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MIT Sloan Management Review - January 2018

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IS YOUR JOB A
COMMODITY?
WHAT SETS GREAT
STRATEGIES APART
THE FLAW IN AI
IMPLEMENTATION
A SMART WAY TO
TEST ASSUMPTIONS
PAGE 16
PAGE 86
PAGE 10
PAGE 89
MIT Slo
sloanreview.mit.edu
WINTER 2018 • VOL. 59 • NO. 2
Management Review
Take Your Organization
to the Next Level
PAGE 27
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FROM THE EDITOR
Don’t Get Caught in
the Middle
Our digital world is rendering traditional intermediaries obsolete.
T
hings move so fast in our
digitized world that it can
feel as if there is no safe
place to position yourself —
or your organization. How
do you anticipate where to go next —
whether considering your company’s
strategy or your own career — when the
winds of change sweep from every direction? You can’t know the right move for
certain. And even when you do make a
good call, you’ll soon face a new threat of
obsolescence. We all need to accept that
there are no safe havens — for us or our
companies. We simply must be prepared
to stay on the move.
But whatever you do next, wherever
you go, don’t head for the middle. Not the
middle of a relationship, not the middle of
an organization. It’s a bad time to be an
intermediary — at least in the traditional
sense of the word. As the makers of products seek to close the distance to those who
buy and use those products, and as layers of
SLOANREVIEW.MIT.EDU
traditional management hierarchy fall
away, the real worth is increasingly found
at the extremes of value chains and organizations, rather than at the center.
There was once a time when the
“middleman” was an indispensable
resource. Intermediaries facilitated
transactions between producers and
consumers; they interpreted high-level
corporate strategy and connected it to
execution; they monitored and herded;
and they closed the gaps between disconnected entities that required one another
for survival. But one by one, they are
being replaced by technology.
• Digital platforms are convening direct
connections between traditionally
intermediated sides of markets by the
thousands — in retailing, dating, personal transportation, and so on.
• Within organizations, advanced
communication technologies — from
corporate messaging apps today to facial
recognition and emotion-sensing
technologies tomorrow — combined
with distributed leadership models are
slowly but surely threatening traditional
middle-management functions.
• New industrial technologies like additive
manufacturing will eliminate links
throughout legacy supply chains. For
example, a company that can make a
needed part through 3-D printing won’t
purchase that part from a distributor.
• We are only now just scratching the
surface of the internet of things (IoT).
IoT promises to deepen connections
between manufacturers and end users
of their products — and that threatens
many traditional intermediaries.
• And if I were in a field such as financial
services, I would be looking at blockchain
with trepidation. Who needs the “trusted
intermediary” when trust has been confirmed through blockchain technology?
For most of the industrial age, we took
the value of intermediaries as a given. But
the more we encounter example after example of their redundancy, the more we
will see middlemen as usurpers of value
rather than creators.
Most intermediaries will not disappear
overnight. For instance, there are few organizational models that can withstand
the wholesale removal of entire management layers in one fell swoop. But over the
long term, genus go-betweenus may well
find itself on the endangered list.
The intermediaries that persevere will
be those that adapt. They will produce
unique value, adding something to a transaction or relationship that is — at least for a
time — irreplaceable. Intermediaries will
increasingly become specialists offering
customized services that are too expensive
or rare for the parties they serve to justify
building on their own.
But for the rest, for those middle players
who exist by tradition rather than by irreplaceability, the future will not be kind.
Paul Michelman // @pmichelman
Editor in Chief
MIT Sloan Management Review
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 1
WINTER 2018 • VOLUME 59 • NUMBER 2
MITSloan Management Review
FEATURES
OPERATIONS
53 Winning With Open
Process Innovation
When manufacturers develop a process
innovation, they often try to keep it under
wraps. That may not be the best approach.
BY GEORG VON KROGH, TORBJØRN NETLAND,
AND MARTIN WÖRTER
27
SPECIAL REPORT
29 A New Approach to Designing Work
REDESIGNING WORK: OPERATIONS
ACCOUNTING
57 The Pitfalls of
Non-GAAP Metrics
Alternative metrics, once used sparingly,
have become increasingly ubiquitous and
more detached from reality.
BY H. DAVID SHERMAN AND S. DAVID YOUNG
For years, management thinkers assumed that there were inevitable trade-offs
between efficiency and flexibility — and that the right organizational design for
each was different. But it’s possible to design an organization’s work in ways
that simultaneously offer agility and efficiency — if you know how.
STRATEGY
BY NELSON P. REPENNING, DON KIEFFER, AND JAMES REPENNING
Executives need to factor scalability attributes
into their business model design or they risk
being left behind.
39 What to Expect From Agile
REDESIGNING WORK: CASE STUDY
What happens when a company whose roots go back over a century —
a bank, no less — decides to adopt agile management methods developed
in the software industry?
BY JULIAN BIRKINSHAW
43 The Trouble With Homogeneous Teams
REDESIGNING WORK: DECISION-MAKING
Diversity in the workplace can increase conflict. But research also suggests that
if teams lack diversity, they will be more susceptible to making flawed decisions.
65 Building Scalable
Business Models
BY CHRISTIAN NIELSEN AND MORTEN LUND
INNOVATION
71 Developing Successful
Strategic Partnerships
With Universities
For many companies, universities have become
essential innovation partners. However, companies often struggle to establish and run university
partnerships effectively.
BY LARS FRØLUND, FIONA MURRAY, AND MAX RIEDEL
EVAN APFELBAUM, INTERVIEWED BY MARTHA E. MANGELSDORF
CONVERSATION WITH THE CEO
49 The Truth About Hierarchy
REDESIGNING WORK: TEAMS
80 Leading in a Time of
Increased Expectations
Hierarchies are often seen as obstacles to innovation. However, a growing body
of research shows that the right kind of hierarchy can help teams become better
innovators and learners.
Lynn Good, CEO of Duke Energy Corp.,
reflects on guiding her company through
a dramatic transformation.
BY BRET SANNER AND J. STUART BUNDERSON
LYNN J. GOOD, INTERVIEWED BY PAUL MICHELMAN
2 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
COVER ILLUSTRATION: CHRIS GASH
FRONTIERS
EXPLORING THE DIGITAL FUTURE OF MANAGEMENT
7 SURVIVING A
DAY WITHOUT
SMARTPHONES
86
COLUMNS
STRATEGY
86 WHAT SETS BREAKTHROUGH STRATEGIES APART
Innovative strategies depend more on novel, well-reasoned
theories than on well-crunched numbers.
BY TEPPO FELIN AND TODD ZENGER
ENTREPRENEURSHIP
89 HOW TO TEST YOUR ASSUMPTIONS
When you’re developing a strategy for a new business, testing
assumptions in a logical order gives you the best chance to make
course corrections early — and not waste time and money.
BY JON FJELD
BACK TALK
96 IS THE THREAT OF DIGITAL DISRUPTION OVERHYPED?
Responding to a recently published essay, a reader pushed back
against the view that managers must prepare for radical and rapid
change in a digital world; he argued that this position may be overly
alarmist. The discussion continues.
BY BRUCE POSNER
IN EVERY ISSUE
91 Executive Briefings
For young adults accustomed
to continually checking their
cellphones, even a single day
without access to them can
be anxiety-producing. What are
the implications for executives
about managing this constantly
connected generation?
BY MARCELLO RUSSO,
MASSIMO BERGAMI, AND
GABRIELE MORANDIN
10 THE FUNDAMENTAL
FLAW IN AI
IMPLEMENTATION
To fully unlock the benefits
of artificial intelligence,
business leaders will need
to upgrade their people’s
skills — and build an
empowered, AI-savvy
workforce.
BY JEANNE ROSS
12 THE BOARD’S
ROLE IN MANAGING
CYBERSECURITY RISKS
Cybersecurity can no longer
be the concern of just the IT
department. Within organizations, it needs to be everyone’s business — including
the board’s.
BY RAY A. ROTHROCK,
JAMES KAPLAN, AND
FRISO VAN DER OORD
16 WHEN JOBS
BECOME COMMODITIES
Even if your job is high paying, it is wise to ask yourself
whether it is common and
repetitive enough to be done
by a machine. If you conclude that it is, it’s time to
look for — or create — less
commoditized work.
BY THOMAS H. DAVENPORT
18 WHY SOME
PLATFORMS ARE
BETTER THAN OTHERS
Although successful digital
platforms can deliver remarkable value to users and riches
to entrepreneurs and investors,
in some sectors it isn’t clear
that anyone will turn a profit.
BY JONATHAN A. KNEE
21 IS YOUR COMPANY
READY FOR A DIGITAL
FUTURE?
There are four different
pathways that businesses
can take to become top
performers in the digital
economy. Leadership’s role
is to determine which pathway to pursue — and how
aggressively to move.
BY PETER WEILL AND
STEPHANIE L. WOERNER
For detailed summaries of articles in this issue.
SLOANREVIEW.MIT.EDU
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 3
MITSloan
Management Review
Editor in Chief
Paul Michelman
Managing Director
Robert W. Holland, Jr.
Head of Planning,
Digital, and Marketing
Deborah I. Gallagher
Director of Business Development
and Sponsorship
Michael Barrette
Editorial Director
Martha E. Mangelsdorf
Executive Editor
David Kiron
Managing Editor
Cheryl Asselin
Senior Project Editor
Allison Ryder
Senior Editor
Bruce Posner
Manager, Sales and Marketing
Jinette Ramos
Design
George Lee
Contributing Editors
Leslie Brokaw
Theodore Kinni
Beth Magura
Custom Content Editor
Elizabeth Heichler
Graphics
Matthew Harless
Senior Production Director
James LaBelle
Senior Advertising and
Sponsorships Manager
Richard Marx
Web Production Editor
Elizabeth Platt Hamblin
Manager, Content Distribution
and Support
Mackenzie Wise
Communications Manager
Sara Peyton
Bookkeeper
Judith White
MIT Sloan
Management Review
Director of Digital Media
Sean M. Brown
Associate Director, Digital Media
Web Developers
Lauren Rosano
Juan Carlos Cruz Dada
Pedro Henrique Santos
Web Designer
Carolyn Ann Geason
Digital Producers
Tyler Davis
Jennifer Martin
Editorial Advisory Board
Deborah Ancona
MIT Sloan School of Management
Andrew Lo
MIT Sloan School of Management
MIT Sloan School
of Management
Julian Birkinshaw
London School of Business
Constantinos C. Markides
London Business School
One Charles Park EE20-601
Cambridge, MA 02142
USA
James Champy
Patricia Moody
Patricia E. Moody Inc.
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University of California, Berkeley
Clayton M. Christensen
Harvard Business School
Michael A. Cusumano
MIT Sloan School of Management
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MIT Sloan School of Management
Stuart L. Hart
University of Vermont Grossman
School of Business
John Hauser
MIT Sloan School of Management
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MIT Sloan School of Management
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George Washington University
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The Governance Laboratory
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4 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
SLOANREVIEW.MIT.EDU
[ELSEWHERE]
Learning from da Vinci
By now, you would think that the contours of
Leonardo da Vinci’s genius would be fairly
well established. Revered for artistic masterpieces such as the “Mona Lisa” and “The
Last Supper,” da Vinci, who died in 1519, was a gifted
engineer as well as a student of, among other things,
anatomy, birds, optics, and geology. For decades,
he recorded his ideas, large and small, in thousands of pages of notebooks.
In a new biography, Walter Isaacson,
who has also written biographies of
Steve Jobs, Albert Einstein, and Benjamin
Franklin, calls da Vinci “history’s consummate innovator.” In a Wall Street Journal
article titled “The Lessons of Leonardo:
How to Be a Creative Genius,” Isaacson
offered three lessons for those seeking to
learn from da Vinci.
The first lesson involves curiosity. In his notebooks, da Vinci made detailed lists of the subjects he
wanted to learn more about, some of which seemed quite
random (for example, investigating what the tongue of a
woodpecker looked like). Yet, as Isaacson sees it, the lists
reflected the depth and range of his interests. Da Vinci
didn’t draw a line between art and science or the humanities and technology. “He never outgrew the child’s need
not just to admire the beauty of a blue sky but to ask why
it is that color,” wrote Isaacson.
The second lesson involves the importance of
observation. Isaacson noted that da Vinci liked
learning about the details of things. Far from
considering reflection an indulgence, da
Vinci is quoted as saying that “men of lofty
genius sometimes accomplish the most
when they work the least.”
The third lesson from da Vinci has to do
with being open to fantasy. Da Vinci used
his imagination to develop props for the
theater and to sketch human-powered
flying machines. In a world where the importance of specialization and technology are seen
as givens, da Vinci provides a timely reminder of the
value of interdisciplinary thinking. “Leonardo,” Isaacson
wrote, “was a grown-up who never stopped indulging in
the sort of fantasy and speculation that we now associate
with childhood.”
The Uncertain Future of Telecommuting
Anticipating the Impact of AI
IBM’s decision in 2017 to ask thousands of its remote workers to start working out of company facilities raises a question that extends beyond Big Blue: Has the telecommuting trend,
which has gathered momentum since the 1980s, reached a peak? In an article in The Atlantic, titled
“When Working From Home Doesn’t Work,” author Jerry Useem explored the pros and cons of
remote work, which has enabled companies (including IBM) to drastically reduce their need for
office space.
According to a recent Gallup survey, some 43% of U.S. workers spend part of their time working
remotely. However, Useem reported, the research on where employees are more productive —
working remotely or from an office surrounded by teammates — includes seemingly contradictory
findings. It depends, Useem concluded, on the type of productivity you want to promote. For personal productivity (for example, closing sales and handling customer complaints), letting people work
remotely often works better. The same goes for jobs that involve working with outside clients. But
work that involves “collaborative efficiency” and lots of communication can frequently be handled
more efficiently in a setting where people can interact directly and in real time. At least some of
IBM’s new physical space, Useem reported, is designed to help teams collaborate more efficiently.
It often features a central table for a small team, with a perimeter of whiteboards for visual displays.
Read The Atlantic article:
www.theatlantic.com/magazine/archive/2017/11/when-working-from-home-doesntwork/540660
How is artificial intelligence (AI) likely to
affect the way business is done? MIT
Sloan Management Review and The Boston
Consulting Group surveyed more than 3,000 executives, managers, and analysts across a range
of industries and regions and also conducted interviews with more than 30 technology experts
and executives. The survey found significant
gaps between companies that are leaders in AI
adoption and those that are laggards, especially
with regard to their approach to data and their
investments in AI talent. While three-quarters
of the executives surveyed expected AI would
help them enter new businesses (and an even
greater number thought it would be a source
of competitive advantage), fewer than four in
10 companies had an AI strategy in place.
The complete report can be found at:
sloanreview.mit.edu/ai2017
SLOANREVIEW.MIT.EDU
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 5
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FRONTIERS
EXPLORING THE DIGITAL FUTURE OF MANAGEMENT
The Fundamental
Flaw in AI
Implementation
10
The Board’s Role
in Managing
Cybersecurity
Risks
When Jobs
Become
Commodities
Why Some
Platforms Are
Better Than Others
Is Your Company
Ready for a
Digital Future?
16
18
21
12
[MOBILE ]
Surviving a Day Without Smartphones
For young adults accustomed to continually checking their cellphones,
even a single day without access to them can be anxiety-producing.
What are the implications for executives about managing this
constantly connected generation?
BY MARCELLO RUSSO, MASSIMO BERGAMI, AND GABRIELE MORANDIN
I
n contemporary society, many people, particularly those under
the age of 30, rely on their smartphones for a variety of important activities, including waking up in the morning, listening to
music, following the news, finding bus schedules, and communicating with friends and family. A 2015 survey by the Pew Research
Center found that 15% of Americans between ages 18 and 29 were
“heavily dependent” on their smartphones for online access. There
is no question that smartphones make our lives easier and more
connected. But at what cost? Several studies have warned that
excessive phone use can affect cognitive abilities, sleep, the quality
of social interactions, and the ability to engage at work.
Based on the behavior we observed in our classrooms and the
extent to which technology is infiltrating young people’s lives, we discussed what we could do to make our students more conscious of the
costs associated with unrestrained use of mobile phones and other
internet-connected devices. After reading about various ideas for
curbing dependency on phones and devices, we decided to initiate
a one-day project in which graduate students in our organizational
behavior and leadership courses at the University of Bologna in Italy
and the Bordeaux, France, campus of Kedge Business School would
be asked to suspend all connectivity and keep a journal about their
experience. This article is based on the experiences of 153 graduate
GETTY IMAGES
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 7
FRONTIERS
Surviving a Day Without Smartphones (Continued from page 7)
students who participated in this project
between 2015 and 2017.
Students reacted to the idea with a mix
of incredulity and skepticism, although
these reactions were often followed by a
feeling of excitement. Some students, particularly foreign students, pushed back,
citing concerns that family members or
partners would worry if they were unreachable. In such cases, we suggested that
students inform their families in advance
and share the contact information of a
friend or professor in case of emergency.
A number of students questioned the very
notion that they were in any way addicted
to their devices. However, we ultimately
decided to make participation in the project a requirement for our courses, and we
advised students to inform their families
that they would be out of touch.
Anticipating the Challenge
For a number of students, the days leading up to the challenge were both busy
and stressful. Individual students were
allowed to choose the day they would be
off-line and away from their phone, and
many were careful about how they prepared. In addition to notifying their
friends and family, some students elected
to announce their upcoming technology
hiatus more publicly, by posting about it
on social media. Many students anticipated some of the challenges they would
face by printing bus schedules, driving directions, and other materials they usually
accessed in real time from their devices.
They consulted with friends and roommates on how to handle mundane tasks
such as how to wake up in the morning
without using their phone alarm.
Students were expected to keep track
of the experience for a paper that would
count for 30% of their final grade for the
course. In the paper, they were to discuss
how they prepared for the experiment,
how they spent their time off-line, how
the experience affected them emotionally,
8 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
and what they learned about disconnecting from technology for one entire day.
How They Spent Their Time Many
students noted that the unplugged day
seemed longer than a typical day. Some
saw this as a good thing, as it allowed
them to complete projects they had postponed for weeks. A German student said
that, despite waking up later than usual
(because he didn’t have an alarm), he had
more time to read, exercise, and prepare a
special meal.
For other students, having more time in
the day was viewed as a negative, leaving
them with gaps some admitted they didn’t
know how to fill. “[Without the smartphone] my breakfast was too short and I
did not know what to do next,” one student
noted. Another wrote about his frustration
at not being able to look at his phone during bus and train rides; he called it “the
longest time of my life.” Not having a
phone to look at made some students feel
exposed. As one put it, “I was feeling uncomfortable, with no possibility to hide
behind the phone screen.” One French student wrote, “I was not capable of doing
nothing. I was thinking about my phone all
the time.” To minimize their discomfort, a
number of students elected to do the experiment on a day they knew would be
busy. “In this way,” wrote a student from
the United States, “I will be distracted and
not think about my phone.”
Many students made a determined effort
to approach the project as a learning opportunity. A few noted that it was a way to
experience what life was like before the rise
of mobile devices (or as one put it, to see
“how my parents communicated when they
were my age”). They reported spending
time visiting new areas of the city (“I had
been living in the city for six months, and
there were still many places I did not know,
so that was a fantastic day for me”). “I
walked around for a couple of hours,” one
student offered, “and it was so relaxing.”
Many participants said the experiment
provided time to reflect on how technologies were shaping their lives and social
interactions. Several said they experienced
more meaningful conversations, which
caused them to feel “closer and more connected” to their friends. One student wrote,
“My friend and I had dinner in the evening, and we both remarked on how much
more present we felt — how we could really hear what each other was saying.”
How They Felt During the unplugged day,
students experienced a mix of emotions. For
many, the strongest feeling was anxiety. Students felt anxious about missing something
important: What if their parents needed
them? What if a dream employer was trying
to contact them with a job offer? How would
they catch up with all the social media updates they were missing? In anticipation of
the experiment, some students reported that
they had difficulty sleeping. In order to calm
themselves, a few students said they used
their smartphones as much as they could
until the experiment began. (One wrote, “I
spent [the] last minutes checking every communication and my social media apps.”)
A common sentiment was that the
phone provided “a sense of safety.” Even
when it was turned off, some students said
they carried their phone with them in airplane mode just in case there was an
emergency. Many noted that they felt the
greatest amount of anxiety in the afternoon,
when they hadn’t received a call for several
hours. One Italian student wrote of her fears
about losing contact with people and being
excluded: “I am not receiving messages,
photos, emails, likes, comments, etc. It
feels as if no one is willing to interact with
me, thinking about me! I am alone!!”
Students also expressed feelings of guilt
at not being able to respond to messages
received in their class chats. They were
concerned that they were hurting their
classmates’ ability to complete class assignments and worried about the repercussions
SLOANREVIEW.MIT.EDU
their disconnection might have on their reputations and social life. A Russian woman
studying at Bologna wrote, “Remaining silent for more than three hours is considered
abnormal, requiring explanation later. Being
fast in responses is a must that is needed in
order to be part of social life.” When the
24-hour period was over, many students
expressed relief. (As one student wrote,
“Thankfully this challenge lasted only one
day.”) Some of them even decided to
insert screen shots in their papers documenting the number of missed messages,
emails, and social media notifications.
What They Learned Many students
came to see that technology has pluses
and minuses. Most of them concluded
that technology was essential and that
living without a mobile phone “would be
impossible.” For example, they acknowledged that connectivity technologies had
improved their lives and enabled opportunities (such as the ability to interact with
people in other parts of the world) that
were once more difficult to access. However, living unplugged — even for a single
day — led many to see that control and
moderation were important. As one male
student from Turkey wrote, “I appreciate
technology and the convenience it [brings]
to our lives. However, too much technology can be detrimental.”
Students developed greater awareness
about their own connectivity habits. As a
Spanish student at Bologna noted, the experiment “was a surprising and revealing
exercise that made [me] aware that [my]
day starts and ends with a smartphone in
my hands.” Even students who didn’t consider themselves overly reliant on their
devices realized how dependent they were
for simple things such as finding a recipe or
setting a timer for cooking. Students also
developed a greater awareness of the extent
to which they were influenced by their peers.
As one student wrote, “Since everybody
is on the smartphone, I also do it.”
SLOANREVIEW.MIT.EDU
Finally, some students said that by observing other people in cafés or on trains
who were totally immersed in their phones,
they became more conscious of the fact
that using phones in front of other people
can be seen as disrespectful. One student
wrote, “My friend checked his phone four
times during our 10-minute encounter!!
This made me realize how superficial some
of our contemporary relationships are becoming.” Inspired by the project, several
students have begun scheduling periods of
respite when they pledge to be disconnected: “I can’t totally give up all my digital
devices, but I’d like to have some unplugged days regularly,” wrote a male
Chinese student at Kedge. An Italian
woman studying at Bologna noted, “At the
end of the day, I was missing neither social
media nor having a digital connection. I
was happy for the opportunity to challenge
my unhealthy daily habits, because this
gave me the opportunity to discover a
slower, more conscious way.”
Implications for Managers
One thing we found was that young
people are more open to adjusting their
technology habits than we expected.
Rather than being totally fixed in their
ways, millennials are surprisingly open
to discovering the value of new tasks
and duties.
During the time they were cut off
from their smartphones, many students
rediscovered the value of other forms of
collaboration. They found new ways to coordinate with classmates for meetings and
sharing class material, class assignments,
and so forth. Such flexibility and resourcefulness could give organizations ideas
about how to approach their digital strategies and what kinds of limits to impose on
internet connectivity. For instance, organizations might find it useful to limit the
use of devices during business meetings,
meals, or interactions with colleagues, and
perhaps to establish guidelines around
sending nonurgent emails during nonwork hours, as a nod toward employees’
private lives and families. Although text
messaging is certainly easy and fast, companies may want to encourage face-to-face
communication when possible.
Working without devices for limited
periods of time can highlight opportunities
for improving self-awareness and selfregulation, two key elements of emotional
intelligence. Following the experiment, we
noticed that many students voluntarily
adopted behavioral rules that limit the use
of mobile devices when interacting with
others as a sign of respect and undivided attention. One German student reported that
he began hiding his phone and keeping it
“out of sight” during social interactions to
control his deeply ingrained habit of checking his phone or simply playing with it
while he was with other people. Considering the pervasiveness of internet-connected
devices in contemporary organizations,
we hope that the unplugging experiment
can be an inspiration for organizations —
and especially for their leaders — as they
attempt to model the right approach to
using mobile technologies at work.
Marcello Russo (@MarcelloRusso6) is an
assistant professor of organizational behavior at the University of Bologna in Bologna,
Italy, and an adjunct professor at the Bordeaux, France, campus of Kedge Business
School. Massimo Bergami (@maxbergami)
is a professor of organizational behavior at
the University of Bologna and dean of Bologna Business School. Gabriele Morandin
(@GabriMorand) is an associate professor
of human resources at the University of
Bologna. Comment on this article at http://
sloanreview.mit.edu/x/59226.
ACKNOWLEDGMENTS
We thank Tammy Allen for encouraging us to
write this paper, Marco Roccetti and Maurizio
Sobrero for their feedback, and the students for
their enthusiasm and insightful reflections.
Reprint 59226. For ordering information, see page 4.
Copyright © Massachusetts Institute of Technology,
2018. All rights reserved.
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 9
FRONTIERS
[ARTIFICIAL INTELLIGENCE ]
The Fundamental Flaw in
AI Implementation
Many executives are enthusiastic about the business potential
of machine learning applications. But business leaders often
overlook a key issue: To fully unlock the benefits of artificial
intelligence, you’ll need to upgrade your people’s skills — and
build an empowered, AI-savvy workforce.
BY JEANNE ROSS
T
here is no question that
artificial intelligence
(AI) is presenting huge
opportunities for companies to
automate business processes.
However, as you prepare to
insert machine learning applications into your business
processes, I recommend that
you not fantasize about how a
computer that can win at Go
or poker can surely help you
win in the marketplace. A better reference point will be your
experience implementing your
enterprise resource planning
(ERP) system or another enterprise system. Yes, effective ERP
implementations enhanced
the competitiveness of many
companies, but many other
companies found the experience more of a nightmare.
The promised opportunity
never came to fruition.
Why am I raining on the
AI parade? Because, as with
enterprise systems, AI inserted
into businesses drives value by
improving processes through
automation. But eventually,
the outputs of most automated processes require
people to do something. As
most managers have learned
the hard way, computers can
process data just fine, but that
processing isn’t worth much
if people are feeding them
bad data in the first place or
don’t know what to do with
information or analysis once
it’s provided.
10 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
With my fellow researchers,
Cynthia Beath, Monideepa
Tarafdar, and Kate Moloney,
I’ve been studying how companies insert value-adding
AI algorithms into their processes. As other researchers
and managers have also observed, we are finding that
most machine learning
applications augment, rather
than replace, human efforts.
In doing so, they demand
changes in what people are
doing. And in the case of AI —
even more than was true with
ERP systems — those changes
eliminate many nonspecialized
tasks and create skilled tasks
that require good judgment
and domain expertise.
For example, fraud detection applications may reduce
the time that people spend
looking for anomalies but
increase requirements for deciding what to do about those
anomalies. An AI application
might allow financial analysts
to spend less time extracting
data on financial performance, but it adds value only
if someone spends more time
considering the implications
of that performance. With the
help of AI applications, customer service staff can spend
fewer hours resolving routine
problems, but they are more
likely to improve operations
if at least some of that saved
time is reallocated to better
understanding the problems
customers are experiencing
with the company’s most
recent offerings.
Many leaders think that
they will generate value from
AI by recruiting more data
scientists. Of course, there’s a
shortage of data scientists —
and some of them are more
attracted to the challenge of
building an application that
wins at poker than solving a
business need. Others will be
inspired to find a cure for
cancer or to mitigate global
A. RICHARD ALLEN/THEISPOT.COM
warming. So financial services
and insurance companies attempting to uncover fraud
and technology companies
hoping to improve customer
satisfaction will be fighting
over the remaining talent.
But recruiting data scientists is not your biggest
challenge. Data scientists can
develop useful algorithms, but
domain experts are needed to
help train the machine to recognize important patterns and
understand new data. Domain
experts include top analysts,
contract managers, salespeople,
recruiters, and other specialists
who are not only experts at
their jobs but are also acutely
aware of how they deliver excellence. That may involve just
a few key people for a given application, but they’d better be
good. And we still haven’t gotten to the really hard part!
Ultimately, you need people who can use probabilistic
output to guide actions that
make your company more effective. Probabilistic outputs
are no problem when, say, an
application such as Salesforce
.com Inc.’s AI tool, Einstein,
indicates that one lead has a
95% chance of converting into
a sale while another has a
60% chance. The salesperson
knows what to do with that
information. But what’s the
next step when a recruiter
learns from an AI application
that a job candidate has a 50%
likelihood of being a good fit
for a particular opening?
When a machine learning
application is helping a lawyer
identify potentially relevant
SLOANREVIEW.MIT.EDU
legal precedents, helping a
vendor management team
ensure compliance with a
contract, or helping a banker
decide whether a customer
qualifies for a loan, the machine is taking over mundane
tasks. Machines can surely
learn to develop spreadsheets
and search large databases for
relevant information. But to
generate competitive advantage from machine learning
applications, you’ll need to
upgrade your employees’ skills.
You’ll also need to redesign
their accountabilities, so that
they are empowered and motivated to deploy machines when
limits, which tend to leave
parts of the tasks — the parts
that don’t fit the algorithms
well — to people. When a machine detects fraud or predicts
customer or employee churn
with 90% accuracy, people
must address the other 10% —
and that will be the toughest
10%. The machine will assuredly take care of the easy cases.
Addressing the toughest
instances is particularly challenging because AI algorithms
can produce indecipherable
results. When a machine
learning algorithm decides
who gets a loan and who
doesn’t, forget about trying to
Companies are succeeding
with AI by partnering smart
machines with smart people
who are learning how to take
advantage of what those machines can do. In short, AI
implementation success depends on your ability to hire
and develop problem-solvers,
equip them with data (and
potentially AI), and then empower them to actually solve
problems. Note that addressing skill requirements this
way may well require major
changes to your existing hiring
and development practices.
Companies that view smart
machines purely as a cost-
Machine intelligence is not a substitute for human
intelligence, because, as organizations, we need to be
able to understand why we’re doing what we’re doing.
they believe that doing so will
enhance outcomes. In short,
you will need to build an entire
workforce of intelligenceconsuming, action-oriented
superstars.
There are, of course, examples of AI algorithms fully
automating a process rather
than augmenting human efforts. Google DeepMind might
automatically adjust temperature settings in a data center.
Similarly, IBM Watson can
trigger automated alerts to insurance customers in an area
likely to be hit by a hailstorm.
But these are exceptions. More
often, machine learning applications are helping people
accomplish something. Like
people, machines have natural
advise a client about how to
qualify. Machine intelligence
is not a substitute for human
intelligence, because, as organizations, we need to be able
to understand why we’re
doing what we’re doing.
None of the issues associated with using AI to augment
your employees’ skills are
insurmountable. Great companies are already empowering
their people with better information produced by smart
machines. Those machines sift
through far more data, and do
it much faster, than people
can. They also discover complex relationships that can be
exposed only with massive
amounts of data and a large
pool of contrasting outcomes.
cutting opportunity are likely
to insert them in all the wrong
places and in all the wrong
ways. These companies will
automate existing processes
rather than imagine new ones.
They will cut jobs rather than
upgrade roles. These are the
companies who will find that
implementing AI is little more
than a reprise of the ERP
nightmare.
Jeanne Ross is a principal
research scientist at the MIT
Center for Information Systems
Research (@mit_cisr) in
Cambridge, Massachusetts.
Comment on this article at http://
sloanreview.mit.edu/x/59212.
Reprint 59212. For ordering information, see page 4. Copyright ©
Massachusetts Institute of Technology,
2018. All rights reserved.
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 11
FRONTIERS
[CORPORATE GOVERNANCE ]
The Board’s Role in Managing
Cybersecurity Risks
Cybersecurity can no longer be the concern of just the IT department. Within
organizations, it needs to be everyone’s business — including the board’s.
BY RAY A. ROTHROCK, JAMES KAPLAN, AND FRISO VAN DER OORD
T
oday, more than ever, the demands
posed by issues of cybersecurity
clash with both the need for innovation and the clamor for productivity.
Increasingly, cybersecurity risk includes
not only the risk of a network data breach
but also the risk of the entire enterprise
being undermined via business activities
that rely on open digital connectivity and
accessibility. As a result, learning how to
deal with cybersecurity risk is of critical
importance to an enterprise, and it must
therefore be addressed strategically from
the very top. Cybersecurity management
can no longer be a concern delegated to the
information technology (IT) department.
It needs to be everyone’s business —
including the board’s.
Cybersecurity Enters
the Boardroom
Network breaches have become so routine
that only the most spectacular events,
such as the recent breach at the credit reporting agency Equifax Inc. that affected
some 143 million U.S. consumers, make
headlines. Corporate boards of directors
are expected to ensure cybersecurity,
despite the fact that most boards are unprepared for this role. A 2017-2018 survey
by the National Association of Corporate
Directors (NACD) found that 58% of
corporate board member respondents
at public companies believe that cyberrelated risk is the most challenging risk
they are expected to oversee. The ability
of companies to manage this risk has
far-reaching implications for stock prices,
company reputations, and the professional
reputations of directors themselves. For
example, following a 2013 data breach
of Target Corp., in which the personal
information of more than 60 million customers was stolen, a shareholder lawsuit
charged directors and officers with having
fallen short in their fiduciary duties by
failing to maintain adequate controls to
ensure the security of data. Although the
board members were ultimately not
found to be at fault, both the company’s
CEO and CIO resigned.
U.S. case law is based on and generally
adheres to the “business judgment rule,”
which sets a high bar for plaintiffs pursuing legal action against board members.
Similar protections for directors are in
12 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
place in most “common law” countries,
including Canada, England, and Australia.
The Equifax cyberattack and future corporate breaches may prompt more
challenges to the business judgment rule.
The view that directors are not sufficiently prepared to deal with cybersecurity
risk has raised alarm bells in boardrooms
nationwide and globally. Even as companies increase their investments in security,
we are seeing more — and more serious —
cyberattacks. If corporate boards are not
sufficiently prepared to deal with cybersecurity, how will they be able to determine
the effectiveness of current and proposed
cybersecurity strategies? How can they
know what operationally effective cybersecurity should look like and how it should
evolve? And how can directors know what
to ask so that they can make the right cybersecurity investment decisions?
Asking the Right Questions
In our work with dozens of companies
and in surveys of executives, we have
found that many directors currently cannot ask the right questions because they
lack meaningful metrics to assess the
cybersecurity of their business. In a 2016
poll of 200 CEOs conducted by RedSeal
Inc., a cybersecurity analytics company
in Sunnyvale, California, 87% of respondents reported needing a better way to
measure the effectiveness of their cybersecurity investments, with 72% calling the
absence of meaningful metrics a “major
JING JING TSONG/THEISPOT.COM
challenge.” Often, executives as well as
directors spend too much time studying
technical reports on such things as the
numbers of intrusion detection system
alerts, antivirus signatures identified, and
software patches implemented.
To improve the situation, companies
need to address two issues. First, directors
need to have basic training in cybersecurity
that addresses the strategic nature, scope,
and implications of cybersecurity risk.
Within companies, managers involved in
operations, security specialists, and
directors alike need to adopt a common
language for talking about cybersecurity
risk. Second, top management needs to
provide meaningful data about not just the
state of data security as defined narrowly
by viruses quarantined or the number of
intrusions detected, but also about the
resilience of the organization’s digital
networks. This means having strategies to
sustain business during a cybersecurity
breach, to recover quickly in its aftermath,
and to investigate needed improvements
to the digital infrastructure. Networks
constantly change, so tracking cyber risks
and vulnerabilities over time and adapting
accordingly is essential.
A few decades ago, when business computers were networked into systems of
record, it made sense for organizations to
focus exclusively on preventing outside
attacks and protecting the network perimeter. However, now that computers have
become systems of engagement, strategies
geared toward perimeter defense are inadequate. Today’s organizations have vast
numbers of network connections and
human-machine interactions taking place
at all hours of the day and night. In this
context, security strategies must extend far
beyond the walls of a single organization to
reflect interactions with suppliers, customers, and vendors. Networks are permeable,
and the relevant question is no longer
“Will the organization’s cyberstructure be
compromised?” but “What do we do when
SLOANREVIEW.MIT.EDU
it is breached?” For organizations, the old
challenge of detecting and neutralizing
threats has expanded to include learning
how to continue doing business during a
breach and how to recover after one. In
other words, it has expanded from security
alone to security and resilience.
Increasing Resilience
Resilience is essential in any effective cyberdefense strategy. Our cyberadversaries are
competent, determined attackers and only
have to succeed once. Resilience assumes
that attacks are immutable features of the
digital business environment and that
Resilience assumes
that attacks are
immutable features
of the digital business
environment and
that some fraction
of these attacks will
inevitably result in
breaches.
some fraction of these attacks will inevitably result in breaches. Therefore, creating
sufficient resilience both to continue doing
business while dealing with a breach and to
recover in the aftermath of a breach is the
most critical element of a contemporary
cyberdefense strategy.
Adequate organizational resilience is
about operating the business while fighting back and recovering. Maintaining this
level of performance requires the ability
to measure an organization’s digital resilience much the way a board oversees its
financial health. For board members, no
fiduciary obligation is more urgent than
overseeing and, where necessary, challenging how executive leadership manages the
risks to the company. Managing cybersecurity risk today requires protecting the
digital networks essential to conducting
business by ensuring effective security
and a high level of resilience in response
to those inevitable cyberattacks. This can
be accomplished through policy, selection
of leadership, and allocation of resources.
It is a whole-enterprise issue, requiring
both full board engagement and superior
execution by management.
