Test Your Innovation IQ Holly Green, Contributor Origina.docx

Test Your Innovation IQ
Holly Green, Contributor
Original Source
Everyone knows that innovation means coming up with the next
great idea in your
industry, right? Actually, there’s a lot more to it than that. Test
your ability to separate
innovation fact from fiction by answering the following
questions true or false:
1. Innovation is the act of coming up with new and creative
ideas.
2. Innovation is a random process.
3. Innovation is the exclusive realm of a few naturally talented
people.
4. The biggest obstacle to innovation is a lack of organizational
resources and
know-how.
5. The most important type of innovation involves bringing new
products and
services to market.
6. Teaching employees to think creatively will guarantee
innovation.
7. The most powerful way to trigger your brain is to simply ask
it a question.
8. Most companies pursue incremental rather than disruptive
innovation.
9. Most companies are not structured to innovate.

10. Listening to your customers is a great way to innovate.
Answers:
1. False. In business, innovation is the act of applying
knowledge, new or old, to the
creation of new processes, products, and services that have
value for at least one of
your stakeholder groups. The key word here is applying.
Generating creative ideas is
certainly part of the process. But in order to produce true
innovation, you have to
actually do something different that has value.
2. False. Innovation is a discipline that can (and should) be
planned, measured, and
managed. If left to chance, it won’t happen.
3. False. Everyone has the power to innovate by letting their
brain wander, explore,
connect, and see the world differently. The problem is that
we’re all running so fast that
we fail to make time for the activities that allow our brains to
see patterns and make
connections. Such as pausing and wondering….what if?
4. False. In most organizations, the biggest obstacle to
innovation is what people
already know to be true about their customers, markets, and
business. Whenever you’re
absolutely, positively sure you’re right, any chance at
meaningful innovation goes out
the window.
https://www.forbes.com/sites/work-in-progress/2011/12/06/test-
your-innovation-iq/#3133e7f0395b

5. False. It’s certainly important to bring new products and
services to market. But the
most important form of innovation, and the #1 challenge for
today’s business leaders
may really be reinventing the way we manage ourselves and our
companies.
6. False. New ideas are a dime a dozen. The hard part is turning
those ideas into new
products and services that customers value and are willing to
pay for -- a process that
requires knowledge about what your customers want and need,
coupled with
implementation.
7. True. Ask a question and the brain responds instinctually to
get closure. The key with
innovation is to ask questions that open people to possibilities,
new ways of looking at
the same data, and new interpretations of the same old thing.
8. True. Most companies focus on using internally generated
ideas to produce slightly
better products (incremental innovation). Then they strive to get
those slightly better
products to market as quickly and as cost-effectively as
possible. This approach is
quicker and cheaper than disruptive innovation. But it rarely
generates the results that
lead to sustainable market leadership.
9. True. Most organizations are physically set up with
accounting in one area, marketing
in another, and management off by itself. Employees rarely

interact with other
departments unless they need something to get their jobs done.
And leaders and
departments often withhold information, believing that it puts
them in a position of
power. Innovation requires teamwork, communication and
collaboration, not isolated
silos.
10. Trick question! The answer is “it depends.” Research shows
that customers can be
a good source of ideas for improving existing products and
services -- if you’re looking
to achieve incremental innovation. However, by itself, customer
research is not
sufficient for generating disruptive innovation because it only
uncovers expressed, or
known, customer needs. Disruptive innovation solves problems
that customers didn’t
even know they had or were unable to clearly articulate to
themselves or their vendors.
It redefines the market at a very fundamental level or, in many
cases, creates a new
market.
If you got 8 or more correct answers, give yourself a pat on the
back. If you scored
between 4 and 7, I recommend some more research and work on
these critical
leadership skills. If you scored less than 4, wake up and smell
the burnt coffee! Get
some help.
If you’re not constantly looking to improve your products,
services, systems, and
managerial processes, you will fall behind. And once you fall

