumbent (really) gets going, that’s when the wheels come off.
After all, incumbents control the lion’s share of most markets at the outset
and have brand recognition across a large customer base. When they
begin moving with an offensive, innovative strategy, they tip the balance.
Digitization goes from being an incremental affair to a headlong rush
as incumbents disrupt multiple reaches of the value chain. Digital natives
generally zero in on one segment.
Our research confirms this. Incumbents moving boldly command a 20 percent
share, on average, of digitizing markets. That compares with only 5 percent
for digital natives on the prowl. Using another measure, we found that revved-up
After a wide-ranging strategic review, Telefónica saw that it was vulnerable to digital
players that were offering mobile customers lower-cost plans and more flexible
models. In an effort to meet the challenges, the company launched an independent
“brownfield” start-up, giffgaff. Its hallmark was an online-first model for customer
support that uses community-based digital forums to resolve customer queries.
Incumbency offered an important advantage: one of the company’s key assets
was its O2 digital network, which provided resources and technical capabilities in
support of giffgaff’s innovative business model.
TELEFÓNICA
LEVERAGING INCUMBENCY
Why digital strategies fail
72 McKinsey Quarterly 2018 Number 1
incumbents create as much risk to the revenues of traditional players as
digital attackers do. And it’s often incumbents’ moves that push an industry
to the tipping point. That’s when the ranks of slow movers get exposed to lifethreatening
competition.
The B2B opportunity
The importance of B2B digitization, and its competitive implications, is easy
to overlook because the digital shifts under way are less immediately obvious
than those in B2C sectors and value chains. However, B2B companies can
be just as disruptive. In the industries we studied, more B2B companies had
digitized their core offerings and operations over the past three years than
had B2C players. Digitizing B2B players are lowering costs and improving
the reach and quality of their offerings. The Internet of Things, combined
with advanced analytics, enables leading-edge manufacturers to predict the
maintenance needs of capital goods, extending their life and creating a
new runway for industrial productivity. Robotic process automation (RPA)
has quietly digitized 50 to 80 percent of back-office operations in some
industries. Artificial intelligence and augmented reality are beginning to
raise manufacturing yields and quality. Meanwhile, blockchain’s digitized
verification of transactions promises to revolutionize complex and paperintensive
processes, with successful applications already cropping up in
smart grids and financial trading. Should the opportunities associated with
shifts like these be inspirational for incumbents? Threatening? The answer
is both.
PITFALL 5: MISSING THE DUALITY OF DIGITAL
The most common response to digital threats we encounter is the following:
“If I’m going to be disrupted, then I need to create something completely new.”
Understandably, that becomes the driving impetus for strategy. Yet for most
companies, the pace of disruption is uneven, and they can’t just walk away
from existing businesses. They need to digitize their current businesses and
innovate new models.
Think of a basic two-by-two matrix such as the exhibit on the following page,
which shows the magnitude and pace of digital disruption. Where incumbents
fall in the matrix determines how they calibrate their dual response. For
those facing massive and rapid disruption, bold moves across the board are
imperative to stay alive. Retail and media industries find themselves in
this quadrant. Others are experiencing variations in the speed and scale of
disruption; to respond to the ebbs and flows, those companies need to
develop a better field of vision for threats and a capacity for more agile action.
Keep in mind that transforming the core leads to much lower costs and
73
Exhibit
greater customer satisfaction for existing products and services (for example,
when digitization shrinks mortgage approvals from weeks to days), thus
magnifying the impact of incumbents’ strategic advantages in people, brand,
and existing customers and their scale over attackers.
Beyond this dual mission, companies face another set of choices that seems
binary at first. As we have indicated, the competitive cost of moving too
slowly puts a high priority on setting an aggressive digital agenda. Yet senior
leaders tell us that their ability to execute their strategy—amid a welter of
cultural cross-currents—is what they worry about most. So they struggle
over where to place their energies—placing game-changing bets or remaking
the place. The fact is that strategy and execution can no longer be tackled
separately or compartmentalized. The pressures of digital mean that you
need to adapt both simultaneously and iteratively to succeed.
Needless to say, the organizational implications are profound. Start with people.
Our colleagues estimate that half the tasks performed by today’s full-time
workforce may ultimately become obsolete as digital competition intensifies.4
New skills in analytics, design, and technology must be acquired to step
up the speed and scale of change. Also needed are new roles such as a more
diverse set of digital product owners and agile-implementation guides.
And a central organizational question remains: whether to separate efforts
to digitize core operations from the perhaps more creative realm of
digital innovation.
4 See “What the future of work will mean for jobs, skills, and wages,” McKinsey Global Institute, November 2017,
McKinsey.com.
Why digital strategies fail
Q1 2018
Digital Strategies Fail
Exhibit 2 of 2
Since the extent and speed of disruption varies, companies will need to
calibrate their response.
These companies need to prepare
themselves for big changes but
cannot lose focus on their existing
businesses in the short term.
Live in two worlds
These companies (eg, those in media,
retail) are faced with severe—and
perhaps fatal—disruption unless they
make big moves.
Take bold steps
These companies can cherrypick
simple plays but are relatively
unaffected.
Make low-risk moves
Degree of
change
Pace of change
These companies need to make
rapid moves but cannot let the
scope of these changes overshadow
existing businesses.
Build agility
74 McKinsey Quarterly 2018 Number 1
While the details of getting this balance right will vary by company, two
broad principles apply:
•Bold aspiration.The first-mover and winner-takes-all dynamics we described
earlier demand big investments in where to play and often major changes
to business models. Our latest research shows that the boldest companies,
those we call digital reinventors, play well beyond the margins. They
invest at much higher levels in technology, are more likely to make digitally
related acquisitions, and are much more aggressive at investing in businessmodel
innovation. This inspired boldness also turns out to be a big performance
differentiator.
• Highly adaptive. Opportunities to move boldly often arise as a result of
changing circumstances and require a willingness to pivot. The watchwords
are failing fast and often and innovating even faster—in other words,
learning from mistakes. Together they allow a nuanced sensing of market
direction, rapid reaction, and a more unified approach to implementation.
Adaptive players flesh out initial ideas through pilots. Minimum viable
products trump overly polished, theoretical business cases. Many companies,
however, have trouble freeing themselves from the mind-sets that take
root in operational silos. This hinders risk taking and makes bold action
difficult. It also diminishes the vital contextual awareness needed to
gauge how close a market is to a competitive break point and what the
disruption will mean to core businesses.
As digital disruption accelerates, we often hear a sense of urgency among
executives—but it rarely reaches the level of specificity needed to address the
disconnects we’ve described in the five aforementioned pitfalls. Leaders
are far more likely to describe initiatives—“taking our business to the cloud”
or “leveraging the Internet of Things”—than they are to face the new realities
of digital competition head-on: “I need to develop a strategy to become
number one, and I need to get there very quickly by creating enormous value
to customers, redefining my role in an ecosystem, and offering new business-value
propositions while driving significant improvement in my existing business.”
Such recognition of the challenge is a first step for leaders. The next one is
to develop a digital strategy that responds. While that’s a topic for a separate
article, we hope it’s clear, from our description of the reasons many digital
strategies are struggling today, that the pillars of strategy (where and how to
75
compete) remain the cornerstones in the digital era. Clearly, though, that’s
just the starting point, so we will leave you with four elements that could
help frame the strategy effort you will need to address the hard truths we had
laid out here.
First there’s the who. The breadth of digital means that strategy exercises
today need to involve the entire management team, not just the head of strategy.
The pace of change requires new, hard thinking on when to set direction.
Annual strategy reviews need to be compressed to a quarterly time frame, with
real-time refinements and sprints to respond to triggering events. Ever
more complex competitive, customer, and stakeholder environments mean
that the what of strategy needs updating to include role playing, scenarioplanning
exercises, and war games. Traditional frameworks such as Porter’s
Five Forces will no longer suffice. Finally, the importance of strategic agility
means that, now more than ever, the “soft stuff” will determine the how of
strategy. This will enable the organization to sense strategic opportunities in
real time and to be prepared to pivot as it tests, learns, and adapts.
Copyright © 2018 McKinsey & Company. All rights reserved.
Jacques Bughin is a director of the McKinsey Global Institute and a senior partner in McKinsey’s
Brussels office, Tanguy Catlin is a senior partner in the Boston office, Martin Hirt is a senior
partner in the Greater China office, and Paul Willmott is a senior partner in the London office.
The authors wish to thank Laura LaBerge, Shannon Varney, and Holger Wilms for their
contributions to this article.
Why digital strategies fail
76 McKinsey Quarterly 2018 Number 1
Why digital transformation
is now on the CEO’s
shoulders
Big data, the Internet of Things, and artificial intelligence
hold such disruptive power that they have inverted the dynamics
of technology leadership.
by Thomas M. Siebel
When science and technology meet social and economic systems, you tend
to see something akin to what the late Stephen Jay Gould called “punctuated
equilibrium” in his description of evolutionary biology. Something that has
been stable for a long period is suddenly disrupted radically—and then settles
into a new equilibrium.1
Analogues across social and economic history
include the discovery of fire, the domestication of dogs, the emergence of agricultural
techniques, and, in more recent times, the Gutenberg printing
press, the Jacquard loom, urban electrification, the automobile, the microprocessor,
and the Internet. Each of these innovations collided with a society
that had been in a period of relative stasis—followed by massive disruption.
Punctuated equilibrium is useful as a framework for thinking about
disruption in today’s economy. US auto technology has been relatively static
since the passage of the federal interstate-highway act, in 1956. Now the
1 See Stephen Jay Gould, Punctuated Equilibrium, Cambridge, MA: Harvard University Press, 2007. Gould
pointed out that fossil records show that species change does not advance gradually but often massively and
disruptively. After the mass extinctions that have occurred several times across evolutionary eras, a minority of
species survived and the voids in the ecosystem rapidly filled with massive speciation. Gould’s theory addresses
the discontinuity in fossil records that puzzled Charles Darwin.
77
synchronous arrival of Tesla, Uber, and autonomous vehicles is creating chaos.
When it’s over, a new equilibrium will emerge. Landline operators were
massively disrupted by cell phones, which in turn were upended by the introduction
of the iPhone, in 2007—which, in the following decade, has settled
into a new stasis, with handheld computing changing the very nature of
interpersonal communication.
The evidence suggests that we are seeing a mass disruption in the corporate
world like Gould’s recurring episodes of mass species extinction. Since 2000,
over 50 percent of Fortune 500 companies have been acquired, merged,
or declared bankruptcy, with no end in sight. In their wake, we are seeing a
mass “speciation” of innovative corporate entities with largely new DNA,
such as Amazon, Box, Facebook, Square, Twilio, Uber, WeWork, and Zappos.
Mass-extinction events don’t just happen for no reason. In the current extinction
event, the causal factor is digital transformation.
AWASH IN INFORMATION
Digital transformation is everywhere on the agendas of corporate boards and
has risen to the top of CEOs’ strategic plans. (For insights into how difficult
it can be to shape an effective digital strategy, see “Why digital strategies
fail,” on page 61.) Before the ubiquity of the personal computer or the Internet,
the late Harvard sociologist Daniel Bell predicted the advent of the Information
Age in his seminal work The Coming of Post-Industrial Society.
2 The resulting
structural change in the global economy, he wrote, would be on the order of
the Industrial Revolution. In the subsequent four decades, the dynamics of
Moore’s law and the associated technological advances of minicomputers,
relational databases, computers, the Internet, and the smartphone have created
a thriving $2 trillion information-technology industry—much as Bell foretold.
In the 21st century, Bell’s dynamic is accelerating, with the introduction
of new disruptive technologies, including big data, artificial intelligence (AI),
elastic cloud computing (the cloud), and the Internet of Things (IoT). The
smart grid is a compelling example of these forces at work. Today’s electricpower
grid—composed of billions of electric meters, transformers, capacitors,
phasor measurement units, and power lines—is perhaps the largest and
most complex machine ever developed.3 An estimated $2 trillion is being
spent this decade to “sensor” that value chain by upgrading or replacing
2 Daniel Bell, The Coming of Post-Industrial Society: A Venture in Social Forecasting, New York, NY: Basic
Books, 1973.