The 2017-2018 survey by NACD reveals
that public company board members are
significantly more skeptical about their
company’s cybersecurity efforts than are
C-suite executives. Just 37% of respondents
reported feeling “confident” or “very confident” that their company was “properly
secured against a cyberattack”; 60% said
they were “slightly” or “moderately” confident. Other surveys, including the 2016 poll
of CEOs by RedSeal, pointed to similar
weaknesses. Given the disconnect between
the risk levels and degree of preparedness,
we believe that most companies need to become more realistic about their vulnerability.
The problem isn’t a lack of investment.
In 2017, worldwide spending on information security was expected to reach $86.4
billion and to further increase to $93 billion in 2018, according to Gartner Inc.
However, cybercrime losses are rising at
more than twice the rate of expenditure increases. Many CEOs continue to focus their
attention on keeping hackers out of their
networks rather than building resilience
for dealing with hackers once they have
broken in. Although most CEOs believe
that cybersecurity is a strategic function
that starts with executives, RedSeal found
that 89% of CEOs surveyed treat it less as a
whole-business issue than as an IT function,
in that the IT team makes all budget decisions on cybersecurity.
Best Practices
Building on insights from the surveys
cited above, we have developed a fourpart approach to help organizations
manage cybersecurity more effectively
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 13
FRONTIERS
The Board’s Role in Managing Cybersecurity Risks (Continued from page 13)
and formulate digital resilience strategies.
It involves educating company leadership;
developing a common language for
management and corporate directors to
discuss cybersecurity issues; understanding the difference between security and
resilience; and making both security and
resilience strategic corporate imperatives.
1. Educate company leadership.
Cybersecurity risk shouldn’t be treated
strictly as an IT issue. In terms of risk management, both security and resilience need
to be managed as issues of importance to
the entire enterprise. Increasingly, directors
and senior management are being held accountable for the security and resilience of
networks and data. Board members must
therefore understand the issues at stake
and accept their fiduciary responsibility for
their organization’s cyberdefense posture.
Company leadership must have an unambiguous understanding of the key elements
of security and resilience. Both management and directors need to be aware of
(1) the limitations of security (no practical
cybersecurity strategy can prevent all
attacks) and (2) the need for resilience
(strategies to sustain business during a
cyberattack and to recover quickly in the
aftermath of a breach).
In order to be effective, directors need
sufficient knowledge to understand and
approach cybersecurity broadly as an
enterprise-wide risk management issue.
Directors need to understand the legal
implications of cybersecurity risks as
they relate to their company’s specific
circumstances.
2. Develop a common language.
Boards must have adequate access to
cybersecurity expertise, and their discussions about cybersecurity risk management
should be a regular part of each board
meeting agenda, with sufficient time
allotted. Moreover, board engagement
regarding cybersecurity issues should not
be restricted to yearly or semiannual reports. A proprietary 2017 McKinsey survey
on chief information security officer
(CISO) and board reporting found that
CISOs who had less-than-productive
board interactions felt they needed more
time with the board to explain and examine critical issues. One CISO who
responded to the survey observed that
“board members have to be able to ask
questions that may be perceived by others
to be ignorant.” No question can be considered bad or inappropriate.
Digital security specialists, like all
subject-area experts, must be able to communicate effectively with board members
and other leaders. Meetings with CISOs
and other security professionals mean
Resilience (the
ability to respond
to incidents and
breaches) should
be prioritized over
the forlorn hope of
security alone as a
silver bullet.
nothing if technical experts and directors
are unable to understand one another.
Information security executives must be
capable of presenting information at a
level and in a format that is accessible
to nontechnical corporate directors.
Ideally, assessments of cybersecurity,
digital resilience, and cybersecurity budgeting should be expressed using metrics
that objectively and unambiguously score
issues of risk, reward, cost, and benefit.
That said, directors should make themselves conversant in basic principles
relevant to digital networking and security.
The goal is for CISOs and other IT executives to engage in frank, mutually
intelligible dialogue with the board and
appropriate subcommittees. Wherever
possible, IT and CISO reports should be
focused on prioritized items on which the
14 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
board can take action, especially those that
can be addressed by the whole company.
3. Distinguish between security and
resilience. Companies should create a
clear distinction between digital security
and digital resilience. Digital security
focuses on essential security measures,
including providing such traditional
defenses as effective antivirus and antimalware software, adequate firewalls, and
employee education in safe computing
practices. Digital security is, therefore, a
security issue.
In contrast, digital resilience is a business
issue, which relates to how the whole organization conducts business in a digital
environment. For example, balancing data
accessibility with the necessity of protecting
customer data and intellectual property
involves a trade-off between security and
interactivity that affects the customer
experience, customer service, customer retention, acquisition of new customers, and
so on. It is therefore a business issue. To the
degree that an element of an organization’s
security implementation impedes business
(for example, by arbitrarily restricting
access to data), it may provide adequate
security. But it is a poor business practice,
which makes the company more liable to
fail and therefore less resilient.
In assessing the organization’s strategic
cybersecurity policy, the board must
balance resilience against security, with
priority given to resilience. Over time,
your network will be penetrated. Therefore, resilience (the ability to respond to
incidents and breaches) should be prioritized over the forlorn hope of security
alone as a silver bullet. Security will not
enable you to continue to conduct business during a breach. Resilience will. The
board must provide necessary leadership
in advocating for whole-enterprise resilience policies and practices.
4. Make security and resilience strategic business issues. Directors must set the
expectation that management will establish
SLOANREVIEW.MIT.EDU
an enterprise-wide cyber-risk management framework with adequate staffing
and budget. The board’s discussions with
management concerning cybersecurity
risk should include identifying which risks
to avoid, which to accept, and which to
mitigate or transfer through insurance —
as well as specific plans associated with
each approach.
In concert with top management, the
board should create a clear statement of its
role in overseeing, evaluating, and challenging the company’s digital security and
resilience strategies. The statement should
clearly define and assign responsibilities and
must delineate the differing roles of the
board and senior management. Within the
board itself, cybersecurity and digital resilience must be the responsibility of all
directors and not be relegated to a committee or subcommittee. Nevertheless, boards
should consider assigning one cyber-savvy
director to take the lead on issues of security
and resilience, and, when recruiting new directors, companies should seek out people
with appropriate cybersecurity expertise.
The board should continually reassess
the overall budget for security and resilience and redirect investments as necessary.
Given the reality that the number and seriousness of breaches are growing, it is clear
that most organizations need to evaluate
their cybersecurity investments more
clearly and effectively. Improving the ability to measure and quantify cyber-related
risks is vital to this step, because it allows
cybersecurity and resilience to be evaluated
for their impact on the entire business.
Ray A. Rothrock (@rayrothrock) is CEO
and chairman of RedSeal Inc. James Kaplan
(@jmk37) is a partner in the New York office
of McKinsey & Co. Friso van der Oord (@
Frisovanderoord) is director of research at
the National Association of Corporate Directors in Washington, D.C. Comment on this
article at http://sloanreview.mit.edu/x/59221.
BREAKING
CONVENTION,
INVENTION
Since its founding, MIT has fostered a spirit of ingenuity and
creative disruption. As a result, our faculty and students are behind
some of the world’s boldest inventions and innovative companies.
That’s also why thousands of global executives and senior managers
come to MIT Sloan Executive Education every year seeking new
frameworks and hands-on experiences to help their companies
be more agile, innovative, and productive.
Keep your company on the leading edge. Enroll now in these upcoming programs:
The Innovator’s DNA: Mastering Five Skills for Disruptive Innovation
March 13–14, 2018
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and Entrepreneurship
March 15–16, 2018
Implementing Enterprise-Wide Transformation
March 27–28, 2018
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March 29–30, 2018
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April 3–4, 2018
executive.mit.edu/smr
Open enrollment courses,
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programs for your organization
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2018. All rights reserved.
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WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 15
FRONTIERS
[AUTOMATION ]
When Jobs Become Commodities
Even if your job is high paying, it is wise to ask yourself whether it is common
and repetitive enough to be done by a machine. If you conclude that it is, it’s time
to look for — or create — less commoditized work.
BY THOMAS H. DAVENPORT
W
e don’t typically think of the jobs that we perform
as commodities. The Merriam-Webster dictionary
entry for commodity describes it as “a massproduced unspecialized product.” But most of us view our
jobs as specialized or somehow differentiated. We typically
believe that we do them differently, and often
better, than anyone else with the same job. In
fact, we’d probably argue that no one does
exactly the same job we do — that we
perform at least a slightly different set
of tasks, or perform them in a slightly
different way, than any coworker.
We may well be right about that, but
the world of business and management may feel otherwise. Jobs are
increasingly viewed as undifferentiated
and interchangeable across humans
and machines — the very definition of
a commodity. Outsourcing — exchanging internal employees for external ones,
often offshore — was a big step toward
job commoditization for many companies. Many recruiting processes lean
toward commoditization, with, for
instance, automated scanning of
résumés. You may think you are unique,
but companies increasingly view you as
just one of many people who can do
whatever your particular skill is, from
writing Python code to managing
financial assets.
Just as there are low-value and
high-value commodities, there are
low-value and high-value commoditized jobs. Sand is a low-value commodity, and working as
a server in a fast-food restaurant is a low-value commodity
job. Gold is a high-value commodity, and financial trading is
16 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
becoming a high-value commodity job (more on this later).
The value of many jobs is driven less by their intrinsic worth
than by market demand. A recent Bloomberg Businessweek visual
analytic suggests that many of the jobs that disappeared in
the highest numbers in the first four months of 2017
compared with the same period in 2016 were not lost
to automation, but were lost because fewer customers
wanted to buy the products and services with which
the jobs were associated. They include jobs in wired
telecommunications, department stores, and
printing.
For many organizations today, the next
big driver of job commoditization is automation driven by smart machines. Simply
put, if a job is viewed as a commodity, it
won’t be long before it is automated. My
research on automation through artificial
intelligence (AI) or cognitive technologies
suggests that, if a job with fairly routine
duties can be outsourced, many of the
tasks typically performed by the jobholder
can probably be automated — even by
relatively “dumb” technologies like robotic
process automation. As a result, many global outsourcers are working desperately to create their
own automation capabilities that could
replace human jobs with machines.
Financial services is a ripe area for
automation, given that many activities
are relatively structured and there is
relatively little product differentiation
(it’s all money!). There are low-value
and high-value commodity jobs in that
industry, and some of the lower-value ones,
such as bank tellers, have been disappearing for a while, albeit slowly.
Now, however, some of the high-value jobs are being commoditized as well. Sophisticated algorithms have begun to replace
LARRY JOST/THEISPOT.COM
financial traders and hedge fund managers, and about one-third
of hedge fund assets are managed in that way, according to Hedge
Fund Research Inc. in Chicago, Illinois. The “robo-adviser,” a
machine that recommends investments to customers, has begun
to replace human financial advisers. As customers turn to mutual
funds, exchange-traded funds (ETFs), and other passive investments, it’s relatively easy to determine an appropriate portfolio
for consumers, rebalance it for asset allocation preferences, and
harvest tax losses — all with little or no human intervention.
In short, even traditionally well-paid financial jobs are becoming commoditized. Since most financial markets are digital,
machines can easily determine which investments perform best.
Intuition and personal experience in picking investments count
for little.
The key for financial professionals and other workers whose
jobs have traditionally seemed safe is to make themselves less
commodity-like. Automation is a game of large numbers, and
it’s not economical to automate unique activities. As long as
human workers’ capabilities are
differentiated from machines’
capabilities, then machines can’t
easily replace them — and few
organizations will be tempted to
automate that niche.
In financial asset management, for
example, picking the right ETF is
commoditized, but advising on other
investments isn’t. If you’re a university investing your endowment and
want to put some of it into alternatives like timber or oil, you will
probably need some expert advice. If
you want to invest in esoteric debt like apartment leases, you will
probably need wise (human) counsel as well. In this industry,
then, astute asset managers should focus on assets that are not
well understood and not traded easily and digitally. Money
manager Rishi Ganti argued in a 2017 Bloomberg Businessweek
article that his future is in managing so-called “esoteric assets” that
require human help — what he terms “high human capital” —
to manage. “It’s like dark matter,” Ganti said. “They dwarf the
visible stuff lit up by markets.”
Another angle for protecting your work from becoming a
commoditized job is to focus on the most human aspects of the
task — that is, those that are most difficult to automate. In the
financial world, this often involves understanding human beings
and the silly financial decisions they frequently make. One of my
students described this as “financial psychiatry,” but a more academically respectable name is “behavioral finance.” Financial
advisers who understand behavioral finance can focus not on
selecting investments for their clients, but on talking them off
the cliff when they’re ready to “sell everything” after a market
decline. Or they can address the tricky problem of reconciling
the differing risk tolerances of husbands and wives. Tackling such
complex and emotional issues is work not likely to be taken over
by machines anytime soon.
Ironically, workers in finance and other industries that have
high-paying, specialized jobs can also make themselves less commoditized by helping with the commoditization process. If
they’re good at structuring decisions and understanding how
those are represented in computers, they will have jobs for quite
a while. They’ll be able to monitor machine-based decisions, pick
up the ball when the machines drop it (because of missing data,
for example), and perhaps even improve machines’ decisionmaking over time.
It’s very difficult to predict how quickly jobs of various types
will become commodities, and how quickly the humans who
Automation often takes longer than we
expect because organizational inertia can
be high, because all jobs are slightly different from one another, and because people
find ways to differentiate themselves.
SLOANREVIEW.MIT.EDU
perform them will be replaced by machines. Jobs are comprised
of a set of tasks, only some of which are usually automated.
Automation often takes longer than we expect because organizational inertia can be high, because all jobs are slightly different
from one another, and because people find ways to differentiate
themselves. But no matter what your field, it pays to ask yourself
whether your job is common and repetitive enough to be done
by a machine. If you conclude that it is, it’s time to look for — or
create — less commoditized work.
Thomas H. Davenport (@tdav) is the President’s Distinguished
Professor of Information Technology and Management at Babson
College in Babson Park, Massachusetts, as well as a fellow at the
MIT Initiative on the Digital Economy. Comment on this article at
http://sloanreview.mit.edu/x/59211.
Reprint 59211. For ordering information, see page 4.
Copyright © Massachusetts Institute of Technology, 2018. All rights reserved.
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 17
FRONTIERS
[STRATEGY ]
Why Some Platforms Are
Better Than Others
Although successful digital platforms can deliver remarkable
value to users and riches to entrepreneurs and investors,
in some sectors it isn’t clear that anyone will turn a profit.
BY JONATHAN A. KNEE
T
he dramatic influence
of the internet on how
businesses operate and
the emergence of a handful of
gigantic, digitally enabled corporations have led to breathless
pronouncements regarding the
importance of a new class of
monopolies built on digital
platforms. Such platforms, it is
said, can fuel network effects
that lead to winner-take-all
marketplaces. This perspective
is often coupled with infectious
optimism and investment
euphoria regarding the extraordinary scale and strength of
network-effects businesses.
In theory, the key attribute
of a network-effects business
is its momentum-driven flywheel. Every new participant
increases the value of the network to existing participants,
attracts more new users, and
makes the prospect of a successful competitive attack
ever more remote — thereby
bolstering the relative attractiveness of the business. The
18 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
imagined innate indomitability of network effects stems at
least in part from the breathtaking strength of notable
platform businesses such as
Facebook Inc.’s social network
or Microsoft Corp.’s Windows
operating system.
The problem is that not
all platform businesses exhibit
network effects that reinforce
a market’s winner-take-all
tendency. For every Facebook
and Microsoft, there are
numerous network-effects
businesses operating in
crowded sectors where it is
not always clear that anyone
will turn a profit.
Nor are digital platforms
necessarily better businesses
than the analog versions that
they displace. Analog malls had
the benefit of their shoppers
being miles away from competing malls, as well as the
benefit of their retail tenants
being committed to long-term
leases. On the internet, platform competitors are only a
click away, and companies
regularly and dynamically optimize their customer reach
across competing platforms
and directly via their own sites.
It is not that marketplace
businesses built on e-commerce
platforms do not have advantages or cannot thrive. Rather, it
is that the mere existence of network effects tells entrepreneurs
and investors relatively little
about the attractiveness of a particular business. For example,
Uber Technologies Inc. and
Airbnb Inc., the global leaders in
the ride-hailing and short-term
lodging marketplaces, respectively, both benefit from network
effects. However, other characteristics of those industries make
it likely that Airbnb will enjoy
dramatically stronger results
than Uber will ever achieve.
Why Airbnb Is
Better Than Uber
Three key structural attributes
drive the value of network
effects in the digital domain.
The first is the minimum market share at which the network
can achieve financial breakeven. The second is the nature
and durability of the customer
relationships spawned by the
network. And the third is the
extent to which the data
generated by the network
facilitates product and pricing
optimization.
These should sound familiar. They are updated versions
of the same core competitive
advantages that have long underpinned the best business
franchises: economies of scale,
customer captivity, and learning. And they are as relevant to
today’s digital platforms as they
were — and continue to be —
to analog ones. Comparing
Uber and Airbnb along these
ADAM MCCAULEY/THEISPOT.COM
dimensions highlights both
their profound relevance and
Airbnb’s inherent advantages.
Minimum Viable Market
Share Two attributes deter-
mine the minimum viable
scale of a network: product/
service complexity and fixedcost requirements. With
regard to these two attributes,
Uber and Airbnb could not be
more different.
In any given city, the financial viability of both companies
is a function of local density —
of drivers on the platform on
the one hand and available
property inventory on the
other. A key distinction between Uber’s and Airbnb’s
respective marketplaces, however, is the level of intrinsic
product complexity and the resulting marketplace liquidity
required to establish a competitive service. In ride-hailing,
the ability to deliver a car
within three to five minutes is
the top customer consideration
(other than price). Having so
many drivers that cars arrive
sooner than that is not useful.
In lodging, there are multiple
customer considerations that
ensure that the value of higher
incremental density in local
short-term lodging listings
does not top out in the same
way. Indeed, more listings
attract more travelers and
drive higher occupancy rates.
The second difference is the
role of fixed costs. Although
both Uber and Airbnb are predominantly variable-cost
businesses, Airbnb’s relative
fixed-cost requirements are far
SLOANREVIEW.MIT.EDU
greater than Uber’s. Both companies have similar technology
and overhead costs, but users
of ride-hailing services primarily use those services in a single
city. In contrast, customers of
companies providing shortterm lodging services use
those services in many different locales. That creates an
incentive for those companies
to incur the higher fixed costs
associated with operating in
multiple popular locations.
It may be that in some
small markets, the fixed operating costs won’t sustain more
than one or two ride-hailing
speed at which market shares
can shift among competitors in
a particular marketplace. When
combined with minimum viable market share, the level of
customer captivity enables a
potential new entrant to
quickly calculate how long it
can expect to lose money before achieving a break-even
market share. So, for instance,
in an industry where customer
loyalty limits annual share
movement to a couple of points
and break-even market share is
20%, an insurgent can expect at
least a decade of losses before
establishing viability.
Unfortunately, even the
best-run ride-hailing company
will struggle to encourage loyalty among drivers and riders.
Already, about two-thirds of
drivers work with two or more
services. Moreover, although a
minority of riders currently
use multiple ride-hailing apps,
that percentage has been growing. Among my MBA students
in New York City, it is greater
than 90%.
There is a difference
between an individual’s willingness to entrust short-term
rentals of his or her home to
multiple companies and a
Customers and business partners operating
in an environment characterized by swift
technological change are generally wary of
long-term commitments.
services. But in larger metropolitan areas, multiple robust
offerings are available, with viability achievable at market
shares of less than 20%. This
effectively translates to a permanent pool of five or more
Uber competitors, severely
limiting achievable returns.
Conversely, the greater
fixed-cost needs in short-term
lodging mean that Airbnb
competitors can break even
only at far higher market
shares. It is not a coincidence
that Airbnb has far fewer direct competitors of size in any
given market than Uber does.
Customer Captivity The nature and durability of customer
relationships determine the
By enhancing the ability
to easily search out, compare,
and switch between sellers,
the internet has set the bar
far higher for businesses to
articulate truly compelling reasons for customers to stay put.
What’s more, customers and
business partners operating in
an environment characterized
by swift technological change
are generally wary of long-term
commitments. Nonetheless,
robust captivity is still achievable when the service quality,
breadth of offering, verification of nuanced counterparty
credentials, and/or seamless
integration of critical data
into buying processes are central to the ultimate decision
to transact.
professional driver’s willingness to drive for multiple
ride services. And, as we’ve
discussed, for the customer,
price and speed are the overwhelming factors influencing
the decision about which ride
service to use for a short trip,
but a variety of factors shape
the decision to a stay in a
stranger’s house. Homeowners want to know who will be
staying in their homes, and
guests want to know the experiences of others who have
used those homes. A single
good experience will make
customers much less likely to
take a chance with an alternative platform that seems to
offer a comparable or even
slightly better proposition.
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 19
FRONTIERS
Why Some Platforms Are Better Than Others (Continued from page 19)
When Airbnb establishes
a leadership position in a
market, competitors are at
a disadvantage in terms of inventory availability. However,
the same is not true for Uber’s
competitors in the ride-hailing
market, because the majority of
drivers use more than one app.
The observance of ride-hailing
market-share shifts of greater
than 5% over a matter of
months nationally (and of
even more in some localities)
suggests that Uber can expect a
platforms discovered that, for
most borrowers, their proprietary data yielded little more
insight than was readily available elsewhere from sources
such as credit scores.
Before I book a stay in a
stranger’s apartment, I pore
over the reviews of previous
visitors before taking the
plunge, no matter how nice the
pictures. By contrast, riders
typically don’t use Uber driver
reviews to select cars. (Mostly,
the reviews are used by the
United States provide limited
advantage. More broadly,
the resilience of Uber’s position hinges on a relentless
aggressiveness rather than a
structural tendency toward
a global winner-take-all (or
even winner-take-most)
equilibrium.
Airbnb’s network effects,
on the other hand, are paired
with significant customer captivity. Given the advantages
afforded by its global fixedcost base, the competition it
The fortunes of network-effects businesses depend
on the value of the data that they can elicit in their
respective markets.
steady stream of new competitors. Such competition is less
likely for Airbnb, where the
time required to recruit and
sign up new lodging units significantly slows the potential
rate of market-share shifting
and the resulting time it would
take a new entrant to break even.
Data Finally, the fortunes of
network-effects businesses depend on the value of the data
that they can elicit in their respective markets. Zillow Group
Inc.’s continued dominance in
the online real estate marketplace, for instance, is in part
a function of its ability to
use its unique access to data
to continually improve its
automated valuation models
and its home search and recommendation engines. In
contrast, peer-to-peer lending
company to manage fleet
quality.) And while feedback
on drivers helps Uber cull out
those who undermine the service and facilitates training, the
nuanced picture that emerges
from travelers around the
world allows Airbnb to direct
regular users to the most appropriate venues and helps
those listing their homes deliver a satisfying experience.
Beware the NetworkEffects Fetish
Uber has built a remarkable
business. However, the structural attributes noted above
suggest that ride-hailing will
continue to be an intensely
competitive business in large,
local markets. Moreover, in
many international markets,
Uber is the insurgent, and the
network effects it enjoys in the
20 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
faces is less intense than the
competition Uber faces. The
lesson for investors and entrepreneurs is to be wary of the
fetishization of network effects as an inherently superior
form of competitive advantage. Excessive optimism
regarding the winning power
of digital platforms’ network
effects is not justified by either
a close study of their structural impact on entry barriers
or any empirical evidence of
generally increasing market
dominance. In fact, many
signs suggest quite the opposite — that in the absence of
the same characteristics (most
notably fixed-cost scale and
customer captivity) that have
long supported the strongest
analog platforms, digital
platforms are likely to be
significantly harder to build
and maintain. It is not a coincidence that two of the largest
and most enduring purely
digital platforms — Google
LLC and Amazon.com Inc. —
are companies that benefit
from leveraging multiple,
complementary sources of
competitive advantage.
With the help of other
sources of competitive
advantage, network-effects
businesses can deliver remarkable value to users and riches
to entrepreneurs and investors.
On their own, however, network effects in a digital context
are a peculiarly fragile barrier
to entry. Seen in this light,
entrepreneurs and investors
should treat the identification
of network effects as the beginning, not the end, of their
analysis. Meanwhile, platform
operators should curb any
complacent confidence that
they may have in their destinies
as the conquerors of global
markets. Instead, they should
redouble their efforts to establish complementary barriers
before being displaced by one
of what are likely to be many
competing platforms.
Jonathan A. Knee is a professor
of professional practice and
codirector of the media and
technology program at Columbia Business School in New
York as well as the author of
Class Clowns: How the Smartest
Investors Lost Billions in Education (Columbia Business School
Publishing, 2017). Comment on
this article at http://sloanreview
.mit.edu/x/59213.
Reprint 59213. For ordering information, see page 4. Copyright ©
Massachusetts Institute of Technology,
2018. All rights reserved.
SLOANREVIEW.MIT.EDU
[DIGITAL STRATEGY ]
a single industry, enterprises can take different paths to becoming future-ready.
This article looks at four banks that have
taken different pathways: Danske Bank,
mBank, BBVA, and ING.
Is Your Company Ready
for a Digital Future?
There are four different pathways that businesses can
take to become top performers in the digital economy.
Leadership’s role is to determine which pathway to
pursue — and how aggressively to move.
Becoming Future-Ready
Becoming future-ready requires changing
the enterprise on two dimensions —
customer experience and operational efficiency. (See “How Companies Compare on
Digital Business Transformation,” p. 22.)
BY PETER WEILL AND STEPHANIE L. WOERNER
I
n preparing for the future, many large,
established enterprises are embarking
on a digital business transformation
journey — often without any sense of
where they are going. In this article, we
will present four viable pathways for
transformation and examine their pros
and cons. However, the goal isn’t digital
transformation but rather business transformation — using digital capabilities to
transform a traditional enterprise into a
top performer in the digital economy.
We call such top-performing enterprises
“future-ready.”
BRIAN STAUFFER/THEISPOT.COM
In 2015 and 2017, we surveyed several
hundred enterprises,1 examining both the
capabilities needed for transformation
and the impacts on performance. We also
had conversations with more than 50 executives to learn about their experiences
with digital business transformation. Respondents represented a wide variety of
industries, with manufacturing, financial
services, and IT software and services
being the biggest groups. Based on our
analysis, future-ready enterprises performed much better than their industry
peers. But we also found that, even within
Future-Ready Future-ready enterprises are
able to innovate to engage and satisfy customers while at the same time reducing
costs. Their goal is to meet customers’ needs
rather than push products, and customers
can expect to have a good experience no
matter which service delivery channel they
choose. On the operations side, the company’s capabilities are modular and agile; data
is a strategic asset that is shared and accessible to all those in the company who need it.
The enterprise is ready to compete in the
digital economy and able to work with a
wide variety of partners through both
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 21
FRONTIERS
Is Your Company Ready for a Digital Future? (Continued from page 21)
digital services and exposed application
programming interfaces (APIs). By these
criteria, 23% of the businesses we surveyed
were future-ready, shown in the upperright quadrant of the exhibit. Their
performance averaged 16 percentage points
better than their industry average, meaning
that if the average net profit margin for a
company in a given industry was 8%,
future-ready enterprises earned 24%.
Silos and Complexity Of the companies
we surveyed, 51% were in the bottom-left
quadrant, with an extensive catalog of
products and services developed over
many years. Their products and services
are supported by a complex set of business
processes, systems, and data. The result is a
fragmented, labor-intensive, and frustrating customer experience, often made worse
by product silos within the company.
Frequently, the ability of such organizations to provide an engaging customer
experience depends heavily on heroics by
employees. For example, we watched one
bank teller work with an elderly customer
who wanted to change her address on six
different bank products. The number of
keystrokes required to make the necessary
changes was dizzying. During a 20-minute encounter, the teller chatted amiably
with the customer about the local sports
team. An amazing effort, to be sure — but
not scalable. It shouldn’t be surprising
that, in our survey, the profit margins of
enterprises from this group were weak;
they averaged 5 percentage points below
their industry average.
each key task (processing an insurance
claim, on-boarding a customer, or assessing risk) and deployed it across the
enterprise. They configured their services
into plug-and-play modules to meet particular customer requirements quickly
and inexpensively. The consolidated data
created from the customer interactions
and operations can become a competitive
asset that anyone involved in the enterprise can access. Over time, many of these
processes and decisions can be automated.
Of the companies we studied, 11% were
in the industrialized group; their net
profit margins averaged 4.6 percentage
points higher than their industry average.
Integrated Experience Enterprises
offering what we call an “integrated experience,” shown in the upper-left quadrant,
provide a better-than-industry-average
customer experience despite having complex operations. Some of the companies
emulated the industry-leading model
Industrialized Companies characterized
by digital industrialization, shown in the
bottom-right quadrant, apply the best
practices of automation to their operations. They use the features that make
them strong as an enterprise and turn
them into modular and standardized digitized services. For example, companies in
this group picked the best way of handling
22 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
epitomized by United Services Automobile
Association (USAA), the San Antonio,
Texas-based financial services group.
USAA is organized around addressing a
customer’s life events (for example, buying
a house, having a baby, or preparing for
retirement) rather than on pushing products. We have seen companies that want to
offer an integrated experience develop
attractive websites and mobile apps and
hire more relationship managers to improve the customer experience. Many
attempt to improve the customer experience by investing in analytics. However,
we have found that these enterprises typically are unable to simplify or automate
the underlying and complex business processes, technology, and data landscape.
As a result, they see their costs of serving
customers increase. About 15% of enterprises we studied offered an integrated
experience; their net profit margins averaged 3.6 percentage points below their
industry average.
HOW COMPANIES COMPARE ON DIGITAL
BUSINESS TRANSFORMATION
In 2015 and 2017, we surveyed several hundred enterprises, examining both the capabilities
needed for transformation and the impacts on performance. Based on our analysis, companies in the future-ready quadrant performed much better than their industry peers. Becoming
future-ready requires changing the enterprise on two dimensions — customer experience
and operational efficiency. We found that enterprises can take one of four different paths to
go from the lower-left quadrant (Silos and Complexity) to the upper-right (Future-ready).
Percentage of
enterprises in survey
15%
Integrated Experience
Transformed
Customer
experience
Increasing
net promoter
score
Traditional
UÊ
ÕÃ̜“iÀÊ}iÌà >˜ ˆ˜Ìi}À>Ìi`
experience despite complex
operations
UÊ-ÌÀœ˜}Ê`iÈ}˜Ê>˜` ÕÃiÀ
experience
UÊ,ˆV…Ê“œLˆiÊiÝ«iÀˆi˜Vi
51%
Silos and Complexity
23%
Future-ready
U œÌ… ˆ˜˜œÛ>̈Ûi >˜`
low-cost
U Ài>Ì VÕÃ̜“iÀ iÝ«iÀˆi˜Vi
U œ`Տ>À >˜` >}ˆi
U >Ì> ˆÃ > ÃÌÀ>Ìi}ˆV >ÃÃiÌ
11%
Industrialized
UÊ*Àœ`ÕV̇`ÀˆÛi˜
UÊ
œ“«iÝʏ>˜`ÃV>«iʜvÊ
processes, systems, and data
UÊ*iÀvœÀ“>˜ViÊÀiµÕˆÀiÃÊ
º…iÀœˆVû
U *Õ}‡>˜`‡«>Þ «Àœ`ÕVÌà >˜`
services
UÊ-…>Ài`Ê`>Ì>ÊV>˜ÊLiÊ>Ê
competitive asset
UÊ"˜Þʜ˜iÊÜ>ÞÊ̜Ê`œÊi>V…ÊŽiÞÊ
Ì>Î
Traditional
Transformed
Operational efficiency
Improving operational efficiency
SLOANREVIEW.MIT.EDU
Four Pathways to
Transformation
We identified four different pathways that
companies took to become future-ready.
Each pathway begins in the bottom-left
quadrant (Silos and Complexity), and
each involves significant organizational
disruption.
PATHWAY 1: Standardize first. Pathway 1 moves enterprises from the Silos
and Complexity quadrant to the Industrialized quadrant. This pathway relies on
building a platform mindset with APIenabled business services that can be
accessed across the enterprise and also
externally. It enables an organization to
eliminate many of its legacy processes
and systems. But, as anyone who’s been
through an enterprise resource planning,
customer relationship management, or
core banking project will attest, replacing
core processes in an enterprise is an expensive, multiyear undertaking. It also
requires putting many other projects on
hold. Cloud computing, APIs, micro services, and better solution architectures
make this industrialization process
quicker, less risky, and less disruptive.2
However, embarking on Pathway 1 takes
time. Among other things, it requires
changing the decision rights to emphasize
integrated services for customers, rather
than focusing on products.3
Danske Bank A/S, headquartered in
Copenhagen, Denmark, and operating in
16 countries, has been pursuing Pathway 1.
The vision it presented on its website in
2012 was: “One platform — exceptional
brands.” Danske Bank’s approach brought
some early benefits, allowing it to acquire
five banks in six years and to reduce operating expenses. In the past few years,
Danske Bank has also revamped its financial products into a set of banking services
that can be combined to create products in
real time across distribution channels in
most markets. In the core banking services,
90% of its applications are shared and
SLOANREVIEW.MIT.EDU
standardized. At the same time, it simplified its management structure, slimming
down its product owner organizations.
Whereas there used to be many executives
responsible for credit cards, for example,
today there’s just one.4
Danske Bank’s “one platform” approach
has also delivered longer-term benefits in
terms of its relationships with customers
and its reputation among peers. In the
five years between November 2012 and
November 2017, its share price rose approximately 150%. Although the bank
cut its number of retail branches by
half between 2012 and 2015, it has seen
Replacing core
processes in an
enterprise is an
expensive, multiyear
undertaking. It also
requires putting
many other projects
on hold.
tremendous increases in e-banking. About
2.2 million of its 3.2 million customers use
Danske Bank’s e-banking platform for such
things as paying bills, accessing accounts,
and managing their retirement savings.
Moreover, the bank’s payment app, called
MobilePay, is so popular that it has been
embraced by other Scandinavian banks.5
PATHWAY 2: Improve customer experience first. Pathway 2 involves moving
from the Silos and Complexity to the
Integrated Experience quadrant. Companies choose this strategy when their most
pressing strategic goal is to improve the
customer experience across the whole enterprise, tackling the problem across
multiple organizational silos. Typically,
they attempt to do several things at once:
develop new attractive offers, build
mobile apps and websites, improve
call centers, and empower relationship
managers — all with the goal of measurably increasing customer satisfaction.
One company following this approach
is mBank S.A., headquartered in Warsaw,
Poland. The bank’s leadership realized back
in 2000 that the typical banking customer
experience in Poland was far from positive.
This led mBank to initiate a series of
changes, including opening call centers,
offering online services, and adding many
new banking products. As it introduced
new products and features, it also expanded
into new markets in two neighboring countries, the Czech Republic and Slovakia.6
Eventually, mBank’s leadership concluded that the company’s old service
platform had reached its limit. Struggling
to deliver the desired flexibility and customer experience — and predicting that
the problems would only worsen — the
bank set out to develop a new banking
platform. Created over 14 months, the
new platform offers customers a wide
range of features, including 30-second
loan approvals, mobile payments, video
chat, integration with Facebook, peerto-peer transfers, and cardless ATM withdrawals. The new platform is designed to
increase efficiency and reduce time to
market. When customers perform transactions or make changes on their mBank
mobile app, the information is available
immediately to customer representatives
and distribution channels.
To grow, mBank has created business
channels that tap into its digitized platform, allowing it to offer services to an
expanded set of customers via partnerships with other companies. It is thus able
to expand the business into new markets
or offer its services through noncompeting banks in other countries.
The advantages of Pathway 2 include focusing on the customer first and improving
the customer experience, which results in
higher customer satisfaction scores and
sometimes increased sales. The biggest disadvantage is that the improvements in the
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 23
FRONTIERS
Is Your Company Ready for a Digital Future? (Continued from page 23)
customer experience typically add more
complexity to already complex systems and
processes, increasing the cost to serve a customer. Employees may still need to perform
heroics to deliver what was promised.
PATHWAY 3: Take stair steps. Enterprises on Pathway 3 move toward
becoming future-ready by alternating
their focus from improving customer
experience to improving operations and
then back again, shifting the focus back
and forth as needed. For example, the first
move might be a project to implement an
omnichannel experience. After that, companies might improve operations, perhaps
by replacing a few legacy processes or
creating an API layer. Then, they might
attempt to put together a more attractive
set of customer offerings by making
smarter use of internal data.
With this approach, the difference
between success and failure is having a
road map that informs everyone’s efforts
versus taking a haphazard approach. The
best way to tell the difference is to ask a
manager how a specific project fits into
the overall plan. The advantage is that the
steps, which consist of tightly coordinated
sets of projects, are smaller, reducing risk.
The disadvantage is that explaining the
intermittent changes in direction can be
difficult and can even confuse employees.
In some enterprises, we have seen organizational whiplash from changes in
direction, with a reduction in employee
effectiveness and an increase in burnout.
An example of Pathway 3 can be found
in Banco Bilbao Vizcaya Argentaria Sociedad Anonima (BBVA), based in Bilbao,
Spain. Responding to challenges he saw in
the banking industry, BBVA executive
chairman Francisco González announced
plans in 2015 to build “the best digital
bank of the 21st century.”7 In its effort to
reshape the customer experience, BBVA
introduced a mobile app in 2014 that offers simple new-customer on-boarding in
less than five minutes. It serves as a digital
wallet and allows customers to set up
appointments and conduct instant messaging conversations with managers.
The app also allows easy, automated
purchases from a self-service suite of
products, including consumer loans and
investment funds. The changes have been
well-received by bank customers; in early
2017, customers interacted with the bank
on average 150 times per year via their
mobile devices, compared to four branch
visits in the same year.
To improve efficiency, BBVA has
worked hard to remove legacy business
processes that had been constructed over
In some enterprises,
we have seen organizational whiplash
from changes in
direction, with a reduction in employee
effectiveness and an
increase in burnout.
time from many different systems and
versions of data, replacing them with
scalable, reusable global digital platforms.