behind, it can be very
difficult and often impossible to catch up!
Jobs-To-Be-Done Theory and Methodology
Seeing innovation through a new lens reveals a new way to
create customer value
Original Source
Fundamentals of Jobs-To-Be-Done Theory
As Theodore Levitt said, “people do not want a quarter-inch
drill, they want a quarter
inch hole.” It is true. People buy products and services to get
jobs done; and while
products come and go, the underlying job-to-be-done does not
go away. This notion is
at the heart of jobs-to-be-done theory.
While most companies innovate by trying to improve their
existing products (creating a
better quarter-inch drill), the innovation process is dramatically
improved by instead
trying to find better ways to create a quarter-inch hole (to get
the job done). The
implication of this thinking is profound: stop studying the
product and instead study the
job that people are trying to get done. By making the job, rather
than the product or the
customer, the unit of analysis, we’ve made it possible for
companies to achieve

predictable growth.
Here’s how our jobs-to-be-done methodology works in practice.
By focusing on the job-
to-be-done, it becomes possible, for the first time, to know all
the customer’s needs and
determine which are unmet. It turns out that when customers are
executing a job, they
have a set of metrics in mind that define the successful
execution of that job. These
metrics (or desired outcomes) can be captured as actionable
customer need statements
that replace the customer inputs companies ordinarily capture
and use to create new
products. We have discovered that customers typically use
between 50 and 150 distinct
outcome statements to describe the successful execution of any
job. These metrics are
uniquely structured statements that describe how customers
measure value. They are
the perfect input into a process that is aimed at creating valued
products and services.
The ramifications of jobs-to-be-done theory do not stop here.
What Jobs-To-Be-Done Theory Tells Us
Jobs-to-be-done theory tells us much more about customers,
strategy, products and
how managers should think about business, growth and
innovation. The theory tells
companies to heed the following bits of advice:
Define your markets around the job-to-be-done. While most
companies tend to
define a market around a product, technology or a solution,

when applying jobs-to-be-
done theory, a market is defined as a job executor and the job
that executor is trying to
get done. For example, “parents” (the job executor) trying to
“pass life lessons to their
https://strategyn.com/jobs-to-be-done/jobs-to-be-done-theory/
children” (the job to be done) is a market. Defining a market
this way opens the door to
a different type of market analysis, as the goal becomes
analyzing the job to discover
where parents struggle to get the job done, rather than analyzing
the products they use
for that purpose.
Help customers get the entire job done. People do not want to
have to cobble
together different products and services to achieve their goals.
They want one product
that helps them get the entire job done. The key to success is
understanding, from the
customer’s perspective, just what the entire job is and making
that job the focal point of
value creation. Keurig and Nespresso products, for example, get
more of the “prepare a
hot beverage” job done than competing solutions such as a
coffee pot, or a tea bag.
Help customers get more jobs done. Products evolve over time
to help people get
more jobs done. Jobs-to-be-done theory tells us that the more
jobs a product can help a
customer get done, the more valuable that product is as a
product platform in that

space. The swiss army knife, for example, helps customers get
dozens of jobs done,
and the smartphone helps customers get thousands of jobs done.
Design a business around a job-to-be-done. Rather than
designing a business
around a product that is certain to become obsolete, companies
should design a
business around the job-to-be-done. This way the company will
always be focused on
creating the solutions that will get the job done best. This will
result in longevity, as the
company will be more likely to disrupt itself.
Target those who will pay the most to get the job done best.
When a market is
highly underserved, the fastest way to profits is to first target
the people who will pay the
most to get the job done the best. We use outcome-based market
segmentation
methods to discover if such a segment exists in a given market.
Nest, for example,
targeted underserved customers who were willing to pay 5 times
more for a solution that
would get the job of “achieving personal comfort” done
significantly better. This strategy
is in stark contrast to a low-end disruption strategy where
companies target over-served
customers who want to pay less to get the job done worse.
Focus R&D and M&A efforts on getting a customer job done
better. Companies
invest lots of cash in R&D and M&A efforts, but those
investments rarely pay off. Why?
Because the investments often times are not made to help
customers get a job done