3 George Constable et al., A Century of Innovation: Twenty Engineering Achievements that Transformed our Lives,
Washington, DC: Joseph Henry Press, 2003.
Why digital transformation is now on the CEO’s shoulders
78 McKinsey Quarterly 2018 Number 1
the multitude of devices in the grid infrastructure so that all of them are
remotely machine addressable.4
When a power grid is fully connected, utilities can aggregate, evaluate, and
correlate the interactions and relationships of vast quantities of data from all
manner of devices—plus weather, load, and generation-capacity information—
in near real time. They can then apply AI machine-learning algorithms to
those data to optimize the operation of the grid, reduce the cost of operation,
enhance resiliency, increase reliability, harden cybersecurity, enable a
bidirectional power flow, and reduce greenhouse-gas emissions. The power
of IoT, cloud computing, and AI spells the digital transformation of the
utility industry.
A virtuous cycle is at work here. The network effects of interconnected and
sensored customers, local power production, and storage (all ever cheaper)
make more data available for analysis, rendering the deep-learning algorithms
of AI more accurate and making for an increasingly efficient smart grid.
Meanwhile, as big data sets become staggeringly large, they change the nature
of business decisions. Historically, computation was performed on data samples,
statistical methods were employed to draw inferences from those samples,
and the inferences were in turn used to inform business decisions. Big data
means we perform calculations on all the data; there is no sampling error.
This enables AI—a previously unattainable class of computation that uses
machine and deep learning to develop self-learning algorithms—to perform
precise predictive and prescriptive analytics.5
The benefits are breathtaking. All value chains will be disrupted: defense, education,
financial services, government services, healthcare, manufacturing,
oil and gas, retail, telecommunications, and more.6 To give some flavor to this:
• Healthcare. Soon all medical devices will be sensored, as will patients.
Healthcare records and genome sequences will be digitized. Sensors will
remotely monitor pulse, blood chemistry, hormone levels, blood pressure,
temperature, and brain waves. With AI, disease onset can be accurately
predicted and prevented. AI-augmented best medical practices will be
more uniformly applied.
4 Derived from Estimating the Costs and Benefits of the Smart Grid: A Preliminary Estimate of the Investment
Requirements and the Resultant Benefits of a Fully Functioning Smart Grid, Electric Power Research Institute,
March 2011.
5 See “How artificial intelligence can deliver real value to companies,” McKinsey Global Institute, June 2017,
McKinsey.com.
6 See “Unlocking the potential of the Internet of Things,” McKinsey Global Institute, June 2015, McKinsey.com.
79
• Oil and gas. Operators will use predictive maintenance to monitor
production assets and predict and prevent device failures, from submersible
oil pumps to offshore oil rigs. The result will be a lower cost of production
and a lower environmental impact.
• Manufacturing. Companies are employing IoT-enabled inventory optimization
to lower inventory carrying costs, predictive maintenance to lower the cost
of production and increase product reliability, and supply-network risk
mitigation to assure timely product delivery and manufacturing efficiency.
THE NEW ENGINE OF CHANGE: CEOS
Perhaps the most unique aspect of this technology trend is that digital
transformation is being driven from the top, personally mandated by the
CEO. This is something new.
In the past 70 years of computing, the world advanced from the vacuum tube
to the transistor to the semiconductor, from mainframe computing to
minicomputing to personal computing to the Internet. Software evolved
from bespoke custom programming to on-premises, packaged enterprise
application software and then to software as a service (SaaS)—cloud-resident
solutions. Among the fruits: increased productivity and profitability, a lower
cost of operation, and economic growth.
I witnessed many of these tech-adoption cycles over the past 30 years. With
the promise of performance improvements and productivity increases, such
innovations were introduced to industry through the IT organization.
Over months or years, and after multiple trials and evaluations, each gained
the attention of the chief information officer, who was responsible for technology
adoption. The CEO was periodically briefed on the cost and result.
With the 21st-century digital transformation, the adoption cycle has inverted.
What I’m seeing now is that, almost invariably, global corporate transformations
are initiated and propelled by the CEO. Visionary CEOs, individually, are the
engines of massive change that is unprecedented in the history of information
technology—possibly unprecedented in the history of commerce.
Something fundamentally important is happening, and it’s something
that corporate leaders find highly motivating—and urgent. Michael Porter
of the Harvard Business School speculates that the new world of smart,
connected devices represents a sea change in the fundamental dynamics
Why digital transformation is now on the CEO’s shoulders
80 McKinsey Quarterly 2018 Number 1
of competition.7 Porter suggests that the Internet of Things isn’t simply
a matter of competitive advantage; it is existential. More darkly, John
Chambers of Cisco Systems predicts that 40 percent of today’s businesses
will fail in the next ten years; 70 percent will attempt to transform
themselves digitally, but only 30 percent will succeed. “If I am not making
you sweat,” he told an executive audience, “I should be.”8
The competitive effects are playing out in the marketplace. In autos, think of
Tesla as IoT on wheels. Tesla’s market capitalization is roughly equivalent
to that of General Motors even though its revenue is less than one-twentieth
of GM’s. Tesla collects terabytes of data from its vehicles and uses machine
learning to improve predictive maintenance, self-driving capabilities, and
the driving experience of its cars significantly and continuously.9 The
more miles driven, the more data Tesla collects, and the more it grows as a
competitive force. A consumer can configure and purchase a customized
new Tesla from the company’s website in eight minutes. In retail, Amazon
is digitally transforming the industry with data, AI, and network effects.
Its share of the US e-commerce market is 34 percent and could increase to
50 percent by 2021.10
In response, some farsighted CEOs are revamping their playbooks. Isabelle
Kocher, CEO of Engie, an integrated energy company based in Paris, has
assembled a C-suite team to step up the transformation of the company. Together
they have updated its strategy with new business targets that include specific
expectations for digital value creation. Other CEOs we work with are
thinking through scenarios to anticipate future disruption, asking questions
like “what are our customers really buying, do they really need us, or could
a digital competitor provide a better insight or product at a lower cost?” They’re
using these “what if” cases to break out of cloistered mind-sets and reallocate
investments for future digital efforts. One healthcare CEO used scenarios
to craft a road map for hundreds of next-generation application improvements
across its businesses. Where new talent is required to bolster C-level efforts,
CEOs are recruiting for roles such as chief digital officer with the authority
and budget to make things happen.
Other CEOs are seeking inspiration by organizing visits to the headwaters
of disruption, at companies like Apple, Tesla, and Uber. (My company has
7 Michael E. Porter and James E. Heppelmann, “How smart, connected products are transforming competition,”
Harvard Business Review, November 2014; and Michael E. Porter and James E. Heppelmann, “How smart,
connected products are transforming companies,” Harvard Business Review, October 2015, hbr.org.
8 Julie Bort, “Retiring Cisco CEO delivers dire prediction: 40 percent of companies will be dead in 10 years,”
Business Insider, June 2015, businessinsider.com.
9 Kirsten Korosec, “Why Morgan Stanley is so bullish on Tesla and the Model 3,” Fortune, March 2017, fortune.com.
10 Phil Wahba, “Amazon will make up 50 percent of all US e-commerce by 2021,” Fortune, April 2017, fortune.com.
Why digital transformation is now on the CEO’s shoulders 81
hosted more than 30 such visits in 2017 alone.) They’re retooling executive
perspectives with boot camps on digital innovation. They’re also reaching
across company and industry borders to share and promulgate best practices.
In Germany, leading industry CEOs formed a working group, Industrie
4.0, to advise the federal government on industrial policy needed for the “fourth
industrial revolution,” grounded in IoT and AI. Hundreds of leading companies
have formed the Industrial Internet Consortium to accelerate the
adoption of “cyberphysical systems” in energy, healthcare, manufacturing,
smart cities, and transportation.
Digital transformation is about sweeping change. It changes everything about
how products are designed, manufactured, sold, delivered, and serviced—
and it forces CEOs to rethink how companies execute, with new business
processes, management practices, and information systems, as well as
everything about the nature of customer relationships. I’m seeing leaders
who get this. They’re all over it: they want to launch five transformation
initiatives right now; they’re talking to me and every digital leader they know
about where the technology threats are coming from; and they’re hiring
the best people to advise them. Yet I’m shocked by—even fearful for—the many
CEOs I know who seem to be asleep at the switch. They just don’t see the
massive disruption headed their way from digital threats, seen or unseen,
and they don’t seem to understand it will happen very quickly.
So when I see CEOs who may be experimenting here and there with AI or the
cloud, I tell them that’s not enough. It’s not about shiny objects. Tinkering
is insufficient. My advice is that they should be talking about this all the time,
with their boards, in the C-suite—and mobilizing the entire company. For
boards, if this isn’t on your agenda, then you’ve got the wrong agenda. If your
CEO isn’t talking about how to ensure the survival of the enterprise amid
digital disruption, well, maybe you’ve got the wrong person in the job. This
may sound extreme, but it’s not.
It’s increasingly clear that we’re entering a highly disruptive extinction event.
Many enterprises that fail to transform themselves will disappear. But as in
evolutionary speciation, many new and unanticipated enterprises will emerge,
and existing ones will be transformed with new business models. The
existential threat is exceeded only by the opportunity.
Copyright © 2018 McKinsey & Company. All rights reserved.
Thomas M. Siebel is the chairman and CEO of C3 IoT. Previously, he founded Siebel
Systems, serving as its CEO and chairman from 1993 until its acquisition by Oracle, in 2006.
82
83
DIGITAL SNAPSHOTS:
FOUR INDUSTRIES
IN TRANSITION
88 Banking needs an
ecosystem play
Miklós Dietz,
Joydeep Sengupta,
and Nicole Zhou
84 The automotive
ecosystem shifts
into gear
Matthias Kässer,
Thibaut Müller, and
Andreas Tschiesner
THE EMERGENCE OF ECOSYSTEMS
PRODUCTIVITY POSSIBILITIES
92 Pulp and paper:
Where digital help far
outweighs the hurt
Peter Berg and
Oskar Lingqvist
83
94 A digital upgrade
for engineering and
construction
Jose Luis Blanco,
Andrew Mullin, and
Mukund Sridhar
It’s no surprise that digital technologies have altered today’s
competitive playbook. But just how much change is afoot?
McKinsey research on the outlook for four industries shows
an extensive range that varies by sector. In automobiles and
banking, a new clutch of ecosystems is set to shape the global
business environment. And in two more traditional industries,
pulp and paper and engineering and construction, digital is
giving productivity a big boost.
84 McKinsey Quarterly 2018 Number 1
The automotive
ecosystem shifts into gear
An analysis of mobility investments reveals how technologies and
players are beginning to interact, and where new opportunities are
starting to appear.
by Matthias Kässer, Thibaut Müller, and Andreas Tschiesner
As digitization reshapes traditional industry boundaries, many are betting
that an “automotive ecosystem” will be one of the first to develop. But what
will it look like in practice, and how will we know when such a competitive
shift really takes place?
As we have recently described,1
the coming ecosystems will comprise diverse
players who provide digitally accessed, multi-industry solutions based on
emerging technologies. In automotive, four such technologies known by the
acronym ACES—autonomous driving, connected to the Internet of Things,
electric, and shared mobility—are likely to be key. A constellation of different
players, including OEMs and their suppliers, competing “frenemies,” and
unexpected attackers, will aim to capture the opportunities these and other
innovations will present.
Thanks to the findings of the Start-up and Investment Landscape Analysis
(SILA), McKinsey’s proprietary, self-optimizing big data engine, we can now
paint a more detailed picture of the evolving battleground. Through SILA’s
semantic analysis of keywords and network analytics of relevant companies,
1 See Venkat Atluri, Miklós Dietz, and Nicolaus Henke, “Competing in a world of sectors without borders,”
McKinsey Quarterly, July 2017, McKinsey.com.