Today, BBVA offers customers a digital
experience via a reliable core banking
platform, enabling new developments
that combine the bank’s open APIs and
other capabilities. A big advantage of this
approach is that other enterprises, including retailers, telcos, and even startups, are
able to tie into the bank’s services, thereby
enhancing their own products.
PATHWAY 4: Create a new organization. Rather than fight an uphill battle
to transform their existing organization,
leaders who choose to pursue Pathway 4
start new enterprises that begin life as
future-ready. In the automobile industry,
for example, German carmaker Audi AG
recently created a wholly owned subsidiary to develop experimental mobility
24 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
services apart from car ownership. In
banking, ING Groep N.V., the multinational banking and financial services
company based in Amsterdam, has pursued a similar approach with ING Direct.
ING launched ING Direct in Canada
in 1997 before expanding into Australia,
Italy, Spain, the United Kingdom, the
United States, and other countries. By
2006, it had 13 million customers in nine
countries. Although ING Direct did have
some ATMs, it had no branches. Customers interacted with the bank by phone,
mail, or online. After beginning as a monoline bank offering high-interest deposit
products, it gradually added multiple new
products, including loans and mortgages.
ING Direct’s country-based businesses
operated autonomously but shared a common set of standardized business solutions
and technical platform components.
Module reuse kept operational costs low,
allowing the businesses to offer higher
savings rates and lower-cost loans.8
It took several years for ING Direct to
establish its brand, culture, products, platforms, and partnerships. In our research,
we have seen that the big challenge for enterprises taking Pathway 4 is figuring out
how to bring the parent company and the
transformed enterprise together.
Everything about them — their business models, their cultures, even the
customers they cater to — tends to be different. Like every parent of a Pathway 4
enterprise, ING had to figure out how to
deal with a successful spin-off. Complicating matters was the fact that there was no
single ING Direct; each country operated
a little differently. In the face of difficulties
following the 2008 financial crisis, ING
sold some of its operations, including ING
Direct in the United States, Canada, and
the United Kingdom,9 while continuing to
hold on to its businesses in other countries,
including Australia and Spain. The company says that it plans to standardize on a
single digital banking platform by 2021,
SLOANREVIEW.MIT.EDU
with data and support functions shared
across countries and product lines.10
The advantage of Pathway 4 is that it
allows an enterprise to build its customer
base, people, culture, processes, and systems from scratch to be future-ready. It
doesn’t need to deal with legacy systems
or silos or culture. The challenge is that
once the new entity is successful, how do
you — or do you — integrate it with the
mother ship?
Choosing a Pathway
Leadership’s role is to determine which
pathway the enterprise (or, depending on
the circumstances, the business unit)
should take and how aggressively to move.
Start by determining where the company is
today — based on metrics such as net promoter score and net margin — compared
to the rest of the industry.
Another important step is selecting the
right executive to lead the transformation.11
The right choice will depend on the
company’s circumstances, the industry
environment, and the direction management wants to go.
• Pathway 1 makes sense if the customer
experience the company provides is
around industry average and the threat
of digital disruption is not high. CIOs
are a good choice to lead Pathway 1.
• Pathway 2 makes sense if the customer
experience the company provides is significantly worse than average and you
can’t wait to improve, or if there are worrisome new competitors. An executive
passionate about customer experience
who is technologically literate is a good
choice to lead Pathway 2.
• Pathway 3 makes sense if the customer
experience the company provides is a
problem, but you can identify a few
limited initiatives that will make a big
difference. Start with those and then
focus on operations — and repeat in
small steps. A chief digital officer is a
good choice to lead Pathway 3.
SLOANREVIEW.MIT.EDU
• Pathway 4 — building a new enterprise —
makes sense when you can’t see a way to
change the culture or the customer experience and operations fast enough to
survive. The CEO or COO are good
choices to lead Pathway 4.
Once the company — that is, the
board, the CEO, and the senior management team — settles on a pathway, the
difficult work begins. The digital era is a
great opportunity for leaders to reinvent
the enterprise. The most successful enterprises will need to become future-ready
and ambidextrous — constantly innovating to improve customer experience while
also working to reduce costs. Those that
don’t become future-ready will likely
suffer a death by a thousand cuts, with
startups, players from other industries,
and agile competitors slicing bits out of
their businesses.
We conclude on a cautionary but realistic note. We recently ran a workshop on
digital business transformation with the
CEO and the executive team of an international financial services firm. We asked
attendees to plot their company’s journey
over the previous three years using the
pathway framework. After the other executives had presented, we invited the CEO
to share his version. He drew a series of
movements, beginning in the Silos and
Complexity quadrant, moving up, then to
the right, then down and back, charting a
convoluted path that continued for several
more squiggles. When the CEO finished,
he stepped back and said, “You know, it’s
not as if we planned to do it that way. But
using the objective metrics against our
industry, this is the path we followed.”
He concluded by expressing his view that
leaders need to pick a pathway and then stick
to it. Ultimately, we think this is good advice.
After all, transformation is difficult. All
of a company’s stakeholders — including
the board, employees, partners, and customers — need to know where the enterprise
is going and how it plans to get there.
Peter Weill (@peterdweill) is chairman
and senior research scientist at the MIT
Sloan School of Management’s Center
for Information Systems Research, where
Stephanie L. Woerner (@SL_Woerner) is a
research scientist. Comment on this article
at http://sloanreview.mit.edu/x/59224.
REFERENCES
1. In the MIT CISR 2015 CIO Digital Disruption
Survey, we surveyed 413 enterprises. In the
MIT CISR 2017 Pathways to Digital Business
Transformation Survey, we surveyed 400
enterprises.
2. J.W. Ross, I.M. Sebastian, and C.M. Beath,
“Digital Design: It’s a Journey,” research briefing, MIT CISR, April 21, 2016, http://cisr.mit.edu.
3. J.W. Ross, P. Weill, and D.C. Robertson,
“Enterprise Architecture as Strategy: Creating
a Foundation for Business Execution” (Boston:
Harvard Business School Press, 2006), chap. 1
and 2.
4. Our sources are company interviews and
documents used with permission; Danske
Bank’s website, www.danskebank.com; and
the MIT CISR 2012 IT Investment Survey,
sample size of 354, developed countries only.
5. V. Vig Nielsen, “Danske Bank: A Winner
in Digitalization,” Feb. 1, 2017, https://digit.hbs
.org.
6. N.O. Fonstad, S.L. Woerner, and P. Weill,
“mBank: Creating the Digital,” research briefing, MIT CISR, Oct. 15, 2015, http://cisr.mit.edu.
7. “Francisco González: ‘We Are Building the
Best Digital Bank of the 21st Century,’” March
13, 2015, www.bbva.com.
8. Ross, Weill, and Robertson, “Enterprise
Architecture as Strategy,” 61-64.
9. “Scotiabank to Buy ING Bank of Canada for
$3.1B,” Aug. 29, 2012, www.cbc.ca; “ING
Direct to Become ‘Capital One 360,’ but
Promises to Remain the Same,” Nov. 7, 2012,
https://consumerist.com; and “ING to Sell ING
Direct UK to Barclays,” press release, Oct. 9,
2012, www.ing.com.
10. “ING to Spend EUR800 Million on Digital
Integration; Shed 7,000 Jobs,” Oct. 3, 2016,
www.finextra.com; and “ING Strategy Update:
Accelerating Think Forward,” press release,
Oct. 3, 2016, www.ing.com.
11. P. Weill and S.L. Woerner, “Becoming
Better Prepared for Digital Disruption,” NACD
Directorship Magazine, March-April 2016,
www.nacdonline.com.
Reprint 59224. For ordering information, see page 4.
Copyright © Massachusetts Institute of Technology,
2018. All rights reserved.
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 25
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Redesigning
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SOMEWHERE ALONG THE LINE, most of us have worked in organizations with inefficient processes,
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Our special report offers some fresh thinking about how to improve the way things get done in your
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SPECIAL
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29
A New Approach
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Work
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What to Expect
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The Truth About
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WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 27
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R E D E S I G N I N G W O R K : O P E R AT I O N S
A New Approach to
Designing Work
For years, management thinkers assumed that there were
inevitable trade-offs between efficiency and flexibility —
and that the right organizational design for each was different.
But it’s possible to design an organization’s work in ways that
simultaneously offer agility and efficiency — if you know how.
BY NELSON P. REPENNING, DON KIEFFER, AND JAMES REPENNING
YOU CAN HARDLY pick up a business publication without reading about the ever-increasing
pace of change in technologies and markets and the consequent need for more adaptable organizations. Given the imperative of adaptability, it is not surprising that few words have received more
attention in recent conversations about management and leadership than “agile.”1 Organizations
ranging from large corporations like General Electric Co. to tiny startups are trying to be both flexible and fast in the ways that they react to new technology and changing market conditions.2
The word “agile” appears to have been first applied to thinking about software by 17 developers in
2001.3 Having experimented with more iterative, less process-laden approaches to developing new applications for several decades, the group
codified its experience in an agile manifesto. “We are uncovering better ways
of developing software by doing it and
helping others do it,” they wrote. In software development, agile now has a variety
of manifestations, including scrum, extreme programming, and feature-driven
development.4 The results have been significant. A variety of studies show that
agile software development methods can
generate a significant improvement over
their more traditional predecessors.5
But what does this mean outside of software? Can agile methods be successfully
applied to other types of work? Many proponents (a number of whom started in the
software industry) argue that the answer is
yes, and a growing collection of books, papers, and blog posts suggests how it might
be done.6 The evidence, however, remains
limited to date, and a recent article by two
KEITH NEGLEY/THEISPOT.COM
THE LEADING
QUESTION
How can
companies
achieve both
agility and
efficiency in
their work?
FINDINGS
Make a distinction
between welldefined and
ambiguous tasks.
Break processes into
smaller units of
work that are more
frequently checked.
Identify points at
which collaboration
is needed.
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 29
R E D E S I G N I N G W O R K : O P E R AT I O N S
ABOUT THE
RESEARCH
Our dynamic work design
framework originated more
than 20 years ago when
two of the authors worked
together to improve both
manufacturing and product
development at HarleyDavidson Inc. (At the time,
one of the authors [Don
Kieffer] was leading HarleyDavidson’s largest engine
development project, and
another [Nelson Repenning]
was doing research on
failures in new product
development.) Following
the principles of action
research, in the ensuing
decades we have regularly
iterated between trying to
help organizations improve
their work design and building a theory grounded in the
underlying social science
for why these interventions
did or did not work. Over
the years, we have done
dozens of projects in a variety of industries, including
oil and gas, software, and
genetic sequencing. We
have also supervised more
than 1,000 work design
projects done by executives
in our courses at MIT.
of agile’s founders cautions against applying agile
indiscriminately.7 The blogosphere is also replete
with discussions of an ongoing agile backlash.
To provide some practical advice to business leaders trying to understand what agile might mean for
their organizations, we take a different approach. Our
research suggests that in applying agile methods from
the software industry to other domains, managers
often confuse practices and principles. When agile
methods work, they do so because the associated practices manifest key behavioral principles in the context
of software development. But, successful as those practices can be when developing software, there is no
guarantee that they will work in other contexts. The
key to transferring a set of practices from one domain
to another is to first understand why they work and
then to modify them in ways that both match the
new context and preserve the underlying principles.
The goal of this article is to help you understand
several key work design principles that undergird not
only agile practices in software but also Toyota Motor
Corp.’s well-known production system in manufacturing. Once you understand these underlying work
design principles — through a framework we call dynamic work design — you can create work processes
in your own organization that are both more flexible
and more efficient. (See “About the Research.”)
Stability Vs. Uncertainty
Academics and managers alike long believed that organizations had to make trade-offs between flexibility
and efficiency. A central notion in the academic theory on organizational design is contingency, the idea
that organizations and their associated processes
need to be designed to match the nature of the work
they do. One of the most common variables in contingency theory is the degree of uncertainty in the
surrounding environment (often also conceptualized
as the need for innovation). When both the competitive environment and the associated work are stable
and well understood, contingency theory suggests
that organizations will do best with highly structured,
mechanistic designs. In contrast, when facing highly
uncertain situations that require ongoing adaptation,
the theory suggests that organizations will do better
with more flexible, organic designs.8
An early advocate of the mechanistic approach
to work design was Frederick Winslow Taylor,
30 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
author of the 1911 book The Principles of Scientific
Management.9 Taylor’s essential insight was simply
that if work is regularly repeated, it can also be
studied and improved. In stable, well-understood
environments, it is thus often best to organize work
in ways that leverage the efficiency that comes with
repetition. For example, in a modern factory, welldefined tasks are specified, and the work proceeds
serially, moving from one carefully constructed and
defined set of activities to the next. There is little
need for collaboration in these settings, and the organizational structure that surrounds stable and
repeatable work tends to be hierarchical to ensure
that everybody follows the prescribed work design.
The cost of such efficiency is adaptability. Due to
the high degree of routinization and formalization,
mechanistic process designs are difficult to change
in response to new requirements. Though efficient,
a mechanistic design is not agile.
When, however, the environment is unstable and
uncertain, discrete tasks are harder to define, and
therefore organizations cannot rely on a sequence of
clearly defined steps. For example, product development teams often face challenges for which there is
little precedent. Contingency theory holds that in
unpredictable environments like new product development, organizations rely more on things like
training and collaboration and less on routinization
and careful specification. Developing a breakthrough
product or service usually can’t be organized like a
factory assembly line. Marketing experts may develop
a set of initial requirements, which are then passed on
to designers and engineers, but the requirements
often evolve through multiple iterations as designers
and engineers determine what is technically feasible.
Consequently, effective development processes often
require ongoing real-time collaboration, rather than
rote adherence to a set of sequentially organized steps.
Though the contingency theory was first developed more than 50 years ago, its basic insights
reappear frequently in contemporary management
thinking. Many flavors of process-focused improvement, such as total quality management, Six Sigma,
and business process reengineering, are extensions
to Taylor’s fundamental insight that work that
is repeated can also be improved. Recently, the
increasingly popular design thinking approach can
be thought of as a charge to tackle ambiguous,
SLOANREVIEW.MIT.EDU
uncertain tasks with a more collaborative, less hierarchical work design. 10 In general, contingency
theory gives managers a straightforward approach
to designing work: Assess the stability of the competitive environment and the resulting work, and
then pick the best mix of defined tasks and collaboration to fit the challenge at hand.(See“A Traditional
Approach to Work Design.”) If the work being designed consists of well-defined tasks (for example,
assembling components), then it is best to organize
it serially, or, as we label the cell on the bottom left,
using the “factory” mode. Conversely, if the work is
highly ambiguous and requires ongoing interaction
(for example, designing new products), then the
work is best organized collaboratively, or, as we label
the cell on the top right, in “studio” mode.
Though powerful, this approach to work design is
not entirely satisfying for two reasons. First, it describes an unpalatable trade-off: Work done using the
serial factory design isn’t very flexible, making it hard
to adapt to changes in external conditions, and work
done using the collaborative studio approach often
isn’t very efficient. Second, few types of work perfectly
fit the archetype of well-defined or ambiguous work.
Even the most routine work has the occasional
moment of surprise, and conversely, even the most
novel work, such as designing a new product or service, often requires executing routine analysis and
testing activities that support each creative iteration.
Academic theory notwithstanding, real work is a
constantly evolving mix of routine and uncertainty.
At first glance, agile methods appear to fall more
toward the collaborative side of the work spectrum.
However, our research suggests a different interpretation. The conventional approach to process and
organizational design is almost entirely static, implicitly presuming that once a piece of work has been
designed, everything will go as planned. In contrast,
a dynamic approach to work design suggests viewing
A TRADITIONAL APPROACH TO WORK DESIGN
In a traditional approach to work design, if the work being designed consists
of well-defined tasks (for example, assembling components), then it should be
organized serially, in what we call the “factory” mode. Conversely, if the work
is highly ambiguous and requires ongoing interaction (for example, designing
new products), then the work should be organized collaboratively, in what we
call the “studio” mode.
Organize
collaboratively
“Studio”
Organize
serially
“Factory”
Well-defined
work
Ambiguous
work
work as an ever-evolving response to the hiccups and
shortfalls that are inevitable in real organizations. As
we will describe later in this article, agile methods actually transcend the traditional serial vs. collaborative
work framework by creating better mechanisms for
moving between the two basic ways of organizing
work. By identifying mechanisms to cycle back and
forth between well-defined factory-style tasks and
collaborative studio modes when appropriate, an
agile approach can considerably reduce the trade-off
between efficiency and adaptability.
Dynamic Work Design at Toyota
What does this look like in practice? Consider a
well-known example of work and organizational
design, Toyota’s Andon cord. Work on Toyota assembly lines is the epitome of the serial, mechanistic
design. Tasks are precisely specified, often detailing
specific arm and hand movements and the time
A dynamic approach to work design suggests viewing work
as an ever-evolving response to the hiccups and shortfalls that
are inevitable in real organizations.
SLOANREVIEW.MIT.EDU
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 31
R E D E S I G N I N G W O R K : O P E R AT I O N S
that each action should take. In a plant we visited
recently, training for a specific role began with the
trainee learning to pick up four bolts at a time —
not three and not five. Only when the trainee could
pick up four bolts regularly was she allowed to learn
the next motion. But, despite an attention to detail
that would have made Taylor proud, sometimes
things go awry. In the Toyota scheme, a worker noticing such an issue is supposed to pull what’s known
as the Andon cord (or push a button) to stop the
production line and fix the problem.
While the management literature has correctly
highlighted the importance of allowing employees
to stop the line,11 what happens after the cord is
pulled might be more important. During a recent
visit we took to a Toyota supplier in Toyota City,
Japan, we observed that one operator on the factory
floor was struggling to complete her task in the allotted time, and so she hit a yellow button, causing
an alarm to sound and a light to flash. (This factory
has replaced the Andon cord with a yellow button
at each operator’s station.) Within seconds, the
line’s supervisor arrived and assisted the operator
in resolving the issue that was preventing her from
following the prescribed process. In less than a
minute, the operator, now able to hit her target,
DYNAMIC WORK DESIGN AT A TOYOTA SUPPLIER
At a Toyota supplier, a worker on an assembly line can press a button if he or she
faces a problem. A manager then helps solve the problem through collaboration;
once the problem is solved, the worker returns to his or her task. Pushing the button
thus initiates a temporary shift in the work design — from serial to collaborative work
and then back again — that increases agility.
Agile as Dynamic Work Design
“Studio”
Problemsolving
Organize
collaboratively
“Factory”
Organize
serially
Push
button
Change
work
mode
Problem
solved
Problem
Well-defined
work
32 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
Ambiguous
work
returned to her normal routine, and the supervisor
went back to other activities.
What, from a work design perspective, happened
in this short episode? Initially, the operator was working in the “factory” mode, executing well-defined
work to a clearly specified time target. (See the box
on the lower left in the exhibit “Dynamic Work
Design at a Toyota Supplier.”) But when something
in that careful design broke down, the operator
couldn’t complete her task in the allotted time.
Once the problem occurred, the operator had two
options for responding. She could have found an ad
hoc adjustment, a workaround or shortcut that
would allow her to keep working. But this choice
often leads to highly dysfunctional outcomes.12
Alternatively, as we observed, she could push the
button, stop the work, and ask for help. By summoning the supervisor to help, pushing the button
temporarily changed the work design. The system
briefly left the mechanistic, serial mode in favor of
a more organic, collaborative approach focused
on problem resolution. Once the problem was resolved, the operator returned to her normal task
and to the serial work design.
The Toyota production system might at first appear to be the ultimate in mechanistic design, but a
closer look suggests something far more dynamic.
When a worker pulls the Andon cord, the system actually moves between two modes based on the state
of the work. Though the nature of the work couldn’t
be more different, such movement between the two
modes is also the key to understanding the success of
agile software development.
As we discussed earlier, the last two decades have witnessed a significant change in the conduct of software
development. Whereas software was once largely
developed using what is known as the waterfall
approach, agile methods have become increasingly
popular. From a dynamic work design perspective,
the waterfall and agile approaches differ significantly.
In the waterfall approach, the software development cycle is typically divided into a few major
phases. A project might include a requirements
phase, an architecture development phase, a detailed
coding phase, and a testing and installation phase. A
waterfall project typically cycles between three basic
SLOANREVIEW.MIT.EDU
Checking in with more senior leadership only in the form
of periodic phase-gate reviews means that the entire team
could work for months before realizing they are not meeting
management’s expectations.
modes of work. First, the bulk of the time is spent by
software architects and engineers working individually or in small groups, completing whatever the
specific phase requires. Second, typically on a weekly
basis, those people leave their individual work to
come together for a project meeting, where they report on their progress, check to ensure mutual
compatibility, and adapt to any changes in direction
provided by leadership. Third, at the end of each
phase, there is a more significant review, often
known as a “phase-gate review,” in which senior
leaders do a detailed check to determine whether the
project is ready to exit that phase and move to the
next. Development cycles for other types of nonsoftware projects often work similarly.13
Agile development processes organize the work
differently. For example, in the scrum approach14
(one version of agile), the work is not divided into
a few major phases but rather into multiple short
“sprints” (often one to two weeks in length) focused on completing all of the work necessary to
deliver a small but working piece of software. At the
end of each sprint, the end user tests the new functionality to determine whether or not it meets the
specified need.
Like the waterfall method, the agile approach to
software development also has three basic work
modes — individual work, team meetings, and customer reviews — but it cycles among them very
differently. First, proponents of agile suggest meeting daily — thus moving from individual work to
teamwork and back every day — in the form of a
stand-up or scrum meeting, where team members
report on the day’s progress, their plans for the
next day, and perceived impediments to progress.
Second, agile recommends that at the end of each
sprint, the team lets the customer test the newly
added functionality. Finally, in something akin to
the Andon cord, some versions of agile also include
an immediate escalation to the entire team when a
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piece of code does not pass the appropriate automated testing, effectively again moving the system
from individual work to the team collaboration mode.
Viewed from a dynamic work design perspective,
agile offers two potential benefits over waterfall. First,
in waterfall development, the frequency of collaborative episodes is usually too low, both among the team
members and between the team and its customers. A
developer working for a week or two without a checkin could waste considerable effort before it’s clear that
he or she has made a mistake or gone off course. In
practice, developers often do not wait this long and informally check in with supervisors or teammates.
While seemingly functional, these check-ins can lead
to a situation in which the entire team is not working
from a common base of information about the state
of the project. In such cases, the operating mode starts
to migrate from the box on the lower left, the “factory”
mode, to the one on the lower right, where ambiguous
work is organized serially. This results in costly and
slow iteration, which we call ineffective iteration. (See
“Dysfunctional Dynamics,” p. 35.) Research suggests
that in R&D processes, this mode can be highly inefficient.15 Similarly, checking in with more senior
leadership only in the form of periodic phase-gate
reviews means that the entire team could work
for months before realizing that it is not meeting
management’s expectations, thus also potentially
causing rework.
The agile approach to software development also
improves the quality of the time that developers spend
working alone. The focus on developing pieces of
functionality means that both the team and the customer are never more than a few weeks away from a
piece of software that can be used, making it far easier
to assess whether it meets the customer’s need. In contrast, in waterfall, the early phases are characterized by
long lists of requirements and features, but there is
nothing to try or test. It’s not surprising that waterfall
methods often lead to projects in which major defects
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 33
R E D E S I G N I N G W O R K : O P E R AT I O N S
and other shortfalls are discovered very late in the development cycle and require costly rework.16
Applying Dynamic Work Design
Both the Toyota production system and agile-based
software methods are thus examples of what we call
good dynamic work design. In contrast to traditional
static approaches, dynamic work design recognizes
the inevitability of change and builds in mechanisms
to respond to that. Once managers recognize the
necessity of moving between more individual and
more collaborative modes of work, they can build on
four principles to create shifting mechanisms that are
well matched to the work of their organization.
1. Separate well-defined and ambiguous work.
Begin by clearly separating well-defined and ambiguous tasks. Trying to handle both types of work
in the same process often leads to trouble. (See
“Dysfunctional Dynamics.”) Often, the two types
can be separated by inspection, but if not, then look
for the signature element of ambiguous work, iteration. When work is well defined, it can be moved to
the next stage like the baton a relay runner hands off.
When done correctly, it doesn’t need to come back.
In contrast, when work is ambiguous, even the best
effort often needs to be revisited. If you find that a
particular task often requires multiple iterations
through the same set of steps, that’s a good sign that
you are confronting ambiguity inefficiently.
2. Break processes into smaller units of work that
are more frequently checked. If you strip away all the
hype, the agility of any work process — meaning its
ability to both adjust the work due to changing external conditions and resolve defects — boils down to
the frequency and effectiveness with which the output
is assessed. In both traditional, pre-Toyota manufacturing and waterfall software development, the
assessments are infrequent and not particularly effective. Consequently, both approaches tend to be slow to
adjust to changes in the external environment, and
quality will be achieved only through slow and costly
rework cycles. In contrast, when assessments are
frequent and effective, the process will be highly
adaptable and quality will improve rapidly. The fundamental recipe for improved process agility is this:
smaller units of work, more frequently checked.
3. Identify the chain of individuals who support
those doing the work. It is also important to identify
34 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
the help chain — the sequence of people who support
those doing the work. In manufacturing, the help
chain starts with a machine operator and extends
from foremen to supervisors all the way up to the
plant manager. In software, the help chain often begins with an engineer and moves through the team
leader to more senior managers, ultimately ending
with the customer. It is critical, in our experience, that
you identify the chains of individuals who do and
support the work, not their roles, departments, or
functions. Increasing agility requires knowing whom
to call when there is a problem or feedback is needed.
4. Introduce triggers and checks that move work
into a collaborative mode. Once you understand the
help chain, you have two basic mechanisms for activating it: triggers and checks. A trigger is a test that
reveals defects or misalignment and then moves the
work from a factory mode to a more collaborative
mode. In our opening example, the Toyota operator’s
inability to complete the assembly task on time triggered her pushing a button and then receiving help
from a supervisor. A check involves a prescheduled
point when the work is moved to a more collaborative
environment for assessment. In agile software development, this shift happens daily in stand-up meetings
where the team quickly assesses the current state of
the project. Completing a sprint creates a second opportunity, this time to check in with the customer.
Improving Procurement
Performance
Using this dynamic work design framework within
a company can lead to significant improvements in
both efficiency and adaptability. Consider the case
of a company we’ll call “RefineCo,” which owns
several oil refineries and distribution terminals in
the United States. The company had a procurement
organization that was uncompetitive by almost any
benchmark. RefineCo paid more for similar parts
and services than its competitors, and the procurement group’s overhead costs were higher than the
industry average. Even more troubling, when critical parts were not delivered to a refinery, it often
turned out that the location was on “credit hold”
due to an inability to pay the supplier in a timely
fashion. Every participant in the system, from senior management down to the shipping and
receiving clerks, was frustrated.
SLOANREVIEW.MIT.EDU
DYSFUNCTIONAL DYNAMICS
What happens when organizations don’t do a good job of cycling between factory and studio modes of work? We have observed two
related failure modes, ineffective iteration and wasted attention. When they are combined, they create a truly unproductive work design —
one we have dubbed the axis of frustration. (See “The Axis of Frustration.”)
meetings. (As a manager we once interviewed said, “I knew
my project was in trouble when I was required to give hourly
updates.”) But the form of those reviews makes all the difference.
If they are well designed and focus on resolving the key problems
that are causing the iteration, then they can move the system
back to a more productive cycling between factory and studio
modes. Such interventions, however, are the exception rather
than the rule.
Ineffective Iteration Consider first what happens when elements
of the work in question are highly ambiguous but are nonetheless
organized serially (captured in the box in the lower right-hand corner). Relative to a more collaborative design, this approach tends to
create slow and costly iteration. The lack of speed comes
because the ambiguity must travel among participants to be resolved, thus requiring multiple rounds, each of which takes time.
Worse, when knowledge work is designed serially, many of these
interactions take place through email or text messaging. Research suggests both that such communication modes are
less effective for reducing ambiguity than face-to-face communication and that those sending such messages are
unaware of those limits.i Trying to resolve ambiguity via email
or text messaging tends to create more misunderstandings
and often necessitates multiple iterations.
Wasted Attention On the flip side, organizing well-defined
work in a collaborative fashion also creates inefficiency. If the
work is clearly defined, then it doesn’t benefit from a collaborative approach, and collaboration just multiplies the cost.
Worse, too much collaboration may prevent the efficiencies
that come with the learning curve that emerges when people
repeat the same task.ii
THE AXIS OF FRUSTRATION
Organize
collaboratively
The Axis of Frustration Whereas functional work processes
move between the factory and studio modes, our research
suggests that absent careful design attention, processes can
devolve to the point where they move between the failure
modes described above, oscillating between wasted attention
and ineffective iteration — the dynamic we call the axis of
frustration.
Getting stuck on the axis of frustration typically starts
with time pressure — a project is behind schedule or a more
repetitive process is not delivering on its targets. When people feel they are behind, they don’t want to take the time to
shift into collaborative studio mode for problem-solving, preferring to stay in the factory box on the lower left and “just get
the work done.” The consequence of this decision is to leave
one or more problems unresolved, whether it is an element of
a product design that doesn’t work or a defect in a manufactured
product. Eventually, these problems will be discovered, usually
by an activity downstream from the one that generated it. And, if
this problem is not then solved in collaborative studio mode (again
due to time pressure) but instead sent back for rework, then the
system has effectively moved from the box on the lower left to the
box on the lower right and is now in “ineffective iteration” mode.
The consequence of ineffective iteration is that the process
becomes increasingly inefficient and incapable of meeting its
targets. Senior leaders are, of course, unlikely to stand idly by
and will eventually intervene. Unfortunately, the typical intervention is often to scrutinize the offending process in more detail,
usually in the form of more frequent and more detailed review
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Wasted attention
“Studio”
The axis of
frustration
Organize
serially
“Factory”
Ineffective iteration
Well-defined
work
Ambiguous
work
When organizations make the mistake of both structuring
well-defined work collaboratively and ambiguous work
serially, the result is a highly inefficient process we call
the axis of frustration. This process oscillates between
wasted attention and ineffective iteration.
Most work processes have not been designed with escalation
mechanisms in mind. So, when senior managers want to intervene
and scrutinize a project, they don’t know where to look and want to
review everything. The result of such scrutiny is long review meetings, the majority of which focus on elements of the process that are
just fine, thereby trapping the process in the upper left-hand box,
“wasted attention.” Worse, long review meetings and the preparation that they require steal time and resources from actual work, thus
intensifying the time pressure that prevented a proper shift between
work modes in the first place. Without careful attention to the mechanisms that move a process between the individual and collaborative
modes, processes can increasingly cycle between ineffective iteration and wasted attention, basically moving between frantically trying
to solve (or at least hide) the latest problem before the next review,
and endless, soul-destroying review meetings that never get to
solving the problems that would really make a difference.
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R E D E S I G N I N G W O R K : O P E R AT I O N S
Long turnaround times created unhappy customers and
suppliers who constantly called to complain and ask about
their particular order or payment.
The procurement system at each of RefineCo’s
sites worked roughly as follows. To purchase an item
or service from an outside vendor, an employee
would enter the requirements into the electronic
procurement system, which would then appear as a
request to the central procurement function. The
staff in the procurement office would then review the
request and issue a purchase order. That order would
go to the supplier. When the product arrived at the
refinery or the service was completed, a packing slip
or service order verification slip would be generated,
which would also be entered into the procurement
system. Later, the supplier would generate an invoice
that was also entered into the system. The electronic
system would then perform a three-way match to
verify that everything was done correctly: The purchase order should match the verification receipt,
which, in turn, should match the invoice. If there was
not a three-way match, the invoice would be “kicked
out” of the system and the supplier would not get
paid until the discrepancy was resolved.
The job of resolving those discrepancies fell
to the staff in the refinery’s purchasing office.
Unfortunately, the products and services procured
frequently failed the three-way match, leading to
both an overburdened purchasing department and
frustrated suppliers. Though the refinery was part of
a large and successful company, it was frequently on
credit hold with its suppliers for failure to pay invoices on time, making it difficult for the staff to do
their jobs and run the plant safely. The dedicated
procurement staff worked 10-plus hours per day and
had hired temporary workers to help manage the
backlog, but they were still falling behind.
Most of the members of the procurement team
complained bitterly about being “overworked” and
how “screwed up the system was.” Nobody saw any
opportunity for improvement beyond adding what
appeared to be much-needed staff. For us, the critical
moment in our work with the procurement staff
came when one of the longtime team members
36 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
explained that a good purchase request contained “all
the information I need” and could be turned into an
official purchase order in “five to 10 minutes.” A difficult one, however, lacked key pieces of information
and might require one to two hours to process as the
purchasing staff traded emails with both the requesting unit and the supplier. Despite this effort, difficult
purchase orders were usually the ones that failed the
three-way matching process and got kicked out of the
system. Further investigation revealed that the purchase order system was completely gridlocked with
the kicked-out orders, and the team spent much of
its time trying to clear the backlog. The system had
descended into the classic “expediting” or “firefighting” trap: There were so many purchase orders in
process that the turnaround time for any given one
was very long. But long turnaround times created unhappy customers and suppliers who constantly called
to complain and ask about their particular order or
payment. Consequently, the procurement team was
constantly reprioritizing its work and reacting to
whichever customer or supplier was most unhappy.
Our first insight came in recognizing that the
procurement team was engaged in two different
types of work that corresponded to what we call serial “factory” work and collaborative “studio” work.
When the requested item was standard and all the
needed information was provided, a single person
could easily process the request without collaboration; then, once the purchase order was entered, it
would easily flow through the system, just like an
item on an assembly line. However, standard requests flowed easily through the system only if the
request came with the correct information. If it did
not, then it could require several rounds of iteration,
usually via email, to issue the purchase order. So the
purchasing function created a simple checklist that
described a good purchase request. The idea was
to ensure that standard orders would always arrive
with the correct information. To give the various departments an incentive to use the checklist, the
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purchasing function promised that any request received by 7 a.m. with the proper information would
result in a purchase order being issued by 2 p.m. that
day. At that time, a one-day turnaround was unheard
of because every order simply went into the “to do”
pile. The purchasing department also created a simple trigger to improve productivity: Purchase orders
that were missing items on the checklist would be
immediately returned to the requesting unit.
The second part of the intervention came in recognizing that not every request could be supported
in factory mode. In the existing system, neither the
requesters nor the purchasing staff distinguished between a standard request and a novel one. Thus,
when a request for a new product or service showed
up, the agent would do his or her best to process it,
typically requiring multiple emails with the requester, often over several days, to nail down all the
relevant information. In many cases, when the
agents couldn’t get the information they needed,
they would make their best guess and then submit an
incomplete or incorrect purchase order. This, too,
created additional iteration, as the supplier, unsure
of what was being requested, would call or email the
agent. The purchasing process was thus living in the
lower right-hand box of our matrix, attempting to
accomplish ambiguous work in a serial fashion and
thereby creating slow and expensive iteration.
Creating an effective collaborative studio mode
to handle the complex purchase orders required two
changes in work design. First, the team created a
clear trigger: If a request was nonstandard, then it
was moved into a separate pile and not dealt with
immediately. Second, each day at 2 p.m., the team
would work together to process the more complex
cases. By working collaboratively (in studio mode),
they were able to resolve many of the more complex
cases without additional intervention — somebody
on the team might have seen a similar order before.
Also, having a face-to-face meeting was far more
efficient than the endless chain of email that it replaced. And, if additional information was needed,
the team could schedule a phone call in the time
window after 2 p.m., rather than send an email,
again reducing the number of expensive iterations.
The results of these two changes were significant.
Creating a factory mode for the standard orders
allowed the team to make good on its “in by 7, out by
2” promise almost immediately, generating an immense amount of goodwill with the requesters.
Spending the afternoon in studio mode also sped the
processing of the complex orders. The two changes
created enough space that the team was able to use studio time to not only process the more complex requests
but also work through the backlog of unresolved older
orders. In the end, due to the efficiency improvements,
the procurement team reduced its staff by the equivalent of two full-time staff members, while providing
far faster and more reliable service. These process
improvement insights were then applied to the
company’s other U.S. sites and, as of this writing,
RefineCo pays more than 90% of its invoices on time,
resulting in a far happier collection of suppliers.
Look for Best Principles
Managers and consultants are often obsessed with
the search for best practices — those activities that
appear to separate leading organizations from the
rest of the pack. The idea behind this search is that
once identified, best practices can be adopted by
other organizations, which will then experience similar gains in performance. While there is certainly
some truth to this idea, the supporting evidence is
decidedly mixed. Organizations frequently struggle
to implement new tools and practices and rarely
experience similar gains in performance. In many
industries, the performance gap between the top and
middle performers remains stubbornly difficult to
close. A key reason for these failures is simply that
organizations are complex configurations of people
Organizations are complex configurations of people and technology, and a set of tools or practices that works well in one
context might not be equally effective in a major competitor —
even if that competitor is located just down the street.
SLOANREVIEW.MIT.EDU
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 37
R E D E S I G N I N G W O R K : O P E R AT I O N S
and technology, and a set of tools or practices that
works well in one context might not be equally effective for a major competitor — even if that competitor
is located just down the street.
Best practices are “best” when they manifest an underlying behavior principle in a way that is well
matched to the organization that uses them. Toyota’s
famed Andon cord and the localized problem-solving
it catalyzes work by capitalizing on the efficiency
that comes from individual repetition and the
innovation that comes with collaborative problemsolving. Conversely, agile development methods
work by channeling the creativity of software engineers through frequent team meetings and customer
interactions. More generally, organizations become
more adaptable when they find defects and
misalignments sooner. A dynamic approach to contingency, supported by triggers and checks, can open
the path to creating practices that support increased
agility in the work of your organization.
Nelson P. Repenning is the School of Management
Distinguished Professor of System Dynamics and
Organization Studies at the MIT Sloan School of
Management in Cambridge, Massachusetts, where he
currently serves as the associate dean for leadership
and special projects and the faculty director of MIT’s
Leadership Center. Don Kieffer is a senior lecturer in
operations management at the MIT Sloan School and
a founder of ShiftGear Work Design, a consulting firm
based in Cambridge. James Repenning is the managing partner at ShiftGear Work Design. Comment on
this article at http://sloanreview.mit.edu/59234.