better. Jobs-to-be-done theory tells us that this is the main
reason such investments
should be made.
Let jobs-to-be-done theory guide your future. What products
will win in the future?
The products that help customers get the job done better.
Knowing where customers
struggle today to execute the job-to-be-done indicates what a
product has to do in the
future to win in the marketplace. Jobs-to-be-done theory
predicts what products will win
in the future.
These insights have resulted in a revolutionary innovation
process, Outcome-Driven
Innovation. Using this process, companies are far more likely to
launch products and
services that get their customer’s jobs done significantly better.
Fundamentals of Jobs-To-Be-Done TheoryWhat Jobs-To-Be-
Done Theory Tells Us
Innovation ExerciseStageCustomer FocusedYour
customers…How to innovateYour innovation…1: DefineHow do
customers determine their goals and plan resources.Simplifying
planning.2: LocateHow do customers gather items and
information needed to do the job.Making required inputs easier
to gather and ensuring they’re available when and where
needed.3: PrepareHow do customers set up the environment to
do the job.Making set-up less difficult and creating guides to
ensure proper set-up of the work area.4: ConfirmHow do
customers verify that they’re ready to perform the job.Giving

customers information they need to confirm readiness.5:
ExecuteHow do customers carry out the job.Preventing
problems or delays.6: MonitorHow do customers assess whether
the job is being successfully executed.Linking monitoring with
improved execution.7: ModifyHow do customers make
alterations to improve execution.Reducing the need to make
alterations and the number of alterations needed.8:
ConcludeHow do customers finish the job or prepare to repeat
it.Designing products that simplify the process of concluding
the job.
ReferenceThis table is from pages 46 and 47 in your book, On
Innovation, and is provided here as a quick reference.During
this step…Customers…Companies can innovate by…Example:1:
DefineDetermine their goals and plan resources.Simplifying
planning.Weight Watchers streamlines diet planning by offering
a system that doesn’t require calorie counting.2: LocateGather
items and information needed to do the job.Making required
inputs easier to gather and ensuring they’re available when and
where needed.U-Haul provides customers with prepackaged
moving kits containing the number and types of boxes required
for a move.3: PrepareSet up the environment to do the
job.Making set-up less difficult and creating guides to ensure
proper set-up of the work area.Bosch added adjustable levers to
its circular saw to accommodate common bevel angles used by
roofers to cut wood.4: ConfirmVerify that they’re ready to
perform the job.Giving customers information they need to
confirm readiness.Oracle’s ProfitLogic merchandising
optimization software confirms optimal timing and level of a
store’s markdowns for each product.5: ExecuteCarry out the
job.Preventing problems or delays.Kimberly-Clark’s Patient
Warning System automatically circulates heated water through
thermal pads placed on surgery patients to maintain their normal
body temperature during surgery.6: MonitorAssess whether the
job is being successfully executed.Linking monitoring with
improved execution.Nike makes a running shoe containing a
sensor that communicates audio feedback about time, distance,

pace, and calories burned to an iPhone or iPod worn by the
runner.7: ModifyMake alterations to improve
execution.Reducing the need to make alterations and the number
of alterations needed.By automatically downloading and
installing updates, Microsoft’s operating systems remove hassle
for computer users. People don’t have to determine which
updates are necessary, find the updates, or ensure the updates
compatible with their operating system.8: ConcludeFinish the
job or prepare to repeat it.Designing products that simplify the
process of concluding the job.3M makes a wound dressing that
stretches and adheres only to itself-not to patients’ skin or
sutures. It thus offers a convenient way for medical personnel to
secure dressing at the conclusion of treatment and then remove
them after a wound has healed.
1
HBR’s 10 Must Reads series is the definitive collection of ideas
and best practices for
aspiring and experienced leaders alike. These books offer
essential reading selected from
the pages of Harvard Business Review on topics critical to the
success of every manager.
Titles include:
HBR’s 10 Must Reads on Change Management
HBR’s 10 Must Reads on Collaboration