© ilbusca/Getty Images
The automotive ecosystem shifts into gear 85
clusters, and industry moves within the investment landscape, we identified
ten technology clusters with more than a thousand companies combined
that have received external investments since 2010 of about $111 billion. This
figure does not include internal R&D expenses by automotive and technology
companies, but it does include acquisitions and stakes in other businesses
made by these companies.
In the past decade, the rate of mobility investments has increased nearly sixfold,
and the median deal size has more than tripled. In 2016 alone, investments
amounted to $31 billion, a little less than half of the total R&D spend by all
automotive OEMs ($77 billion). Around 60 percent of the total investment
volume went into very large, industry-shaping deals, whereas the rest went
into a huge number of smaller deals. Notably, these investments were focused
not on products but on the technologies underlying the changes in mobility.
In other words, investors are betting on an ecosystem.
No less compelling is the evidence as to who the investors are. More than
90 percent of the investments identified by SILA have been made by tech
companies, on the one hand, and venture-capital (VC) and private-equity
(PE) firms, on the other. These two sectors are investing about equal amounts
(that is, slightly more than 45 percent of the total investments); OEMs and
major suppliers make up the remainder. And while VC and PE firms are
making these investments because they expect significant growth and will
likely look to exit in the foreseeable future, tech companies seem intent
on staying put—staking out emerging control points and getting ahead of
critical trends.
Our SILA analysis shows ten major clusters based on the four ACES
technologies (exhibit). Among these technologies, autonomous driving received
the largest amount of funding. Sharing solutions came in second, with
A mobility ecosystem is quickly taking shape
across the world. And this ecosystem is more
than just “Automotive Industry 2.0.”
86 McKinsey Quarterly 2018 Number 1
Exhibit
Q1 2018
Automobile Ecosystem
Exhibit 1 of 1
Mapping mobility start-ups and investments in the evolving automotive
ecosystem shows activities across ten clusters.
Connectivity Autonomous driving
Smart mobility Electrification
Vehicle leasing/fleet management
Sharing solutions
Parking and mobility optimization
Electrification/energy storage
Back end and cybersecurity
Gesture/voice recognition
User-interface technologies
Telematics
Autonomous solutions
Sensors/semiconductors
10 clusters loosely categorized into 4 areas, includes >1,000 companies with
investments of ~$111 billion, 2010−17
Source: Capital IQ; PitchBook Data; McKinsey Center for Future Mobility
87
around one-third of the funding—surprisingly little, given the media attention.
In both areas, the investments were dominated by a few large investments
in major companies (for example, Didi, Mobileye, and Uber); autonomous
driving also had a long tail of smaller investments in technology start-ups.
The picture is very different in the connectivity cluster, where investments
have focused almost entirely on specialized small and midsize companies.
Electrification and energy-storage investments are smaller than investments
in other technologies, most likely because automotive companies are
investing in these technologies in-house.
The analysis also reveals strong links between the different ACES clusters
(as shown by their proximity on the node map), which emphasizes the
underlying technologies’ wide-ranging applicability. For example, machine
learning is the underlying technology for both autonomous driving and
voice-recognition software, among others. This suggests that companies
should consider opportunities in light of the technology to be used rather
than the offerings to be developed.
Not surprisingly, more than half of the start-ups currently receiving investment
are based in the United States, which leads both in the number of companies
and in investment volumes. China follows and Europe lags well behind.
But as the SILA data show, a mobility ecosystem is quickly taking shape across
the world. And this ecosystem is more than just “Automotive Industry 2.0.”
Leading in the new landscape will require contending with multiple new
players—many not from a traditional automotive background—and integrating
different capabilities. For traditional OEMs and suppliers, as well as new
entrants, it will be essential to adopt an ecosystem mind-set.
Copyright © 2018 McKinsey & Company. All rights reserved.
Matthias Kässer is a partner in McKinsey’s Munich office, where Andreas Tschiesner is a
senior partner; Thibaut Müller is a consultant in the Geneva office.
The authors wish to thank the McKinsey Center for Future Mobility (MCFM) for their
contributions to this article. For more information about MCFM, visit McKinsey.com/mcfm.
The automotive ecosystem shifts into gear
88 McKinsey Quarterly 2018 Number 1
Banking needs an
ecosystem play
To regain ground lost to challengers, the industry must digitize
core operations and adapt to an era of markets without borders.
by Miklós Dietz, Joydeep Sengupta, and Nicole Zhou
Digital competition threatens to upend business models across sectors. So
what’s happening in banking—with attackers targeting some of the most
profitable income streams, so-called platform companies entering the fray,
and many incumbent players struggling to respond—is a stark reminder
for all senior executives of what’s at stake.
Fast-moving fintechs, many of them start-ups, launched the first salvo in
banking using smartphone apps, cloud-based infrastructure, and intuitive
interactions to lure banks’ customers. Fintechs forced banks to innovate their
digital offerings and even their business models. While this first wave of
intrusion has mostly abated, platform companies such as China’s Tencent,
Japanese retailer Rakuten, and Amazon in the United States are now using
their customer knowledge, scale advantage, and data capabilities to target
a range of retail, corporate, and commercial segments. Such companies use
information from their huge base of customers to build ecosystems—networks
that span industries and functional capabilities and enable them to attract
customers from adjacent and previously stand-alone industries at high
speed and low cost. In banking, for instance, using data analytics and other
capabilities, digital players can make credit decisions nearly instantly.
© Rostislav_Sedlacek/Getty Images
Banking needs an ecosystem play 89
THE HIT TO PERFORMANCE
Using proprietary data across banking segments and geographies, we
looked at the extent to which current and future digital competition may
potentially damage returns and the degree to which technology choices
are important. We found that attackers—whether fintechs or platform players—
favor incumbent banks’ choicest businesses, namely fee-based offerings
such as transactions and payments as well as asset management. At the moment,
these produce 47 percent of banking revenues but an outsized 65 percent
of profits and a return on equity (ROE) of 20 percent. There is relatively less
interest in banks’ “manufacturing” areas, the core finance and lending
businesses that pivot off balance sheets. These represent 53 percent of revenues
and 35 percent of profits and have an ROE of 4.4 percent.
Absent any mitigating actions, we estimate that the ongoing digitization of
the industry could cost banks more than four percentage points of ROE
by 2025 (exhibit)—an unsustainable loss that will drop returns well below
Exhibit
Q1 2018
Automobile Ecosystem
Exhibit 1 of 1
Banks that execute a successful ecosystem strategy could restore their return
on equity to double digits.
Projected 2025 return on equity for average bank, %
9.3
–4.1
Postdigitization
Steady state—
no disruption
+2.5
7.7
Effect of margin reduction
before mitigation1
Full deployment of today’s
digital tools
Digital disruption
and banks’
industrialization efforts
10.6−14.0
+0.5−3.4
+0.5−1.0
Boost to core revenue via
margin improvement
Move beyond banking
(eg, housing services)
Acquiring new customers
at lower cost
Potential upside
of ecosystem moves
+1.9
Successful
ecosystem strategy 10.1– 10.6
1
Average results across sectors and geographies, generally more severe in consumer finance, payments, and asset/wealth
management sectors (up to –20% or more in United Kingdom and Japan).
Source: S&P Global Market Intelligence; Global Banking Pools and Panorama by McKinsey
90 McKinsey Quarterly 2018 Number 1
even the cheapest cost of capital. Banks could win back some of that erosion
by better deploying core technologies now being used against them—
“industrializing” operations with digital automation or using new digitalmarketing
tools and analytics more effectively—but on its own, this will
not be enough to recover the lost ground.
ECOSYSTEM PLAY
Our research shows that, for the past several years, banking returns have been
stuck between 8 and 10 percent. The best option for many banks to lift
returns to something like the go-go years of the early 2000s—to say nothing
of the tremendous margins that digital firms now command—will be to
embrace the ecosystem environment. They must use their inherent advantages,
including customer trust, regulatory knowledge, a big customer base, and
unexploited data. Many banks could scan their markets and regions and then
join these new business systems—and banks with strong digital capabilities
might even build an ecosystem, enlisting other financial and nonfinancial
players to join them.
In a basic ecosystem “play,” platform power helps banks retain their core
customers and improves cross-selling. Banks will be much more conspicuous
to digitally minded customers and will be able to offer products better
suited to customer needs—even as better data help banks make sharper underwriting
decisions. In our estimate, these improvements can add close to
two percentage points to ROE. Further ROE increases are possible as networks
of ecosystem partners and access to more data lower costs of customer
acquisition, in some cases to as little as 1 percent of historical costs.
For some banks with the necessary digital “chops” and insights into potential
opportunities, a deeper ecosystem strategy can be even more decisive. Many
banks are already surveying related revenue pools, ranging from housing and
In a basic ecosystem “play,” platform power
helps banks retain their core customers and
improves cross-selling.
91
transportation to participation in B2B and B2C marketplaces. A mediumsize
bank, for example, in partnership with regional real-estate developers
and agents, might capture 15 percent of ecosystem revenues in home sales,
financing, and aftermarket services such as moving, decorating, insurance,
and so on. Even this small slice could be enough to lift returns into the
midteens again.
Over time, digitization will sharply reduce banking revenue pools. The
“vertical” business system may be in its final lap, but by shifting today’s
organizations to ecosystems, banks can claim their share of the expanded
revenue pools in markets that transcend industry boundaries.
Banking needs an ecosystem play
Copyright © 2018 McKinsey & Company. All rights reserved.
Miklós Dietz is a senior partner in McKinsey’s Vancouver office, Joydeep Sengupta is a senior
partner in the Singapore office, and Nicole Zhou is a partner in the Shanghai office.
The authors wish to thank Matthieu Lemerle, Asheet Mehta, and Miklós Radnai for their
contributions to this research.
For the full report from which this article is adapted, see “Remaking the bank for an
ecosystem world,” on McKinsey.com.
92 McKinsey Quarterly 2018 Number 1
Pulp and paper:
Where digital help far
outweighs the hurt
While the industry’s prospects vary by product and region,
digital offers opportunities across the board to improve costs—
and capture new growth.
by Peter Berg and Oskar Lingqvist
With the strong tide pulling readers away from paper to digital modes of
communication, it’s no surprise that paper demand has suffered. But for
the paper and forest-products industry overall, digital is giving as well
as taking away. Most conspicuously, ever-increasing online purchasing
is generating new sales of fiber-based transport packaging. Less visibly,
digital technologies are driving across-the-board opportunities to improve
efficiency throughout the value chain.
Paper and board producers already collect a lot of data, and companies that
are able to apply advanced analytics and artificial intelligence to it can
learn how to better run their plants. Improvements include predictive maintenance,
which helps keep machinery running, as well as more stable
production processes, which in turn lead to lower consumption of energy and
bleaching chemicals. Remote process controls for mills and other uses
of automation can also reduce costs.
The exhibit shows our rough estimate of the new benefits accruing from
adoption of existing technologies at the plant level for pulp and paper
© Roberto Pangiarella/EyeEm/Getty Images
93
manufacturing—based on what is already starting to be achieved. It also offers
a cautious interpretation of potential gains, as digital technologies evolve and
are applied to new areas in plant operations. Meanwhile, digital has potential
elsewhere in the industry. In forestry, drones are already boosting the precision
with which tree growth is monitored, harvesting decisions are made, and
logging crews are deployed. Downstream, there are new product-development
opportunities, for example, in packaging that can be better traced or that
incorporates new security features. Digital also opens the potential for more
efficient customer interactions and even direct B2C relationships between
paper-product makers and end consumers, for example, in tissue products.
While opportunities exist across the technology spectrum, perhaps
unsurprisingly, data-intensive applications involving artificial intelligence
and advanced analytics offer the biggest opportunities for gains.
Peter Berg is a senior expert in McKinsey’s Stockholm office, where Oskar Lingqvist is a
senior partner.
For a more complete set of findings, see “Pulp, paper, and packaging in the next decade:
Transformational change,” on McKinsey.com.
Copyright © 2018 McKinsey & Company. All rights reserved.