REFERENCES
1. A quick trip to the web reveals multiple manifestations,
including “agile principles,” “enterprise agile,” “agile organizations,” and a variety of suggestions, including the
charge to “apply agile development to every aspect of
business.” See, for example, S.D. Goldstein, “Apply Agile
Development to Every Aspect of Business,” Mint, Dec. 4,
2016, http://www.livemint.com.
2. D. Leonard and R. Clough, “How GE Exorcised the
Ghost of Jack Welch to Become a 124-Year-Old Startup,”
Bloomberg, March 17, 2016, www.bloomberg.com.
3. See their manifesto here: http://agilemanifesto.org.
4. For a summary, see F.S. Glaiel, A. Moulton, and S.E.
Madnick, “Agile Project Dynamics: A System Dynamics
Investigation of Agile Software Development Methods,”
Massachusetts Institute of Technology, Engineering
Systems Division working paper, Oct. 2014, available at
http://dspace.mit.edu.
5. See C.J. Stettina and J. Hörz, “Agile Portfolio Management: An Empirical Perspective on the Practice in Use,”
International Journal of Project Management 33, no. 1
38 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
(January 2015): 140-152; J. Sutherland and J.J. Sutherland,
“Scrum: The Art of Doing Twice the Work in Half the Time”
(New York: Crown Business, 2014); and D. West and
T. Grant, “Agile Development: Mainstream Adoption
Has Changed Agility,” Forrester, Jan. 20, 2010,
www.forrester.com.
6. See, for example, M.E. Moreira, “Being Agile: Your
Roadmap to Successful Adoption of Agile” (New York:
Apress, 2013).
7. D.K. Rigby, J. Sutherland, and H. Takeuchi, “Embracing
Agile,” Harvard Business Review 94, no. 5 (May 2016):
40-50.
8. The classic descriptions of contingency theory can be
found in T. Burns and G.M. Stalker, “The Management of
Innovation,” rev. ed. (Oxford, U.K.: Oxford University
Press, 1994) and in P.R. Lawrence and J.W. Lorsch,
“Organization and Environment: Managing Differentiation
and Integration,” rev. ed. (Boston: Harvard Business
School Press, 1986). An updated summary can be found
in several textbooks, including R.M. Burton, B. Eriksen,
D.D. Håkonsson, and C.C. Snow “Organization Design:
The Evolving State-of-the-Art” (New York: Springer
Science+Business Media, 2006).
9. F.W. Taylor, “The Principles of Scientific Management”
(New York and London: Harper & Brothers, 1911).
10. T. Brown, “Change by Design: How Design Thinking
Transforms Organizations and Inspires Innovation” (New
York: HarperBusiness, 2009).
11. J. Liker, M. Hoseus, and the Center for Quality People
and Organizations, “Toyota Culture: The Heart and Soul of
the Toyota Way” (New York: McGraw-Hill Education, 2008).
12. See, for example, N.R. Repenning and J.D. Sterman,
“Capability Traps and Self-Confirming Attribution Errors in
the Dynamics of Process Improvement,” Administrative
Science Quarterly 47, no. 2 (June 2002): 265-295; and
N. Leveson, “A Systems Approach to Risk Management
Through Leading Safety Indicators,” Reliability Engineering and System Safety 136 (April 2015): 17-34.
13. K.T. Ulrich and S.D. Eppinger, “Product Design
and Development,” 6th ed. (New York: McGraw-Hill
Education, 2016).
14. Sutherland and Sutherland, “Scrum”; and Scrum
Guides, www.scrumguides.org.
15. L.A. Perlow, “The Time Famine: Toward a Sociology
of Work Time,” Administrative Science Quarterly 44,
no. 1 (March 1999): 57-81.
16. See Sutherland and Sutherland, “Scrum”;
and J. Kamensky, “Digging Out of the Digital Stone
Age, ” Government Executive, March 9, 2017,
www.govexec.com.
i. N. Epley and J. Kruger, “When What You Type Isn’t
What They Read: The Perseverance of Stereotypes and
Expectancies Over E-Mail,” Journal of Experimental
Social Psychology 41, no. 4 (July 2005): 414-422.
ii. L. Argote and D. Epple, “Learning Curves in Manufacturing,” Science 247, no. 4945 (Feb. 23, 1990): 920-924.
Reprint 59234. For ordering information, see page 4.
Copyright © Massachusetts Institute of Technology, 2018.
All rights reserved.
SLOANREVIEW.MIT.EDU
R E D E S I G N I N G WO R K : C A S E S T U DY
What to Expect From Agile
What happens when a company whose roots go back over a century — a bank, no less —
decides to adopt agile management methods developed in the software industry?
BY JULIAN BIRKINSHAW
THE LEADING
QUESTION
HOW IS TECHNOLOGY transforming the practice of management? As everyone knows, technological innovation enables changes in how we work, for example, helping people collaborate, access
information more quickly, and make smarter decisions. Less obvious, but no less important, is the
observation that technological innovation inspires new approaches to management. For example, the
shift from mainframe computers to personal computers gave impetus to the empowerment trend of
the 1980s, and the emergence of collaborative software tools shaped the knowledge management
movement in the 1990s. In such cases, new technologies expand our capabilities and broaden our
horizons, and it is this combination that enables management to evolve.
A case in point is agile. This emerged during the 1990s as a software methodology, made possible
by new programming languages that made it much easier for developers to build prototypes and
gain rapid user feedback. The concept of agile software development was formally defined in 2001,1
and over the next decade it gathered momentum as a more responsive and collaborative approach
to software development than the traditional “waterfall” methodology.2 In recent years, agile has
started to move into mainstream management thinking, with some observers proclaiming it the
next big thing. Forbes.com contributor Steve
Denning calls it a “vast global movement that is
transforming the world of work.” In a 2016
Harvard Business Review article, Darrell Rigby,
Jeff Sutherland, and Hirotaka Takeuchi wrote
that “agile innovation has revolutionized the
software industry. … Now it is poised to transform
nearly every other function in every industry.”3
The purpose of this article is to shed light on
agile as a management practice. To do this, I report
on a detailed case study of the operations of ING
bank in the Netherlands, which has adopted agile
across its headquarters in Amsterdam. Though
ING’s Dutch operations are less than three years
into the process — and it’s therefore premature to
declare the initiative a success — taking a deep
dive into the organization’s early experience with
adopting agile is nonetheless instructive.
Most IT departments in large companies today
are adopting agile techniques to some extent, although with varying degrees of success. 4 And
many fast-growing technology companies, such as
London-based Spotify Ltd. and Los Angeles-based
NEIL WEBB/THEISPOT.COM
What happens
when an
established
company
decides to
adopt agile
management
methods?
FINDINGS
Executives need to
be willing to share
power and embrace
new ways of
working.
It’s important to pre
pare stakeholders
for the change.
Build the organiza
tional structure
around customers —
and keep it fluid.
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 39
R E D E S I G N I N G WO R K : C A S E S T U DY
ABOUT THE
RESEARCH
This article is based primarily
on a detailed case study of
ING in the Netherlands, involving in-person and phone
interviews with 15 executives over an eight-month
period (June 2016 to February 2017). The two key
executives who led the
change process were
Peter Jacobs (CIO) and
Bart Schlatmann (COO of
the Netherlands operation,
who has recently left the
company). Other people
we spoke to, some several
times, include Josje Schiltmanns, Leonoor Koomen,
Karin van der Pol, Heidi van
Eijk, Jaap Kok, Maartje
Geven, Lieke Jansen,
Henk Kolk, Payam Djavdan,
Maartje Aangenendt, Tom
Degen, and Saloua Essalhi.
In the text, statements in
quotation marks are verbatim from our interviews
(which were recorded), but
to improve the flow, we
have not attributed them to
the individuals in question.
The author thanks Scott
Duncan for his help with interviews and transcription.
The article also builds
on the author’s ongoing
research into new ways of
working in large traditional
organizations, including interviews with executives at
Barclays, King.com, Roche,
Bayer, Unilever, BMW, and
the U.K. Government Digital
Service, as well as consultants and training providers
such as the Agile Business
Consortium, Lean Kanban,
and the Scrum Alliance.
To contextualize the analysis
of agile in this paper, a review was also undertaken
of other contemporary
innovations in management
thinking, including design
thinking, lean startup, and
holacracy.
Riot Games Inc., have embraced agile not just as an IT
methodology but as a way of working.5 By contrast,
ING is a bank whose roots go back more than a century. It is the first case I know of in which an established
company in a traditional industry is reinventing its
management model throughout its operations in a
particular country — not just its IT or software development management model — using agile principles.
By studying the experience of ING’s operation in the
Netherlands, leaders at other established companies
should be able to make more informed decisions
about whether pursuing agile is right for them.
In this article, I highlight key learnings at ING in
the Netherlands, largely from the point of view of
the senior executives of the bank during this transition period, and I reflect on some of the broader
implications. I don’t spend much time on the internal workings of the agile teams, or squads as they
are known within ING, because others have written
extensively on those.6 Instead, my focus is on implementing agile on an organization-wide basis.
This is where the ING experience is unique — and
hopefully most useful to other established companies that are seeking to embrace agile working.
My research is based on in-depth interviews
with 15 ING executives and many front-line employees. (See “About the Research.”) In addition, I
spoke to leaders tackling similar issues about new
ways of working at other large companies, including Barclays, Roche, Bayer, Unilever, and BMW.
Tellingly, one of the ING leaders I interviewed, Bart
Schlatmann, left ING early in 2017 when another
large global bank recruited him to help them implement agile methods. (He had spent 22 years with
ING, the last 10 as COO of ING Netherlands.)
Why ING Adopted Agile
ING has always been open to new ways of working. It
was an early mover in internet banking, creating ING
Direct in the late 1990s as a nonbranch offering. In
2007, ING merged its two Netherlands-based businesses, Postbank (a savings-only bank with no
branches) and ING (a traditional retail bank). The
transformation process was called TANGO (together
achieving new growth opportunities), and it achieved
annual savings of 280 million euros (roughly $330
million at today’s exchange rates). In 2014, with the
emergence of mobile banking, ING began rethinking
40 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
its entire model through a process called RIO (redesign into omnichannel). ING quickly realized that it
needed to look beyond the banking industry for
guidance. Specifically, ING found inspiration from
Amazon, Spotify, and Zappos, where agile methods
had demonstrably improved customer orientation
and employee engagement.
ING also conducted an internal study, which
highlighted how bureaucracy, silos, and risk aversion were cultural problems. Based on this analysis,
ING decided on a top-to-bottom restructuring of
its operations in the Netherlands, based mostly on
Spotify’s model but also on practices from Google,
Netflix, and Zappos. The plan was to organize the
3,500 employees in Amsterdam into squads: teams
of up to nine people with end-to-end responsibility
for a specific customer-related activity. The squads
would then work according to agile principles: a series of short “sprints” with frequent user feedback
and daily progress updates.
ING went live with the new structure for its
Netherlands operations on June 15, 2015. Eighteen
months later, employee engagement was up (according to an internal survey to which I had access). In
addition, ING’s Net Promoter Score for its business
in the Netherlands rose from –21% in 2015 to –7%
in 2017, and its cost-to-income ratio in that business
dropped over the same period from 65% to 51%.
While the transformation is not finished, it is still
fruitful to reflect on what ING has learned so far. I’ve
grouped ING’s lessons into five points.
1. Decide how much power you are willing to
give up. In December 2014, ING executives flew to
meet executives at Spotify. At that meeting, a Spotify
executive said: “I can see you are fascinated by our
way of working, but it’s not that easy. You need to ask
yourself honestly, how much are you willing to give
up?” His point was that agile shifts power away from
those at the top and puts ownership in the hands of
those closest to the action. That is a difficult shift for
executives at established companies.
How did ING handle this shift? The “big bang”
approach meant that senior managers in the
Netherlands had to embrace the new way or leave the
company. Those who stayed had to reapply for the
newly categorized jobs. This led to major personnel
changes and a significant downsizing in the organization (a net reduction of about 1,500 employees in
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the Netherlands from 2014 to 2016). All told, about
one-third of the senior managers left.
The overarching lesson is that you cannot implement agile unless top executives accept that they are
surrendering some status and power. “It requires sacrifices and a willingness to give up fundamental parts
of your current way of working,” said Schlatmann.
2. Prepare stakeholders for the leap. Some ING
stakeholders were “completely freaked out by our proposals — they thought it would be complete chaos,”
recalled Schlatmann. It was one thing for Spotify and
Netflix to adopt agile; it was quite another for a large
bank to do so in the post-financial-crisis era.
How did ING’s executives sell agile to nervous
stakeholders? To the board, the executive team cited
its track record with TANGO and RIO. They also
argued that ING needed a new way of working to
stay competitive in the lightning-fast digital marketplace. In addition, ING executives sought early
buy-in from the works council representing employees, explaining how agile would engage and
empower rank-and-file employees. “They accepted
the notion that this was a one-time chance to really
change the organization, and they ended up supporting us in a very positive way,” said Schlatmann.
Meanwhile, bank regulators were concerned:
They had never seen this structure in the industry.
So ING’s executives invited regulators to headquarters, where they could observe how agile brought to
operations a habit of daily communication about
progress and customer solutions. Most important,
ING’s executives assured regulators that finance,
compliance, and legal functions would continue to
be managed in their traditional way.
The lesson here is to assess — as early as you can —
how stakeholders will react to a major change. Then
find the right arguments to allay their concerns.
3. Build the structure around customers — and
keep it fluid. The notion that work should be focused
on customers is as old as the hills. Management thinkers such as Peter Drucker, W. Edwards Deming, Philip
Kotler, and Theodore Levitt have all espoused their
own variants of customer orientation. But agile goes a
step further, forging a structure around customer
needs. The basic building block for ING was a selfmanaged team, or squad, of up to nine people focused
on a particular customer group. These squads were
then clustered into larger tribes working on related
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activities. ING distinguished between experience tribes
that attract new customers and service tribes that take
care of existing customers. ING also defined two enabling tribes to serve these two customer-facing tribes.
For example, one of the enabling tribes built blackbox technical solutions for customer identification.
ING also seeks to keep its structure fluid so that it
can evolve to do what’s best for customers. For example, the experience tribe in charge of daily banking
was, for a time, handling some customer communications duties. But eventually they shifted these duties
to the tribe that specialized in communications.
Likewise, ING has created the concept of “pop-up
squads” to manage one-off, short-term projects.
The lesson here — regardless of any decision
you make about agile — is to revisit your organizational structure to make sure it maps to the real
needs of customers.
4. Give employees the right balance of oversight and autonomy. How do you ensure that
squads prioritize important work? ING in the
Netherlands moved to a quarterly business review
(QBR) process adapted from Google LLC and
Netflix Inc. Four times a year, each tribe lead writes
a maximum six-page summary of what the tribe
achieved, what they did not achieve (and why not),
what they are going to achieve next quarter, and any
dependencies outside the tribe’s control.
These summaries are then discussed in a big
meeting (the QBR Market) attended by tribe leads
and other relevant leaders — about 20 people overall. They challenge one another’s achievements and
plans, and in doing so often resolve tensions or
overlaps. Each tribe lead emerges with a set of objectives and key results (OKRs) for the following
quarter. The OKRs then get translated into tasks for
the individual squads within the tribe.
All of that has been a learning process for ING
employees accustomed to a traditional goal-setting
process. At first, tribe leads defined quarterly goals
that were comfortably achievable. ING’s executives
had to urge them toward more ambitious targets —
since the whole point was setting stretch goals, not
erring on the safe side.
ING’s experience is a reminder that you still
need top-level oversight in an agile organization —
to continually tweak the framework for goals and
reporting, and to keep the level of ambition high.
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 41
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5. Provide employees with development and
growth opportunities. Squad-based structures can
be scary for employees used to having their personal
development and career progression mapped out by
HR departments or the mainstream career trajectories of a given industry. Indeed, one risk of agile is
that employees become too task-focused and resultsoriented. They burn out and neglect to think about
their careers over the long term. Having discovered
this risk in its advance research, ING has taken steps
to attend to employee development.
For example, ING in the Netherlands instituted
weekly POCLAC meetings for each squad, where the
activities of the squad and the development needs of
individuals are discussed in tandem. POCLAC stands
for product owner, chapter lead, agile coach — the
three people responsible for empowering a squad.
Though these meetings are undoubtedly a sound
idea, they remain a work in progress. In fact, one area
in which ING in the Netherlands has struggled in its
agile transition is that the POCLAC meetings do not
always happen on a weekly basis. Perhaps this isn’t
surprising: In most organizations, long-term individual goals easily get subsumed by and subordinated
to short-term urgencies. What’s more, in ING’s old
way of working, a manager was responsible for everything: the product, the process, and the people. With
agile, each element is the responsibility of a different
person. It’s been an adjustment for chapter leads,
product owners, and agile coaches to grow comfortable with all of their new responsibilities, let alone
nonurgent matters like career development.
The lesson? Finding proper coaching and support
for agile — and the new, long-term responsibilities
employees must embrace — is one of the hardest
parts of the transformation.
Lessons From ING
ING’s experiences are a reminder that implementing
new practices is much more difficult than suggesting
them. The key challenges — shifting power from the
top, getting buy-in from stakeholders, and changing
employees’ views about professional development —
are operational concerns, rather than big-picture
ones. No wonder new management practices often
work better at young companies than they do at old
ones, where the employees have entrenched expectations and habits.
42 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
But agile does have one advantage for established
companies: It is now a bona fide way of working,
with its own set of principles and a track record of
success in certain sectors (mostly tech) and functions (mostly IT). In discussions with stakeholders,
leaders can say that they are exploring a tested management model, rather than reinventing the wheel.
Moreover, agile is starting to migrate into mainstream business — and ING in the Netherlands is
at the forefront of this movement. By discussing
the details of its experiences, I hope others can be
encouraged toward comparable experiments and
explorations.
Julian Birkinshaw (@JBirkinshaw) is a professor of
strategy and entrepreneurship, academic director of
the Institute of Innovation and Entrepreneurship, and
deputy dean (programs) at London Business School.
Comment on this article at http://sloanreview.mit
.edu/59201.
REFERENCES
1. The original “Agile Manifesto” is available at
http://agilemanifesto.org.
2. As part of this gathering momentum, a wide range of industry associations and training providers have emerged,
such as Scrum.org, the Scrum Alliance, the Agile Business
Consortium, the Agile Centre, and Lean Kanban.
3. S. Denning, “Explaining Agile,” www.forbes.com, Sept.
8, 2016; and D.K. Rigby, J. Sutherland, and H. Takeuchi,
“Embracing Agile,” Harvard Business Review 94, no. 5
(May 2016): 40-50.
4. Challenges with implementing agile have been reported in several places. See, for example, J. Birkinshaw
and M. Guest, “Digital Transformation in Practice,” London Business School and Deloitte Institute of Innovation
and Entrepreneurship, October 2016, www.london.edu;
and B. Boehm and R. Turner, “Management Challenges
to Implementing Agile Processes in Traditional Development Organizations,” IEEE Software 22, no. 5
(September-October 2005): 30-39.
5. S. Denning, “Can Big Organizations Be Agile?”
www.forbes.com, Nov. 26, 2016; and H. Kniberg,
“Spotify Engineering Culture (Part 1),” labs.spotify.com,
posted March 27, 2014.
6. J. Sutherland and J.J. Sutherland, “Scrum: The Art of
Doing Twice the Work in Half the Time” (New York: Crown
Business, 2014); S. McChrystal, with T. Collins, D. Silverman, and C. Fussell, “Team of Teams: New Rules of
Engagement for a Complex World” (New York: Penguin,
2015); and K.S. Rubin, “Essential Scrum: A Practical Guide
to the Most Popular Agile Process” (Upper Saddle River,
N.J.: Pearson Education, 2013).
Reprint 59201. For ordering information, see page 4.
Copyright © Massachusetts Institute of Technology, 2018.
All rights reserved.
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R E D E S I G N I N G WO R K : D E C I S I O N - M A K I N G
The Trouble With
Homogeneous Teams
Diversity in the workplace can increase conflict. But research also suggests that
if teams lack diversity, they will be more susceptible to making flawed decisions.
EVAN APFELBAUM, INTERVIEWED BY MARTHA E. MANGELSDORF
MANY COMPANIES TODAY understandably focus on workplace diversity — issues
such as how to increase diversity, how to foster
sensitivity to it, and how to manage a diverse
workforce. But, according to MIT Sloan School
professor Evan Apfelbaum, managers should
also be cognizant of another, related topic: the
problems associated with homogeneity. Recent
research, including Apfelbaum’s own, has found,
for example, that racially homogeneous groups
are less rigorous in their decision-making — and
make more mistakes — than diverse ones.
Apfelbaum, the W. Maurice Young (1961)
Career Development Professor of Management
and an associate professor of work and organization studies at the MIT Sloan School, spoke with
MIT Sloan Management Review editorial director
Martha E. Mangelsdorf. What follows is a condensed and edited version of their conversation.
MIT Sloan Management Review: You’re an
expert in research on diversity and how it affects group decision-making. And one thing
you and others have found is that diverse
groups often do better in decision-making
than more homogeneous ones. Can you tell
us a bit about some of the important studies
in that area and what they found?
APFELBAUM: Sure. A good way to think about
it is that diverse groups have the potential to
do better than homogeneous ones. In reality,
there are a number of examples and reasons
why that often doesn’t happen. But I do think
JING JING SONG/THEISPOT.COM
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there’s a unique advantage to diverse groups in certain areas.
I’ll start off by talking about cooperative decision-making scenarios, where people are trying to
work together to come to some best solution. Early
work from several decades ago provided the first
evidence that diverse groups yielded more creative
solutions, and that spurred much of the more recent research in that area.
One paper that was particularly important and
useful took place in a legal setting, with jurors. In
that study, a researcher now at Tufts University got
access to a real jury pool and randomly assigned jurors to deliberate in six-person, all-white or racially
diverse juries. The groups all considered the same
fictitious case, and their deliberations were recorded on video.
In general, the diverse juries were far more rigorous in how they approached their decisions. The
racially diverse juries spent a longer time deliberating. They considered a wider range of perspectives
and angles with respect to the case — different
things that could have happened or might have
been important. And they made fewer factually inaccurate statements in their discussions.
It wasn’t the case that diverse juries were outperforming homogeneous ones primarily because, say,
the black jurors were adding new information that
wasn’t there in the all-white juries. It was actually
white jurors on diverse juries whose behavior
showed the most dramatic change. This suggested
that it was something about being in the presence
of a racially diverse environment that changed how
people thought and discussed issues.
Subsequent research has looked at student groups
working on projects, and this work has shown similar
effects. For example, one project demonstrated that
when you tell students that they’re going to be having
a discussion regarding a written article, they prepare
more rigorously if they know that they’re going to
have to debate in a more diverse group.
In that case, it might have been because the article
had some race-related component to it, and the
students wanted to make sure that they had a wellrehearsed way to think about that. The study also
showed that when researchers asked students to
write essays after the fact to reflect on what they took
away from the discussion, these essays typically were
44 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
more complex and of higher quality when the discussion took place in a racially diverse group.
Those are some important effects of diversity.
Now, let’s take a more traditional business setting,
and one that’s competitive, not cooperative. In one
paper that I coauthored, we asked: What happens if
you put diverse versus homogeneous groups together in a naturally occurring competitive scenario?
To examine this case, we looked at trading markets.
We randomly assigned people to either racially
diverse or homogeneous groups in two different
studies, one in Asia and one in North America.
People were brought to the lab, and we formed mini
trading markets of six people. Think of it as a sixperson competitive group. The participants were
given real money, and there were several rounds of
trading where the groups were networked through
computers. The participants were making decisions
about whether they would like to buy and sell assets,
and their goal was to end up with the most money
at the end.
The only difference between the two groups was
that at the very beginning of the study, people saw
who would be in their market based on who was sitting with them in the waiting room. And we randomly
assigned half of these groups to be homogeneous. The
beauty of this is that it wasn’t just an all-white sort of
homogeneity, because we were able to do this experiment in Asia in a way where the dominant culture
identity was not white. So half of the groups see a
singular ethnicity, and half see a more diverse one.
Then the group members are separated and begin
trading. A couple of interesting results emerge here.
The first thing is that there’s a difference in accuracy,
in how closely people are pricing assets to their actual
value. In the homogeneous groups, there was more
inaccuracy and mispricing; there was a tendency to
spend more for things than they were actually worth.
The other thing that was interesting is that these
mistakes were, in a sense, more contagious in homogeneous than diverse groups. That is, not only were
people in homogeneous groups more likely to make
pricing errors, but other people in those groups were
more likely to copy those errors. People in homogeneous groups were more likely to assume that other
people in the group knew what they were doing.
In diverse groups, people were less likely to trust
the wisdom of other people’s purchasing choices.
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“People in homogeneous groups were more likely
to copy another person’s mistake — presumably
assuming that the mistake had some value that
they just didn’t understand.”
— EVAN APFELBAUM
And the result of these two different dynamics that
played out is that pricing bubbles, which are very
problematic in financial markets, and for society at
large, were more likely in this experimental context
to form in homogeneous groups. The reason for
this is that people in homogeneous groups were
more likely to copy another person’s mistake —
presumably assuming that the mistake had some
value that they just didn’t understand. In homogeneous groups, there was this escalating effect where
people would copy poor decisions.
So we were able to see very different trajectories
even within the lab, and in very similar ways in two
very different cultures, suggesting that there is something fundamental about working with similar
versus different others that affects individuals’ decision-making. Again, this is a competitive context:
People were really motivated to try to eke out as
much money as they could because, at the end of the
experiment, they kept the money they had made.
That suggests that people in the homogeneous
groups were trying to make the right decisions —
but something about the group context constrained
their ability to do so.
One of the ideas we had is that maybe this is just
something basic about conformity. So we essentially ran a variation of what is one of the most
famous social psychological experiments ever to be
run in the domain of groups: psychologist Solomon
Asch’s conformity paradigm from the 1950s. In
that famous study, participants would sit at a table
with people they thought were other study participants, and they would simply look at a picture of
three lines of three different lengths.
The study participants believed that the other
people at the table were also participants, but the
other people were actually working for the person
running the experiment. And the experimenter said,
for example, “Tell me which line is the longest.” What
the participants would experience is that they would
hear other people answer before them and all say that
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the third line was the longest, when it was clearly evident that actually it was the second line that was the
longest. It was such an obvious answer, but the experimenters in this case were looking at how likely
participants are to yield to majority opinion, even
when they know that it’s the wrong answer. In other
words, how much can social pressure affect us?
Asch’s research found that people yield to what
they know to be the wrong answer around roughly
30% of the time — which is a pretty large frequency.
We wondered if diversity would change people’s
susceptibility to this bias.
We replicated the same paradigm with a few
changes; instead of lines, we used a task involving fictitious college applications, where we could establish
that one candidate was a clearly stronger applicant
for admission than another. What we found is that in
all-white groups, the rate of conformity to the clearly
wrong applicant was about 30% — which was similar to the classic research on conformity. In diverse
groups, however, the frequency with which people
would yield to what they know to be the wrong
answer dropped significantly, to 20% in some experiments, and even lower in others.
What’s interesting about these studies is that we
didn’t allow people to talk with the other people in
the room. What we were looking at is not the effect
of having a discussion or being persuaded by arguments. We were asking: Does simply sitting down
in a room and seeing the demographic makeup of
the people at the table affect people’s propensity to
conform to others’ decisions?
And the answer was that it does — and that people were less likely to conform in diverse groups.
Similar to our suspicions in the stock-pricing experiment, there’s almost this benefit-of-the-doubt effect
that happens in homogeneous groups that we don’t
see in diverse groups; people in homogeneous
groups are more likely to assume that the other people in the group must know something or have
picked up on something that they didn’t. In diverse
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groups, people are more likely to not rely on those
types of assumptions and come to an independent
assessment of what they think to be the case.
I wonder if homogeneous groups make people
feel more comfortable, and they then work less
hard cognitively. Do researchers know what the
mechanisms are that cause these differences in
behavior?
APFELBAUM: What I say is, diversity is not better
or worse — it’s just harder. It’s harder socially, it’s
harder cognitively, and it makes us work. And I think
that’s a useful framework to think about why diversity can be both advantageous and complicated in
the workplace and in decision-making groups.
When we find ourselves in social events, there’s a
natural inclination to gravitate toward people who are
similar to us. It’s easier. It’s easier to find common
ground with people who have similar backgrounds to
us — whether it’s in terms of culture, organizational
expertise, language, or school affiliation. That’s natural. It provides us with a sense of belonging and it’s
easier — and I think that’s OK. Diversity is harder for
the same kind of reasons. It doesn’t allow us to rest on
our laurels, and we are less concerned, in some sense,
with retaining our membership in diverse groups.
I think people just end up being more independent and objective in diverse groups. And that can
go well in the scenarios I just talked about, but it is
also, I believe, at the root of other research that has
shown that diversity can breed conflict and mistrust. Some research, for example, has shown that
even a normal level of team conflict is more quickly
perceived as a really serious type of conflict in the
eyes of managers when the group is racially diverse
as compared to homogeneous. One very recent
study presented participants with an exchange between members of a team. And the researchers just
changed a very, very small component of that exchange — which was whether people believed that
the people involved in this team exchange, which
was designed to be medium-level debate and back
and forth — were a diverse or homogeneous group.
What the researchers found is that people in the
role of managers were much more likely to think that
the diverse teams’ level of conflict was higher, even
though it was exactly the same exchange. And in
turn, the managers were less likely to suggest that
46 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
greater resources be provided to the diverse teams to
assist them with completing future projects. In some
sense, managers were saying, “We can’t invest; this is
an irreparable form of conflict.” So the level of conflict seems to be perceived to be artificially higher in
diverse groups than it is in homogeneous groups.
I think that when it comes to gender, race, and
ethnicity, these are issues in our society that are
fraught and laced with mistrust and uncertainty, so
there’s a lower threshold for people to find evidence
that is consistent with that and either disengage
from their groups, accuse others, or devolve into
unproductive forms of conflict in groups.
Interesting. It sounds like in most of the studies
you’re discussing, the diversity is racial. And
are there similar findings with different types of
diversity, such as gender diversity?
APFELBAUM: I would say race and culture are two
of the most frequent ones that have been explored.
Gender has been explored, and there’s been some
similar experiments there. I think that research now
is really only just beginning to look at, for example,
how race and gender may play out differently. There
is also some research out there that has looked at cognitive diversity — for example, diversity in the way
people think about problems.
You mentioned research about the cost of diversity and the conflicts that can happen, and how
they can quickly get unproductive. Tell me a little
about that research.
APFELBAUM: There is a good amount of research
that’s happened in the past few decades that has
found in real work teams that people in diverse
teams report higher degrees of conflict. They like it
less. They’re less comfortable there. And there are a
number of different studies that have demonstrated
more interpersonal conflict in diverse teams.
Let me tell you about another finding that I think is
pretty interesting. In one paper, researchers looked
at a decision-making task that was cooperative.
Participants in the group had to put together disparate pieces of information — clues — to make a single
recommendation about the correct suspect to arrest.
What the researchers found in this task, as has been
shown in previous research, is that the diverse groups
tended to consider more perspectives, and ultimately
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were more likely than people in the homogeneous
groups to narrow in on the correct suspect.
But another result that came out that was very, very
interesting was that in this study the researchers also
asked: How confident are you that you have identified
the correct suspect? And though the diverse groups
were factually more accurate than the homogeneous
groups, it was actually the homogeneous groups that
were more confident in their results — the exact
opposite of what you would expect.
Now you could think of this as a challenge to
diversity or a limitation to homogeneity. It suggests
that diverse groups with the same results as homogeneous groups can come out of a meeting stating
that they’re less confident that they have achieved
the correct objective or have landed on a workable
solution than a homogeneous group will be. And
what we know about confidence in organizational
settings is that it is reinforced. If you’re managing
two groups and one group comes to you and says,
“We are 95% sure this is going to be on time, under
budget, and workable,” and the other group comes
to you and says, “We’re 75% sure,” in the real world,
nine times out of 10 it’s the group that says that
they’re 95% sure who is going to get the opportunity to run with their project and try to deliver.
And what we’ve seen from this data is that you’re
more likely to hear that 95% story from the homogeneous group — but it’s not because they are more
likely to deliver better results. The homogeneous
groups may just be less accurate. In homogeneous
groups, there seems to be this inflated sense of confidence, in part because of the phenomena unearthed
in the research that I’ve talked about earlier. Those
groups may not be considering all the perspectives.
And there is more of a tendency to narrowly see the
issues in ways consistent with other people’s views
and perhaps less comfort to disagree with others.
So the diverse groups are actually mitigating
overconfidence bias in a way?
APFELBAUM: Yes.
What should managers take away from this research? What advice would you give?
APFELBAUM: The takeaway for me is that the di-
versity needs to be carefully managed. Managers
need to mitigate the concerns about people not
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feeling comfortable in order to harness what can be
some of these real distinct advantages of diversity.
And what about these findings about teams?
Should executives be thinking, if we have a really
complex decision to make, the decision-making
group should be more diverse?
APFELBAUM: Certainly, when you have to make a
large-level organizational change or you’re making
a big decision, people are often involved from many
different functional groups, so everyone can see
each other’s blind spots to some degree.
I think that should sort of be the status quo in organizations. And I think that with a really inclusive
culture, the lack of ease and comfort that people typically associate with diverse groups can be normalized.
If you think back to a lot of the data that we’ve
just gone over, at least a good portion of it says
that, well, in objective ways, homogeneity is the
thing that’s producing the strange results. Think
back to the overconfidence results. Think back to
the amount of inaccuracy.
But how many leaders in organizations do you
know who have thought, “Wow, what can we do
about the problems of homogeneity? Where are the
most homogeneous teams that we have in our organization, and what can we do to make sure that they
are thinking really carefully and there’s some productive conflict?” I don’t know of a single program
anywhere in the world that is focusing on the potential blind spots of homogeneous teams. And I think
that’s just not the narrative, because, in many industries, homogeneous teams are normal in terms of
their frequency.
But even if homogeneous teams are normal in
the sense that they’re common, there’s reason to
question how normal they are in these other ways.
Instead of just looking at the management of
diverse groups as a problem to be solved, it’s useful
to flip that for a second and think of it from the
other side: What can we do about the problematic
aspects of homogeneous teams?
Comment on this article at http://sloanreview.mit
.edu/x/59232.
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Copyright © Massachusetts Institute of Technology, 2018.
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WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 47
SHAPING THE FUTURE.
TOGETHER.
The Boston Consulting Group (BCG) is a global management
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™ŠŽ—Š“™Š—•—Ž˜Š˜ǀ”—’”—ŠŽ“‹”—’†™Ž”“ƽ•‘Š†˜Š›Ž˜Ž™‡ˆŒǀˆ”’ǀ
R E D E S I G N I N G WO R K : T E A M S
The Truth About Hierarchy
Hierarchies are often seen as obstacles to innovation. However, a growing
body of research shows that the right kind of hierarchy can help teams
become better innovators and learners.
BY BRET SANNER AND J. STUART BUNDERSON
THE LEADING
QUESTION
EXPERTS, ACADEMICS, AND experienced innovators frequently espouse the virtues of
eliminating hierarchies to make sure every idea is heard and to unlock innovation.1 As intuitively
appealing as this view is, it does not stand up to scrutiny. In fact, a growing body of research, including studies by one of this article’s authors, shows that the right hierarchy can help teams become
better innovators and learners.2 We have also seen what happens when teams insist upon being flat.
They often become unfocused, tumultuous, and inefficient because their pursuit of perfect equality
prevents the more expert team members from resolving conflicts and playing leadership roles in
group learning and innovation.
Debunking the Myths
FINDINGS
A hierarchy can
help teams generate, identify, and
select new ideas.
Hierarchies can
create ground rules
that enable and
encourage members
to speak up.
If goals and
Research on social species ranging from ants to zebras shows that hierarchies are important for group
functioning.3 When a group has a chain of command, disagreements can be more easily resolved
so that the group can take coordinated action. Coordinated action improves the odds of survival.
Human beings also have a tendency to think and act
hierarchically.4 In fact, hierarchies — distinct differences in group members’ power and status — can
be found in virtually every human group, from
children on the playground to executives in the
boardroom. Depending on the circumstances, hierarchies can be formally designated or emerge
naturally. And while the idea of hierarchies may go
against democratic instincts and beliefs, they can
and do play useful roles.
IDEO, the product design and consulting firm,
offers a useful example. In 1999, ABC News’
“Nightline” chronicled the efforts of an interdisciplinary IDEO team to redesign the supermarket
shopping cart. Since airing, the video has become a
classic example of how innovation works. Initially,
IDEO founder David Kelley expresses strongly
negative views about hierarchy, saying, “In a very
innovative culture, you can’t have a kind of hierarchy.”5 But as the story unfolds, a small group of
senior IDEO people step in to direct how the product development team allocates its time. When
asked why the intervention was necessary, one
DANIEL HERTZBERG/THEISPOT.COM
How can
teams
benefit from
hierarchy?
feedback are
group-oriented,
hierarchy can
promote learning
and performance.
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 49
R E D E S I G N I N G WO R K : T E A M S
senior person explains that the process of finding
creative solutions sometimes needs to be “very
autocratic for a very short period.”
In reviewing the empirical research on the role
of hierarchies in learning and the innovation that
results from learning, and through our own studies, we have found that a properly deployed
hierarchy is an essential ingredient for helping a
team engage in and get the most out of its efforts to
learn and innovate.6 (See “About the Research.”)
The Role of Hierarchy
Hierarchies help teams of people innovate much the
same way they help animals survive in the wild —
they keep teams moving in the same direction even
when strong disagreements threaten to keep the
teams from progressing or even tear them apart.
Specifically, we found that hierarchies help teams
generate, identify, and select new ideas by performing three critical functions (and then getting out of
the way): bounding solutions, converging ideas, and
structuring processes.