HBR’s 10 Must Reads on Communication
HBR’s 10 Must Reads on Innovation
HBR’s 10 Must Reads on Leadership
HBR’s 10 Must Reads on Making Smart Decisions
HBR’s 10 Must Reads on Managing People
HBR’s 10 Must Reads on Managing Yourself
HBR’s 10 Must Reads on Strategic Marketing
HBR’s 10 Must Reads on Strategy
HBR’s 10 Must Reads on Teams
HBR’s 10 Must Reads: The Essentials
2
http://hbr.org/product/hbr-s-10-must-reads-on-change-
management-with-feat/an/12599E-KND-ENG?Ntt=12599
http://hbr.org/product/hbr-s-10-must-reads-on-collaboration-
with-featured/an/11378-PBK-ENG
http://hbr.org/product/hbr-s-10-must-reads-on-communication-
with-featured/an/11364-PBK-ENG
http://hbr.org/product/hbr-s-10-must-reads-on-innovation-with-
featured-ar/an/11363-PBK-ENG
http://hbr.org/product/hbr-s-10-must-reads-on-leadership-with-
featured-ar/an/12546E-KND-ENG?Ntt=12546
http://hbr.org/product/hbr-s-10-must-reads-on-making-smart-
decisions-with/an/11367-PBK-ENG
http://hbr.org/product/hbr-s-10-must-reads-on-managing-
people-with-featur/an/12575E-KND-ENG?Ntt=12575

http://hbr.org/product/hbr-s-10-must-reads-on-managing-
yourself-with-bonu/an/12572E-KND-ENG?Ntt=12572
http://hbr.org/product/hbr-s-10-must-reads-on-strategic-
marketing-with-fe/an/11366-PBK-ENG
http://hbr.org/product/hbr-s-10-must-reads-on-strategy-with-
featured-arti/an/12601E-KND-ENG?Ntt=12601
http://hbr.org/product/hbr-s-10-must-reads-on-teams-with-
featured-article/an/11365-PBK-ENG
http://hbr.org/product/hbr-s-10-must-reads-the-
essentials/an/13292E-KND-ENG?Ntt=13292
On
Innovation
HARVARD BUSINESS REVIEW PRESS
Boston, Massachusetts
3
Copyright 2013 Harvard Business School Publishing
Corporation
All rights reserved
No part of this publication may be reproduced, stored in or
introduced into a retrieval
system, or transmitted, in any form, or by any means
(electronic, mechanical, photocopying,
recording, or otherwise), without the prior permission of the
publisher. Requests for
permission should be directed to [email protected], or mailed to
Permissions, Harvard Business School Publishing, 60 Harvard
Way, Boston, Massachusetts
02163.

The web addresses referenced in this book were live and correct
at the time of book’s
publication but may be subject to change.
Library of Congress Cataloging-in-Publication Data
HBR’s 10 must reads on innovation.
pages cm
Includes index.
ISBN 978-1-4221-8985-6 (alk. paper)
1. Creative ability in business. 2. Creative thinking. 3.
Diffusion of innovations—
Management. 4. Technological innovations—Management. 5.
New products. I. Harvard
business review II. Title: HBR’s ten must reads on innovation.
HD53.H394 2013
658.4'063—dc23
2012045970
Find more digital content or join the discussion on
www.hbr.org.
eISBN: 978-1-4221-9150-7
4
mailto:[email protected]
http://www.hbr.org
Contents

The Innovation Catalysts
by Roger L. Martin
Stop the Innovation Wars
by Vijay Govindarajan and Chris Trimble
How GE Is Disrupting Itself
by Jeffrey R. Immelt, Vijay Govindarajan, and Chris Trimble
The Customer-Centered Innovation Map
by Lance A. Bettencourt and Anthony W. Ulwick
Is It Real? Can We Win? Is It Worth Doing?
by George S. Day
Six Myths of Product Development
by Stefan Thomke and Donald Reinertsen
Innovation: The Classic Traps
by Rosabeth Moss Kanter
Discovery-Driven Planning
by Rita Gunther McGrath and Ian C. MacMillan
The Discipline of Innovation
by Peter F. Drucker
Innovation Killers
5
by Clayton M. Christensen, Stephen P. Kaufman, and Willy
C. Shih