Pulp and paper: Where digital help far outweighs the hurt
Exhibit
Q1 2018
Pulp and paper
Exhibit 1 of 1
The digital revolution offers cost-improvement opportunities.
1
In addition to cost savings, digital applications in predictive maintenance, throughput debottlenecking, and quality control
could improve overall equipment eectiveness by ~5 percentage points.
2 Excluding purchasing, marketing and sales, and upstream areas such as forestry.
Example: pulp and paper manufacturing, all figures are approximate
Estimated cost savings
from digital,¹ % of total
cost base
Artificial intelligence
and analytics
Automation
Mobile
Total opportunity,2 % of total cost base Use-case examples
~15% cost
reduction
Existing technologies
Future technologies
Fiber yield, chemical and energy
consumption, predictive maintenance
Logistics and process automation,
remote process control and inspection
Digital field-force apps, digital 1.5 business-support functions
9.5
4.0
94 McKinsey Quarterly 2018 Number 1
A digital upgrade
for engineering and
construction
Construction-technology start-ups are helping the industry tackle
long-standing productivity problems.
by Jose Luis Blanco, Andrew Mullin, and Mukund Sridhar
Engineering and construction companies have struggled with low productivity
for decades. But digital solutions, many developed by specialized technology
start-ups, are helping the industry identify and extract new sources of value.
To better understand the evolving productivity landscape, we examined the
products of more than 1,000 construction-software start-ups (representing
$10 billion in investment funding) between 2011 and 2017. Those start-ups
have brought to market thousands of innovative project tools, whose
capabilities include everything from improved quality control to predictive
analytics. New ones are emerging all the time, and the mix of capabilities
on offer appears to be changing.
Overall, the preponderance of tools created by these companies has been for
the construction phase, with far fewer aimed at design, preconstruction,
operations, or management. Many start-ups have focused on basic collaboration
tools that compile or share project information (such as documentmanagement
solutions) or core back-office digitization (such as enterpriseresource-planning
systems).
© Glowimages/Getty Images
95
The priorities of newer start-ups—those actually founded in the last five
years—suggest digital productivity opportunities are becoming richer.
Almost 30 percent of those companies offer on-site performance-management
and field-productivity tools. Quality-control tools, including GPS and
images to monitor sites, also ranked high: 27 percent of recent start-ups offer
them (exhibit). More advanced tools are in demand, including predictive
analytics to help manage projects, the use of drones and the Internet of
Things for monitoring, and wearable and virtual-reality technologies to
improve safety.
With productivity within the construction sector about half that of the total
economy, digital solutions alone will not close the gap. But as the range
of digital possibilities grows, the importance of engaging with the start-ups
offering them will, too.
Jose Luis Blanco is a partner in McKinsey’s Philadelphia office, Andrew Mullin is a partner
in the Toronto office, and Mukund Sridhar is a partner in the Singapore office.
The authors wish to thank Kaustubh Pandya for his contributions to this article.
For the full article, see “The new age of engineering and construction technology,”
on McKinsey.com.
Copyright © 2018 McKinsey & Company. All rights reserved.
A digital upgrade for engineering and construction
Exhibit
Q1 2018
Construction
Exhibit 1 of 1
1
Those founded in past 5 years.
2 ERP = enterprise resource planning.
Total investment
(all start-ups), $ billion
Field productivity
% of newer start-ups1
investing in application
25–30
Performance management
~4.4
~1.0 Quality control
Top 3 applications
by investment
Most popular with
newer start-ups
5–10
3–5
Document management
Equipment management
ERP2 systems
15–20
25–30
25–30
When it comes to investing in construction technologies, newer start-ups
break rank with others in their choice of tools.
96
AI can eliminate the need for large, labeled data sets.
Here, a CycleGAN application learns from a small set
of data how to translate bears into pandas.
What AI can and can’t do (yet) for your business 97
What AI can and can’t do
(yet) for your business
Artificial intelligence is a moving target. Here’s how to take
better aim.
by Michael Chui, James Manyika, and Mehdi Miremadi
Artificial intelligence (AI) seems to be everywhere. We experience it at home
and on our phones. Before we know it—if entrepreneurs and business innovators
are to be believed—AI will be in just about every product and service we buy
and use. In addition, its application to business problem solving is growing
in leaps and bounds. And at the same time, concerns about AI’s implications
are rising: we worry about the impact of AI-enabled automation on the
workplace, employment, and society.
A reality sometimes lost amid both the fears and the headline triumphs, such
as Alexa, Siri, and AlphaGo, is that the AI technologies themselves—namely,
machine learning and its subset, deep learning—have plenty of limitations
that will still require considerable effort to overcome. This is an article about
those limitations, aimed at helping executives better understand what may be
holding back their AI efforts. Along the way, we will also highlight promising
advances that are poised to address some of the limitations and create a new
wave of opportunities.
Our perspectives rest on a combination of work at the front lines—researching,
analyzing, and assessing hundreds of real-world use cases—and our collaborations
with some of the thought leaders, pioneering scientists, and engineers working
at the frontiers of AI. We’ve sought to distill this experience to help executives
98 McKinsey Quarterly 2018 Number 1
who often, in our experience, are exposed only to their own initiatives
and not well calibrated as to where the frontier is or what the pace setters are
already doing with AI.
Simply put, AI’s challenges and limitations are creating a “moving target”
problem for leaders: It is hard to reach a leading edge that’s always advancing.
It is also disappointing when AI efforts run into real-world barriers, which
can lessen the appetite for further investment or encourage a wait-and-see
attitude, while others charge ahead. As recent McKinsey Global Institute
research indicates, there’s a yawning divide between leaders and laggards in
the application of AI both across and within sectors (Exhibit 1).
Executives hoping to narrow the gap must be able to address AI in an informed
way. In other words, they need to understand not just where AI can boost
innovation, insight, and decision making; lead to revenue growth; and
capture of efficiencies—but also where AI can’t yet provide value. What’s
more, they must appreciate the relationship and distinctions between
Exhibit 1
Q1 2018
AI Limitations
Exhibit 1 of 2
Leaders in the adoption of AI also intend to invest more in the near future
compared with laggards.
Future AI demand trajectory, % change in AI spending over next 3 years1
Leading sectors
Falling behind
Current AI adoption, % of companies2
Media and
entertainment
1
13
12
11
10
9
8
7
6
5
4
3
2
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32
High tech and
communications
Automotive
and assembly
Financial
services
Energy and
resources
Transportation
and logistics
Consumer
and packaged
Building materials goods
and construction
Professional
services
Travel and
tourism
Retail
Education
Healthcare
1
Estimated average, weighted by company size; demand trajectory based on midpoint of range selected by survey respondent.
2 Adopting 1 or more AI technologies at scale or in business core; weighted by company size.
Source: McKinsey Global Institute (MGI) AI adoption and use survey; MGI analysis
What AI can and can’t do (yet) for your business 99
technical constraints and organizational ones, such as cultural barriers;
a dearth of personnel capable of building business-ready, AI-powered
applications; and the “last mile” challenge of embedding AI in products and
processes. If you want to become a leader who understands some of the
critical technical challenges slowing AI’s advance and is prepared to exploit
promising developments that could overcome those limitations and
potentially bend the trajectory of AI—read on.
CHALLENGES, LIMITATIONS, AND OPPORTUNITIES
A useful starting point is to understand recent advances in deep-learning techniques.
Arguably the most exciting developments in AI, these advances
are delivering jumps in the accuracy of classification and prediction, and are
doing so without the usual “feature engineering” associated with traditional
supervised learning. Deep learning uses large-scale neural networks that
can contain millions of simulated “neurons” structured in layers. The most
common networks are called convolutional neural networks (CNNs) and
recurrent neural networks (RNNs). These neural networks learn through
the use of training data and backpropagation algorithms.
While much progress has been made, more still needs to be done.1 A critical
step is to fit the AI approach to the problem and the availability of data. Since
these systems are “trained” rather than programmed, the various processes
often require huge amounts of labeled data to perform complex tasks accurately.
Obtaining large data sets can be difficult. In some domains, they may simply
not be available, but even when available, the labeling efforts can require
enormous human resources.
Further, it can be difficult to discern how a mathematical model trained by
deep learning arrives at a particular prediction, recommendation, or decision.
A black box, even one that does what it’s supposed to, may have limited utility,
especially where the predictions or decisions impact society and hold
ramifications that can affect individual well-being. In such cases, users sometimes
need to know the “whys” behind the workings, such as why an algorithm
reached its recommendations—from making factual findings with legal
repercussions to arriving at business decisions, such as lending, that have
regulatory repercussions—and why certain factors (and not others) were
so critical in a given instance.
1 Stuart Russell et al., “Research priorities for robust and beneficial artificial intelligence,” AI Magazine, Winter 2015,
Volume 36, Number 4, pp. 105–14, aaai.org.
100 McKinsey Quarterly 2018 Number 1
Let’s explore five interconnected ways in which these limitations, and the
solutions emerging to address them, are starting to play out.
Limitation 1: Data labeling
Most current AI models are trained through “supervised learning.” This
means that humans must label and categorize the underlying data, which can
be a sizable and error-prone chore. For example, companies developing selfdriving-car
technologies are hiring hundreds of people to manually annotate
hours of video feeds from prototype vehicles to help train these systems.
At the same time, promising new techniques are emerging, such as in-stream
supervision (demonstrated by Eric Horvitz and his colleagues at Microsoft
Research), in which data can be labeled in the course of natural usage.2
Unsupervised or semisupervised approaches reduce the need for large, labeled
data sets. Two promising techniques are reinforcement learning and
generative adversarial networks.
Reinforcement learning. This unsupervised technique allows algorithms
to learn tasks simply by trial and error. The methodology hearkens to a “carrot
and stick” approach: for every attempt an algorithm makes at performing a
task, it receives a “reward” (such as a higher score) if the behavior is successful
or a “punishment” if it isn’t. With repetition, performance improves, in many
cases surpassing human capabilities—so long as the learning environment
is representative of the real world.
Reinforcement learning has famously been used in training computers to play
games—most recently, in conjunction with deep-learning techniques. In
May 2017, for example, it helped the AI system AlphaGo to defeat world champion
Ke Jie in the game of Go. In another example, Microsoft has fielded decision
services that draw on reinforcement learning and adapt to user preferences.
The potential application of reinforcement learning cuts across many business
arenas. Possibilities include an AI-driven trading portfolio that acquires or
loses points for gains or losses in value, respectively; a product-recommendation
engine that receives points for every recommendation-driven sale; and truckrouting
software that receives a reward for on-time deliveries or reducing
fuel consumption.
Reinforcement learning can also help AI transcend the natural and social
limitations of human labeling by developing previously unimagined
solutions and strategies that even seasoned practitioners might never have
2 Eric Horvitz, “Machine learning, reasoning, and intelligence in daily life: Directions and challenges,” Proceedings of
Artificial Intelligence Techniques for Ambient Intelligence, Hyderabad, India, January 2007.
What AI can and can’t do (yet) for your business 101
considered. Recently, for example, the system AlphaGo Zero, using a
novel form of reinforcement learning, defeated its predecessor AlphaGo
after learning to play Go from scratch. That meant starting with completely
random play against itself rather than training on Go games played by and
with humans.3
Generative adversarial networks (GANs). In this semisupervised learning
method, two networks compete against each other to improve and refine
their understanding of a concept. To recognize what birds look like, for example,
one network attempts to distinguish between genuine and fake images of
birds, and its opposing network attempts to trick it by producing what look
very much like images of birds, but aren’t. As the two networks square off,
each model’s representation of a bird becomes more accurate.
The ability of GANs to generate increasingly believable examples of data
can significantly reduce the need for data sets labeled by humans. Training
an algorithm to identify different types of tumors from medical images, for
example, would typically require millions of human-labeled images with the
type or stage of a given tumor. By using a GAN trained to generate increasingly
realistic images of different types of tumors, researchers could train a
tumor-detection algorithm that combines a much smaller human-labeled
data set with the GAN’s output.