Bounding Solutions During idea generation, hierarchies set the parameters and goals of innovation. A
paradox of creativity is that people are more innovative when they have clear constraints (such as time,
budget, customer requirements, etc.) within which
their solutions must fit.7 But teams aren’t very good
at establishing constraints on their own. Team members with influence can accelerate the learning
process by clearly setting the bounds for innovation
and then giving the team wide latitude to explore
within those bounds.
Converging Ideas In the early stages of innovation,
teams come up with a large assortment of ideas and
possibilities. Ultimately, however, some ideas are
more promising than others, in part because they
better line up with the company’s capabilities and
resources.8 Hierarchies can assist here by helping
teams decide which ideas have promise and should
be pursued, which ideas should be put on the back
burner, and which ideas go on the waste pile. As
IDEO’s Kelley noted, innovation in its early phases is
“a messy process.”9 To transition from generating to
refining and implementing ideas, teams need to develop mechanisms for deciding which ideas to hone
in on. This can be easier said than done — different
team members may be emotionally attached to
different ideas. By helping teams converge on a
direction, hierarchies keep teams from getting lost in
aimless exploration.
Structuring Processes Finally, effectively going
through the learning process requires members to
use their specialized knowledge to propose potentially wild ideas and challenge potentially sacred
beliefs. These behaviors are interpersonally risky
in that they open up members to ridicule and social sanctions. As a result, teams must have norms
and processes in place that lower those risks so that
team members are able to engage in the learning
process.10
Hierarchies can actually help here, too, by creating ground rules that enable and encourage
members to speak up. Research has shown that
brainstorming groups struggle without a hierarchy
to provide structure to what can be a haphazard
process.11 To that point, one of us surveyed and
interviewed teams at a Fortune 100 high-tech company and demonstrated that teams with clear
hierarchies did a better job of creating an environment and establishing norms that encouraged each
member to speak up and share what they know.12 In
short, clarity about who is in charge and how each
member contributes can help everyone on a team
feel like they could — and should — engage in the
learning process, and it can give them a structured
way to do so.
Clarity about who is in charge and how each member contributes can help everyone on a team feel like they could — and
should — engage in the learning process, and it can give them
a structured way to do so.
50 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
SLOANREVIEW.MIT.EDU
When those with more power in a group aren’t
needed to help with bounding, converging, or structuring, they need to get out of the way so that the
team can do what teams do best — share, discuss,
and integrate diverse perspectives and knowledge to
come up with new ways of solving problems. In
other words, the best hierarchies are invisible most
of the time, operating in the background and only
coming out of the shadows when power differences
are needed to keep things moving along. Even wellmeaning hierarchies become problematic when
people at the top are too heavy-handed and interfere
when their interference isn’t needed.
Making Hierarchies Work
People are suspicious of hierarchies for a reason —
they sometimes stifle good ideas and the learning
process that leads to good ideas. For example, dysfunctional hierarchies have been blamed for long
periods of stagnation that companies such as
General Motors Co. experienced.13
So, how can organizations foster learning and
innovation? Here are three things leaders can do to
leverage the power of hierarchy on teams yet avoid
its pitfalls.
Have a clear chain of command. With other researchers, one of us recently surveyed teams at more
than 50 organizations in order to understand how
the shape of a team’s hierarchy affects conflicts and
performance.14 The study found that hierarchies
work best when there is no confusion about who defers to whom. Teams with a clear chain of command
(clarity and agreement about who defers to whom)
were less likely to get bogged down in conflicts and
stalemates than teams where influence was more cyclical. Indeed, a study of 62 pharmaceutical research
and development teams found that teams with a
clear hierarchy were more involved in the learning
process that is central to innovating.15
Create a performance-based culture. A clear
chain of command means that some team members will defer to others who are “higher up” in
terms of status or respect. Many of us know what
it is like to be in a situation where incompetent
people are running the show. So, how can teams
improve the chance that members with the most
relevant knowledge are higher up in the hierarchy?
The key is getting teams to identify the members
SLOANREVIEW.MIT.EDU
ABOUT THE RESEARCH
Our research was motivated by what we saw as a disconnect between the
rhetoric and the reality in management theory and practice concerning hierarchy.
The view that hierarchies are the enemy of new ideas seemed misguided in light
of past research suggesting that hierarchies are unavoidable in groups and that
they actually serve important functions. We decided to take a closer look at hierarchy and its implications for learning and innovation within groups. The article
summarizes findings from four of J. Stuart Bunderson’s studies examining teams
in a variety of organizations and industries. It was also informed by some of our
other research and additional research and findings from other scholars doing
work in this area. Our goal was to provide an overview so that managers and
consultants can question popular negative assumptions about hierarchies.
who possess real knowledge. This is often easier
said than done, in part because we tend to have
implicit biases about the characteristics or backgrounds that signal expertise. For example, a study
at a high-technology Fortune 100 company found
that, not surprisingly, teams perform better when
their more expert members rank higher in the
team’s hierarchy. That study also found, however,
that teams often pay attention to the wrong things
as they sort out who will have more or less influence — things like gender or ethnicity rather than
training, expertise, and experience.16 Because these
biases operate below our conscious awareness, they
are difficult to correct unless more accurate expertise signals are emphasized.
One way to counter the biases is to create a performance-based culture, where performance is
measured, publicized, and celebrated. Studies suggest that when people have opportunities to
demonstrate what they can do, experts tend to rise
to the top. 17 Moreover, when credible evidence
shows that less vocal members are better qualified
to make informed decisions, groups will limit the
influence of bombastic pseudo-experts.18 In other
words, groups listen to experts when they can identify who the experts are. Group hierarchies in
performance-based cultures are more likely to be
based on expertise and less likely to be based on
physical characteristics.
Use team feedback. Another way to improve the
way hierarchies function is to encourage those at
the top to act in ways that support the group rather
than acting in their own best interest. How do you
make sure this happens? One of the authors worked
with a group of researchers from the Netherlands
to examine this question in 46 teams from a wide
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 51
R E D E S I G N I N G WO R K : T E A M S
range of industries including banking, medicine,
software development, and management consulting. 19 The study found that, contrary to what
previous research had shown,20 power differences
within teams did not necessarily hurt team learning.
In fact, hierarchies promoted learning and performance when goals and feedback were group-oriented,
but they stifled learning and performance when goals
and feedback were individually oriented.
Group goals and feedback encourage higherups to use their advantaged position to encourage
members to collaborate through information sharing, experimentation, and reflection. Individual
goals and feedback keep people focused on their
own tasks and outcomes.
Bret Sanner is an assistant professor of management at Shenandoah University in Winchester,
Virginia. J. Stuart Bunderson is the George and
Carol Bauer Professor of Organizational Ethics and
Governance and codirector of the Bauer Leadership
Center at Olin Business School at Washington
University in St. Louis. Comment on this article at
http://sloanreview.mit.edu/x/59217.
REFERENCES
1. A.C. Edmondson, R.M.J. Bohmer, and Gary P. Pisano,
“Speeding Up Team Learning,” Harvard Business Review
79, no. 9 (October 2001): 125-134; and T. Kastelle, “Hierarchy Is Overrated,” Harvard Business Review, Nov. 20,
2013, https://hbr.org.
2. J.S. Bunderson and B. Sanner, “How and When
Can Social Hierarchy Promote Learning in Groups?”
in “Oxford Handbook of Group and Organizational
Learning,” eds. L. Argote and J.M. Levine (New York:
Oxford University Press, in press).
3. M.M. Moosa and S.M.M. Ud-Dean, “The Role of
Dominance Hierarchy in the Evolution of Social Species,”
Journal for the Theory of Social Behaviour 41, no. 2
(January 2011): 203-208.
4. E.M. Zitek and L.Z. Tiedens, “The Fluency of Social
Hierarchy: The Ease With Which Hierarchical Relationships Are Seen, Remembered, Learned, and Liked,”
Journal of Personality and Social Psychology 102, no. 1
(January 2012): 98-115.
5. ABC News, “The Deep Dive,” July 13, 1999,
www.youtube.com.
6. Bunderson and Sanner, “How and When Can Social
Hierarchy Promote Learning in Groups?”
7. B.A. Hennessey and T.M. Amabile, “Creativity,” Annual
Review of Psychology 61, no. 1 (January 2010): 569-598.
8. J. Barney, “Firm Resources and Sustained Competitive
Advantage,” Journal of Management 17, no. 1 (March
1991): 99-120.
52 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
9. ABC News, “The Deep Dive.”
10. B. Sanner and J.S. Bunderson, “When Feeling
Safe Isn’t Enough: Contextualizing Models of Safety
and Learning in Teams,” Organizational Psychology
Review 5, no. 3 (August 2015): 224-243.
11. N.L. Oxley, M.T. Dzindolet, and P.B. Paulus,
“The Effects of Facilitators on the Performance of
Brainstorming Groups,” Journal of Social Behavior
and Personality 11, no. 4: (1996): 633-646.
12. J.S. Bunderson and P. Boumgarden, “Structure and
Learning in Self-Managed Teams: Why ‘Bureaucratic’
Teams Can Be Better Learners,” Organization Science
21, no. 3 (December 2009): 609-624.
13. S. Helper and R. Henderson, “Management Practices, Relational Contracts, and the Decline of General
Motors,” Journal of Economic Perspectives 28, no. 1
(winter 2014): 49-72.
14. J.S. Bunderson, G.S. van der Vegt, Y. Cantimur,
and F. Rink, “Different Views of Hierarchy and Why
They Matter: Hierarchy as Inequality or as Cascading
Influence,” Academy of Management Journal 59, no. 4
(August 2016): 1265-1289.
15. H. Bresman and M. Zellmer-Bruhn, “The Structural
Context of Team Learning: Effects of Organizational
and Team Structure on Internal and External Learning,”
Organization Science 24, no. 4 (July-August 2013):
1120-1139.
16. J.S. Bunderson, “Recognizing and Utilizing Expertise
in Work Groups: A Status Characteristics Perspective,”
Administrative Science Quarterly 48, no. 4 (December
2003): 557-591.
17. C. Anderson and G.J. Kilduff, “The Pursuit of
Status in Social Groups,” Current Directions in
Psychological Science 18, no. 5 (October 2009):
295-298.
18. C. Anderson, D.R. Ames, and S.D. Gosling, “Punishing Hubris: The Perils of Overestimating One’s Status in a
Group,” Personality and Social Psychology Bulletin 34,
no. 1 (January 2008): 90-101.
19. G.S. van der Vegt, B. de Jong, J.S. Bunderson,
and E. Molleman, “Power Asymmetry and Learning
in Teams: The Moderating Role of Performance Feedback,” Organization Science 21, no. 2 (March-April
2010): 347-361.
20. C. Anderson and C.E. Brown, “The Functions
and Dysfunctions of Hierarchy,” Research in Organizational Behavior 30, no. 10 (2010): 55-89; A.K. Brooks,
“Power and the Production of Knowledge: Collective
Team Learning in Work Organizations,” Human Resource Development Quarterly 5, no. 3 (fall 1994):
213-235; and E.G. Foldy, P. Rivard, and T.R. Buckley,
“Power, Safety, and Learning in Racially Diverse
Groups,” Academy of Management Learning &
Education 8, no. 1 (March 2009): 25-41.
Reprint 59217. For ordering information, see page 4.
Copyright © Massachusetts Institute of Technology, 2018.
All rights reserved.
SLOANREVIEW.MIT.EDU
O P E R AT I O N S
Winning With Open
Process Innovation
When manufacturers develop a process innovation,
they frequently seek to keep it under wraps. But that’s
often not the best approach.
THE LEADING
QUESTION
How can
companies
apply open
innovation to
processes?
FINDINGS
BY GEORG VON KROGH, TORBJØRN NETLAND, AND MARTIN WÖRTER
Many operations
managers can build
greater advantage
through openness
than through
secrecy.
MOST RESEARCH ON open innovation has focused on the use of ideas and knowledge from
outside the organization in the development of products and services. But openness can be useful for
process innovation, too. Our research shows that manufacturers can benefit substantially when they
look for ideas beyond the factory gates, especially when their operations are already advanced.
We often meet managers in manufacturing companies who keep process innovation activities
tightly under wraps. Some see their processes as a source
of competitive advantage that should not be shared with
anyone. Others consider them organizational knowledge
that could be detrimental to expose to outsiders.
Some companies have good reasons for keeping process innovations concealed. For example, a combination
of process and product innovation often jointly results in
competitive advantage for a company. If you have found a
unique production process with which to manufacture a
differentiated product — for example, a new metal alloy
or a medicine — it can be wise to keep that know-how
within the company. In such cases, there is an obvious
risk of loss of intellectual property.
However, our research suggests that for many manufacturers, such defensiveness deprives companies of a valuable
source of ideas for productivity improvement. We draw our
conclusions from an analysis of nine years of survey responses from 1,000 Swiss manufacturers, as well as 200
interviews with personnel at the Volvo Group (AB Volvo), a
manufacturer of trucks, buses, construction equipment, and
marine and industrial engines that is based in Gothenburg,
Sweden.1 One of the authors also visited 45 Volvo Group factories around the world. (See “About the Research,” p. 54.)
Even for an industry leader, walling off process innovations from the outside world can be a losing strategy,
because sooner or later, competitors usually catch up.2
As counterintuitive as it may seem, our research suggests
CARL WIENS/THEISPOT.COM
Focus on increasing
the pace of process
innovation.
Build your organiza
tion’s capacity to
absorb ideas from
the outside.
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 53
O P E R AT I O N S
ABOUT THE
RESEARCH
Two of the authors were
part of a team that used nine
years of panel data to study
the open process innovation
activities of Swiss manufacturers. The study tested
how search involving external knowledge sources
(such as customers, suppliers, universities, research
institutes, and competitors)
and the use of information
technology to boost internal
knowledge absorption affect process innovation
performance. Data was
drawn from three waves of
the Swiss Innovation Survey
(2005, 2008, and 2011), collected by the KOF Swiss
Economic Institute. The
surveys are based on a stratified random sample of
companies known as the
KOF enterprise panel. Each
wave contains over 1,000
responses from Swiss manufacturing industries. The
details of our research report were published in the
academic journal Management Information Systems
Quarterly in 2017.i
We complemented this
research with qualitative
data from the global process
innovation activities of a
leading equipment manufacturer, the Volvo Group (AB
Volvo). The Volvo Group
produces trucks, buses,
construction equipment, engines, and powertrains in 18
countries worldwide. One of
the authors studied and visited 45 of the Volvo Group’s
factories across five continents, conducted 200
interviews with factory and
headquarters personnel, and
collected longitudinal plantlevel operational data related
to process innovation.ii We
used this deep insight into
the Volvo Group’s factory
network to discuss and exemplify how practices of
open process innovation can
boost a company’s process
innovation capabilities.
that most operations managers can build greater advantage for their company by following a policy of
open process innovation rather than secrecy. However, evolving from a closed culture to an open one is
not easy, and it generally requires taking six big steps.
1. Open up internally. Most large global manufacturers encourage their factories to share
innovative practices and success stories with one
another. The best ideas that emerge from this sharing become part of the overall corporate program.
Empirical evidence shows that sharing process
ideas has a profoundly positive effect on operational performance.3 Companies that already do
this informally can extend the process improvement activities with a systematic effort inside their
factory networks. In this way, they gain some of the
advantages of open innovation without the risk —
while laying the groundwork for other open
information sharing about processes.
This tends to work well. Because the factories
belong to the same “family,” their operations and
contexts are usually comparable. This means the
hurdles for implementing novel ideas are often
lower than when technology or knowledge stem
from outside the network. Through open process
innovation within the company, the factories lift
the productivity bar together.
For approximately 10 years, the Volvo Group
has worked intensively to share process innovation
practices among its manufacturing sites. One goal
is to raise all truck factories in the network to a defined “gold standard” by 2018. One initiative is a
corporate process innovation program that collects
best practices from factories in a global database
accessible through the Volvo Group’s intranet. Another initiative is a global online knowledge-sharing
conference that brings together about 200 to 300
attendees from across the company’s operations.
Held about 10 times a year, the conference is scheduled in the morning according to the U.S. Eastern
time zone so that the majority of factories located
in the U.S., Europe, South Africa, and East Asia can
participate. The conference slogan captures the
idea behind intracompany open process innovation: “Everyone has something to teach; everyone
has something to learn.”
2. Focus on the pace of process innovation. We
find that many managers tend to overrate the quality
54 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
of their company’s process innovation. The truth is
that not everybody can be above average. Even in the
exceptional case where a factory’s processes are indeed state of the art, hiding them can usually fend off
competition for a limited time only. The only way to
know how advanced your practices actually are is to
compare them with someone else’s practices.
A more sustainable way to create competitive advantage in the manufacturing industry is not to keep
your manufacturing excellence off the radar screen but
to be faster than your competitors at process innovation. In Lyon, France, for instance, Renault Trucks, a
subsidiary of the Volvo Group, has a state-of-the-art
engine factory. In a central, highly visible part of the
factory, quality rejects are put on display. Anybody
who visits the factory — employees, customers, suppliers, sister plant managers, collaborating researchers,
or others — can immediately see whether the factory
has unresolved quality issues. Such exposure motivates
factory managers and employees alike to speed up
problem-solving and idea generation, as a way to keep
the rejects out of sight. The “open” strategy increases
the creativity, motivation, and, most importantly, the
pace of process innovation at the plant. This gentle
nudge provided by openness has helped Lyon become
one of the Volvo Group’s flagship factories for process
innovation, motivating the factory to strive always
to be the best possible version of itself.
3. Exploit connectivity technologies. Our
research found that data access systems help companies capture process innovations from the outside
and spread them internally. Many business systems
come with preinstalled “production know-how”
that vendors have already integrated from their experiences across a multitude of customers. Although
off-the-shelf solutions never guarantee that operations will improve, our findings show that increased
use of data access systems leads to greater production cost reductions as employees adopt process
innovations recommended by the software.
Customer relationship management, supplier relationship management, supply chain management,
and enterprise resource planning (ERP) software
systems all require codification of tacit knowledge,
which makes it easier to understand and transfer a
process. This enhances a company’s capacity to
spread external process ideas and technology to the
people who need it.
SLOANREVIEW.MIT.EDU
A medium-size Volvo Group remanufacturing
factory for engines and transmission boxes in North
Carolina offers an interesting case in point. Until a
few years ago, the factory was digitally disconnected
from the rest of the Volvo Group’s dispersed remanufacturing factories. Remanufacturing operations
tend to incorporate a lot of tacit know-how, and the
factory had previously not seen any particular need
for what managers described as “static software to
plan its highly dynamic business.” In spite of the
managers’ opposition, Volvo Group headquarters
mandated that the factory implement the same ERP
suite as other remanufacturing plants within the
group. Since then, the new business software implementation forced the factory managers to think
harder about their current practices and learn about
new best practices from other units in the group.
Although such global ERP implementations can
be difficult and expensive, they offer many benefits
to users. A process innovation elsewhere in the factory network — which can be codified as an ERP
parameter or a new planning procedure — can be
shared across all the network’s factories quickly.
4. Improve your organization’s ability to absorb and implement ideas from external sources.
To make innovations matter for production cost
reduction, factories must strengthen their ability to
make learning from the outside stick — something
scholars call an organization’s absorptive capacity.4
Absorptive capacity starts with a deep belief that
there are important lessons to be learned from others.
In addition, factory management must establish
routines for gathering ideas from external sources
and putting them to use. A good way to do this is to
specify and codify the existing knowledge in a set of
standards. While standards should never be taken as
generally valid across all areas of a company, they
make it easier for people to use past learning and
help focus improvement efforts. The use of standards and regular standards revision meetings are
practical ways to build absorptive capacity, particularly when those standards can be shared online with
the entire plant and any sister factories.
A Volvo Group powertrain plant in the Kantǀ region of Japan offers an excellent example of what strong
absorptive capacity can do for process innovation. For
decades, the plant’s managers benchmarked their
operations against others in Japan and incorporated
SLOANREVIEW.MIT.EDU
practices that they found better than their own.
After many years of systematic internalization of external best practices, the factory found itself at the
“performance frontier.” Seeking new inspiration,
the factory teamed up with Volvo Group headquarters to access the group’s powertrain R&D
departments in Sweden and external technology
partners. Today, managers have tried to combine
the best of Japanese kaizen culture with the latest
engine assembly technology from abroad — and
they’re not done yet: The managers report that
leveraging their proficiency in absorptive capacity
helps them stay at the forefront of competition.
5. Open up to the outside. It is not surprising that
factories that lag in operational performance tend to
improve when they participate in open process innovation. The benefits for the best-in-class factories
are not so obvious, but they are real. Cutting-edge
factories can attain deeper expertise by teaching others, but they often need to search outside their factory
network for new inspiration. Our research indicates
that the deeper a company searches for a source of
external knowledge (for example, understanding a
novel casting technology researched at a university),
the greater the cost reduction it will experience,
whether or not it is a leader in its industry.
In fact, under normal circumstances, the better
you get, the more you can gain by opening up. In a
Volvo Group truck assembly plant in Virginia, the
management team decided to move their customer
fairs from exotic locations to the factory site. This
turned out to be wildly successful: During the fairs,
old and new customers would ask blue-collar operators questions directly on the line. The customers
received passionate answers from skilled people who
were not trying to sell anything and just wanted to
convey their expertise. At the same time, operators
learned firsthand what customers really wanted from
Volvo trucks. Opening up to the outside paid off in
terms of both higher sales and increased productivity.
6. Utilize unconventional sources of knowledge.
Art Fry, who co-invented the Post-it note at 3M Co.,
has proposed that creativity is “a numbers game”5:
The more ideas you have, the more good ones you
find. Innovation fairs, internal contests, conferences
and exhibitions in other industries, and joint projects with research institutions and universities are
all good sources of fresh ideas that enable managers
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 55
O P E R AT I O N S
to step back and think outside the box. Provocative
ideas from nontraditional sources of knowledge
may spark process innovation and help overcome
difficult problems.6
One good example is from a truck plant in
Pennsylvania. Operators had identified a safety
hazard when technicians worked on top of the cab
to do final installations. Searching for ideas from
outside the organization, they came up with a tailored bungee jump cord that safeguarded the
technicians without limiting their mobility. The
role of unconventional sources of ideas at the Volvo
Group resonates with other iconic examples from
the manufacturing industry. For example, Toyota’s
Taiichi Ohno took his inspiration from American
supermarkets when designing and introducing justin-time parts delivery to Toyota’s assembly lines after
World War II.7 Another example is the way that
GlaxoSmithKline plc learned to minimize downtime from McLaren Honda, a British Formula 1
automotive racing team company that shared its expertise about pit stop operations.8
How to Get Started
Open product innovation is already a well-known
strategy. We think open process innovation is a
logical extension. As product life cycles continue
to decrease and demand for individualization
increases, companies that master the combination
of superior product and process development will
be better positioned.
Ultimately — and ironically — the success of a
program of operational openness will depend most
of all on how well a company knows itself. Managers
will need to ask: What part of our product innovation would benefit most from the search for external
knowledge? What part of our process innovation
could benefit most? Where should we combine the
search for product and process knowledge? Given
our strengths and weaknesses, from whom would it
be most beneficial for us to learn? What can we offer
them in terms of product and process know-how in
return for what they can teach us?
As with many organizational changes, open
innovation is best begun gradually. We do not recommend switching from closed to open process
innovation in a day. However, that is not to say that
companies should not start now. The bulk of our
56 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
research persuades us that the businesses that win
in the future will be those that master both the process and product sides of open innovation.
Georg von Krogh is a professor and Chair of Strategic Management and Innovation in the department
of management, technology, and economics at ETH
Zurich in Switzerland. Torbjørn Netland (@tnetland)
is an assistant professor and Chair of Production
and Operations Management in the same department. Martin Wörter is a senior researcher at ETH
Zurich’s KOF Swiss Economic Institute. Comment on
this article at http://sloanreview.mit.edu/x/59220.
REFERENCES
1. Note that AB Volvo, the Volvo Group, is not the manufacturer of Volvo cars. Volvo Car Group is owned by the
Chinese company Zhejiang Geely Holding Group Co. Ltd.
2. E. v. Hippel and G. v. Krogh, “Open Source Software
and the ‘Private-Collective’ Innovation Model: Issues for
Organization Science,” Organization Science 14, no. 2
(March-April 2003): 209-223.
3. T. Netland and K. Ferdows, “What to Expect From a
Corporate Lean Program,” MIT Sloan Management
Review 55, no. 3 (summer 2014): 83-89; and T.H. Netland
and K. Ferdows, “The S-Curve Effect of Lean Implementation,” Production and Operations Management 25, no. 6
(June 2016): 1106-1120; and G. Szulanski and R.J. Jensen,
“Presumptive Adaptation and the Effectiveness of
Knowledge Transfer,” Strategic Management Journal 27,
no.10 (October 2006): 937-957.
4. S.A. Zahra and G. George, “Absorptive Capacity: A
Review, Reconceptualization, and Extension,” Academy
of Management Review 27, no. 2 (April 2002): 185-203.
5. B. Hindo, “At 3M, a Struggle Between Efficiency
and Creativity,” BusinessWeek, June 11, 2007,
www.bloomberg.com.
6. E. v. Hippel and G. v. Krogh, “Identifying Viable
‘Need–Solution Pairs’: Problem Solving Without
Problem Formulation,” Organization Science 27,
no. 1 (January-February 2016): 207-221.
7. See “Taiichi Ohno,” The Economist, www.economist
.com, July 3, 2009; and T. Ohno, “Workplace Management” (Portland, Oregon: Productivity Press, 1988).
8. See, for example, McLaren Technology Group,
“Case Study: GSK,” May 15, 2014, www.mclaren.com.
i. K. Trantopoulos, G. v. Krogh, M.W. Wallin, and
M. Woerter, “External Knowledge and Information
Technology: Implications for Process Innovation
Performance,” Management Information Systems
Quarterly 41, no. 1 (March 2017): 287-300.
ii. Netland and Ferdows, “What to Expect From a
Corporate Lean Program”; and Netland and Ferdows,
“The S-Curve Effect of Lean Implementation.”
Reprint 59220. For ordering information, see page 4.
Copyright © Massachusetts Institute of Technology, 2018.
All rights reserved.
SLOANREVIEW.MIT.EDU
ACCOUNTING
The Pitfalls of
Non-GAAP Metrics
Lurking within the financial statements and communications of
public companies is a troubling trend. Alternative metrics, once
used sparingly, have become increasingly ubiquitous and more
detached from reality.
BY H. DAVID SHERMAN AND S. DAVID YOUNG
THE LEADING
QUESTION
Should companies use
non-GAAP
measures
to report
performance?
FINDINGS
An increasing num
ber of companies
are using alternative
metrics.
At times, nonstan
IN 2011, Groupon Inc. announced plans for a highly anticipated initial public offering. But
enthusiasm for the offering waned when the U.S. Securities and Exchange Commission (SEC)
issued a comment letter questioning Groupon’s use of a profit metric it called “adjusted consolidated segment operating income.” To our knowledge, no company had ever used that metric before;
it was intended to measure operating profit without including marketing expenses, stock-based
compensation, and acquisition-related costs. Management argued that a $420 million loss
from operations reported on its 2010 income statement should really be
considered a $60 million gain.
For decades, companies have used custom metrics that don’t
conform to generally accepted accounting principles (GAAP)
or international financial reporting standards (IFRS) as supplements to their official financial statements. (Although we
use the term “non-GAAP” throughout this article, the arguments we make here apply equally to companies that report
under IFRS and the related disclosure of “non-IFRS
measures.”) Some common non-GAAP measures include free cash flow, funds from operations, adjusted
revenues, adjusted earnings, adjusted earnings per
share, adjusted earnings before interest, taxes, depreciation, and amortization (known as adjusted
EBITDA), and net debt.
However, it’s not unusual for the alternative measures to lead to problems. Since companies devise
their own methods of calculation, there’s no way to
compare the metrics from company to company —
or, in many cases, even from year to year within the
same company. Lately, in the course of examining the
financial statements and communications of thousands of public companies in at least a dozen countries,
we have seen a troubling trend. Alternative measures,
once used fairly sparingly and shared mostly with a small
ROBERT NEUBECKER/THEISPOT.COM
dard metrics can
add a useful
perspective.
However, non-GAAP
measures can mislead investors and
obscure a company’s
financial health.
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 57
ACCOUNTING
ABOUT THE
RESEARCH
This article reflects our continued focus and research
on developing ways to
improve the quality of
corporate reporting to shareholders and to enhance the
understandability and relevance of financial reports.
Since 2000, we have
worked together, individually
scanning corporate reports
in the U.S. and world markets, and have interviewed
numerous CFOs, board
members, audit committee
chairs, auditors, consultants,
and regulators. Since 2005,
we have interviewed auditors and CFOs as they
adapted to requirements of
GAAP and IFRS accounting.
Initially, our work was designed to help investors in
analyzing corporate financial
statements.i Our analysis
and conclusions are also
based on reviewing corporate reports, press releases,
earnings calls, and other
corporate shareholder
disclosures, including correspondence with the SEC.
For many years, both individually and together, we have
tracked the management
press to understand how
corporate reports are interpreted and misinterpreted.
We have seen how the information prepared for reports
to shareholders can be
misinterpreted and even
misused to present unsupportable conclusions about
corporate financial performance. Our goal is to
improve the state of shareholder reporting globally.
group of professional investors, have become more
ubiquitous and further and further disconnected
from reality. (See “About the Research.”)
The proliferation of alternative metrics not only
poses a problem for investors, but it can also harm the
companies themselves by obscuring their financial
health, overstating their growth prospects beyond
what standard GAAP measures would support, and
rewarding executives beyond what can be justified.
Board members, top executives, compliance officers,
and corporate strategists need to make sure that whatever alternative measures companies use improve
transparency and reduce bias in financial reports.
The Rise of Alternative Financials
Among large companies, alternative financial metrics have become increasingly common. In 2013,
McKinsey & Co. found that all of the 25 largest
U.S.-based nonfinancial companies reported some
form of non-GAAP earnings.1 A survey by PricewaterhouseCoopers similarly found that 95 of the
Financial Times Stock Exchange 100 Index companies used some form of non-GAAP measure. 2 In
2016, Hans Hoogervorst, chairman of the International Accounting Standards Board (IASB),
reported in an address to the European Accounting
Association that more than 88% of the companies
making up the S&P 500 included non-GAAP metrics in their earnings releases. What’s more,
Hoogervorst added, fully 82% of those earnings releases that used non-GAAP metrics showed higher
profits than they would have with GAAP-based
measures and were “clearly designed to present results in a more favorable light.”3
To be sure, there remain some contexts in which alternative financials don’t appear. In the United States,
for example, they are generally not included in annual
Form 10-K reports or quarterly Form 10-Q filings,
which are subject to oversight by the SEC; for those
documents, the SEC requires that non-GAAP metrics
be reconciled with their closest GAAP equivalent.
However, alternative financials are becoming more the
norm than the exception elsewhere. Press releases and
earnings-call summaries frequently present nonGAAP measures that are increasingly detached from
their GAAP-based equivalents. What’s more, reports by
third parties that are derived from SEC filings (for example, analysts’ reports and articles in the business
58 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
press) can — and often do — use non-GAAP measures without reference to GAAP earnings. Recent
articles about Groupon, Yahoo, LinkedIn and other
companies, for instance, do not mention GAAP losses
but do point to non-GAAP profits.4 In many settings,
non-GAAP measures have taken on a life of their own.
Separating Signals From Noise
In our view, the least problematic and most justifiable unofficial metrics are those that offer important
information that official GAAP metrics don’t provide, such as sales figures. Officially, for example,
McDonald’s Corp. is expected to report sales only for
its company-owned McDonald’s restaurants and
should not include numbers from units owned by
franchisees in its consolidated accounts. However, in
the belief that some stakeholders will be interested in
the combined numbers, the company reports “system-wide sales” in its annual report, which includes
sales from both company-owned and franchiseeowned locations. For their part, both Wal-Mart
Stores Inc. and Starbucks Corp. track same-store
sales and sales growth relative to previous years in
order to separate out growth in the underlying business from that created by adding new stores. These
are the types of supplementary disclosures that can
enhance people’s understanding of a business.
More problematic are custom metrics that suggest an alternate way of counting things that official
figures already measure. In many cases, they present an alternative view of earnings by leaving out
one or more expenses required by GAAP.
Although alternative financials can provide a
useful perspective on a company’s ability to deliver
sustainable or repeatable earnings, it is not always
clear whether the company’s rationale for using
non-GAAP measures is to help people understand
the business better or merely to improve the way
the business is perceived. PepsiCo Inc.’s 2015 annual
report offers a good example: The report included a
three-page reconciliation of GAAP and non-GAAP
information. Even so, some of the explanations
were not entirely convincing.
PepsiCo’s net revenues for 2015 were $63
billion, 5.4% lower than the previous year, and
some of the company’s profit measures also
declined. The revenue decline was at least partly
attributable to the strength of the dollar in 2015,
SLOANREVIEW.MIT.EDU
which prompted management to argue that “organic revenue growth” — a non-GAAP number that
PepsiCo calculates from revenue growth adjusted
for, among other things, the effects of foreign
currencies and the impacts of acquisitions and
divestitures — went up, not down.5 The argument
might have been stronger had it not been for PepsiCo’s explanations in previous years, which took an
entirely different view on adjustments for foreign exchange fluctuations and the impacts of acquisitions
and divestitures. In her 2007 letter to shareholders,
for example, PepsiCo CEO Indra Nooyi pointed to
the company’s 12% sales growth based on official
GAAP figures. However, that 12% included two percentage points of net revenue growth attributable to
foreign exchange effects resulting from a weak U.S.
dollar and three percentage points of growth from
acquisitions.6 In other words, PepsiCo’s non-GAAP
“organic” revenues increased by only 7% rather than
12% in 2007. In short, the company’s communications focused on GAAP revenues (in 2007) or
non-GAAP revenues (in 2015), depending on which
figure sent the more favorable message.
Similarly, companies have been known to adjust
their reported earnings to exclude mainstream expenses such as pension costs, regulatory fines,
“rebranding” expenses, fees paid to directors, executive bonuses, and severance payments.7 As companies
have become more brazen, the justifications for using
alternative metrics can be more elaborate, as reflected
in the following examples involving stock grants,
nonrecurring expenses, and unwelcome news.
Stock Grants Between January 2013 and December
2015, LinkedIn reported net operating losses of $180
million in its official income statements. However,
management wanted the analyst community to
focus on different numbers. So, rather than presenting its losses based on GAAP, the company reported
an “adjusted EBITDA” for 2014 and 2015 of $1.37
billion. How? By removing depreciation and amortization charges, and removing the cost of stock-based
compensation.
LinkedIn isn’t alone in attempting to separate the
effects of stock-based compensation from its financial performance reports. Twitter Inc. has used a
similar approach. From 2014 through 2015, Twitter
issued shares to employees valued at more than 120%
SLOANREVIEW.MIT.EDU
of operating income. By excluding this expense from
its earnings calculations, it was able to transform a
negative earnings report into positive “pro forma”
earnings. Although proponents of this approach concede that stock and option grants have dilutive effects
on the company’s other shareholders, they argue that
adding such costs back into earnings provides a more
accurate picture of what’s happening to the company.
Since stock grants are noncash expenses, they argue
that the grants are not “real” money.
There are at least two problems with this view. First,
although stock-based compensation does not involve
a direct cash payment, it does involve an outlay of the
company’s shares. Distributing shares to employees
means that shareholders will own less of the company.
The shares in question could have been sold at the
market price, with the proceeds going to the company
and, by extension, the existing shareholders. Second,
earnings are not supposed to reflect cash flow; rather,
they are meant to reflect the company’s performance
(with accruals included). Accrual accounting is supposed to show expenses in the period when the related
revenues are booked and not necessarily when the cash
outflow occurs; we already have a statement of cash
flows for that. By excluding noncash obligations, companies fail to tell the story earnings should be telling.
Recent studies support the view that non-GAAP
earnings measures that exclude stock-based compensation have less predictive power for future earnings
than metrics that don’t exclude that expense.8
Nonrecurring Expenses Companies often seek to
explain away poor performance by suggesting that
particular expenses were “one-time events” that
shouldn’t be counted. Restructuring expenses, new
product development expenses, and acquisition
costs are some of the expenses most often listed as
nonrecurring. But when a company frequently acquires other businesses, it doesn’t make sense that
the expenses associated with making acquisitions
are not factored into the equation.
If a company acquires intangible assets that produce revenues, there are costs associated with
producing those revenues. Admittedly, the rules
governing how those costs are to be amortized may
not be perfect. But calculating the earnings by
counting the revenues flowing from the assets while
ignoring the costs does not seem logical.
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 59
ACCOUNTING
Unwelcome News Corporate managers prefer to
report good news and downplay bad news. This tendency is frequently reflected in non-GAAP financial
measures. In the energy sector, for example, oil reserves are carried as assets based on world oil prices.
When oil prices fall, the underlying value of the reserves will decline; oil companies are then required to
reduce the carrying value on the balance sheet to the
reserves’ current market value. In accounting circles,
this is known as an impairment and reflects a real cost
to the company that is included as an expense in its
income statements. But some energy companies have
in the past few years adjusted their non-GAAP earnings so that they didn’t reflect the asset impairment
charges brought on by a decline in oil prices — in effect, attempting to dismiss this bad news.9 Does it
make sense to exclude such asset impairment charges
from earnings without making the opposite adjustment for the earnings windfalls that come from oil
price increases? We think not.
This practice of minimizing bad news is not just
limited to energy companies. The Wall Street Journal
reported that 40 companies with initial public offerings
in 2014 showed losses under GAAP but profits using
non-GAAP measures.10 Or consider Vonage Holdings
Corp., an internet telephony service provider based in
Holmdel, New Jersey. In its 2012 financial disclosures,
Vonage talked about “pre-marketing operating
income,” which went so far as to exclude certain marketing costs, customer equipment, shipping costs, and
direct costs of goods sold from net income. At the very
least, such practices may cause shareholders to wonder
what the companies are trying to show.