While the application of GANs in precise disease diagnoses is still a way off,
researchers have begun using GANs in increasingly sophisticated contexts.
These include understanding and producing artwork in the style of a
particular artist and using satellite imagery, along with an understanding of
geographical features, to create up-to-date maps of rapidly developing areas.
Limitation 2: Obtaining massive training data sets
It has already been shown that simple AI techniques using linear models
can, in some cases, approximate the power of experts in medicine and other
fields.4 The current wave of machine learning, however, requires training
data sets that are not only labeled but also sufficiently large and comprehensive.
Deep-learning methods call for thousands of data records for models to
become relatively good at classification tasks and, in some cases, millions for
them to perform at the level of humans.5
3 Demis Hassabis et al., AlphaGo Zero: Learning from scratch, deepmind.com.
4 Robyn M. Dawes, “The robust beauty of improper linear models in decision making,” American Psychologist,
July 1979, Volume 34, Number 7, pp. 571–82.
5 Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, Cambridge, MA: MIT Press, 2016.
102 McKinsey Quarterly 2018 Number 1
The complication is that massive data sets can be difficult to obtain or create
for many business use cases (think: limited clinical-trial data to predict
treatment outcomes more accurately). And each minor variation in an assigned
task could require another large data set to conduct even more training. For
example, teaching an autonomous vehicle to navigate a mining site where
the weather continually changes will require a data set that encompasses the
different environmental conditions the vehicle might encounter.
One-shot learning is a technique that could reduce the need for large data
sets, allowing an AI model to learn about a subject when it’s given a small
number of real-world demonstrations or examples (even one, in some cases).
AI’s capabilities will move closer to those of humans, who can recognize
multiple instances of a category relatively accurately after having been shown
just a single sample—for example, of a pickup truck. In this still-developing
methodology, data scientists would first pre-train a model in a simulated
virtual environment that presents variants of a task or, in the case of image
recognition, of what an object looks like. Then, after being shown just a few
real-world variations that the AI model did not see in virtual training, the
model would draw on its knowledge to reach the right solution.6
This sort of one-shot learning could eventually help power a system to scan
texts for copyright violations or to identify a corporate logo in a video after
being shown just one labeled example. Today, such applications are only in
their early stages. But their utility and efficiency may well expand the use of
AI quickly, across multiple industries.
Limitation 3: The explainability problem
Explainability is not a new issue for AI systems.7 But it has grown along with
the success and adoption of deep learning, which has given rise both to
more diverse and advanced applications and to more opaqueness. Larger and
more complex models make it hard to explain, in human terms, why a certain
decision was reached (and even harder when it was reached in real time).
This is one reason that adoption of some AI tools remains low in application
areas where explainability is useful or indeed required. Furthermore, as
the application of AI expands, regulatory requirements could also drive the
need for more explainable AI models.8
6 Yan Duan et al., One-shot imitation learning, December 2017, arxiv.org.
7 Eric Horvitz et al., “The use of a heuristic problem-solving hierarchy to facilitate the explanation of hypothesisdirected
reasoning,” Proceedings of Medinfo, October 1986, pp. 27–31.
8 See, for example, the European Union’s proposed General Data Protection Regulation, which would introduce
new requirements for the use of data.
What AI can and can’t do (yet) for your business 103
Two nascent approaches that hold promise for increasing model transparency
are local interpretable model-agnostic explanations (LIME) and attention
techniques (Exhibit 2). LIME attempts to identify which parts of input data
a trained model relies on most to make predictions in developing a proxy
interpretable model. This technique considers certain segments of data at a
time and observes the resulting changes in prediction to fine-tune the proxy
model and develop a more refined interpretation (for example, by excluding
eyes rather than, say, noses to test which are more important for facial
recognition). Attention techniques visualize those pieces of input data that a
model considers most as it makes a particular decision (such as focusing on
a mouth to determine if an image depicts a human being).
Exhibit 2
Q1 2018
AI Limitations
Exhibit 2 of 2
1
LIME = local interpretable model-agnostic explanations.
Source: Carlos Guestrin, Marco Tulio Ribeiro, and Sameer Singh, “Introduction to local interpretable model-agnostic
explanations (LIME),” August 12, 2016, O’Reilly, oreilly.com; Minlie Huang, Yequan Wang, Li Zhao, and Xiaoyan Zhu,
Attention-based LSTM for aspect-level sentiment classification, Tsinghua University; Pixabay
Turning off all but
a few interpretable
components of this
image reveals the
probability that the
model will identify …
Words relevant to food quality …
… or to service
is a sensitivity analysis that reveals which parts of an input matter most
to the eventual output.
… a tree frog
54%
… billiard balls
7%
… a balloon
5%
shines a spotlight on where the model is looking when it makes Attention a particular decision.
They have one of the fastest delivery times in the city.
The fajita we tried was tasteless and burned and the mole sauce was way too sweet.
New techniques hold promise for making AI more transparent.
104 McKinsey Quarterly 2018 Number 1
Another technique that has been used for some time is the application of
generalized additive models (GAMs). By using single-feature models, GAMs
limit interactions between features, thereby making each one more easily
interpretable by users.9 Employing these techniques, among others, to
demystify AI decisions is expected to go a long way toward increasing the
adoption of AI.
Limitation 4: Generalizability of learning
Unlike the way humans learn, AI models have difficulty carrying their
experiences from one set of circumstances to another. In effect, whatever a
model has achieved for a given use case remains applicable to that use
case only. As a result, companies must repeatedly commit resources to train
yet another model, even when the use cases are very similar.
One promising response to this challenge is transfer learning.10 In this approach,
an AI model is trained to accomplish a certain task and then quickly applies
that learning to a similar but distinct activity. DeepMind researchers have also
shown promising results with transfer learning in experiments in which
training done in simulation is then transferred to real robotic arms.11
As transfer learning and other generalized approaches mature, they could help
organizations build new applications more quickly and imbue existing
applications with more diverse functionality. In creating a virtual personal
assistant, for example, transfer learning could generalize user preferences
in one area (such as music) to others (books). And users are not restricted to
digital natives. Transfer learning can enable an oil-and-gas producer, for
instance, to expand its use of AI algorithms trained to provide predictive
maintenance for wells to other equipment, such as pipelines and drilling
platforms. Transfer learning even has the potential to revolutionize business
intelligence: consider a data-analyzing AI tool that understands how to
optimize airline revenues and can then adapt its model to changes in weather
or local economics.
Another approach is the use of something approximating a generalized
structure that can be applied in multiple problems. DeepMind’s AlphaZero,
9 Yin Lou, Rich Caruana, and Johannes Gehrke, “Intelligible models for classification and regression,” Proceedings
of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York: ACM,
2012, pp. 150–58.
10 For an earlier example application, see John Guttag, Eric Horvitz, and Jenna Wiens, “A study in transfer learning:
Leveraging data from multiple hospitals to enhance hospital-specific predictions,” Journal of the American
Medical Informatics Association, 2014, Volume 21, Number 4, pp. 699–706.
11 Andrei A. Rusu et al., Sim-to-real robot learning from pixels with progressive nets, October 2016, arxiv.org.
What AI can and can’t do (yet) for your business 105
for example, has made use of the same structure for three different games:
it has been possible to train a new model with that generalized structure
to learn chess in a single day, and it then soundly beat a world-champion
chess program.12
Finally, consider the possibilities in emerging meta-learning techniques that
attempt to automate the design of machine-learning models. The Google
Brain team, for example, uses AutoML to automate the design of neural networks
for classifying images in large-scale data sets. These techniques now perform
as well as those designed by humans.13 That’s a promising development,
particularly as talent continues to be in short supply for many organizations.
It’s also possible that meta-learning approaches will surpass human
capabilities and yield even better results. Importantly, however, these
techniques are still in their early days.
Limitation 5: Bias in data and algorithms
So far, we’ve focused on limitations that could be overcome through technical
solutions already in the works, some of which we have described. Bias is a
different kind of challenge. Potentially devastating social repercussions can
arise when human predilections (conscious or unaware) are brought to
bear in choosing which data points to use and which to disregard. Furthermore,
when the process and frequency of data collection itself are uneven
across groups and observed behaviors, it’s easy for problems to arise in how
algorithms analyze that data, learn, and make predictions.14 Negative
consequences can include misinformed recruiting decisions, misrepresented
scientific or medical prognoses, distorted financial models and criminaljustice
decisions, and misapplied (virtual) fingers on legal scales.15 In many
cases, these biases go unrecognized or disregarded under the veil of “advanced
data sciences,” “proprietary data and algorithms,” or “objective analysis.”
As we deploy machine learning and AI algorithms in new areas, there probably
will be more instances in which these issues of potential bias become baked
into data sets and algorithms. Such biases have a tendency to stay embedded
because recognizing them, and taking steps to address them, requires a deep
12 David Silver et al., Mastering chess and shogi by self-play with a general reinforcement learning algorithm,
December 2017, arxiv.org.
13 Google Research Blog, “AutoML for large scale image classification and object detection,” blog entry by Barret
Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc Le, November 2, 2017, research.googleblog.com.
14 Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan, Inherent trade-offs in the fair determination of risk
scores, November 2016, arxiv.org.
15 See the work of Julia Angwin, Jeff Larson, Surya Mattu, Lauren Kirchner, and Terry Parris Jr. of ProPublica.
106 McKinsey Quarterly 2018 Number 1
mastery of data-science techniques, as well as a more meta-understanding
of existing social forces, including data collection. In all, debiasing is proving
to be among the most daunting obstacles, and certainly the most socially
fraught, to date.
There are now multiple research efforts under way, as well as efforts to capture
best practices, that address these issues in academic, nonprofit, and privatesector
research. It’s none too soon, because the challenge is likely to become
even more critical, and more questions will arise. Consider, for example, the
fact that many of these learning and statistically based predictive approaches
implicitly assume that the future will be like the past. What should we do
in sociocultural settings where efforts are under way to spur change—and
where making decisions based on past behavior could inhibit progress (or,
worse, build in resistance to change)? A wide variety of leaders, including
business leaders, may soon be called upon to answer such questions.
HITTING THE MOVING TARGET
Solutions to the limitations we have described, along with the widespread
commercial implementation of many of the advances described here, could
be years away. But the breathtaking range of possibilities from AI adoption
suggests that the greatest constraint for AI may be imagination. Here are a
few suggestions for leaders striving to stay ahead of—or at least not fall too
far behind—the curve:
Do your homework, get calibrated, and keep up. While most executives
won’t need to know the difference between convolutional and recurrent neural
networks, you should have a general familiarity with the capabilities of
today’s tools, a sense of where short-term advances are likely to occur, and
a perspective on what’s further beyond the horizon. Tap your data-science
and machine-learning experts for their knowledge, talk to some AI pioneers
to get calibrated, and attend an AI conference or two to help you get the
real facts; news outlets can be helpful, but they can also be part of the hype
machine. Ongoing tracking studies by knowledgeable practitioners, such
as the AI Index (a project of the Stanford-based One Hundred Year Study on
Artificial Intelligence), are another helpful way to keep up.16
Adopt a sophisticated data strategy. AI algorithms need assistance to unlock
the valuable insights lurking in the data your systems generate. You can
help by developing a comprehensive data strategy that focuses not only on the
16 See the AI Index (aiindex.org) and the One Hundred Year Study (ai100.stanford.edu).
What AI can and can’t do (yet) for your business 107
technology required to pool data from disparate systems but also on
data availability and acquisition, data labeling, and data governance. Although
newer techniques promise to reduce the amount of data required for training
AI algorithms, data-hungry supervised learning remains the most prevalent
technique today. And even techniques that aim to minimize the amount of
data required still need some data. So a key part of this is fully knowing your
own data points and how to leverage them.
Think laterally. Transfer-learning techniques remain in their infancy, but there
are ways to leverage an AI solution in more than one area. If you solve a problem
such as predictive maintenance for large warehouse equipment, can you
also apply the same solution to consumer products? Can an effective nextproduct-to-buy
solution be used in more than one distribution channel?