The Trouble With Alternative Metrics
The potential risks and difficulties of non-GAAP
measures have implications for both investors and
companies. Based on our research, we identified
several challenges.
Difficulties in Comparing Performance NonGAAP measures can muddy comparisons between
companies involving performance. For example,
there is a running debate about whether companies
should include the cost of stock grants in their
earnings calculations or whether they should remove the cost. One argument for removing the cost
is that it’s a noncash expense. The counterargument
60 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
is that stock options have a cost that should be considered as an expense under GAAP. Microsoft Corp.
and many other technology companies report nonGAAP earnings that do adjust for the cost of
stock-based compensation. At a conference in
December 2016, Frank Brod, Microsoft’s corporate
vice president, finance and administration, and
chief accounting officer, explained that Microsoft
viewed stock options not only as something valued
by employees but also as a transaction that diluted
the ownership of other shareholders and, therefore,
should be included as an expense.11
The question of how to treat stock options is often
academic. But it became more real for Microsoft
when it acquired LinkedIn at the end of 2016.12
Following the acquisition, LinkedIn earnings were
no longer recast to exclude the value of the stock options, so it became difficult to compare LinkedIn’s
performance pre- and post-acquisition. Since the
way non-GAAP performance measures are calculated can change from year to year, executives, board
members, and internal strategists should be prepared to use several sources to form a meaningful
picture of the company’s performance and make
comparisons to competitors.
Risks in Setting Executive Compensation Most
executive remuneration packages are based on adjusted earnings. Knowing that even GAAP numbers
are subject to earnings management, paying bonuses based on measures selected by executives
themselves can be extremely risky for compensation committees. To appreciate the risk, consider
the case of BP PLC, the global energy company. In
2015, BP CEO Bob Dudley received compensation
of $20 million, an increase from $16.4 million the
previous year, despite the fact that the company
reported its worst loss ever.13 Shareholders were
dismayed and expressed their disapproval by voting down the company’s report on director pay, but
the shareholder resolution was nonbinding.
Implications for Stock Price A vivid example of
what can happen when a company abuses nonGAAP measures came to light in 2016 when the
SEC raised questions about the accounting practices of Valeant Pharmaceuticals International Inc.,
a drug company headquartered in Laval, Quebec,
SLOANREVIEW.MIT.EDU
Canada. The SEC noted that, over the course of the
previous four years, Valeant had reported GAAP net
losses of approximately $330 million yet claimed
non-GAAP net income of about $9.8 billion during
that four-year period — a difference of more than
$10 billion.14 The SEC challenged Valeant’s practice
of removing acquisition-related expenses, particularly in light of the company’s reliance on large,
frequent acquisitions.15
Valeant argued that acquisition expenses were
not related to the company’s core operating performance. But the SEC was not convinced, and neither
were analysts and investors. The company’s stock
price fell about 90% in the months leading up to the
SEC announcement. The company finally relented
and agreed to stop referring to “core” results.16
Rules Governing
Non-GAAP Measures
In the aftermath of the Enron and WorldCom scandals, the SEC introduced a set of guidelines on the
public disclosure of non-GAAP accounting measures (referred to as Regulation G).17 Starting in
2003, companies presenting non-GAAP financial
measures were required to (1) give equal or greater
prominence to the most comparable GAAP measures, (2) include appropriate disclosures (such as
reconciliations between GAAP and non-GAAP figures), (3) label non-GAAP measures properly, and
(4) identify adjustments as nonrecurring only
when such characterization is justified.
In May 2016, the SEC announced additional
guidelines on non-GAAP measures, forbidding their
use as the only measure in public communications.
In the new rules, companies are prohibited from
using bigger fonts for non-GAAP measures than for
their GAAP counterparts in public disclosures and
from issuing press releases that feature only nonGAAP measures.18 In addition, companies are not
allowed to include gains in non-GAAP measures if
they have excluded losses on similar transactions;
this practice has always been frowned upon, but
the new guidance makes it clear that such cherrypicking is not acceptable. Companies are also
forbidden from creating individually tailored measures that are counter to official GAAP measures,
and new restrictions have been placed on the presentation of per-share non-GAAP metrics.
SLOANREVIEW.MIT.EDU
We see the recent changes as a step in the right
direction, although it’s too early to know if they will
have an impact on corporate behavior. One question has to do with the role of external auditors.
Professional standards require auditors to read all
information in all documents containing official
financial statements and accompanying audit
reports to ensure that the official information appearing in the financial statements is consistent
with other information the auditors have. But since
non-GAAP measures are rarely included in official
financial statements, outside auditors are rarely in
a position to review this information. However, a
growing number of companies have been asking
their audit committee to review the measurement
and use of non-GAAP measures. Eventually, such
audit-committee reviews may become a legal
requirement.
Some companies, such as Microsoft, 19 do ask
external auditors to review non-GAAP disclosures.
So far, such reviews have served to augment management’s and the board’s confidence in the
reasonableness of the non-GAAP measures (and
have not generally been visible to external parties).
For example, auditors can evaluate whether the composition of the non-GAAP measure is consistent
from year to year and accurately reflects the balances
in those accounts that are adjustments to GAAP. In
cases where the auditor finds discrepancies, management can respond proactively, thereby preventing
embarrassing and potentially costly problems resulting from violating an SEC regulation.
Where Do We Go From Here?
In the U.S., the Financial Accounting Standards
Board (FASB) is currently conducting research on
whether to improve the information presented in
official income statements. The idea would be to
develop more precise definitions of operating activities and how to distinguish between recurring
and infrequent items. If adopted, such changes
could have a profound effect on corporate practices
regarding non-GAAP measures.
For example, when a company says that something
is a “one-off,” how frequently does it actually occur?
Clearly, if a loss (or gain) results from a truly bizarre
circumstance, it’s reasonable to call it “nonrecurring.”
But we like the idea of defining “nonrecurring” more
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 61
ACCOUNTING
stringently than the SEC’s current rule (which is
once every two years).
How might such a change be enacted? The SEC and
other regulators could require that events be considered “nonrecurring” only if the time frame for
recurrence is every five years. Although five years is arbitrary, we believe it is preferable to two years because it
is closer to the length of a typical business cycle. The
SEC could also turn its attention to the definition of a
relevant “event.” For example, should a company that
is writing down the value of its foreign operations due
to currency devaluation and economic uncertainty be
able to record that as a “one-off”?20 Or is that uncertainty something that any global company should have
to endure (and therefore not be subject to an adjustment in calculating non-GAAP earnings)?
One reason why so many companies use unofficial figures is to track performance at a level of detail
that exceeds what is available in the income statement. In response, the FASB and the IASB could
offer to include new subtotals on the income statement, in effect establishing official versions of some
THE QUESTIONS BOARDS SHOULD ASK
The principal responsibility for ensuring proper practices in the use of non-GAAP metrics
rests with the audit committee of a company’s board. Audit committees should exercise
their oversight responsibility by posing the following questions:
• Why has management chosen a customized financial performance measure to
communicate with investors? What information does the alternative measure
convey that official GAAP or IFRS numbers do not?
• Are the measures properly labeled?
• How are the metrics calculated? Do the calculations produce financial performance
results that are fair and unbiased?
• How do the non-GAAP measures differ from related official GAAP measures?
Do the trend lines of GAAP figures track with those of non-GAAP figures across
time? Are official GAAP measures presented with equal or greater prominence
than non-GAAP measures?
• Are the non-GAAP measures calculated consistently from one period to the next?
If a company provides a non-GAAP financial measure in one period, does it continue
to present the same measure?
• Does the company’s portfolio of non-GAAP measures differ from those of its
competitors? If so, how?
• Is the company’s communication of earnings and other performance measures
consistent across reporting mediums — annual reports, press releases, webcasts,
etc.? Are official figures included with unofficial ones in press releases and investor
presentations?
• Does the compensation committee use non-GAAP measures to calculate the
compensation level of key executives? If so, which adjustments are used, and
how are they justified?
• Has the audit committee reviewed and approved the use of all reported non-GAAP
measures as well as management’s explanations of the need for and computation
of these measures?
62 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
of the more common unofficial metrics, such as
EBITDA. However, that would not address the more
customized versions of EBITDA that are sometimes
labeled “adjusted EBITDA” (which exclude items
management thinks are unrepresentative of operating results). In such cases, comparability and
consistency would remain a problem, and would
perhaps require statements from management explaining how the metrics were calculated and how
they compare with those used in previous years.
Finally, the FASB and the IASB could greatly improve matters by clarifying the way disclosures
relating to recurring versus nonrecurring activities
are presented. For example, the income statement
could be modified to produce a measure of recurring operating income as an official measure, with all
of the financial and nonrecurring items appearing
“below the line,” after recurring operating income.
Such a measure would go a long way toward mitigating the perceived need to produce non-GAAP
metrics. We think such changes are not only desirable but plausible over the next few years. And
independent of these efforts, there are steps that corporate boards can take now to improve the reliability,
credibility, and integrity of their non-GAAP disclosures. (See “The Questions Boards Should Ask.”)
Management and boards have a responsibility
to report the performance of the business accurately to shareholders. GAAP and IFRS are the
standards for public disclosure for public companies in the world’s major financial markets.
Although no standard is perfect, standards provide
a foundation for consistent measurement of corporate performance over time and across businesses.
However, although we see arguments for additional constraints and regulations, we believe that
businesses should be free to add value to their
GAAP financial reports by including non-GAAP
measures customized to their particular operations, in much the same way as McDonald’s
currently does when reporting system-wide sales.
Management has the right — and even the responsibility — to adopt metrics that enable them to
communicate with stakeholders about their corporate performance. But there’s a difference
between non-GAAP measures that add value and
measures that mislead, deliberately or otherwise.
When money-losing companies report robust
SLOANREVIEW.MIT.EDU
profits using non-GAAP measures, people may suspect that the intent is to disguise disappointing
performance with alternate facts. In many cases,
that view will not be wrong.
H. David Sherman is a professor of accounting at
Northeastern University’s D’Amore-McKim School of
Business in Boston, Massachusetts. S. David Young is
a professor of accounting and control at INSEAD in
Fontainebleau, France. Comment on this article at
http://sloanreview.mit.edu/x/59202.
ACKNOWLEDGMENTS
The authors would like to thank Andrea Ovans for her
insightful editorial guidance.
REFERENCES
1. A. Jagannath and T. Koller, “Building a Better Income
Statement,” November 2013, www.mckinsey.com.
2. “An Alternative Picture of Performance: Alternative
Performance Measure Reporting Practices in the FTSE
100,” January 2016, www.pwc.co.uk.
3. H. Hoogervorst, “Performance Reporting and the Pitfalls
of Non-GAAP Metrics” (presentation at the Annual
Congress of the European Accounting Association,
Maastricht, Netherlands, May 11, 2016). There are
occasional exceptions to this trend. For example, in 2015,
GlaxoSmithKline PLC reported adjusted operating profit
(a non-GAAP metric) of 5.7 billion pounds while operating
profit (an official GAAP metric) was 10.3 billion pounds.
The difference between these two figures was caused
by gains on asset and business disposals that were removed in the non-GAAP measure.
4. D. Seetharaman,“Yahoo’s Marissa Mayer to Reap $187
Million After Verizon Deal,” The Wall Street Journal, April
25, 2017, p. B1; Trefis Team, “Groupon Misses on TopLine, but Beats on Profitability in Q1,” May 11, 2015,
www.forbes.com; and E. Rosenfeld and D. Chu, “LinkedIn
Plunges 40% in Early Trading, Erases $10B in Market
Cap,” Feb. 5, 2016, www.nbr.com.
5. On p. 1 of PepsiCo’s 2015 annual report, chairman and
CEO Indra K. Nooyi wrote, “Organic growth grew 5% in
2015.” However, official (or GAAP-based) revenue declined by 5%. “Organic growth” is defined by PepsiCo in
this instance — it’s not an official number. Now turn to
p. 59 of the PepsiCo Form 10-K included as part of the
2015 annual report online. The far right-hand column on
p. 59 reconciles these two numbers. The key item is the
line for foreign exchange translations of negative 10%.
This accounts for the difference between the official income statement result (a reduction in net revenue of 5%)
with organic growth of positive 5%. See “PepsiCo 2015
Annual Report,” 2015, www.pepsico.com.
8. M.E. Barth, I.D. Gow, and D.J. Taylor, “Why Do Pro
Forma and Street Earnings Not Reflect Changes in GAAP?
Evidence From SFAS 123R,” Review of Accounting
Studies 17, no. 3 (September 2012): 526-562.
9. See M. Fahey, “Mind the GAAP: Buffett Warns of
Deceptive Earnings,” March 1, 2016, www.cnbc.com.
10. M. Rapoport, “Tailored Accounting at IPOs Raises
Flags,” Jan. 7, 2015, www.wsj.com.
11. F. Brod was a panelist on non-GAAP measures at the
Association of International Certified Public Accountants
(AICPA) Conference on Current SEC and PCAOB Developments 2016, Dec. 5-Dec. 7, 2016, Washington, D.C.
See also E. Odoner and Weil, Gotshal, and Manges LLC,
“SEC Guidance on ‘New GAAP’ Transition Disclosures
and Non-GAAP Measures,” Jan. 14, 2017, http://corpgov
.law.harvard.edu.
12. Microsoft closed the $26 billion deal in late December
2016. See J. Jamerson, “Microsoft Closes Acquisition of
LinkedIn,” Dec. 28, 2016, www.wsj.com.
13. In fairness to Dudley, some of his 2015 pay was linked
to an adjustment in the accounting rules that determine
how pension benefits are calculated.
14. M. Rapoport, “SEC Has Reviewed Valeant’s Use
of ‘Non-GAAP’ Financial Measures — Comment
Letters,” Dow Jones Newswires, May 24, 2016;
and U.S. Securities and Exchange Commission, comment letter re: Valeant Pharmaceuticals International,
Inc. Form 8-K filed March 15, 2016, and amended
March 15, 2016, file no. 001-14956, March 18, 2016,
www.sec.gov.
15. N. Grover, “SEC Raises Concerns About Valeant’s
Use of ‘Non-GAAP’ Measures,” Reuters, May 24, 2016,
www.reuters.com.
16. S. Gandel and Reuters, “What Caused Valeant’s Epic
90% Plunge,” March 20, 2016, www.fortune.com.
17. U.S. Securities and Exchange Commission,
“Final Rule: Conditions for Use of Non-GAAP
Financial Measures,” Jan. 24, 2002, www.sec.gov.
18. U.S. Securities and Exchange Commission, “NonGAAP Financial Measures,” May 17, 2016, www.sec.gov.
19. Comments by Brod at the Association of International
Certified Public Accountants (AICPA) Conference on
Current SEC and PCAOB Developments 2016.
20. T. McLaughlin, “Venezuela Roils Corporate Profits
Around the Globe,” July 27, 2016, www.reuters.com.
6. “PepsiCo 2007 Annual Report,” www.pepsico.com.
i. H.D. Sherman, D. Carey, and R. Brust, “The Audit Committee’s New Agenda,” Harvard Business Review 87,
no. 6 (June 2009): 92-99; H.D. Sherman and S.D. Young,
“Where Financial Reporting Still Falls Short,” Harvard
Business Review 94, no. 7/8 (July-August 2016: 76-84);
and H.D. Sherman, S.D. Young, and H. Collingwood,
“Profits You Can Trust: Spotting and Surviving Accounting
Landmines” (Upper Saddle River, New Jersey: Financial
Times Prentice Hall, 2003).
7. M. Rapoport, “What Companies Strip Out of ‘NonGAAP’ Earnings: Fines, Exec Bonuses, Severance,
Rebranding Costs…,” Moneybeat (blog), Jan. 8, 2015,
http://blogs.wsj.com.
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Copyright © Massachusetts Institute of Technology, 2018.
All rights reserved.
SLOANREVIEW.MIT.EDU
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 63
Blockchain sees the tree for the forest
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which improves sustainability and adds value to every table, latte, and ring sold.
Look again.™ See supply chain reimagined.
Copyright © 2017 Deloitte Development LLC. All rights reserved.
S T R AT E G Y
Building Scalable
Business Models
THE LEADING
QUESTION
How do
companies
develop scalable business
models?
FINDINGS
Scalable business
Many of today’s most successful companies are able to
leverage business model scalability to achieve profitable
growth. Executives need to factor scalability attributes into
their business model design or they risk being left behind.
BY CHRISTIAN NIELSEN AND MORTEN LUND
models are flexible
and turn new
resources into
increasing returns.
Scalability often
involves connecting
strategic partners to
a company’s value
proposition.
One key is to find
smart ways to leverage the resources of
partners.
BUSINESS MODEL INNOVATION has become an increasingly hot topic in management circles,
and understandably so. No management activity is
more important than having clarity about how the organization creates, delivers, and captures value. It
requires, among other things, knowing what customers want, how value can be best delivered, and how to
enlist strategic partners to achieve maximum benefit.
Although the ability to develop strong value propositions can enable companies to “get by,” in our view
many of today’s most successful businesses are those
that are able to place themselves in the “sweet spot” of
business model scalability. Scalability is about achieving profitable growth and is therefore a fundamental
consideration for managers and investors alike. If
managers are incapable of factoring scalability attributes into their business model design, they risk
being left behind, much the way bookstores owned by
Borders Group Inc. were eclipsed by Amazon.com Inc.
Over a five-year period, we studied scalability in the
context of more than 90 Scandinavian businesses and
also examined the experiences of a number of wellknown businesses, including Google, Apple, and
Groupon. (See “About the Research,” p. 67.) In the course
of our research, we identified five patterns by which
companies can achieve scalability. The first pattern involved adding new distribution channels. The second
entailed freeing the business from traditional capacity
constraints. The third involved outsourcing capital investments to partners who, in effect, became participants
in the business model. The fourth was to have customers
RICHARD BORGE/THEISPOT.COM
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 65
S T R AT E G Y
and other partners assume multiple roles in the business model. And the fifth pattern was to establish
platform models in which even competitors may
become customers. Based on these patterns, we have
developed a framework for identifying potential
levers for business model scalability, along with a
road map that managers can use to improve their
business models.
Over and above the need to create value propositions that are difficult for competitors to replicate,
managers need to develop business models that are capable of achieving positive and accelerating returns on
the investments made. When companies restructure
or invest in acquisitions, it’s common for them to identify synergies that reduce costs and simplify workflows
and product offerings. However, simply thinking in
terms of synergies isn’t enough; such synergies don’t
necessarily lead to improvements in business model
scalability. To achieve scalability, managers and entrepreneurs need to remove capacity constraints. They
have opportunities to do this in a variety of ways: by
collaborating with partners, by encouraging partners
to play multiple roles in the business model, by creating platforms to attract new partners, or even by
working with current competitors.
Accelerating Returns to Scale
What do we mean by “scalable”? We use the term
scalability to identify where changes in size or volume are possible and seem worthwhile. Scalability
refers to a system’s ability to expand output on demand when resources are added. Linking scalability
to business models provides us with a framework
for discussing and estimating business potential,
which is important to both executives and many
stakeholders because, among other things, it has
implications for hiring and skill development.
Another important characteristic of scalability is
that the organization has sufficient flexibility to
grow while incorporating the effects of external
pressures, such as new competitors, altered regulation, or macroeconomic pressure.
The first dimension of scalability is the degree to
which increased input can create higher output.
The second dimension of scalability relates to the
ability of the business model to accelerate the returns on the additional investment. Accelerating
returns to scale are typically found in business
66 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
models where new resources, capabilities, or value
propositions provide completely new properties to
an existing industry.1 Amazon.com’s retailing business model offers a good example. For example, the
company’s algorithms introduce customers to
products they may not have considered but might
be of interest to them as they shop online.
In those situations where returns to scale are declining rather than increasing, managers should
figure out how quickly to exit the business. If the
returns are falling precipitously, it might make
sense to pull out quickly. Even when returns are flat,
further investments may be unattractive. As a
general rule, executives should invest capital where
they can generate increasing returns to scale.
Scalability Patterns in
Business Models
A scalable business model is one that is flexible and
where the addition of new resources brings increasing returns. In the course of our research, we searched
for business model attributes that were sufficiently
flexible to cope with internal demands and external
forces and where the potential wasn’t constrained by
physical or material assets (such as labor shortages,
machine capacity, cash liquidity, or storage capacity). Below we will examine the five patterns of
business model scalability individually.
PATTERN A: Add new distribution channels.
While the notion of selling through multiple distribution channels isn’t novel, it’s useful to understand
what happens when an additional channel is added.
As long as the implementation of a new distribution channel does not cannibalize sales in existing
channels, adding a new sales channel can allow a
company to spread the costs of overhead and reap
benefits from increased sales.
We found this to be the case at Copenhagen
Seafood A/S, a Danish supplier of fresh fish. The
company, which had traditionally sold only to highend restaurants, added the sale of fresh fish directly
to retail customers, enabling it to offer restaurantquality seafood to individuals at reasonable prices.
Because restaurants typically ask for specific cuts
of fish, the percentage of waste can be high. By adding the retail channel, Copenhagen Seafood was
able to cultivate a new clientele with people who
relished the opportunity to buy from a seafood
SLOANREVIEW.MIT.EDU
supplier closely associated with some of the city’s
best-known restaurants.2
PATTERN B: Explore ways to work around traditional capacity constraints. Scalability often
means finding ways to overcome traditional capacity
constraints. Obviously, constraints vary from industry to industry. In the pharmaceutical industry, the
constraints might involve the cost of establishing research infrastructure and the ability to develop new
products and receive approval for new products.
However, when viewing constraints from the perspective of business model innovation, companies
should ask themselves if they can find ways to work
around existing constraints. In the private banking
sector, for example, a company might bypass capacity constraints by focusing on customer relationship
activities and outsourcing infrastructure management to others. In a similar vein, a consulting
company with a business model focused on hourly
billing for large government organizations explored
bypassing that constraint by marketing standard
outputs and simpler reports to a new customer segment consisting of smaller businesses.
PATTERN C: Shift capital requirements to
partners. Every organization needs to prioritize its
investments and determine which are most critical.
CFOs are encouraged to optimize the cash liquidity
constraints, cash flow, and working capital attributes of their business models. Given that many
companies place a high value on cash, business
models that shift capital requirements to strategic
partners can be desirable.3
One company we studied was Sky-Watch A/S, a
company based in Støvring, Denmark, that develops and manufactures drones suited for a variety of
industrial settings. Sky-Watch’s business model has
fewer resource constraints than some of its close
competitors thanks to management’s decision to
concentrate on turning the core platform into an
open platform that allows customers and strategic
partners to add their own hardware and software.
PATTERN D: Leverage the work of partners.
Companies need to pay attention to what their customers and strategic partners value. Managers should
use this knowledge to optimize the value proposition
of the products and services they offer to customers.
The key is to find smart ways to leverage the resources
of partners. For example, Tupperware Brands Corp.,
SLOANREVIEW.MIT.EDU
ABOUT THE RESEARCH
Business models offer a novel perspective from which to understand
how companies can become profitable, competitive, and sustainable.
They offer distinct recipes for how companies do business,i including
activities and resources, customer relationships, partnering strategies,
and revenue models.ii
Our research focused on business model innovation in conjunction
with 10 networks of collaborating companies, where the companies were
collaborating either through joint ventures or via more open and informal
arrangements. The research, which was conducted between 2008 and
2013, was aimed at helping participating companies develop a process
for pursuing new global business opportunities and providing a solid
base of relevant qualitative data. A total of 92 Scandinavian companies
participated. We used longitudinal methods, augmented by a series of
semi-structured interviews, to examine business model innovation processes. Our team followed the companies through workshops, company
meetings, board meetings, and observations, which were recorded and/or
documented with minutes, pictures, or video.
based in Orlando, Florida, is famous for leveraging a
community of sales representatives who have an interest in selling the company’s food-storage products
to a widening circle of people. Groupon Inc. likewise
turns customers into partners by giving them incentives to spread the word about the company. Similar
strategies can be leveraged for distribution methods,
building customer loyalty, giving access to resources,
and performing other activities according to the
value configuration of the business model.
PATTERN E: Implement platform models.
A variation on leveraging partners involves using
platform-based business models. Platform models
are based on collaboration and can take different
forms. For example, PrintConnect.com of Würselen,
Germany, operates a web-based workflow platform
for printing and packaging that links partners across
the value chain. Some platform business models
predate the web: Visa Inc., which connects businesses with credit card users, is an example.
When looking at business model innovation from
a platform perspective, an important question is,
“How do we turn competitors into partners or perhaps
even customers?” For example, The Relationship
Factory,4 a company based in Aarhus, Denmark,
that organizes professional networking groups for
managers, opted for a platform model to achieve
business model scalability. It makes its software
platform available to competitors on a private-label
basis, thereby providing the company with a supplemental and recurring revenue stream on top
of its traditional service-based activities. While
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 67
S T R AT E G Y
competitors continue to rely heavily on their sale of
service hours, the company is able to generate incremental revenue by selling “ease of use” to its
competitors as well as benchmarking data across
the industry.
A Road Map to Business
Model Scalability
The patterns we have discussed above describe how
companies can adjust their business models to make
them scalable. While traditional thinking typically
leads to synergy effects and, at best, positive returns
that are linear to the investments, some of the companies we studied showed that it was possible to
redesign business models to achieve accelerating
returns. However, achieving accelerating returns is
not easy. It requires thinking strategically in terms of
the value propositions of stakeholders, strategic
partners, and customers involved in the immediate
business ecosystem. Aligning and leveraging the
competencies and motivations of these stakeholders
can lead to better cooperation. It can also build
greater trust and loyalty among partners, which will
pay off in the long term.
To implement the patterns for scalability, it is
often necessary to identify activities and resources
where collaborating with partners is advantageous
and can strengthen the offering’s value proposition
to customers. These patterns can assist managers in
rethinking how their business models make use of
partners, customers, and other stakeholders. Rather
than just relying on traditional analytical exercises
such as analyzing cost structures, product-segment
profitability, and market-segment growth, managers can work on achieving business model scalability
by asking a different set of questions. The questions
will often lead to the identification of new partners
and potentially new roles.
We suggest that companies pursue three steps:
1. Identify potential strategic partners. Scalability
typically involves connecting strategic partners to
the value proposition, either through sharing activities or resources. Given that scalability requires
thinking beyond simply sharing costs, executives
should ask themselves the following:
• Are there potential strategic partners that could
perform activities in our business model — or
provide resources to it — in ways that would help
improve the value proposition to our customers?
2. Ask questions that reveal a road map to scalability. Asking questions can trigger ideas about
how to reconfigure a business model. When encountering novel ways of doing business, managers
should analyze how such a business model would
play out for their own company. We have found
that the following questions can be helpful:
• How does this novel business model challenge our
existing way of thinking about the business?
• What would we need to do differently to implement this business model?
• Which other companies excel at what we are trying to do, and what can we learn from them?
• What are the key value drivers of this particular
business model?
• Could this business model lead to scalability?
Based on the ideas you are able to generate, we
recommend using the following questions to help
clarify potential avenues for scalability:
• Are there potential strategic partners that can
offer features (at minimal or no cost to our company) that enrich the existing value proposition to
our customers, while receiving value themselves?
• Are there alternative configurations that free the
business model from existing capacity constraints?
• Would it make sense to establish a platform for
other businesses to buy into — and thus create
alternative ways of generating revenue?
• Is it possible to change the role of existing stakeholders and utilize them in multiple roles in the
business model?
Achieving acclerating returns is not easy. It requires thinking
strategically in terms of the value propositions of stakeholders,
strategic partners, and customers.
68 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
SLOANREVIEW.MIT.EDU
• Who would pay for either access to our customer
base or knowledge about our customers and their
characteristics?
• Which mechanisms are in place to create customer
lock-in?
• How agile is our company in reacting to threats
from new entrants or new technologies?
3. Analyze the scalability attributes of business
model options. When all of the ideas generated have
been presented, executives should facilitate a discussion to start to evaluate potential business models.
They should analyze the attributes of the various options and consider how they might be configured to
achieve accelerating returns on investments.
Traditionally, some companies have developed
business models that focus on achieving economies
of scale while other companies have been more
geared toward creating economies of scope through
differentiation. We have found that scalability goes
beyond this traditional distinction and that identifying the sweet spot of business model scalability
involves identifying accelerating returns on input.
In cases of declining returns to scale, managers
should focus on downsizing the business so as not
to cannibalize existing value. In cases where the returns on additional inputs are constant, managers
should attempt to find ways to increase returns or
invest excess capital elsewhere. When the business is
able to generate positive, albeit linear, returns on additional inputs, the existence of synergies can make
this a favorable place to be, although the company
may be stuck with a business model that is at best
average. In this case, managers should attempt to
improve their business model using one of the five
patterns described above.
Having a road map for business model scalability
can be enormously helpful for managers, whether
they are involved in developing new business models
from scratch or innovating, rejuvenating, or redesigning existing business models. Although much of the
recent research about business model innovation examines the alignment between value propositions
and customer needs,5 business model scalability
depends on close alignment between the value proposition and strategic partners.
The patterns we have identified as gateways to
scalable business models (for example, enriching
value propositions, removing capacity constraints,
SLOANREVIEW.MIT.EDU
and changing the role of stakeholders in business
models) provide avenues for managers to explore.
Identifying business model configurations that
allow for such characteristics should be a top priority for managers as they develop and review their
corporate strategies.
Christian Nielsen is a professor of business models
and performance reporting at Aalborg University
in Aalborg, Denmark, and at Inland Norway University of Applied Sciences in Norway. Morten Lund
(@mortenlunddk) is an assistant professor and
director of the Business Model Design Center at
Aalborg University. Comment on this article at
http://sloanreview.mit.edu/x/59206.
REFERENCES
1. C. Nielsen and H. Dane-Nielsen, “The Emergent
Properties of Intellectual Capital: A Conceptual Offering,”
Journal of Human Resource Costing & Accounting 14,
no. 1 (2010): 6-27; and H. Dane-Nielsen and C. Nielsen,
“Understanding Business Models from an Intellectual
Capital Perspective,” in “Handbook of Intellectual Capital
Research,” ed. J. Guthrie, F. Ricceri, J. Dumay, and
C. Nielsen (London: Routledge, 2017).
2. This is an example of the type of complementary fit,
where activities are mutually reinforcing, identified by
C. Zott and R. Amit in “The Business Model: A Theoretically Anchored Robust Construct for Strategic Analysis,”
Strategic Organization 11, no. 4 (November 2013):
403-411. See also P. Milgrom and J. Roberts, “Complementarities and Fit Strategy, Structure, and Organizational
Change in Manufacturing,” Journal of Accounting and
Economics 19, no. 2-3 (March-May 1995): 179-208.
According to Milgrom and Roberts, activities are complements when the marginal value of one activity increases
as the other activity is increased.
3. See Y. Taran, C. Nielsen, M. Montemari, P. Thomsen,
and F. Paolone, “Business Model Configurations: A Five-V
Framework to Map Out Potential Innovation Routes,” European Journal of Innovation Management 19, no. 4 (2016):
492-527; and H. W. Chesbrough, “Open Innovation: The
New Imperative for Creating and Profiting from Technology” (Boston: Harvard Business School Press, 2005).
4. This is an English translation of the company’s name,
Relationsfabrikken ApS. See www.relationsfabrikken.dk.
5. A. Osterwalder, Y. Pigneur, G. Bernarda, and A. Smith,
“Value Proposition Design: How to Create Products and
Services Customers Want” (New York: John Wiley &
Sons, 2014).
i. C. Baden-Fuller and M.S. Morgan, “Business Models
as Models,” Long Range Planning 43, no. 2 -3 (April-June
2010): 156-171.
ii. Taran et al., “Business Model Configurations: A Five-V
Framework.”
Reprint 59206. For ordering information, see page 4.
Copyright © Massachusetts Institute of Technology, 2018.
All rights reserved.
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 69
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I N N O VAT I O N
Developing Successful
Strategic Partnerships
With Universities
THE LEADING
QUESTION
How can
companies
improve
their partnerships with
universities?
For many companies, universities have become essential
innovation partners. However, companies often struggle
to establish and run university partnerships effectively.
FINDINGS
Universities offer a
BY LARS FRØLUND, FIONA MURRAY, AND MAX RIEDEL
wide and at times
bewildering array
of modes of
engagement.
Articulate strategic
COLLABORATIONS BETWEEN COMPANIES and universities are critical drivers of the
innovation economy. These relationships have long been a mainstay of corporate research and development (R&D) — from creating the knowledge foundations for the next generation of solutions, to
serving as an extended “workbench” to solve short-term, incremental problems, to providing a flow of
newly minted talent. As many corporations look to open innovation to augment their internal R&D
efforts, universities have become essential partners. Indeed, companies now look to universities to
anchor an increasingly broad set of innovation activities, especially those grounded in
engaging with regional innovation ecosystems. Silicon Valley, Kendall Square in
Cambridge, Massachusetts, and Block 71 in
Singapore are among the most visible innovation ecosystems where universities are
essential stakeholders in an innovation community that also includes corporations,
government entities, venture investors, and
entrepreneurs. Thus, in addition to serving
as sources of people and ideas for corporations, university collaborations are an
important mechanism for corporations
seeking to open up new avenues of engagement with a broader innovation ecosystem.
Following corporate giants like General
Electric, Siemens, Rolls-Royce, and IBM,
which have collaborated with universities for
years, a variety of younger companies including Amazon, Facebook, Google, and
Uber are using universities as a key part of
HARRY CAMPBELL/THEISPOT.COM
goals for partnerships and then
choose collaboration structures
that align with
those goals.
Identify key perfor
mance indicators
to evaluate the
partnerships.
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 71
I N N O VAT I O N
ABOUT THE
RESEARCH
The article draws on the results of a four-year research
project, as well as on many
years of experience in
advising companies and
universities on establishing
and managing universityindustry partnerships in
specific regional innovation
ecosystems. The research
project investigated strategic programs for industryuniversity collaborations
with a focus on (1) the development of specialized
units and people for university relations and their
professionalization, (2) the
change from an ad hoc
approach to a strategic
approach to university
partnerships, and (3) the
corporate success factors
for university partnerships.
The research was conducted
through a qualitative inquiry
consisting of participant observation, semi-structured
interviews, and workshops.
The article also draws on
challenges that participants
in the MIT Regional Entrepreneurship Acceleration
Program have encountered
in university-industry
partnerships.i
their early-stage innovation and new ventures
strategy.1 Even smaller, more regionally oriented
companies in diverse sectors such as mining and automotive have come to believe that universities are
key ecosystem stakeholders in supporting and shaping their regional economies. For example, IQE plc,
a compound semiconductor company based in
Cardiff, U.K., supports a regional innovation ecosystem through its collaborative relationship with
Cardiff University. The partners have developed a
translational research facility to train scientists and
technicians in compound semiconductor technologies and support an R&D facility to help U.K.
businesses exploit advances in these technologies.
Such collaborations between corporations and universities foster the innovation ecosystem.
While the aspirations of university-industry
partnerships can be easily described, many companies find it challenging to establish and run these
partnerships effectively, even when key financial
resources and human capital are available. The
challenge is amplified in an ecosystem where the
various stakeholders, all with their own ambitions,
need to be properly aligned to achieve impact. In
our research, we have found that both corporations
and universities confront a general level of frustration and a mismatch in culture and governance
when they collaborate. (See “About the Research.”)
Although many factors contribute to the frustration, the core reason is that university culture —
characterized by high autonomy and distributed
governance — maps poorly to corporate culture.
Universities offer companies a wide and at times
bewildering array of faculty, programs, and other
modes of engagement. Even when the formats for
interaction are established, there is often a profound mismatch in the expectations and goals for
joint engagement.
Given both the promise and the challenge of
university-industry interactions today, it’s important to explore the factors that make such
collaborations successful. We have found that a systematic approach to university partnerships within
innovation ecosystems requires both companies
and universities to be well prepared before the engagement even begins. In particular, companies
need to move from an ad hoc to a strategic approach to partnerships with universities.
72 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
From Ad Hoc to Strategic
Partnerships
In an ad hoc approach, university collaborations are
first and foremost established by individual researchers or engineers in the company and focus on
specific R&D needs identified by those individuals.
This means that collaboration partners are likely
chosen based on personal experience and the networks of the researchers and engineers in the
company. The rationale for university partner selection is familiarity between individual researchers,
not between the two organizations as a whole.
Although this may mean that many potentially valuable aspects of a university partnership are ignored,
such approaches create what has been described as
an “extended workbench.”2 Such collaborations,
though small, are often agile. From the perspective
of the corporation, the university collaboration is
limited to the specific project (typically within a
business unit), and thus there is no centralized organization. From the university’s perspective,
individual researchers and their students gain a
source of funding, insight into relevant problems,
and opportunities to access novel assets or partners.
Ad hoc approaches often lead to a large number
of collaborations (sometimes numbering in the
hundreds) with little synergy. Each agreement is
negotiated individually, which tends to put a heavy
workload on legal departments, leading to delays.
In addition, opportunities for broader engagement
and impact are lost. Consequently, large companies
and many leading universities have shown interest
in more strategic programs.
As companies enter into strategic agreements
with universities, they have begun to organize their
relationships with universities into tiers. “Top-tier”
relationships are no longer simply based on personal connections between an academic and a
corporate researcher. Increasingly, companies select universities based on their expertise in an area
of strategic importance and their familiarity to the
company. In fact, companies have started to use
company-wide master research agreements to create transparency in their collaboration activities,
improve their negotiating positions, accelerate the
deployment of projects, and encourage interfaculty
collaboration on topics of shared interest. Such
approaches are reminiscent of relationships
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established back in the 1980s between Harvard
Medical School and Hoechst A.G., Washington
University and Monsanto, and MIT and Exxon;
and the longstanding relationship between the
University of Oxford and Rolls-Royce.3 More comprehensive agreements are particularly attractive to
universities because they enable individuals from
corporate labs to be embedded on-site at the university, provide a more stable source of funding,
and allow for more multifaceted interactions.