Encourage business units to share knowledge that may reveal ways to use
your best AI solutions and thinking in more than one area of the company.
Be a trailblazer. Keeping up with today’s AI technologies and use cases is not
enough to remain competitive for the long haul. Engage your data-science
staff or partner with outside experts to solve a high-impact use case with
nascent techniques, such as the ones discussed in this article, that are poised
for a breakthrough. Further, stay informed about what’s possible and what’s
available. Many machine-learning tools, data sets, and trained models for
standard applications (including speech, vision, and emotion detection) are
being made widely available. Sometimes they come in open source and in
other cases through application programming interfaces (APIs) created by
pioneering researchers and companies. Keep an eye on such possibilities to
boost your odds of staking out a first-mover or early-adopter advantage.
AI’s challenges and limitations are creating
a “moving target” problem for leaders: It
is hard to reach a leading edge that’s always
advancing.
108 McKinsey Quarterly 2018 Number 1
The promise of AI is immense, and the technologies, tools, and processes needed
to fulfill that promise haven’t fully arrived. If you think you can let the
technology develop and then be a successful fast follower, think again. It’s
very difficult to leapfrog from a standing start, particularly when the target
is moving so rapidly and you don’t understand what AI tools can and can’t
do now. With researchers and AI pioneers poised to solve some of today’s
thorniest problems, it’s time to start understanding what is happening at
the AI frontier so you can position your organization to learn, exploit, and
maybe even advance the new possibilities.
Copyright © 2018 McKinsey & Company. All rights reserved.
Michael Chui is a partner of the McKinsey Global Institute (MGI) and is based in McKinsey’s
San Francisco office; James Manyika is the chairman of MGI and a senior partner in the San
Francisco office; and Mehdi Miremadi is a partner in the Chicago office.
The authors wish to thank Jack Clark at OpenAI, Jeffrey Dean and Martin Wicke at Google
Brain, Professor Barbara Grosz at Harvard University, Demis Hassabis at DeepMind, and Eric
Horvitz at Microsoft Research for their insights on the ideas in this article. They also wish to
thank their McKinsey colleagues Steven Adler, Ali Akhtar, Adib Ayay, Ira Chadha, Rita Chung,
Nicolaus Henke, Sankalp Malhotra, and Pieter Nel for their contributions to this article.
About the artwork: Bear and panda images provided by software engineer Tatsuya
Hatanaka. For more on CycleGAN, see Jun-Yan Zhu et al., Unpaired image-to-image
translation using cycle-consistent adversarial networks, November 2017, arxiv.org.
The four questions to ask when serving on a nonprofit board 109
The four questions to
ask when serving on a
nonprofit board
Directors need to probe, nudge, and prod to make sure the
organization achieves its full potential.
by William F. Meehan III and Kim Starkey Jonker
Sooner or later, you may follow in the footsteps of countless business leaders
onto the board of one or more nonprofit organizations. Maybe it’s the board
of a local institution you care about personally, such as a small-scale theater,
public radio station, or your child’s school. It also could be a national or even
global organization—an international development group, a major university,
or the like.
Whatever the board, it’s an opportunity to make a difference, provided
you’re prepared. Some of that opportunity stems from the growing potential
of these organizations to generate social impact. Even as the cash-strapped
public sector retrenches, nonprofits are poised to enjoy new sources of
financial support: some $59 trillion will move from US households into other
hands between 2007 and 2061, according to one estimate. Nonprofits
also can leverage new sets of tools, including robust digital infrastructure.
The nature of the opportunity runs deeper, though. Our research, as well as
that of others, shows that a great many nonprofit boards are underdelivering.
A majority of respondents to a 2015 survey on nonprofit governance,
© Robert Daly/Getty Images
110 McKinsey Quarterly 2018 Number 1
conducted by researchers at Stanford University, said they did not believe that
their fellow board members were very experienced or very engaged in their
work. More than two-thirds of directors said their organization had faced one
or more serious governance-related problems over the years—a finding
reinforced by a survey we conducted with more than 3,000 stakeholders in
the nonprofit sector, 56 percent of whom indicated that their organizations
struggled with board governance.
If you know how to probe, nudge, and prod, you can help your board perform
better. Doing so starts with courage. In our experience, nonprofit board
members are often reluctant to contribute actively to discussions for fear
that they will appear uninformed or cause an embarrassing ruckus. To be
effective, you must overcome that fear. And then you must ask questions. Ask
all your questions, even ones you fear might seem stupid, and keep asking
them until you figure out what the smart questions are. Then demand answers
to the smart questions. If you don’t get good answers to your smart questions,
or if you don’t get support from your fellow board members when you ask
those questions, then resign.
While many questions will be specific to your organization, there are four
crucial ones that apply to all nonprofits. We’ll lay those out in this article,
which builds on a model of strategic nonprofit leadership we’ve distilled in
our book, Engine of Impact: Essentials of Strategic Leadership in the Nonprofit
Sector (Stanford Business Books, November 2017). As we show in the book,
board effectiveness is a critical enabler of all the components that, collectively,
are indispensable to the achievement of a nonprofit’s potential. Happily,
it’s one that you can start helping with the moment you get on a board.
QUESTION 1: ARE WE SUCCUMBING TO MISSION CREEP?
Companies in the private sector have a built-in sense of focus: they exist to
maximize shareholder value. Because nonprofits lack that clarity of purpose,
they need a crystal-clear mission statement that can unite stakeholders
with different—and often competing—goals and expectations. When a mission
statement is clearly formulated, it guides decisions about which programs
and projects to undertake, which to avoid, and which to exit.
In too many cases, though, nonprofits develop mission statements that are
vague or too lofty. In fact, many board members do not know or fully understand
their organization’s mission. When BoardSource asked nonprofit board
members and CEOs to “grade your board’s performance in understanding your
organization’s mission,” only 50 percent of respondents gave their board an A.
The four questions to ask when serving on a nonprofit board 111
An unintended consequence of such fuzziness is mission creep, a debilitating
virus that takes nonprofits far beyond their core competencies. It’s worth
remembering that a fundamental axiom of strategy in the corporate sector
is that more focused strategies outperform less focused ones. If a for-profit
bakery decided to begin making not just bread and pastry but also tennis rackets,
software, and pianos, people would raise an eyebrow. When that kind of
expansion happens in the nonprofit sector, no one blinks. Often mission creep
arises from a compelling funding opportunity. For example, a neighborhood
after-school tutoring organization that decides to offer midnight basketball
can invariably trace that decision to a top donor’s special enthusiasm for
midnight basketball.
Helping an organization avoid such problems is one of the main duties of a nonprofit
board. Too often, board members just accept that a nonprofit’s mission
“is what it is.” Even in cases where an organization has a clear and well-focused
mission statement, board members and senior staff should thoroughly review
that statement every three to five years. In doing so, they will sharpen both
their understanding of the mission and their commitment to maintaining it.
The board of Helen Keller International (HKI) periodically reviews its mission
in this way as part of its strategic planning. According to its mission statement,
HKI “saves and improves the sight and lives of the world’s most vulnerable
by combating the causes and consequences of blindness, poor health and
malnutrition.” (The interventions are linked; malnutrition is a leading
cause of blindness.) President and CEO Kathy Spahn says the organization
requires board members to visit programs in Africa and Asia at least once
every three years, allowing them “to come back not only inspired and passionate
about our mission, but also with a deep understanding of what is involved
in executing on that mission.” That approach has paid off. When a devastating
cyclone struck in Bangladesh, for example, the HKI board ensured that the
organization limited its role to helping villagers reestablish home gardens
and did not attempt to provide emergency food supplies. Emergency relief is
not HKI’s mission or core competency.
QUESTION 2: HOW IS OUR ‘THEORY OF CHANGE’ INFORMING OUR
STRATEGY?
Board members who are used to robust strategy formulation in the private
sector are often surprised by how nonprofit organizations struggle to translate
their mission into a concrete plan for marshaling and deploying resources.
In many cases, boards themselves are part of the problem. Only 20 percent
of respondents in the BoardSource survey said that they would give an A to
their board’s ability to adopt and follow a strategic plan.
112 McKinsey Quarterly 2018 Number 1
One way to make the strategic conversation more concrete is to probe on
a nonprofit’s “theory of change.” A theory of change is a rigorous description
of exactly how an organization’s work—its portfolio of initiatives and
interventions—will help achieve the given mission. Often discussed in the nonprofit
world, but infrequently employed as a tool for ensuring strategic
coherence, a theory of change is a step-by-step outline, ideally informed by
empirical evidence, of how organizational activity will translate into
impact for beneficiaries.
When reviewing any proposed activity, you should ask the executives and
program officers of the nonprofit, “How does this activity align with a logical,
achievable theory of change?” When you are clear on the answer to that
question, you can do a better job of assessing that individual initiative. You are
also better able to have a coherent conversation about big-picture strategic
issues that may be rumbling beneath the surface, such as the degree to which
your strategy incorporates a clear-eyed view of potential competitors and
collaborators, or the sustainability of your revenue model. These are critical
issues that a business leader naturally would ask about in a corporate setting
but that can seem out of place unless they are integrated with a theory
of change.
Landesa, an organization that has worked in more than 50 countries to obtain
land rights for the rural poor, consciously divides its theory of change into
five discrete steps, each of which is informed by empirical evidence. Here,
for example, is how it articulates the final step: “A small group of focused
professionals working collaboratively with governments and other stakeholders
can help to change and implement laws and policies that provide opportunity
to the world’s poorest women and men.” Landesa also developed a graphical
picture of its theory of change that uses arrows depicting causality to delineate
specific goals, activities, outcomes, and impact.
For Landesa, as for most organizations, the process of developing and obtaining
stakeholder agreement on its theory of change has been as important as
the end product. Tim Hanstad, former president and CEO of Landesa, who
is now a special adviser to the organization, explains: “Some of our richest
discussions as an organization—with management, staff, board members,
and donors—have occurred during the process of developing . . . our theory of
change. . . . We are forced to ask ourselves as a group, ‘What evidence do we
have that our intervention will bring about the intended results?’” Landesa
not only has a sound theory of change; it also uses that tool. “We have an
The four questions to ask when serving on a nonprofit board 113
internal process—called the Project Life Cycle process—that requires
every new project concept and design to be justified by our theory of change,”
Hanstad says.
QUESTION 3: HOW ARE WE EVALUATING OUR IMPACT?
Corporate boards enjoy the benefit of a range of financial metrics, including
a company’s share price, to help them evaluate their performance. Without
them, nonprofit boards unsurprisingly tend to fall short in this area: in the
2015 BoardSource survey, for example, only 13 percent of respondents gave
their board an A for monitoring organizational performance and impact, and
38 percent gave their board a C or worse.
If you are serious about helping your nonprofit achieve its mission, you need
to insist on regular impact measurement, not as a pro forma obligation but
as part of a dynamic feedback loop that helps drive organizational strategy.
Far from being a mere box to tick, evaluation can drive a virtuous cycle
in which an organization tests its theory of change and strategy and then
improves its programs in response to what it learns.
In recent years, randomized controlled trials (RCTs)—studies that test an
intervention against a counterfactual case in which it is not in effect—have
emerged as a powerful way to demonstrate whether a nonprofit intervention
actually works. Boards should encourage this approach. Pratham, an organization
that works to improve learning outcomes among children in India,
has embraced RCTs with the full support of its directors. Over a 12-year period,
the organization completed 11 such evaluations. “The RCT process is
expensive, but the value is enormous because it builds internal capacity,” said
Madhav Chavan, Pratham’s founder. “After we started doing the RCTs, our
entire organization started understanding data much better, and we acquired
down the line a better understanding of how to think of impact.” Through
its investment in this approach, Pratham has shown a definitive, causal link
between its program and the impact on beneficiaries—and in turn this has
helped unlock millions of dollars in funding.
QUESTION 4: DO WE HAVE THE RIGHT ‘FUEL’ TO DRIVE OUR
ORGANIZATION?
A nonprofit is more than its mission, strategy, and impact. It’s also a living,
breathing organism that requires “fuel”—great people, an effective organization,
sufficient funding, and the like—to operate. As a nonprofit board member,
you need to check your organization’s “fuel gauges” on a regular basis.
114 McKinsey Quarterly 2018 Number 1
This should start with a clear-eyed view of the board itself. Significant
mismatches between a nonprofit’s mission and the composition of its board
are common. An egregious example arose on the board of an international
poverty-alleviation organization that, for nearly a decade, consisted only
of a handful of the founders’ childhood friends, all of whom were based in the
United States and none of whom had any substantive experience or relevant
professional expertise in international poverty alleviation. How could such
a board operate as anything other than a rubber stamp for the decisions of the
organization’s executives?
If you find yourself on a board like this, you have a duty to speak up, and to
vote with your feet if you don’t see progress. You may be surprised at the
receptiveness of your fellow directors, whose time is valuable and who may be
harboring similar feelings but remaining quiet out of politeness or habit.
As you work through these issues, heed the venerable principle of the three
Ws: work, wisdom, and wealth. You and your fellow board members should
ask, “Do we have members who offer their time, energy, and insight to
committee work, fund-raising events, outreach to donors, and the like? Do
we have members whose special talent or area of expertise will help us
achieve our mission? And do we have members who can and will support the
organization financially?” While this last topic may be uncomfortable,
helping your organization to raise money—whether through direct giving,
providing introductions to prospective donors, or continually examining
your organization’s overall approach to fund-raising—is the only way to
sustain its impact.
Keeping an eye on the fuel gauge also means regularly asking at board
meetings, “Does our organization have the people needed to achieve our
mission?” Board members have a special duty to insist on both paying highly
effective executives appropriately, so they can be retained, and ensuring
that underperforming employees move on. The latter is an area where
nonprofits particularly struggle. In our Stanford survey, only about half of
nonprofit executives, staff, and board members agreed with the assertion
that underperforming employees “do not stay for long in my organization.”
But as every manager in the for-profit sector knows, removing laggards, when
done responsibly, not only improves organizational efficiency but sends a
powerful signal about organizational values.
The four questions to ask when serving on a nonprofit board 115
Serving on a nonprofit board in the years ahead represents an extraordinary
opportunity for impact on society, and on the nonprofit itself. But if you want
to be an effective strategic leader, you can’t settle for a regimen of reading
board books and showing up for quarterly meetings. You must engage fully
on your organization’s mission; seize opportunities to observe frontline
work; and, at each board meeting, take every chance to confront the big, longterm
issues by asking tough questions. The best quip that we ever heard on
this subject conveys a vital truth: “I have no objection to a good discussion
breaking out in the middle of a board meeting.”
Copyright © 2018 McKinsey & Company. All rights reserved.
William F. Meehan III is the Lafayette Partners Lecturer in Strategic Management at
the Stanford Graduate School of Business and a director emeritus of McKinsey & Company.
Kim Starkey Jonker is president and CEO of King Philanthropies and a lecturer in
management at the Stanford Graduate School of Business.
This article is adapted from the
authors’ recent book, Engine
of Impact: Essentials of Strategic
Leadership in the Nonprofit
Sector (Stanford Business Books,
November 2017).
116 McKinsey Quarterly 2018 Number 1
Working across many
cultures at Western Union
The CEO of the global money-transfer company explains how it
brings in the multicultural voice of the consumer through a broadly
diverse team of top executives.
When Western Union Holdings CEO Hikmet Ersek rang the opening bell of
the New York Stock Exchange in May 2015, it marked 150 years since the
WU ticker was the first listed on Wall Street. Few businesses are as long lived.
Western Union is one of only two companies still left from the original 11 in
the Dow Jones Transportation Average.
Since its founding, Western Union has played a prominent role in American
culture and commerce. The company built the first transcontinental telegraph
line across the United States in 1861, issued one of the first consumer charge
cards in 1914, launched the first domestic commercial satellite into orbit in 1974,
and sold the first prepaid telephone card in 1993—not to mention sending
the first CandyGram in 1959. Some of the world’s great tragedies have played
out by Western Union telegraph. These include the last message sent from
the Titanic, a distress call reading: “SOS SOS CQD CQD Titanic.1 We are sinking
fast. Passengers are being put into boats. Titanic.”
You can’t send a telegram by Western Union anymore, but the company continues
to thrive at the forefront of the cross-border, cross-currency money-transfer and
payments industry. Across more than 200 countries and territories, the company
has more than half a million agent locations, and it offers services through more
1 CQD was the contemporary maritime distress signal meaning “Come Quickly: Distress.”
Working across many cultures at Western Union 117
than 150,000 ATMs and kiosks, along with the ability to send money to billions
of accounts. In 2016, Western Union completed 268 million consumer-toconsumer
transactions and 523 million business payments worldwide, moving
more than $150 billion of principal for consumers and businesses.
McKinsey’s Kausik Rajgopal and Lang Davison recently sat down with Western
Union’s CEO to talk about its multicultural customer base (and leadership team),
finding the simplicity within complexity, and how Ersek surprised everybody
with his choice to lead the company’s digital innovation lab in San Francisco.
The Quarterly:Western Union has a big global network of agents on the ground
in a wide variety of countries. What makes this network distinctive?
Hikmet Ersek: For one thing, our customers aren’t like those of many other
companies. We actually have two types of people we serve, the sender of the
money and the person who receives it. For example, the sender could be an
immigrant from a rural part of Tamil Nadu, who’s left India to find work in
Canada. In this case, we have to understand that his relatives—the receivers—
are in Tamil Nadu. They’re not in Punjab; they’re not in Pakistan. And that
understanding has to drive where and how we open locations in Tamil Nadu,
as well as where and how we open them in Canada. That’s a bit more complex
than opening a typical retail location.
But what really sets us apart is the interplay between our digital business
and the retail network. Our senders can send money from the phone in
their hand, and the receiver can pick it up in cash. NGOs [nongovernmental
organizations] can send money from their global headquarters in London,
and their fieldworkers can pick it up in cash in a conflict zone. In India,
parents of a university student in Canada can give cash to our agent in Mumbai,
and the tuition payment is made to the university’s bank account.
In order to build a unique physical and digital network like this, you can’t sit in
a corner office in Denver or San Francisco. You have to be in and understand
the diverse marketplaces in the world. There is a lot of fundamental prework
that has to occur before you can open anything. First you have to negotiate
with the reserve banks. You have to talk things over with the regulators. You
have to find the right agent for the location. And you have to begin all this
with the voice of the customer in your head.
Many people say the voice of the CEO is very powerful. I don’t think so. The
voice of the customer has more power. But if you can combine both voices in
your day-to-day actions, it’s even stronger.
118 McKinsey Quarterly 2018 Number 1
The Quarterly:You have built this network during a unique historical time, too.
Hikmet Ersek: Yes, to be fair, we have been lucky. Globalization has helped
us—the expansion and mobility not only of goods and information but also
of the global workforce. The increased movement of people across borders
has been very helpful to our expansion. Globalization has also helped us have
a unique brand. People may not speak English, but they recognize Western
Union. Ours is a global language for moving money to support your loved ones.
That’s Western Union.
The Quarterly:That would seem to put a premium on multicultural skills within
the organization.
Hikmet Ersek: It does. Our customers have broadly diverse religious
celebrations, school systems, languages, and beliefs. A multicultural understanding
of these differences is required if we are to stay close to our
customers—not only the senders and receivers of money but also the bankers,
regulators, and agents. You need a multicultural competence simply to
select the right agent for a given location, or to create the right app for a given
country, one that reflects our brand in the right way. Cultural differences
are complex, and therefore our business is, too. Thank God it’s complex. If it
weren’t then maybe we wouldn’t be so successful.
The Quarterly:What kind of management approach do you need for this unique
customer context?
Hikmet Ersek: Our people need their own multicultural competency if they
are to understand the diverse needs of our customers. I call it “cultural dancing.”
You don’t have to be Filipino to have that competence. You don’t have to be
Indian or Turkish. But you do have to be open-minded to people’s needs and
willing to step away from the perspective with which you see the world.
You also have to be willing to look beneath the surface, to look beyond the
apparent first meaning of the words someone is using. Because the person
speaking may not be using their primary language, it’s up to the listener
to actively participate in finding out what the person actually means by what
they say. If you’re only used to your home culture, you don’t have to do that.
You can take things more at face value. But if you grew up in a multicultural
environment, you think to yourself, “Maybe they didn’t mean it exactly like
it sounds. Maybe there’s a second thought, a second meaning behind the first
one.” That openness is important if you are on my leadership team.
Working across many cultures at Western Union 119
By the way, I find that people in the US are more multicultural than they
are given credit for. The business leaders in the US adapt themselves more
easily than do those of some other countries. Perhaps one reason is that
the US is built with and by immigrants. This country has an understanding
of immigrants and an openness to diverse cultures that isn’t always present
in other countries. I hope that doesn’t get put aside in the new political
environment that seems to be emerging in the US.
The Quarterly: Your customers are diverse. And your leadership team is
similarly diverse, right?
Hikmet Ersek: Among the nine executives that make up my top leadership
team, we have 13 nationalities. These leaders have together worked in more
than 40 cities globally—from Kabul to London, from Frankfurt to Riyadh.
So they’re truly international, but they also have deep market experience,
which enables them to stay connected to our diverse customer base.
The Quarterly: How does that diversity play out in your leadership assignments
and in the roles you ask your leaders to take on?
Hikmet Ersek: I’ll give you an example. A few years ago, we decided to open
a new office in San Francisco with a team that would be responsible for
building WU Digital, Western Union’s digital and mobile business, a startup
within the broader company responsible for reinventing and expanding
our money-transfer business for the mobile age. Who did I pick to lead
this new effort? Not a cool, new tech genius from the Bay Area and Silicon
Valley. I picked the leader of our Africa business, Khalid Fellahi. I picked
someone who has the multicultural competence we’re talking about, the
understanding of our diverse customer base and their needs. Even a new
start-up within the company has to begin with the voice of the customer, and
“Our people need their own multicultural
competency if they are to understand
the diverse needs of our customers. I call
it ‘cultural dancing.’”
120 McKinsey Quarterly 2018 Number 1
that’s what we got with Khalid. With that in place, he then hired 250 smart
people from Silicon Valley, including the many engineers the effort needed.
Now our digital business is the fastest-growing part of Western Union.
Many companies or investors would never dream of pulling a leader out of
Africa to establish and run a multimillion-dollar digital business in the heart
of the Silicon Valley. In fact, many people, both inside the company and out,
said, “What are you doing?” Even Khalid was surprised. But I believe that
if you understand the voice of the customer, then everything else will follow
thereafter. And I think this decision was the right one, since WU.com has
been growing in the double digits.
The Quarterly:Are there downsides to being multicultural?
Hikmet Ersek: Well, the upside to multiculturalism is you tend to learn
quickly. But the downside, to generalize, at least, is that multicultural
executives tend to be a bit less disciplined. Something about dancing between
cultures that means you can sometimes be less disciplined. Or at least that
it doesn’t come naturally; you have to learn it. You may have an ability to
be forward looking and visionary, but you have to look backward, too, in order
to fix what’s less efficient and effective. So the question becomes how you
combine those two things in an organizational culture.
HIKMET ERSEK
Vital statistics
Born in Istanbul, Turkey
Married, with with 2 children
and 1 grandson
Education
Holds a master’s degree in
economics and business
administration from Vienna
University of Economics
and Business
Career highlights
Western Union Holdings
(1999–present)
President, CEO,
and director
General Electric
(1996–99)
National executive for
Austria and Slovenia
Europay/MasterCard
(1986–96)
Sales and business
development
Fast facts
Recognized as one of the
“most socially responsible
chief executives” by
Corporate Responsibility
Magazine, receiving its
Responsible CEO of the
Year Award in 2012
Recipient of the Austrian
of the Year Award in 2016
and serves as the Austrian
Honorary Consul for
Colorado and Wyoming
Member of the International
Business Council of
the World Economic
Forum and the Business
Roundtable
Citizen of both Austria and
Turkey; advocates for
migrant and refugee rights
worldwide
Working across many cultures at Western Union 121
I was fortunate to have been trained in one of the best places, General Electric,
where I spent the first years of my career. Jack Welch was one of the first
guys to bring Six Sigma to Europe. And Six Sigma is all about discipline, even
if it’s not exclusively that.