The change from incremental problem-solving
to shared strategic work on grand challenges or deep
exploration is important because it signals that universities are places not just to establish an extended
workbench to solve predefined problems but also to
tackle more ambitious challenges that have a more
open-ended, exploratory emphasis.4 Within corporations, the creation of strategic programs has led to
the institutionalization of specialized units for university relations, often situated in the corporate R&D
organization with reporting lines to senior management. Such units play a leading role in defining the
focus areas for collaboration, designing formats,
selecting partner universities, offering advice regarding intellectual property rights, evaluating
collaborations, and continually managing the
interactions between the company and the universities. Universities have made fewer organizational
changes, but the development of specific corporate
programs for engagement with particular research
centers, departments, or initiatives has become more
commonplace, and the number of licensing and
contract professionals has grown.
As a next step in the evolution of university partnerships, strategic programs are now increasingly
seen as the fulcrum for broader innovation ecosystem engagement (in part because large corporations
are seeking external input throughout the innovation process, from initial idea to impact). Companies
may link to the ecosystem via a range of local entities
(for example, local governments, school systems,
and startup communities). However, particularly
when companies are working with universities actively engaged in startup creation and research
translation, familiarity with the university and its
collection of innovation activities can become a natural entry point for companies to develop broader
links into the innovation ecosystem. The shift also
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aligns with the ways in which universities today are
participating in local and regional economic development and playing a part on a global stage.
Conversely, it would be difficult to imagine innovation ecosystem engagement taking place without a
deep connection to the local university.
Moving from ad hoc to strategic to ecosystem
partnerships places ever more demands on university-corporate interactions. On the corporate side,
business units, global R&D, and venturing units all
want seats at the table, with each party bringing its
respective needs and values. On the university side,
individual labs, centers and initiatives, and entrepreneurship programs all have an interest in the
engagement. Our research is based on our interest
in both understanding and learning how to optimize these naturally complex relationships. As part
of that research, we have found that companies that
work through six important questions are better
positioned to develop an effective approach to
interaction with a range of universities across different innovation ecosystems.
Preparing for Systematic
Engagement With Universities
We recommend that companies consider these six
fundamental questions:
1. What business goals drive your university
partnerships?
2. What are the key focus areas of your university
partnerships, and how are they selected to ensure
alignment with your business goals?
3. Who are your primary university partners, and
by what criteria are they chosen?
4. What collaboration formats match your focus
areas and business goals?
5. What people, processes, and organizational
structures support your university partnerships?
6. What key performance indicators are most useful for evaluating your university partnerships?
These questions are closely linked; when the
answers are aligned, they provide a logic for engagement that is more strategic and, in our experience,
more likely to be effective. The six questions can be divided into three groups. The first two are about the
business goals — the strategic goals that university
partnerships should deliver for the company. The next
two questions emphasize the partners (who) and
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collaboration formats (how). Together, the first four
questions form the core of a systematic approach to
university partnerships. The remaining two questions
are about ensuring that the right people, processes, organizational structures, and evaluation tools are in
place so that the university and the company can ensure
that the partnership is delivering value to both parties.
QUESTION 1: What business goals drive your
university partnerships? Companies have various
reasons for wanting to engage in university partnerships, and they often have difficulty articulating their
goals in clear business terms. Working with companies with emerging best practices in this field, we
found the business goals that drive university interactions can often be grouped into five categories:
Short-Term, Incremental Problem-Solving This
often occurs within an existing product line and is,
as already mentioned, sometimes referred to as an
“extended workbench.”
Talent Identification and Hiring For many compa-
nies, talent acquisition at levels from undergraduates
to Ph.D.s to postdocs is a primary business goal of
partnerships with universities.
Long-Term Development of New Technologies
These interactions are often referred to as “grand
challenges” or “deep exploration,” and they involve
companies seeking new technologies and solutions
to broad-based customer needs that may lead to
new product lines or new businesses.
Systematic Exposure to Startups For many
companies, exposure to startups (either researchor student-driven) while the startups are still part
of the university can be an important incentive for
interacting with universities, often with the involvement of corporate venturing units. Although
there is no agreed-upon term for this, we will refer
to it as exposure to the “startup pipeline.”
Publicity and Political Influence A high-profile
partnership with a prestigious university can lend
luster to a company. In some regional innovation
ecosystems, a partnership with a top research institution can provide access to high-level government
officials.
74 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
Although each of these goals is distinct, they
may be interrelated. For example, working collaboratively on grand challenges can serve as a pathway
to talent identification and, at times, to a startup (to
move a solution out of the lab and into the world).
Likewise, short-term engagements focused on incremental problem-solving might drive the hiring
of individuals with specific skills needed for the
company’s R&D activities. Addressing this first
question — something few companies do — creates an opportunity for a corporation to articulate a
cohesive view of its business goals and how they relate to its university interactions.
QUESTION 2: What are the key focus areas
of your university partnerships, and how are they
selected to ensure alignment with your business
goals? Within the business goals outlined, a company’s key focus areas for university partnerships must
be specified in terms of particular innovation priorities. For a business goal such as talent identification,
for example, the focus areas must be prioritized so
that the “what” can be defined in terms of technical
competence/capability (for example, bioprocess
engineers); challenge areas (for example, new distributed power systems); or product domains (for
example, more efficient turbine blades). To ensure
that university collaborations are properly aligned
with the company’s business goals, the selection process for such partnerships should be as rigorous as a
comparable internal process would be.
For example, a large European automotive company has an innovation board comprising the heads
of R&D, production, and marketing innovation —
the three divisions most relevant to the business goals
for university partnerships. The board makes decisions on which focus areas should be part of future
projects with universities and monitors ongoing projects with partner universities. This process ensures
that the focus areas of the current and future projects
with universities are constantly aligned with the business goals that drive the university partnerships.
QUESTION 3: Who are your primary university
partners, and by what criteria are they chosen?
Selecting university partners is no easy task. However,
many leading corporations are making their selection criteria more explicit — in ways that universities
often welcome. The most common criteria include
the following:
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• Familiarity and fit (previous joint projects, personal relationships, many hires);
• Location (if proximity, to headquarters or to an
innovation ecosystem, is desirable);
• Excellence (the reputation of the university, topjournal publications of a lab, or involvement of a
particular researcher);
• Legal framework (especially regarding issues such
as intellectual property rights and access to university-based startups); and
• Culture (especially regarding entrepreneurial culture, openness to industry, and interdisciplinary
collaboration).
Our research indicates that successful companies
continually define and refine their university selection criteria as their experience grows and their
goals change. One large U.S. corporation, for example, has developed an online tool that tracks the
productivity and impact of its university partnerships. The tool helps its R&D group make informed
decisions about whom to partner with and ensures
that projects are aligned with business goals of the
company.
QUESTION 4: What collaboration formats
match your focus areas and business goals?
Choosing the right collaboration format is at the
heart of a successful university partnership. While
traditional formats have mainly taken the form of
companies sponsoring research in one or many
projects, the variety of formats has been expanding
in recent years. Among the formats we see today
are contract research with a single lab, individual
corporate employees embedded in a lab or research center, consortia membership, large
co-created research centers, open calls for grant
proposals in a particular research area, student/
corporate hackathons and idea contests, collaboration on publicly funded projects, fellowship
programs, and jointly sponsored conferences and
workshops. (See “A Variety of Industry-University
Collaboration Formats.”)
More novel and ambitious formats include the
Cisco-University of British Columbia relationship,
which aims to turn the university campus into a
living lab for smart building systems, 5 and the
Global Innovation Exchange, funded initially by
Microsoft and established in collaboration with
Tsinghua University in Beijing and the University
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A VARIETY OF INDUSTRY-UNIVERSITY
COLLABORATION FORMATS
Contract research with a single lab is one of the most common forms of
industry-university collaboration, but there are many other collaboration
formats as well. Here is a sampling, with an example of each format.
FORMAT
EXAMPLE
Embedded
individuals
MIT’s Microsystems Technology Laboratories, which
encourage corporate researchers to work in a specific
on-campus lab
Consortia
membership
University of Cambridge Institute for Manufacturing
corporate membership program
Co-created
research centers
MIT-IBM Watson AI Lab
Focused calls
for research
proposals
Amazon Catalyst research grants initiated and piloted
with the University of Washington
Student
hackathons
MIT hackathon with the Advanced Functional Fabrics
of America (AFFOA) and the U.S. Department of
Defense
Student
competitions
The Siemens Global University Challenge
Collaboration
on publicly
funded research
Defense Advanced Research Project Agency (DARPA)
proposals
of Washington in Seattle, which provides a compelling example of more complex but increasingly
relevant multiparty, multicontinent relationships.6
Of course, the collaboration format a company
chooses will depend on the goals it wishes to pursue.
For companies prioritizing short-term, incremental
problem-solving, contract research with a single lab
may be the best way to seamlessly extend the
workbench. If the goal is talent acquisition, studentoriented activities such as hackathons, competitions,
and fellowships are highly effective in that they enable the company to get to know a large number of
talented students and evaluate their fit. Embedding
employees within the university is a particularly effective way to identify talent, particularly at the Ph.D.
and postdoctoral level. To meet grand challenges,
several formats are emerging as best practices: targeted but open calls for research proposals (perhaps
preceded by a hackathon to raise awareness and excitement) and the co-creation of research centers.
The opportunity to engage with the startup
pipeline has emerged as another attractive business
goal for university partnerships. Here again, there
are various potential formats, all geared toward
identifying and connecting to startups or even
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project teams that have yet to formally incorporate
(which we refer to as “proto-startups”). Many
proto-startups are associated with innovation and
entrepreneurship programs in universities and in
the wider innovation ecosystem. We found in our
research that successful companies draw a distinction between engaging with student-led startups
(for example, via business plan competitions or student accelerators) and lab-based startups, where
interaction typically includes consideration of intellectual property, engagement with faculty, and
sharing of expertise around scale-up and testing,
along with sponsored research.
Companies that have expansive goals need to
actively manage a complex portfolio of relationships and formats. The university-relations unit at
one large pharmaceutical company, for example,
continually evaluates and prioritizes its collaboration formats based on the evolving business needs
and phases in the drug development process. The
company uses fellowship programs to fund Ph.D.s
and postdoctoral researchers and provide opportunities to bring them into the organization, both to
drive know-how about grand challenges and for
talent acquisition.
Ultimately, successful partnering with universities isn’t about choosing a particular collaboration
format but about systematically prioritizing and
reprioritizing different formats in response to
changing business goals. Beyond that, it is about
having the right people, processes, and organizational support to ensure success and to identify and
manage sources of tension. In our last two questions, we will examine these issues.
QUESTION 5: What people, processes, and organizational structures support your university
partnerships? Corporations with partnerships
across a range of universities must create internal
structures and processes to drive success. This raises
questions about what structures will enable effective interactions, what competencies universityrelationship managers need, and what processes are
best suited to support internal alignment between
the technical expert level and the management level
in the company and also between the company and
the university.
In terms of structure, the shift from an ad hoc
approach to a strategic approach has spawned
76 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
specialized units for university relations. But what
are the best organizational structures to support
the partnerships? If it makes sense for companies
to establish units for university relations, should
they be part of a centralized R&D function or operate as part of more decentralized business units?
Our findings show that strategic partnerships do
not have to be supported by a centralized unit that
reports to the chief technical officer (CTO) or a senior vice president. Rather, it’s important that the
primary business goals (and the particular choice
of who, where, and how) shape the choices. If the
problems you are trying to solve have been defined
at the business-unit level, then partnership support should take place within the business units
themselves. If the goal is to undertake something
larger (for example, a grand challenge), then it
makes sense for the university-relations unit to be
centralized and to report to a senior manager such
as the CTO.7
Regardless of organizational setup, company
employees who serve at the university-corporate
interface must be capable of acting as knowledge
brokers between university partners and the company. We suggest that companies designate two
roles for each university partner: a management
sponsor (usually a top manager such as a board
member or country CEO), who is assigned to a
specific university; and a university-relationship
manager for R&D, who supports the sponsor
within the company.
Based on our experience, it works well if the university likewise appoints people to mirror those two
corporate roles (ideally, a vice president or dean and
an industry-partnership manager). On the corporate side, the core team drives the processes that are
covered by our six questions. Of course, when considering the portfolio of university partnerships, the
top executives and key managers involved with the
relationships need to take part in the conversations
and have an overview of the company’s engagement
in various innovation ecosystems. But as noted
above, the overall reporting structure needs to reflect
the goals of the relationships. Beyond the internal
organizational decisions, the corporate team and its
university counterparts should explicitly address
our six questions and use them to build a shared understanding of success.
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QUESTION 6: What key performance indicators are most useful for evaluating your university
partnerships? Evaluation is a critical element of an
effective university-industry engagement. But as
with any essential activity, the metrics of success
must be carefully defined to ensure that what’s
measured and tracked is closely aligned with the
business goals. Both the key performance indicators (KPIs) you choose and the evaluation process
are fundamental to ongoing effectiveness.
Some of the most commonly used KPIs for university partnerships are cash investment, number of
joint projects initiated per year, number of students
hired, number of patents or licensing agreements,
amount of public funding leveraged, effectiveness
and efficiency of projects, number of faculty members and students involved in projects per year,
number of ideas that turn into product development, and number of investments in startups. Our
research suggests that successful companies tend to
use a variety of KPIs (both quantitative and qualitative), and they define and redefine them periodically
in terms of how they fit with the business goals and
the collaboration formats.
• If the goal is incremental problem-solving, then
the KPIs should prioritize the effectiveness, timeliness, and efficiency of the researcher, group, or
lab in delivering a solution.
• For talent identification and hiring goals, the metrics
might include number of applicants for key roles,
successful hiring ratios, retention, and, over time, the
hires’ performance within the corporation.
• For grand challenges, the KPIs might include the
number of proposals submitted, the diversity of the
proposals, the number of faculty engaged, the extent
to which external funding is leveraged, and, later, the
effectiveness and breadth of the solutions developed.
• For goals involving access to the startup pipeline,
the KPIs might include the number of new startups
coming out of the university in fields of interest to
ASSESSING PARTNERSHIPS WITH UNIVERSITIES
We have created this form, which we call the “university partnership canvas,” to allow business executives to address six key questions
about their university partnerships visually. Working through these six questions can help companies develop a strategic perspective on
their partnerships, thus setting up both companies and universities for more effective interactions. Note: This form is available for download
as part of the online version of this article, which can be found at http://sloanreview.mit.edu/x/59205.
UNIVERSITY PARTNERSHIP CANVAS
Created for:
Created by:
Date:
FOCUS AREAS
What are the key focus areas of your university
partnership, and how are they selected to
ensure alignment with your business goals?
PARTNERS
Who are your primary
university partners, and by
what criteria are they chosen?
3
2
GOALS
What business goals drive your
university partnerships?
PEOPLE, PROCESSES, AND ORGANIZATION
What people, processes, and organizational
structures support your university partnerships?
Version:
5
1
FORMATS
What collaboration formats
match your focus areas and
business goals?
EVALUATION
What key performance indicators are most useful
for evaluating your university partnerships?
4
6
Designed and developed by Lars Frølund, Max Riedel, and Fiona Murray
SLOANREVIEW.MIT.EDU
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 77
I N N O VAT I O N
ADDITIONAL
RESOURCES
To download a copy of
the university partnership canvas form or view
examples of completed
forms, visit the online
version of this article at
http://sloanreview.mit
.edu/x/59205.
the company, and the number and amount of the
company’s investments in such startups.
• Finally, if the goal is publicity and political influence, then the number of high-level meetings,
media mentions, and the level of satisfaction on the
media relations team are among the relevant KPIs.
As we have outlined, working through the six
questions can help companies develop a strategic perspective on their partnerships, thus setting up both
companies and universities for more effective interactions and more successful ecosystem engagement. To
implement the process, we have created a form we call
the “university partnership canvas,”8 which allows executives to represent the six questions visually. (See
“Assessing Partnerships With Universities,” p. 77.)
The University Partnership Canvas
As we have noted, many companies have recently
shifted their focus in university and innovation
ecosystem engagement from incremental problemsolving toward long-term development and
systematic exposure to new startups. In doing so,
however, our research shows that companies have
not always done enough to reconfigure the other
elements of their engagement approach.
In such instances, the university partnership canvas offers a tool to systematically help executives assess
their existing approaches and identify inconsistencies
between their business goals and, for example, the
structure of their existing partnerships. Against this
background, the canvas can help executives define
possible solutions to overcome any mismatches or
tensions. They can also use the canvas to explore the
impact of changing business goals on their existing
university partnerships and, against this background,
make timely decisions on what to change.
A technology company used the canvas to assess a
strategic university partnership program that had
been under way for more than five years, the primary
business goal of which was to drive the development
of new product lines or new businesses. As a first step
in the assessment, we asked the people responsible
for university relations globally to fill out the canvas,
answering the questions one by one and inserting
red lines and/or remarks when they found mismatches or tensions — in other words, when their
answers to the six questions didn’t reinforce each
other. (See “Additional Resources.”)
78 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
The assessment highlighted four mismatches
and tensions:
Format Selection In spite of the business goal of
driving long-term development of new product
lines or new businesses, the company’s preferred
collaboration format was contract research.
Contract research is a good match for short-term,
incremental problem-solving but not for driving
long-term development of new business lines.
Focus Areas The company had no central process
for selecting focus areas that were aligned to its
innovation priorities. Instead, the focus areas
were selected at the business-unit level, which led
to narrowly scoped projects that weren’t aligned
with primary business goals.
Partnership Selection Although the company
was focused on long-term development of new
product lines or new businesses, it gave low priority
to “entrepreneurial culture” in its criteria for selecting university partners.
Partnership Evaluation The company did not
have a KPI that was useful for evaluating the impact
of projects in the partnership and the creation of
new product lines or new businesses.
We then asked the university-relations managers
from the company to come up with tentative solutions to address the mismatches and tensions, and
to write the solutions on the canvas. They prioritized sponsored research and hackathons, with the
expectation that hackathons could inform new
projects that could lead to new business creation.
As for selection criteria, they decided to still give
the top priority to “familiarity” but also decided to
give “entrepreneurial culture” a higher priority
than “scientific excellence.” In selecting focus areas,
university-relations staff wanted to create a centralized call for proposals (funded, if possible, by the
CTO) as a way to drive more sponsored research
projects and hackathons within areas they thought
would impact several business units. Finally, they
decided to create a KPI measuring the number of
new product lines or new businesses based on joint
research projects.
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In another example, a global technology company
wanted to reprioritize its business goals from primarily short-term, incremental problem-solving toward a
focus on systematic exposure to new business ideas
and research-based startups, talent acquisition, and
long-term development of new business lines. The
company first assessed its current approach to university partnerships by using the canvas. It then
inserted the reprioritized business goals and against
this background worked through the rest of the
questions to explore the impact of the revised goals
on its approach to university partnerships.
When working through the questions on the canvas, the company realized that the change in business
goals had a profound impact on its approach to university partnerships. Executives saw that they would
need to (1) set up internal processes to make sure
that the focus areas for university partnerships are
aligned to the overall R&D priorities of the company, (2) apply resources to startup scouting and
hackathons, (3) expand their set of knowledge
brokers embedded in their preferred regional innovation ecosystems, and (4) develop KPIs that
measure investments in startups and the number of
ideas (from sources such as hackathons) that lead to
the development of new product lines.
Unlocking More Value
Max Riedel is a consultant on university relations
for Siemens AG in Munich, Germany. Comment on
this article at http://sloanreview.mit.edu/x/59205.
ACKNOWLEDGMENTS
We would like to thank the following people for giving us
insights into university relations and for many productive
discussions (presented alphabetically by affiliation name):
John Westensee and Anette Miltoft from Aarhus University; Louise Leong and Joe de Sousa from AstraZeneca;
Nicole Eichmeier and Mirjam Storim from BMW; Ciro
Acedo Boria, Alberto Lopez-Oleaga, and Manuel Martines
Alonso from Ferrovial; Michel Benard from Google; Alessandro Curioni and Chris Sciacca from IBM; Karsten Keller
from Nitto Avecia; Søren Bregenholt and Uli Stilz from
Novo Nordisk; Kate Barnard and Mark Jefferies from
Rolls-Royce; Rajiv Dhawan from Samsung; Najib Abusalbi
from Schlumberger; and Natascha Eckert from Siemens.
Finally, we would like to thank the participants in the seminar “Success Factors for University Partnerships,” who
tested our university partnership canvas and gave us very
valuable feedback.
REFERENCES
1. An example of the partnerships is the MIT-IBM Watson
AI Lab. See http://mitibmwatsonailab.mit.edu/.
2. M. Perkmann and A. Salter, “How to Create Productive
Partnerships With Universities,” MIT Sloan Management
Review 53, no. 4 (summer 2012): 79-88.
3. D.E. Sanger, “Corporate Links Worry Scholars,”
New York Times, Oct. 17, 1982, www.nytimes.com;
and University of Oxford Department of Engineering
Science, “Celebrating 25th Anniversary of the RollsRoyce University Technology Centre in Solid Mechanics,”
n.d., www.eng.ox.ac.uk.
Partnering with universities in innovation ecosystems
can be challenging, but the university partnership
canvas can help companies develop a systematic
approach to their interaction with universities, thus
enabling both parties to unlock more value and
pursue a more strategic approach for ecosystem engagement. In our view, companies shouldn’t use the
canvas just as an internal tool for assessing and developing their approach to university partnerships. They
should also use it in their ongoing dialogues with universities. In this way, the canvas can be a tool that
corporations and universities use together to create
transparency about the goals, formats, KPIs, and organizational structures of the partnerships under
consideration for further development.
4. Perkmann and Salter, “How to Create Productive
Partnerships.”
Lars Frølund (@LarsFrolund) is a visiting fellow
at the MIT Innovation Initiative. Fiona Murray (@
Fiona_MIT) is the William Porter Professor of Entrepreneurship at MIT’s Sloan School of Management
and codirector of the MIT Innovation Initiative.
i. See http://reap.mit.edu.
SLOANREVIEW.MIT.EDU
5. “The University of British Columbia and Cisco
Collaborate on Smart+ Connected Buildings and
Smart Energy,” press release, May 27, 2013,
https://newsroom.cisco.com.
6. E. Redden, “From Beijing to Puget Sound,” Inside
Higher Ed, June 19, 2015, www.insidehighered.com.
7. Siemens, for example, has created an organization of
internal and university-based relationship managers to
run their strategic programs (Centers of Knowledge Interchange, or CKI) with universities. For an example of a CKI
university and some activities of the program, see http://
cki.rwth-aachen.de/en. More information is available at
www.siemens.com.
8. The “university partnership canvas” is inspired by the
“business model canvas” developed by A. Osterwalder
and Y. Pigneur in their book “Business Model Generation:
A Handbook for Visionaries, Game Changers, and Challengers” (New York: Wiley, 2010).
Reprint 59205. For ordering information, see page 4.
Copyright © Massachusetts Institute of Technology, 2018.
All rights reserved.
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 79
C O N V E R S AT I O N W I T H T H E C E O
Leading in a
Time of Increased
Expectations
Traditionally, big energy companies focused primarily on power generation,
not customer-centricity. But that’s changing — and today’s digitally empowered
customers have opinions about everything from where their energy should come
from to when their bills should arrive. Lynn Good, CEO of Duke Energy Corp.,
reflects on guiding her company through this transformation.
LYNN J. GOOD, INTERVIEWED BY PAUL MICHELMAN
WHEN YOU LEAD one of the world’s largest
electric utilities, you deal with a rather interesting
set of challenges. For example:
A large percentage of your workforce is nearing
retirement, yet your industry does not seem to hold
the attractiveness of the dynamic technology companies you must compete with for young talent.
You manage stakeholders who often have opposing interests, which means whatever you do to
satisfy one risks alienating another.
After decades of stability and uniformity, your
customers’ expectations for service, choice, and
customization have dramatically transformed and
diverged.
Your industry is exposed to changes in public
policies that are under continual debate — at the
local, national, and global level.
And while nature is literally the source of your
power, it can also be your most vicious enemy,
especially when huge swaths of your customers live
in hurricane zones like the Carolinas and Florida.
Meet Lynn J. Good, chairman, president, and
80 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
CEO of Charlotte, North Carolina-based Duke
Energy Corp. Good, whose calm and upbeat
demeanor belies the very long set of complex
tensions she faces every day, met with MIT
Sloan Management Review editor in chief Paul
Michelman at Duke’s headquarters in Charlotte,
with further exchanges via email. What follows is a
condensed and edited version of their conversation
about leadership, strategy, and an industry undergoing dramatic transformation.
MIT Sloan Management Review: The first topic
I’d like to cover is industry transformation and
the nature of competition. You wrote recently
that the transformational change the electric
industry is undergoing “is surprisingly intense.”
What are the key drivers of this surprisingly
intense transformation?
GOOD: I point to three things: customer expecta-
tions, technology, and public policy. What’s
surprising is that all three of them are changing at
the same time. To have rapid advancement on three
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We have to keep up with
what customers expect
from an experience:
information, control,
convenience, and choice.”
— LYNN GOOD
distinct dimensions simultaneously makes for an
extraordinary transformation.
Let’s look at how our customer relationships have
evolved. And to do that, we have to look at where our
industry has come from. We are engineers who built
big networks. We manage assets. We maintain the reliability of those assets. Our traditional focus was on
the network, not the customer. And then you have
today’s world of empowered consumers, who expect
choice, convenience at their fingertips, and to have
their questions answered in real time. Historically,
we have not invested in the ways necessary to keep
pace with their expectations.
Now that’s changing. The amount of capital
that we’re deploying around new solutions for
customers is more than it was five years ago —
and a lot more than it was 10 or 15 years ago. We
have to keep up with what customers expect from
an experience: information, control, convenience,
and choice. People expect that from their energy
provider just as they do with all the other services
in their lives.
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So what are some of those investments?
What are some of the dynamics of the new
relationship?
GOOD: First, there’s mobile. Customers today care
deeply about where their power comes from. They
may want a fully green solution. They expect choice
about how to pay their bill. They want access to
data on their usage. People are accustomed to having unique customer-specific information about
the products that they’re buying. Mobile apps are
the centerpiece of giving customers access to the
data and options they are seeking.
And then we are putting new devices on our
systems: smart meters, sensing devices, and communication technologies that allow the network to
gather and deliver all this information.
And we need to address our legacy IT [information
technology] systems. Our legacy customer system was
pegged to a meter. It didn’t know if you had a telephone
number. We didn’t have your email address. We knew
the customer as simply a meter. Well, that doesn’t work
anymore. Those legacy systems weren’t built for the
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customer expectations and functionality we’ve been
discussing. We need to invest to bring them up to date.
Given the complexity of your industry relative
to the other industries that consumers interact
with, how reasonable or unreasonable are customer expectations?
GOOD: Well, I’m not sure it matters, because there’s
no customer who thinks they’re unreasonable. They
have expectations, and it’s my job to meet them. If
someone has an expectation that the delivery or service window should be two hours and we’re telling
them eight hours, which means they have to take a
day off of work, we need to adjust that.
So here’s another example. We bill you when we
read your meter. We run a big network, and we tend
to do things on our own timeline. Now, think about
a senior citizen who has a monthly check that comes
the first week of the month. They want very much
for their electric bill to come the first week of the
month, too. It helps them manage their life. Why
can’t we send the bill the first week of the month? Or
why can’t we provide an option where they can send
us a prepayment on the first of the month? It’s a
reasonable expectation and something that other
industries accommodate for customers all the time.
So we’re not solving rocket science problems
here. No one is asking me to explain how the nuclear
reactor works. What people want is to be able to pay
their bill when they want. They want to know how
much energy they’re using. They want to know how
it compares to last year. They want to know if they’re
about to go over their budget. Those are things that
we should be able to enable, but it takes investment.
Presumably, customer expectations are a moving
target, particularly now, with new smartphones
coming out every year with new abilities, right?
How do you think about keeping pace?
GOOD: It’s a really important question for us. Think
about the big legacy systems we operate. Historically,
we have worked with long development windows.
Define requirements, design it, test it, and roll it out.
It might take five years. Now, we are shifting toward
agile methodologies, so we’re delivering customer
solutions much more quickly. We’re testing, piloting,
getting feedback, and continuing to move forward.
We can’t live with a five-year cycle.
82 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
That’s not to say that we’re necessarily going fast
enough, but we’re trying to keep pace with what we
know to be a rapidly developing set of expectations.
There’s a cultural element to this. We are trying to
foster an environment where flexibility, change, and
agility is the way we do business. And this is where
you start to see some of the tensions with the complexity of our business. We have safety and reliability
to be concerned with; we manage big equipment. It
takes us a long time to do things at times. But nonetheless, we have to be an organization that embraces
change and is agile enough to keep moving.
So I see that agility at two levels. It’s not only
products and services and trying to keep pace with
customers, but it’s increasing the metabolism of the
organization to embrace change internally more
readily.
As the organization’s top leader, how do you
talk about this?
GOOD: Over the last year, I have been working to
define for the organization what I call our leadership imperative. I’m trying to paint a picture of
what it means to lead at Duke Energy.
There are five elements of the imperative. The
first one is “live our purpose,” which is nothing
more than a way to say that we have a very important mission. People count on us 24 hours a day,
seven days a week, all seasons, all times. I’m looking
for personal conviction and commitment. I won’t
get the discretionary effort I need — think about
our Hurricane Irma response — if our leaders
don’t feel a clear connection between their purpose
as a leader and the big mission of Duke Energy.
The second is that you must be a leader who
understands how to lead change. You can set a
vision and you can lead your organization toward
the vision in an agile, flexible way. You embrace all
of the things we are talking about here that are
transforming in the business.
The third is to deliver results the right way. We
can sit in this conference room and talk about all
kinds of good things, but we need to deliver an
outcome to our customers, an outcome to our
stakeholders. And it has to be the right way — with
safety, integrity, and customer service.
The fourth, “work as one,” is our term for how we
go about working together in this big, siloed
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organization with our nuclear professionals and
distribution employees and legal experts and regulatory managers. The outside world doesn’t see us that
way. They want one solution from Duke Energy.
If you are a leader wrestling with a problem that requires you to go across the company, the company
needs to support you. We need to show up as Duke
Energy, not as leaders of siloed organizations.
That requires great collaboration, joint problemsolving, and enterprise leadership.
And finally, “inspire our employees.” We can’t be
successful in any period of change or transformation
unless we have absolutely every employee aligned
and getting up in the morning with enthusiasm
about how they’re going to add to the future of the
organization. This part of the imperative means embracing inclusion — embracing different cultures
and generations, from millennials to people who are
nearing the end of their career. It’s leading across geographies. The focus on inspiration emphasizes that
we win as an organization when all 29,000 of us are
moving in the same direction.
I talk a lot about these leadership imperatives.
We’re developing leaders with these qualities in mind.
We talk about leaders as being strong in one quality
and not as strong in others. We look at what competencies underlie the imperatives. How do we put those
into a leadership academy for developing leaders?
They are a part of ongoing performance discussions
so that we are translating the ideal of what we want
into actual applications in the ways we behave.
It sounds like you are emphasizing behavior
and activities that may be hard to quantify and
measure.
GOOD: There are plenty of things to quantify around
here. We are flush with metrics. Whether it’s earnings or reliability or capacity or generation or
turnover rates, we’re a metric-rich environment.
We’re accustomed to running the business by
metrics. But I don’t think running the business
by metrics alone gets you through a period of
transformation.
Has your approach to planning evolved?
GOOD: Over the past five years, I’ve probably spent
more time on planning than in the previous 10
years combined.
We are dealing with so many more variables today.
We’ve talked about a number of them already. Then
there is the focus on renewables and curtailing our
reliance on fossil fuels; deregulation; and changes in
market structure. These changes can incent lots of
different, even conflicting, behaviors and decisions.
So we undertake an ongoing assessment of all of
these trends to develop the Duke point of view. And
then we plan scenarios in which we are wrong.
What happens if there is no more shale gas or there
is a regulatory mandate to go 100% in a new direction? Amidst the scenarios, we need to identify
where we believe — under the majority of the circumstances — it’s going to make sense to invest.
You can have a horizon probably through 2020,
maybe 2025, on that basis. I think renewables that
are cost-effective are a good investment. Investing
in our grid makes sense — to accommodate renewables, develop more storm-resistant infrastructure,
and address all these customer expectations we’ve
talked about. Cybersecurity is another.
And we believe natural gas is a good place to
invest, at least in the medium term, since we don’t
have a set of technology solutions that will make
reliance on 100% renewable energy possible. Meanwhile, I know coal is under pressure. I know nuclear
is challenged. What else do I have in my portfolio
of choices that complements the renewable, lowercarbon solutions and that I believe could allow me
to keep delivering reliable 24/7 power?
We’re constantly testing our assumptions. We
have signposts that we monitor — prices, adoption
We need to show up as Duke Energy, not as leaders of
siloed organizations. That requires great collaboration, joint
problem-solving, and enterprise leadership.”
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rates, regulatory trends, public policy, what’s happening in the [U.S. Department of Energy’s] National
Labs. What’s happening in Europe, Australia, and
other markets that may be ahead of us on some of
these issues? We are challenging our view of the future.
we complement that with experience, support, and
intensive knowledge transfer from our more senior
employees, you’ve got a combination that can work.
Given the earlier part of our conversation about
customers’ expectations being driven by their ex-
How frequently do you revisit your assumptions
periences with other services or products in their
and objectives?
lives, has that led you to think more broadly
GOOD: Very frequently. In our business, as in many,
about where employees come from?
you’re always challenged on both the short and
long term. How much time do I spend on quarterly
earnings versus the longer horizon?
We are trying to manage that through process.
First, my weekly leadership staff meeting focuses on
current events: What’s coming at us right now; where
are we on the current year plan? And then, every couple of months, we take a day to a day and a half to look
out further. We discuss 2018, 2020. We revisit renewables and what’s going on with battery technology.
We’ll bring in an outside speaker — for example,
someone who has embraced agile techniques. And
then we bring those topics in at the board level, so
that every board meeting has a longer-term topic.
We take a similar approach to strategy. We set up a
framework that articulates what we believe are foundational elements to success in this dynamic industry.
I’ll give you several examples: meeting customer
expectations, being very good at stakeholder engagement, and operating with excellence — which we
absolutely must do. We have to deliver safe, reliable,
environmentally responsible power, day in and day
out. If we don’t do those things well, it really doesn’t
matter what we’re investing in.
GOOD: We recently named a chief customer officer
Let’s talk about people. As your workforce ages,
how do you recruit young talent to a very mature
industry that might not match their idealism?
GOOD: I actually do think our industry taps into
that idealism, because we do something that matters.
Help us figure out how we’re going to embrace a
100% renewable future. Come help an industry that
matters so much to survive for another 100 years. It
will take your creativity and agility to do that.
Energy is a part of the conversation everywhere,
so we have a good story to tell young people. And we
put them to work with the skills they bring; they are
technology-literate, interested in sustainability and
innovation, and willing to embrace change. When
84 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
from outside the industry, someone who has a
background with Disney and United Airlines, to
help create the picture for what customer relationships should look like.
In our customer organization, we already have
more segmentation experts, more data analytics
professionals, more service-oriented people who
understand how we can make a better connection
with the customers. We’ll continue to look at new
initiatives through that lens, and I think that will
lead to more differentiated hiring over time.
How do you think about finding the right balance
between generating cleaner energy and meeting
other demands like affordability?
GOOD: We sit at an intersection of affordability,
reliability, clean, safe. We can’t afford to run down any
single path all by itself. We can’t just say we’re going to
produce the cheapest power no matter where it comes
from. Or that we’re going to spend all the money in
the world to make sure your power never goes out. Or
that we’re going to drive carbon emissions down to
zero in the next four years. It’s impossible to do. No
one has the resources to accomplish that. So we are
constantly trying to strike the balance. We have targets
and expectations around each.
Affordability is really important, whether you are
a consumer with low discretionary income or you’re
an industrial company that’s competing against
Georgia or China or Latin America. If you’re in heavy
manufacturing, the cost of power is one of the top
three factors that makes you competitive. So we can’t
walk away from affordability.
We can’t walk away from sustainability, either.
We need to demonstrate that we are reducing our
carbon footprint, that we’re being good stewards of
water, we’re being good stewards of solid waste and
recycling, and doing all of the things our customers
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Think about the complex world we’ve been discussing.
Wouldn’t you love to have different points of view on
how we deal with these really tough business issues?”
expect, and frankly, I expect. The communities we
operate in expect us to be responsible.
Our carbon emissions are down 30% from 2005.
We’re working toward 40%. It’s going to take a combination of renewables, natural gas conversion, and
energy efficiency for us to continue to move in that
direction. Some stakeholders may argue we’re not
moving fast enough. Others may argue that we are
prioritizing sustainability over lower prices.
And that’s just one part of the business. We are
always trying to work with stakeholders to find common ground — to manage the tensions between
different priorities and to keep the business running,
affordable, safe, reliable, clean.
I’d like to hear your thoughts on where we
Are there other levers that we can be pulling?
GOOD: I wish I knew what the other ways were. I
don’t think there’s a single solution to this. To get to
the C-suite, you need to put together 25 to 30 years
of career. There are a lot of things that could happen
in 25 or 30 years. The objective is to get a lot of diverse talent in the pipeline so you’re building a robust
set of candidates with that type of experience.
When it comes to recruiting, we’ve been trying
to cast broader nets. We’ve done more sourcing —
where we go find people, as opposed to waiting for
them to come to us. We’ve tried recruitment boot
camps for our field crews in diverse communities
to try to increase the interest and understanding of
what our job opportunities are. There’s a lot of
work to do to continue that pipeline development.
are with the number of women leading large
organizations.
And what about board representation?
GOOD: I believe great progress has been made.
GOOD: I think it’s a similar issue. There’s been some
Now, great against a low base, right? But if you look
at our industry in particular, there are more women
CEOs than certainly when I started in the industry.
I look at the executive ranks here at Duke. Our general counsel is a woman, my head of administration
is a woman. We have very important leaders at the
next level who are women.