The Quarterly:Does that mean, that you’ve brought Six Sigma into
Western Union?
Hikmet Ersek: We’re developing our own version of it, yes, with something
called the WU Way. The WU Way is a kind of lean-management processoptimization
environment, a disciplined approach based on the voice of the
customer that can help this multicultural organization increase the
discipline it needs.
RAPID REFLECTIONS
FROM HIKMET ERSEK
IF YOU WEREN’T CEO, WHAT OTHER JOB WOULD YOU DO FOR A DAY?
A professional basketball coach. I played semipro basketball in Europe years
ago, and I still love the sport.
IS THERE A COMMON PIECE OF LEADERSHIP ADVICE THAT YOU THINK IS
WRONG OR MISLEADING?
Many of us were taught that managers and CEOs should be the experts. But the
truth is that leaders who don’t trust and empower their people lose in the long term.
WHAT IS THE MOST INTERESTING THING THAT YOU HAVE LEARNED ABOUT
ANOTHER CULTURE?
Being culturally competent means being a good listener and being humble when
interacting with others.
WHAT MEMORY STANDS OUT THE MOST FROM YOUR EARLY YEARS GROWING
UP AS A CHILD FROM A MULTICULTURAL BACKGROUND IN EUROPE?
Celebrating both Christmas and Eid with my family gave me flexibility for life.
1
2
4
3
122 McKinsey Quarterly 2018 Number 1
The Quarterly:One area the company has had to instill discipline is in the culture
of compliance, given the regulated environment in which you operate. And
in 2017, Western Union paid a $586 million fine imposed by the US Justice
Department and Federal Trade Commission. Can you discuss some of the
things you’ve done with regard to compliance?
Hikmet Ersek: When I became CEO in 2010, it became clear that compliance
was one of the first strategic areas that we needed to invest in. The regulatory
environment only continues to get more complex, and we needed to invest in
the relationships and infrastructure to ensure we could succeed.
We announced the settlement in 2017, but the truth is that the conduct at
issue mainly occurred more than five years ago. Over the past five years, we’ve
made significant enhancements and investments in our programs, and
today we invest 3.5 to 4.0 percent of our revenue in compliance. Part of this
investment is in employees—more than 2,000 are dedicated to compliance—
and in sophisticated technology to help keep “bad money” out of the system.
We also strengthened our agent and customer education, and we put in
place a new compliance governance structure.
Today, I think we all can see that globalization looks different than it did
in 2010, and part of what that means is that there is less harmonization
of regulations than many might have imagined. What that means for WU
is that we believe these compliance investments can become a long-term
competitive advantage. We’re one of the world’s most global companies, yet
we have the relationships and infrastructure to successfully navigate local
regulations—across more than 200 countries and territories.
The Quarterly:Did that mean creating a compliance department?
Hikmet Ersek: We already had a compliance department, but we decided
that compliance had to be part of our culture as a whole, and not just the
responsibility of one department. So we created a compliance committee at
the board level, and then we looked to instill a culture of compliance at the
other levels of the company, too. The same way everybody in a company is a
brand ambassador, well, everyone has to be a compliance ambassador.
And they have to carry out their daily activities with the discipline needed
for compliance. For instance, every employee has to complete a compliance
training class and become certified. And the tests are not easy, either.
Working across many cultures at Western Union 123
It wasn’t popular on Wall Street, by the way. Our stock took a hit when we
announced we’d be investing about 3 to 5 percent of revenues in compliance
activities. It took a while to tell the story, to convince them that we could
create a long-term competitive advantage.
The Quarterly:Describe your own growth as an executive.
Hikmet Ersek: One of my biggest growth areas has been to learn to put my own
ego aside. Don’t think that I was always like that. I learned it. It’s something
that you learn over the years. I may have my own ideas about something—for
instance, about the importance of the WU Way—but I have to carry that to
my team, taking the time to do that properly. The notion is to make your idea
their idea. Then, once the idea takes hold, you can’t say, “Well, it was my idea
in the first place.”
In the past, I would have said, “Hey, it was my idea first! Don’t forget me!
I want to have the credit!” Right? You develop over the years. Or you don’t.
Some people never develop. But being multicultural helps in this regard.
You learn to adapt yourself more easily, to learn and to grow.
The Quarterly:What else have you learned over the years?
Hikmet Ersek: One thing I’ve learned is that leaders have to balance the
complexity of the world by keeping things simple. Many people will show you
how complex or how difficult an issue is. In some cases, they may be right, but
most of the time it is their insecurity or they are just afraid to solve a problem.
As a leader, especially as a business leader, in a complex environment, it is
important to keep things simple. If you have products and services that are
too difficult to market and do not match customer needs, you will lose. The
advice from me would be to create products and services that are simple
for the customers. That will make you successful. The communication and
the marketing of the complicated products and services, whatever they
are—spaceships, medicine, hamburgers, or financial funds—has to be simple.
Also, the company’s vision and goals for the employees, shareholders, board
members, and all its other stakeholders must be stated simply so that all
over the globe, in any culture, in any language, the intent of the message and
the direction of the company are clear.
124 McKinsey Quarterly 2018 Number 1
Copyright © 2018 McKinsey & Company. All rights reserved.
Hikmet Ersek is the president, CEO, and director of Western Union. This interview was
conducted by Lang Davison, a member of McKinsey Publishing who is based in McKinsey’s
Seattle office, and Kausik Rajgopal, a senior partner in the Silicon Valley office.
The Quarterly: How do you go about making things simple?
Hikmet Ersek: I do it by asking “why.” Asking why has been a recurring theme
throughout my business life. During the Jack Welch period at GE, I went
through the Six Sigma training and learned the concept of asking “why” five
times. Asking why generates simple solutions that overcome complexities.
By asking why, you can be innovative, even within a long-established business,
where your own success risks blinding you to future opportunities and
transformation. I started the transformation at Western Union—into the
digital era and into the compliance era—by asking the question why, and it
kicked off an entirely new set of business solutions for our company to offer.
125
HOW COMPANIES CAN GUARD
AGAINST GENDER FATIGUE
Most of the corporate world has set a bold aspiration to achieve
equality for women in the workplace. Ninety percent of US companies
in our latest research, for example, say they are “very committed”
to this goal, and just about all of them are taking action.
It’s also obvious that we’re still in the early stages of the journey:
Currently, just 20 percent of C-suite executives in the United States
are female. Although that figure is inching up—from 19 percent a
year ago—more than one CEO has confided to us, “We’re
implementing all the best practices, but the numbers aren’t moving
fast enough, and I’m worried about maintaining the energy we
need to keep going.”
The good news is there are ways to counter change fatigue. Our third
annual Women in the Workplace report, developed in collaboration
with LeanIn.Org, shows the importance of executing the basics with
conviction. The experience of 70,000 surveyed employees, coupled
with performance benchmarking of the 222 participating companies,
shines a light on bolder actions we see from companies that are
top performers in employing and promoting women.
Break through on the basics
Many companies have put in place the right building blocks: They’re
developing a business case, tracking gender representation across
the workforce, and developing training, flexibility, and networking
programs. Breaking through on the basics isn’t easy, though.
Consider the metrics: Some 85 percent of companies surveyed track
gender representation. Yet less than a third set targets, and
transparency is rarer still. Most companies say they share a majority
of diversity metrics with senior leaders, but just 23 percent do
so with managers, and a mere 8 percent with all employees. It’s
the same with the business case: 78 percent of companies say they
articulate one, but only 16 percent back up the case with data.
Show you are serious about basics such as mentoring and
work–life flexibility—then hold yourself accountable.
Dominic Barton
is the global
managing partner
of McKinsey &
Company.
Lareina Yee is a
senior partner in
McKinsey’s San
Francisco office.
Closing View
126 McKinsey Quarterly 2018 Number 1
Top-performing companies are executing with greater intensity and have
the results to show for it. For example, while many managers work with their
teams to identify development opportunities, top companies also have
programs aimed specifically at boosting the mentorship of women and their
promotion rates.
Or consider flexibility: The top-performing companies in our research are
more than twice as likely as those at the bottom to offer emergency backup
childcare services; three times as likely to offer on-site childcare; and more
likely to offer extended maternity and paternity leave, as well as programs to
smooth the transition to and from extended leave. Moves such as these
build broad-based enthusiasm because they help men and women alike.
Maintaining momentum
Despite these encouraging signs, the overall picture is one of uneven results,
which sometimes breeds skepticism. Barely half of the men and women in
our survey expressed confidence that their company is doing what it takes to
advance women. To keep organizational uncertainty from slowing progress,
leaders should take additional steps like these:
• Hold yourself accountable. A majority of companies say they don’t hold
their senior leaders accountable for performance against gender-diversity
metrics, or use financial incentives to encourage action. Employees
notice: less than 20 percent in the survey said they saw leaders regularly
being held accountable for performance on gender diversity. If you want
to help keep your organization on track, show your people that senior
leaders are taking responsibility for the outcomes of the initiatives they
are driving. Forty percent of the companies in our survey do emphasize
top-management accountability, and many of them are seeing much
better results.
• Make men part of the solution. Less than half of men report that advancing
women is an important priority for them. Leaders hoping to bring them
on board need to show, through actions, not just words, how things can
be different: the data show that when men think their company or direct
manager is highly committed, or get explicit guidance from a senior leader
on how to improve, they are more likely to embrace the cause.
• Emphasize race and gender. Sometimes change efforts benefit from
widening the lens, such as addressing the reality that there is still a
disquieting racial component to gender bias. Just 3 percent of C-suite
127
roles are held by Asian, black, Latina, or other women of color. Black
women face the longest odds. Promotion rates for them are 50 percent
below those of white women, and only 23 percent of black women
say managers help them navigate organizational politics, compared with
36 percent for white women. These challenges are a critical, too-often
overlooked piece of the gender puzzle that demand their own attention,
commitment, and solutions.
In the first year of our research, we shared data suggesting that American
corporations were 100 years from parity at the top. Two years later, even if
the top-performing companies are still early in the journey, they’re providing
the clues on how to break through. That’s encouraging: The data is getting
clearer, and the answers are in front of us. If we stay committed, lead boldly,
and execute relentlessly, we can build momentum and accelerate change.
Copyright © 2018 McKinsey & Company. All rights reserved.
Read the full report, Women in
the Workplace 2017, conducted
by LeanIn.Org and McKinsey,
on womenintheworkplace.com.
This article first appeared in the Wall Street Journal.
For more on the social side of strategy and the power of bold moves, see “Strategy to beat the odds,”
on page 30, which is adapted from Strategy Beyond the Hockey Stick: People, Probabilities, and Big
Moves to Beat the Odds (Wiley, February 2018), by Chris Bradley, Martin Hirt, and Sven Smit.
128 McKinsey Quarterly 2018 Number 1
Extra Point
Copyright © 2018 McKinsey & Company. All rights reserved.
The most dangerous
strategy? Make no bold
moves
“Occasionally, in the strategy room we’ll see things as they
really are and where they’re going, and come up with a truly
bold plan. Your job will be to talk us out of it.”
McKinsey.com/quarterly
Highlights
Welcome to the strategy room: How
to tame the social side of strategy and
make big, winning moves
What AI can and can’t do (yet) for
your business
Organizing for the age of urgency:
It’s the only way to compete at the
speed of digital
The four questions to ask when serving
on a nonprofit board
Data as jet fuel: Boeing’s CIO on
harnessing the power of data analytics
Shaking up the leadership model in
higher education
Reaching for the digital prize: Snapshots
of four industries in transition
How companies can guard against
gender fatigue
Western Union’s CEO on the link
between diversity in the top team and
serving multicultural consumers

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