The question is generally, is it enough? Is it fast
enough? Are we on a curve that’s going to lead to
percentages that are more consistent with percentages entering the workforce? When do we get to
50/50? When do we get to 30%? I think that’s the
challenge for diversity in general. How do you continue to recruit, promote, develop, and mentor an
increasingly diverse population?
And I think the business imperative for this is undeniable. We absolutely must do it. The way we think
about it at Duke is that it’s a pipeline issue first. We
need to make sure that we see enough diverse candidates in our hiring practices. And that we have
intentional development programs and mentoring
programs directed at helping people be successful.
discussion by certain institutional investors of trying to get to 30% board representation for women.
Board recruitment is challenging. You want diversity, but you don’t want people who are “over-boarded.”
You should sit on one or two boards — three maybe, if
you’re retired. There’s a lot of demand for director candidates who will increase the diversity in a boardroom,
so there’s a pipeline issue there, too. If we can get more
diversity in the C-suites of corporations, the diversity
of the boards will improve as well.
I’m a believer that diverse perspectives are very
valuable. You think about the complex world we’ve
been discussing. Wouldn’t you love to have different
points of view on how we deal with these really
tough business issues? Diversity is a business imperative, just like good capital stewardship.
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WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 85
S T R AT E G Y
What Sets Breakthrough
Strategies Apart
Innovative strategies depend more on novel, well-reasoned theories
than on well-crunched numbers.
BY TEPPO FELIN AND TODD ZENGER
STRATEGY ADVICE HAS taken a rather negative
tone of late. Consultants and scholars alike
seem obsessed with eradicating bias and error
in human judgment and decision-making. A
virtual cottage industry has emerged to offer
advice about how to do that, often pushing
managers to replace flawed human judgment
with big data analytics and various computational tools. Given this abysmal view of
human judgment, it’s no wonder that some
authors have suggested that algorithms and
artificial intelligence (AI) should play a
greater role in strategic decisions.
No doubt bias and error are important
concerns in strategic decision-making. Yet
it seems quite a stretch to suggest that the
original strategies developed by people
like Apple’s Steve Jobs, Starbucks’ Howard
Schultz, or even Walmart’s Sam Walton had
much to do with error-free calculations
based on big data. Their strategies, like most
breakthrough strategies, emerged in settings
with remarkably little data to process and
little basis for calculation — situations in
which the paths to value creation were highly
uncertain and evidence was sparse. We are
highly skeptical that debiasing decisionmaking, eradicating errors, or ceding
strategy to AI will improve strategizing, let
alone lead to breakthrough strategies.
What Do You See?
Composing valuable strategies requires seeing the world in new and unique ways. It
requires asking novel questions that prompt
fresh insight. Even the most sophisticated,
deep-learning-enhanced computers or
algorithms simply cannot generate such an
outlook.
But where does the uniqueness and
novelty so essential to innovative strategic
thinking come from? It comes from
86 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
contrarian, perhaps even “distorted,” perceptions and beliefs about reality and the
“facts” that surround us. We think that venture capitalist and PayPal cofounder Peter
Thiel gets it roughly right when he asks
prospective entrepreneurs to tell him something they believe is true that nobody agrees
with them about. If everyone believes the
ALEX NABAUM/THEISPOT.COM
same thing — or if everyone uses the same
variables, information, and computational
tools — the logical result is computational
consistency, shared conclusions, and me-too
strategies. Thus, while renowned behavioral
economist Daniel Kahneman and his coauthors Andrew M. Rosenfield, Linnea
Gandhi, and Tom Blaser argued in a 2016
Harvard Business Review article that it is
problematic that professionals “often make
decisions that deviate significantly from
those of their peers,” it is this seeming
pathology that provides the underlying
raw material — the essential ingredient —
for valuable strategies. In setting strategy,
deviation in judgment is a feature, not a bug.
Examples abound. In the mid-1970s,
computers were used for large-scale industrial and office applications. A mass-market
personal computer was a reality few envisioned to be feasible, and any number of
facts, surveys, expert opinions, and data
could corroborate that conclusion. Yet
despite the evidence and widespread agreement, Steve Jobs, cofounder of Apple,
somehow believed otherwise. Similar narratives could be told about Herb Kelleher
of Southwest Airlines or Jeff Bezos of
Amazon.com. All three entrepreneurs ignored current evidence to pursue a future
reality that only they and perhaps a handful
of others envisioned.
It is tempting to believe that the right evidence and the right analysis will yield the
right strategy. But just as customer surveys
seldom lead to breakthrough products that
capture the imagination of customers and
markets, substantive strategy-making requires that we see well beyond the available
data. As Polaroid Corp. founder Edwin H.
Land once noted, “every significant invention … must be startling, unexpected, and
must come into a world that is not prepared
for it.” The story is no different for managers
seeking to advance valuable new functional
strategies — supply chain solutions, product
development ideas, or marketing strategies.
Paths to substantive value creation emerge
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from those capable of envisioning a reality
that others simply can’t imagine.
We view the strategist’s task as akin to an
inkblot test, where participants are presented with highly ambiguous evidence and
signals that afford many possible realities,
but offer no single correct answer. With
such tests, the very same evidence — an
ambiguous picture or set of marks — can
be interpreted correctly in many different
ways. Indeed, Jobs and the rest of the nascent
computer industry all had the same data.
But in the words of an old Apple slogan,
Jobs did indeed see and “think different.”
Valuable strategizing demands this novel
perception — an ability to see in ambiguous
cues and data what others can’t see. Strategic
thinking is fueled by the novelty of our observation, not its consistency. The object of
strategic thinking is not to ensure that we all
observe the same information and derive
the same conclusion. It is precisely the opposite: If your desire is to be a value creator,
you must aspire to see what others cannot.
Strategy as Theory
This is not to say that we believe strategic
thinking sits outside the realm of logic,
science, and experimentation. Quite the
contrary: We argue that strategic thinkers
engage in an exercise that parallels that of
scientists. Like scientists, they start with
a significant problem to solve, and then
use this problem as a prompt to compose
a theory — in this case, a theory of value
creation. This theory then becomes their
unique perspective and point of view
about the opportunity they see.
One role of a theory is to shape sight
and perception, to enable seeing — often
from simple observations — what was
previously unnoticed. As Albert Einstein
observed, “whether you can observe a
thing or not depends on the theory which
you use.” Through a novel business theory,
you see value in choices, in combinations,
and in purchases that others cannot. And
most importantly, like theories in science,
your theory of value should lead to hypotheses and experiments that help realize
opportunities unseen by others.
Of course, whether you are an entrepreneur, a corporate strategist, or a mid-level
manager, generating the value you envision
typically demands convincing others of the
merits of your theory. Convincing others to
believe in your envisioned reality over theirs
is no small task. In 2009, the founders of
Airbnb Inc. pitched their now-famous idea
to the venture capitalist Fred Wilson and his
firm Union Square Ventures, known for its
prescient investments in entrepreneurial
growth companies such as Twitter, Tumblr,
and Kickstarter. Airbnb needed an infusion
of cash, but Wilson and his partners were
tremendously skeptical — and with good
reason. After all, why would anyone want to
stay with strangers while traveling? Why
would individuals agree to rent their homes
to complete strangers? And how on earth
would a small startup — without any experience in the industry — take on large
established players and brands in the sophisticated hotel market? Given these concerns,
Wilson’s company passed on the investment
opportunity. The rest, of course, is history. In
2017, Airbnb claimed more than 3 million
listings in 65,000 cities in 191 countries.
Only with hindsight is it easy to see the
value in Airbnb. After all, Wilson’s company’s
If everyone believes the same thing — or if
everyone uses the same variables, information, and computational tools — the logical
result is computational consistency, shared
conclusions, and me-too strategies.
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 87
S T R AT E G Y
decision was entirely consistent with the facts
at the time. But selling a theory like Airbnb’s
takes more than selling facts. It is instead
about selling assumptions and logic — convincing those whose resources you need
that your assumptions and logic are reasonable and compelling. It is about selling a
series of if-then statements. For the Airbnb
founders, those entailed convincing investors that if they could solve a number of key
problems — including secure payment,
efficient matching of those seeking accommodations with those renting homes, and
development of a mechanism to generate
reputation and trust between the two parties — then the business would thrive. Of
course, Airbnb could point to eBay Inc. and
Amazon as examples of partial solutions to
the trust problem. But fundamentally, the
path to gaining others’ support and resources depended on selling their theory
through a compelling logical narrative.
Keep in mind that the most valuable theories often face the greatest resistance. In both
the world of science and the world of entrepreneurship, stories abound of persistent
scientists or entrepreneurs facing consistent
rejection — until one day they don’t. Novel
theories are consistently resisted. And you too
will likely face similar resistance in selling
your novel theories. But clarity of assumptions, persuasive logic, and persistence are key
to breaking through this resistance.
Testing Theories
Of course, the ultimate test of any theory of
value rests on whether the strategic experiments you undertake generate the value
anticipated. Fortunately, successful theories
do tend to share some common features.
First, valuable theories are novel. As
discussed above, they are built around
novel beliefs and often try to solve previously unrecognized problems. Think of
Uber, Apple, Airbnb, eBay, Amazon, or
Walmart. At their origin is some form of
contrarian or divergent thinking.
Second, valuable theories are simple and
We believe strategic leaders should focus
their efforts on positing theories, testing
their underlying logic and assumptions, and
crafting strategic actions and experiments.
clear. They indicate clearly what problems to
solve and experiments to run. They also
make it easier to spot solutions others have
overlooked. Consider the famous 1979 visit
of Steve Jobs to the Xerox PARC research
center, where he observed many of the central technologies that today shape personal
computers: the graphical user interface,
bitmapped graphics, and networking technology. Jobs’ theory of value in personal
computers focused on generating seamless
and intuitive interactions between a user
and a computer. Thus, when he walked into
Xerox PARC and found technologies that
were languishing there, he instantly recognized that they could solve problems framed
by his theory. He later recalled one of the
technologies he saw that day as the “best
thing I’d ever seen in my life.”
Third, particularly valuable theories
have broad and general application. They
solve not one but a host of problems, and
then continue to identify problems to solve.
This happened with Apple. Jobs’ theory of
seamless interaction between a user and
a device has continued to direct Apple’s
value-creating efforts, leading to a remarkable succession of devices that have
included computers, music players, phones,
tablets, and watches. Something similar
happened with The Walt Disney Co. In the
1920s and 1930s, Walt Disney began creating fantasy worlds and fantasy characters
through animated film; then, once opportunities for licensing those characters
started to emerge, Disney developed a
broader theory of value, recognizing that
these characters could be replicated and
resold in other entertainment businesses,
including books, music, character licensing, and later theme parks, hotels, and
88 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
television. This theory has continued to fuel
Disney’s strategic experiments for decades,
prompting moves into retail stores, cruise
ships, and Broadway shows. More recently,
it prompted Disney to purchase Marvel
Entertainment LLC and Lucasfilm Ltd. LLC
and expand its content into superheroes
and science fiction characters.
Getting Strategy Right
The human capacity for calculation is admittedly flawed and error-prone. Strategic
decision-makers should do their best to
avoid succumbing to any number of biases,
including overconfidence, confirmation, and
anchoring biases. But the cumulative negative effects of these biases pale in comparison
to the capacity for enhanced strategic
decision-making that can be provided by a
well-crafted theory. Humans in general are
endowed with a remarkable capacity to compose theories that facilitate novel perception,
experimentation, and value creation. We believe strategic leaders should focus their
efforts on positing theories, testing their underlying logic and assumptions, and crafting
strategic actions and experiments. It is those
activities — rather than computation or the
avoidance of biases and errors — that lead to
true breakthroughs.
Teppo Felin (@teppofelin) is a professor of
strategy at the Saïd Business School at the
University of Oxford in the United Kingdom.
Todd Zenger (@toddzenger) is the N. Eldon
Tanner Professor of Strategy and Strategic
Leadership and a Presidential Professor
at the University of Utah in Salt Lake City,
Utah. Comment on this article at http://
sloanreview.mit.edu/x/59223.
Reprint 59223. For ordering information, see page 4.
Copyright © Massachusetts Institute of Technology,
2018. All rights reserved.
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ENTREPRENEURSHIP
How to Test Your
Assumptions
When you’re developing a strategy for a new business, testing assumptions in a
logical order gives you the best chance to make course corrections early — and not
waste time and money.
BY JON FJELD
FOR A NEW BUSINESS to succeed, many assumptions have to prove true. Testing
assumptions in a logical order gives the team
the best chance of making course corrections early — and not wasting time and
money. In this essay, I outline a method for
(1) identifying the assumptions or unknowns and (2) resolving these assumptions
on the basis of three parameters: severity,
probability, and cost of resolution.
Between stints in academia, I spent more
than 20 years in marketing, engineering, and
general management in both startups and
public companies. Most pertinent to this
essay, I served as vice president of engineering
for Align Technology Inc. from December
2000 to July 2004. It was at Align — now a
6,800-employee company based in San Jose,
California — where I learned firsthand the
importance of testing assumptions. Since
leaving Align, I have worked with more than
100 startup teams and developed a systematic
approach for testing assumptions. Below, I’ll
explain my method, illustrating how testing
assumptions helped Rent the Runway Inc., a
505-employee company based in New York
City, become a fast-growing success. I’ll also
explain how my method might have helped
Align — prosperous though it has now become — bleed less cash during its early days.
The enthusiasm surrounding the “lean
startup methodology” and its many offshoots
has created a mindset that entrepreneurs
A. RICHARD ALLEN/THEISPOT.COM
Failure alone does
not teach. If there are
an infinite number of
bad ideas, eliminating
one gets us no closer
to a good idea.
should just launch, failing early and often —
iterating, to use startup parlance. But failure
alone does not teach. If there are an infinite
number of bad ideas, eliminating one gets
us no closer to a good idea. Rather, the
businessperson contemplating a new venture must begin by evaluating factors that
have to be true for the venture to succeed.
He or she also must model these factors in
a way that allows for reasonable testing.
For example, the assumption that people
will buy a product for the asking price is
a big one; it would take a full launch to
completely validate this. Therefore, the entrepreneur must split big assumptions into
discrete, manageable assumptions that can
be tested at a level of detail allowing for
efficient learning.
Identifying the assumptions is the first
step. The second is determining a sequence
for testing them. Each step should resolve a
critical unknown. And each resolution
should spur the entrepreneur to continue,
change direction (in other words, “pivot”),
or, in the worst case, abandon the enterprise. The core of this method is prioritizing
assumptions according to three factors:
severity, probability, and cost of resolution.
Severity is the impact on the venture if
an assumption is not true. The most severe
assumption is that there is a customer need
at all. Rent the Runway is a case in point.
Launched in 2009 by two Harvard Business
School graduates, Rent the Runway allows
consumers to rent ultrafashionable, luxury-brand designer clothing. The business
model depended on the assumption that
consumers would want to rent dresses over
the internet. Accordingly, the founders
tested this assumption before proceeding.
The business model also depended on
whether the founders could acquire
dresses at wholesale prices, so before
WINTER 2018 MIT SLOAN MANAGEMENT REVIEW 89
ENTREPRENEURSHIP
proceeding, the founders tested whether
such partnerships were feasible.
But assumptions about partnerships
or technology do not have the same severity for all ventures. For example, a medical
device startup’s viability often hinges on
a particular technology rather than any
partnerships. In short, severity can be difficult to quantify, but the intuition behind
it is clear. The simple rule is: If all else is
equal and A has higher severity than B, then
test A before B.
The second factor is the probability of an
assumption being true. What is counterintuitive to many is that assumptions that have
a lower probability of being true should be
tested first. It seems natural to validate likelier assumptions; positive feedback feels
good. But this is the wrong approach. If the
goal is to minimize the time and money expended before a key pivot or the decision to
abandon the venture, then the assumptions
that are least likely to be true should be addressed first. That is: If all else is equal and
assumption A is less probable than B, then attempt to validate A before B.
The third factor is the cost of resolving
uncertainty. What constitutes resolution?
What evidence is sufficient to declare the assumption validated? In Rent the Runway’s
case, the founders resolved uncertainty by
mailing PDF photos of dresses to 1,000
prospective customers. The overwhelmingly positive response validated their
assumption that customers would rent
dresses from a website. Tellingly, the cost
for validating this key assumption was
merely the low price of a bulk mailing —
not the potentially high cost of building a
full-fledged website.
In estimating the cost of resolution for an
assumption, entrepreneurs should (1) assess
what evidence they need for resolution and
(2) find the simplest, lowest-cost way of
gathering that evidence. Hence, the third
simple rule of my method: If all else is equal
and resolving assumption A costs less than resolving B, then address A before B.
Having considered these three factors
individually, we can now put them together.
If we can judge both the potential severity
of unknowns and their probability, we can
construct a ranking, using an index that is
something like severity of potential impact
times probability of the negative occurrence.
(This quantity is what we mean by “risk,” in
the colloquial sense.) If we add the estimated cost in money and time to resolve the
unknown, we can construct a ratio of risk to
the cost of resolution:
Severity x Probability assumption is false
Cost of resolution
This ratio creates a ranking of assumptions. Addressing the assumptions according
to this order — wherein the highest-risk,
fastest/cheapest-to-resolve assumptions are
ranked first — provides a path to removing
the greatest risk for the lowest cost.
I believe that this method could have
saved Align Technology significant time and
money. Founded in 1997, Align raised venture capital to develop a device for correcting
malocclusion (crooked teeth). The company’s flagship product, Invisalign, debuted in
1999. The concept was a line of custommade, clear plastic retainers with the ability
to move teeth. Today, Align is a dominant
supplier to the orthodontic market, with
2016 revenues of $1.1 billion. But in its first
six years, Align, despite having raised more
than $250 million, at one point almost ran
out of cash. Therefore, I believe it’s worth
asking: Could Align have avoided those
major early losses if it had taken a smarter
approach to identifying and testing
assumptions?
Align’s founders believed they could
create an automated, technology-driven
manufacturing platform that produced
Invisalign at a fraction of the market’s willingness to pay. Years later, they’ve achieved
90 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
this goal. But it took a number of costly
generations of technology — and much
trial and error. Exacerbating the problems
was a focus on pricey marketing programs
targeting consumers.
Admittedly, hindsight is 20/20 — and
Align’s present-day success casts an optimistic “all’s well that ends well” sheen on its
early struggles. However, I believe that testing certain assumptions could have helped
Align have a smoother path in its early
years. For example, with some regional testing, Align could have discovered that it was
not patient demand alone — but rather a
combination of patients’ awareness and
orthodontists’ acceptance — that was the
true driver of widespread adoption. Of
course, testing the market in this manner
would have involved creating a manufacturing system capable of producing the
Invisalign product. But this could have
happened on a scale that was smaller and
more controlled than that of the company’s
actual operations.
Practical realities will influence how entrepreneurs put these assumption-testing
principles into action. For example, investors may have different opinions than
entrepreneurs about which assumptions
are most severe. And time to market may require testing multiple assumptions at the
same time — instead of following the
sequential path in the guidelines I’ve described. Founders will, to be sure, always
work in an environment of unknowns and
insufficient information. But to the extent it
is possible, I hope I have offered a practical
method for managing the uncertainty that
lies at the heart of new-venture creation.
Jon Fjeld is a professor of the practice of
strategy and philosophy, and the executive
director of the Center for Entrepreneurship
and Innovation at Duke University’s Fuqua
School of Business, in Durham, North
Carolina. Comment on this article at
http://sloanreview.mit.edu/59203.
Reprint 59203. For ordering information, see page 4.
Copyright © Massachusetts Institute of Technology,
2018. All rights reserved.
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WINTER 2018 • VOLUME 59 • NUMBER 2
A New Approach to Designing Work
Nelson P. Repenning (MIT Sloan School), Don Kieffer (MIT Sloan School and ShiftGear Work Design),
and James Repenning (ShiftGear Work Design) pp. 29-38
The goal of this article, according to the authors, is to help managers understand several key work
design principles that undergird not only agile practices in software but also Toyota Motor Corp.’s
well-known production system. Understanding these underlying work design principles — through
a framework the authors call dynamic work design — enables managers to create work processes in
their own organizations that are both more flexible and more efficient.
Traditionally, an academic theory known as contingency theory has suggested that if work consists
of well-defined tasks (for example, assembling components), then it is best to organize it serially, like a
factory assembly line. Conversely, if work is highly ambiguous and requires ongoing interaction (for
example, designing new products), then it is best organized collaboratively.
The authors argue that this approach to work design is not entirely satisfying for two reasons. First, it
describes an unpalatable trade-off: Work done using a serial factory design isn’t very flexible, making it
hard to adapt to changes in external conditions, and work done using the collaborative approach often
isn’t very efficient. Second, few types of work perfectly fit the archetype of well-defined or ambiguous work.
The authors instead advocate a dynamic approach to process and organizational design that transcends the serial versus collaborative work framework by creating mechanisms for moving between the
two basic ways of organizing work at appropriate intervals. By identifying mechanisms to cycle back
and forth between well-defined factory-style tasks and collaborative modes when appropriate, managers
can considerably reduce the trade-off between efficiency and adaptability.
For example, work on Toyota assembly lines is the epitome of the serial, mechanistic work design,
and tasks are precisely specified. But sometimes things go awry. In the Toyota scheme, a worker noticing
such an issue is supposed to pull the Andon cord (or push a button) to stop the production line and
fix the problem, which temporarily changes the work design to a collaborative problem-solving mode.
Once the problem is resolved, the operator returns to the serial work design. The authors argue that
movement between the two work modes is also the key to understanding the success of agile software
development methods.
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What to Expect From Agile
Julian Birkinshaw (London Business School) pp. 39-42
In this article, the author examines agile as a management practice through a case study of ING bank in
the Netherlands, which has adopted agile across its headquarters in Amsterdam. The research is based
on in-depth interviews with 15 ING executives and many front-line employees. The article highlights
key learnings at ING, largely from the point of view of the senior executives of the bank, and explores
the challenges of implementing agile in an established organization, focusing on five lessons:
1. Decide how much power you are willing to give up. Agile shifts power away from those at the top
and puts ownership in the hands of those closest to the action. Unless top executives are willing to
accept that they are surrendering some status and power, agile will not be a good fit.
2. Prepare stakeholders for the leap. ING executives had to sell agile to nervous stakeholders, including
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EXECUTIVE BRIEFINGS
board members, employees, and bank regulators. For example, executives assured regulators that
finance, compliance, and legal functions would continue to be managed in the traditional way.
3. Build the structure around customers — and keep it fluid. Like other approaches to management,
agile is focused on customers. ING in the Netherlands has tried to keep its organizational structure
fluid so that it can evolve to do what’s best for customers.
4. Give employees the right balance of oversight and autonomy. A quarterly goal-setting process is part of
the agile structure at ING in the Netherlands. This has been a learning process. Initially, groups defined
goals that were comfortably achievable, and executives had to urge them toward more ambitious targets.
5. Provide employees with development and growth opportunities. The team-based structures used in
agile can be scary for employees used to having their personal development and career progression
mapped out by HR departments or the mainstream career trajectories of a given industry. One lesson
from ING’s experience is that finding proper coaching and support for agile — and the new, long-term
responsibilities employees must embrace — is one of the hardest parts of the transformation.
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The Trouble With Homogeneous Teams
Evan Apfelbaum (MIT Sloan School), interviewed by Martha E. Mangelsdorf (MIT Sloan Management
Review) pp. 43-47
Many companies are paying increased attention to workplace diversity — issues such as how to increase
diversity, how to foster sensitivity to it, and how to manage a diverse workforce. But, according to MIT
Sloan School of Management professor Evan Apfelbaum, managers should also factor in issues associated
with a related problem: workplace homogeneity. In this interview with MIT Sloan Management Review
editorial director Martha E. Mangelsdorf, Apfelbaum explains why diverse groups are sometimes able to
reach better decisions than homogenous groups. Recent research, including Apfelbaum’s own, has found,
for example, that racially homogeneous groups are less rigorous in their decision-making — and make
more mistakes — than groups composed of people with racially diverse backgrounds.
For example, Apfelbaum notes that in a study that compared trading practices of homogeneous and
diverse groups in both Asia and the U.S., members of the racially homogeneous groups showed a greater
willingness to pay more than things were worth. What’s more, people within such groups were “more likely
to copy another person’s mistake — presumably assuming that the mistake had some value that they just
didn’t understand.” According to Apfelbaum, this finding suggests “that there is something fundamental
about working with similar versus different others that affects individuals’ decision-making.” Other studies
have similarly indicated that diverse groups have fewer blind spots. In diverse groups, Apfelbaum says,
people are more likely to “come to an independent assessment of what they think to be the case.”
In the interview, Apfelbaum observes that “diversity can be both advantageous and complicated in
the workplace and in decision-making groups.” Many people in social settings gravitate toward people
with similar backgrounds, and research has also shown that diverse groups can experience conflict and
mistrust. However, conflict isn’t necessarily a negative. In one study, for example, different groups were
asked to review identical information before reaching their recommendations. The diverse groups
tended to consider more perspectives than the homogeneous ones and were more accurate in both their
decisions and their assessments of their performance. The homogeneous groups had more confidence
in their decisions, but those decisions were actually less accurate.
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The Truth About Hierarchy
Bret Sanner (Shenandoah University) and J. Stuart Bunderson (Olin Business School, Washington
University in St. Louis) pp. 49-52
Research on social species shows that hierarchies are important for group functioning. Human beings
also have a tendency to think and act hierarchically. In fact, hierarchies — distinct differences in group
members’ power and status — can be found in virtually every human group, from children on the
92 MIT SLOAN MANAGEMENT REVIEW WINTER 2018
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playground to executives in the boardroom. Although many people have argued that flat organizations
promote innovative thinking, the authors maintain that a properly deployed hierarchy can help teams
engage in and get the most out of their efforts to learn and innovate.
Specifically, the authors observe that hierarchies help teams generate, identify, and select new ideas
by performing three critical functions: bounding solutions, converging ideas, and structuring processes.
“A paradox of creativity,” the authors write, “is that people are more innovative when they have clear
constraints (such as time, budget, customer requirements, etc.) within which their solutions must fit.”
Early on, teams tend to come up with an array of ideas and possibilities. Hierarchies, the authors
explain, can help sort through which ideas should be pursued and which ones are less promising.
The authors provide three recommendations for leaders seeking to leverage the power of hierarchy
on teams and avoid its pitfalls. First, organizations should have a clear chain of command. In one study,
teams with a clear chain of command were less likely to get bogged down in conflicts and stalemates
than other teams.
Second, organizations need to create performance-based cultures in which performance gets measured, publicized, and celebrated. Hierarchies in performance-based cultures are more likely to be based
on expertise, and that can counteract unconscious biases against women and minorities.
Third, people at the top of the organization should act in ways that support group goals as opposed
to promoting their own interests. Citing a study one of the authors participated in, they write:
“Hierarchies promoted learning and performance when goals and feedback were group-oriented, but
they stifled learning and performance when goals and feedback were individually oriented.”
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Winning With Open Process Innovation
Georg von Krogh (ETH Zurich), Torbjørn Netland (ETH Zurich), and Martin Wörter (ETH Zurich) pp. 53-56
Most research on open innovation has focused on the use of ideas and knowledge from outside the
organization in the development of products and services. But openness can be useful for process
innovation, too. The authors’ research shows that manufacturers can benefit substantially when they
look for ideas beyond the factory gates, especially when their operations are already advanced.
In this article, the authors analyzed nine years of survey responses from 1,000 Swiss manufacturers,
as well as 200 interviews with personnel at the Volvo Group (AB Volvo), a manufacturer of trucks,
buses, construction equipment, and marine and industrial engines that is based in Gothenburg, Sweden.
Although the authors concede that some companies have good reasons for keeping process innovations
concealed, they found that for many manufacturers, such defensiveness deprives companies of a valuable source of ideas for productivity improvement.
The authors present six ideas to help manufacturing companies open up their innovation process.
The first idea is to encourage factories within a large company to share innovative practices and success stories with one another. Companies that already do this informally, the authors say, can extend
their activities with a systematic effort inside their factory networks and lay the groundwork for other
open information sharing about processes. The second idea is to focus on the pace of process
improvement.
The third idea is to recognize that increased use of data access systems leads to greater production
cost reductions. Customer relationship management, supply chain management, and enterprise resource
planning software systems all require codification of tacit knowledge, which enhances a company’s
capacity to spread external process ideas and technology to the people who need it.
As a fourth step, the authors recommend improving the organization’s ability to absorb and
implement ideas from external sources. A good way to achieve this, they say, is to establish routines
for gathering ideas from external sources and putting them to use. Fifth, the authors advise reaching
beyond a company’s internal factory networks for inspiration. Finally, they say companies should
remember that “nontraditional sources of knowledge may spark process innovation and help overcome
difficult problems.”
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The Pitfalls of Non-GAAP Metrics
H. David Sherman (Northeastern University) and S. David Young (INSEAD) pp. 57-63
For decades, companies have used custom metrics that don’t conform to generally accepted accounting
principles (GAAP) or international financial reporting standards (IFRS) as supplements to their official
financial statements. Some common non-GAAP measures include adjusted earnings before interest,
taxes, depreciation, and amortization (known as adjusted EBITDA), free cash flow, funds from operations,
adjusted revenues, adjusted earnings, adjusted earnings per share, and net debt.
However, as the authors point out, it’s not unusual for these alternative measures to lead to problems.
Since companies devise their own methods of calculation, it’s difficult to compare the metrics from
company to company — or, in many cases, from year to year within the same company. According to
the authors, alternative measures, once used fairly sparingly and shared mostly with a small group of
professional investors, have become more ubiquitous and further and further disconnected from reality.
In 2013, McKinsey & Co. found that all of the 25 largest U.S.-based nonfinancial companies reported
some form of non-GAAP earnings. Press releases and earnings-call summaries often present non-GAAP
measures that are increasingly detached from their GAAP-based equivalents.
In addition to creating potential problems for investors, the authors argue, alternative metrics can harm
companies themselves by obscuring their financial health, overstating their growth prospects beyond what
standard GAAP measures would support, and rewarding executives beyond what is justified. Board members, top executives, compliance officers, and corporate strategists need to make sure that whatever alternative measures companies use improve transparency and reduce bias in financial reports.
Although no standard is perfect, the authors note that GAAP and IFRS standards provide a foundation
for consistent measurement of corporate performance over time and across businesses.
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Building Scalable Business Models
Christian Nielsen (Aalborg University and Inland Norway University of Applied Sciences)
and Morten Lund (Aalborg University) pp. 65-69
Business model innovation has become a hot topic in management circles, and understandably so. No
management activity is more important than having clarity about how the organization creates, delivers,
and captures value. It requires, among other things, knowing what customers want, how value can be
best delivered, and how to enlist strategic partners to achieve maximum benefit.
Although the ability to develop strong value propositions can enable companies to “get by, ” the
authors argue that many of today’s most successful businesses are those that can place themselves in the
“sweet spot” of business model scalability. If managers are incapable of factoring scalability attributes
into their business model design, they risk being left behind, much the way bookstores owned by
Borders Group Inc. were eclipsed by Amazon.com Inc.
In the course of their research, the authors identified five patterns by which companies can achieve scalability.
The first pattern involves adding new distribution channels. The second entails freeing the business from traditional capacity constraints. The third involves outsourcing capital investments to partners that, in effect, became
participants in the business model. The fourth is having customers and other partners assume multiple roles in
the business model. And the fifth pattern is to establish platform models in which even competitors may
become customers. Based on these patterns, the authors developed a framework for identifying potential levers
for business model scalability along with a road map that managers can use to improve their business models.
Over and above the need to create value propositions that are difficult for competitors to replicate,
managers need to develop business models that are capable of achieving positive returns and accelerating returns on the investments made. Merely identifying synergies, the authors note, doesn’t necessarily
lead to improvements in business model scalability. They write: “To achieve scalability, managers and
entrepreneurs need to remove capacity constraints. They have opportunities to do this in a variety of
ways: by collaborating with partners; by encouraging partners to play multiple roles in the business
model; by creating platforms to attract new partners; or even by working with current competitors.”
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Developing Successful Strategic Partnerships With Universities
Lars Frølund (MIT Innovation Institute), Fiona Murray (MIT Sloan School), and Max Riedel (Siemens AG)
pp. 71-79
Collaborations between companies and universities are critical drivers of the innovation economy.
These relationships have long been a mainstay of corporate R&D — from creating the knowledge foundations for the next generation of solutions, to serving as an extended “workbench” to solve short-term,
incremental problems, to providing a flow of newly minted talent. As corporations look to open innovation to augment their internal R&D efforts, many of them are turning to universities to anchor an
increasingly broad set of activities, especially those grounded in engaging with regional innovation ecosystems such as Silicon Valley, Kendall Square in Cambridge, Massachusetts, and Block 71 in Singapore.
Universities are essential stakeholders in innovation communities that also include corporations, government entities, venture investors, and entrepreneurs. In addition to being sources of people and ideas
for corporations, university collaborations assist corporations in opening up new avenues of engagement
with a broader innovation ecosystem.
While the aspirations of university-industry partnerships can be easily described, many companies are finding that establishing and running partnerships effectively can be difficult, even when key financial resources
and human capital are available. A core reason for the difficulty, the authors write, “is that university culture —
characterized by high autonomy and distributed governance — maps poorly to corporate culture.”
The authors provide a set of six questions for managers, which make up the basis of a form they call
the “university partnership canvas.” They designed a form (which is downloadable from the digital version
of the article) to help corporations assess and develop strategic approaches to their university partnerships.
By working through the six questions, companies can develop a strategic perspective on what types of
partnerships are best suited to their needs. The spectrum goes from what might be seen as an ad hoc
approach to a strategic and broader engagement with an innovation ecosystem.
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Leading in a Time of Increased Expectations
Lynn J. Good (Duke Energy Corp.), interviewed by Paul Michelman (MIT Sloan Management Review)
pp. 80-85
Traditionally, the focus of big energy companies has been on power generation and maintaining and
managing their assets. But in electric power, as in many businesses, today’s consumers want convenience
and answers in real time. Since taking over as CEO of Duke Energy Corp., a large energy company
based in Charlotte, North Carolina, Lynn Good has faced the challenge of reorienting an old-line business to meet the needs of today’s customers. As Good says, “We have to keep up with what customers
expect from an experience: information, control, convenience, and choice. People expect that from their
energy provider just as they do with all the other services in their lives.”
In this interview with MIT Sloan Management Review editor in chief Paul Michelman, Good discusses three big changes that are affecting the electric power industry: customer expectations, technology, and public policy. She explains how she is attempting to reshape Duke Energy, which has 29,000
employees, around what she calls “our leadership imperative.” The imperative, says Good, is composed
of five elements. The first element involves a commitment to “live” the company’s core purpose and
mission of serving its customers 24/7, during every season. The second element is knowing how to lead
change and guiding the organization “toward the vision in an agile, flexible way.” The third element is
the need to deliver outcomes to customers and other stakeholders “with safety, integrity, and customer
service.” The fourth is to “work as one” to meet customer needs. The final piece, she said, has to do with
inspiring employees. The company can’t succeed in a period of change, she notes, “unless we have absolutely every employee aligned and getting up in the morning with enthusiasm about what they’re going
to add to the future of the organization.”
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B A C K TA L K
Is the Threat of Digital
Disruption Overhyped?
Responding to a recently published essay, a reader pushed back against the view that
managers must prepare for radical and rapid change in a digital world; he argued that
this position may be overly alarmist. The discussion continues.
BY BRUCE POSNER
IN A RECENT short essay titled
“Survival Skills for a Digital
World,” which appeared in our fall
2017 issue, MIT Sloan Management
Re v iew editor in chief Paul
Michelman wrote that today’s
managers need “to embrace the
demands of change.” Given the rate
of change and the extent to which
business environments are shifting
as a result of digital technologies,
Michelman noted that it’s helpful for businesspeople to accept
change as a constant. “The kind of transformation we are experiencing in business today isn’t easy for anyone,” he wrote. “However,
once you accept the truth that change is ongoing, change becomes
more about opportunity and less about challenge.”
One MIT SMR subscriber had a different view. Responding to
Michelman’s essay, reader Tony Pavone, director of process engineering
at IHS Markit Ltd., a global information services company, wrote that
he wished MIT Sloan Management Review and others would move past
what he perceives as an “obsession with radical transformation, chaotic
disruption, and lust for digital tools.” Pavone further argued that, “for
the most part, organizations that require radical anything are failures to
me because they haven’t kept the pulse of their customers, haven’t
benchmarked their competitors, and haven’t stayed in tune with the
tools that allow [them] to do things better.” Neither capabilities nor
deficiencies, he noted, are created or destroyed overnight. Winning
companies, Pavone offered, “mind their knitting” and may often do
what their competitors do — “but do it measurably better.”
It’s true that pressures to change don’t affect every industry with
equal force. A recent McKinsey & Co. survey, for example, found that
executives of companies in industries such as telecommunications
and media and entertainment sensed greater instability than those
in, say, basic materials. That seems to suggest that some companies
will have more time to adjust to the future than others. Moreover,
as Joshua S. Gans, author of a 2016 MIT SMR article titled “Keep
Calm and Manage Disruption,” has argued, for many companies,
fears of quick disruption are overblown.
So how does this jibe with Michelman’s view? Although the
question about how fast companies need to respond to technologyinduced change is complicated, we asked Michelman to address
Pavone’s comments. Here’s what he wrote:
“At the strategic level, Mr. Pavone’s comments are well-taken.
Leaders who are truly attuned to their markets, customers, and
competitors (both current and potential); who maintain a
keen eye on how technological shifts may drive their businesses
in new directions; and who are nimble enough to act on that
foresight can indeed avoid existential crises. But they cannot
avoid change.
Indeed, they are managing it all the time. Even in seemingly
stable industries and even within companies producing consistent and predictable returns, pieces are always moving, and the
ways we work are evolving. It’s important that we accept and
embrace change on a personal level and we recognize that, as
with every major technological shift, the nature of work and
what it demands from us are far from stable.”
Comment on this article at http://sloanreview.mit.edu/59230, or send
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