About the Contributors
Index
6
The Innovation Catalysts
by Roger L. Martin
ONE DAY IN 2007, midway through a five-hour PowerPoint
presentation, Scott Cook
realized that he wasn’t another Steve Jobs. At first it was a
bitter disappointment. Like
many entrepreneurs, Cook wanted the company he had
cofounded to be like Apple—design
driven, innovation intensive, wowing consumers year in and
year out with fantastic
offerings. But that kind of success always seemed to need a
powerful visionary at the top.
This article is about how Cook and his colleagues at the
software development company
Intuit found an alternative to the Steve Jobs model: one that has
enabled Intuit to become a
design-driven innovation machine. Any corporation—no matter
how small or prosaic its
business—can make the same grassroots transformation if it
really wants to.

7
The Birth of the Idea
Intuit’s transformation arguably began in 2004, with its
adoption of the famous Net
Promoter Score. Developed by Fred Reichheld, of Bain &
Company, NPS depends on one
simple question for customers: How likely are you, on a scale of
0 (not at all likely) to 10
(extremely likely), to recommend this product or service to a
colleague or friend?
“Detractors” answer from 0 to 6, “passives” answer 7 or 8, and
“promoters” answer 9 or
10. A company’s Net Promoter Score is the percentage of
promoters less the percentage of
detractors.
For the first couple of years, Intuit saw its NPS rise
significantly, owing to a number of
marketing initiatives. But by 2007 NPS growth had stalled. It
was not hard to see why.
Although Intuit had lowered its detractor percentage
substantially, it had made little
headway with promoters. Customer recommendations of new

products were especially
disappointing.
Clearly, Intuit needed to figure out how to galvanize its
customers. Cook, a member of
Procter & Gamble’s board of directors, approached Claudia
Kotchka, then P&G’s vice
president of design innovation and strategy, for advice.
Following their discussions, Cook
and Steve Bennett, then Intuit’s CEO, decided to focus on the
role of design in innovation at
a two-day off-site for the company’s top 300 managers. Cook
created a one-day program on
what he called Design for Delight (D4D)—an event aimed at
launching Intuit’s reinvention
as a design-driven company.
The centerpiece of the day was that five-hour PowerPoint
presentation, in which Cook
laid out the wonders of design and how it could entice Intuit’s
customers. The managers
listened dutifully and clapped appreciatively at the end, as they
were supposed to; Cook
was, after all, a company founder. Nevertheless, he was
disappointed by his reception.

Despite some interest in the ideas presented, there was little
energy in the room.
But although the main event fell flat, the one that followed did
not. Cook had met a young
consulting associate professor at Stanford named Alex Kazaks,
whom he’d invited to
present for an hour at the off-site. Like Cook, Kazaks began
with a PowerPoint
8
presentation, but he ended his after 10 minutes and used the rest
of the time for a
participatory exercise: The managers worked through a design
challenge, creating
prototypes, getting feedback, iterating, and refining.
The group was mesmerized. Afterward Cook informally polled
the participants, asking
what takeaways they’d gotten from the daylong session. Two-
thirds of the lessons they
reported came from the hands-on exercise. This reaction made
Cook think: He might not be
the next Steve Jobs, but perhaps his company didn’t need one.
Given a few tools, coaching,

and practice, could the grass roots of the company drive success
in innovation and customer
delight?
Idea in Brief
A few years ago the software development company Intuit
realized that it needed a
new approach to galvanizing customers. The company’s Net
Promoter Score was
faltering, and customer recommendations of new products were
especially
disappointing. Intuit decided to hold a two-day, off-site meeting
for the company’s
top 300 managers with a focus on the role of design in
innovation. One of the days
was dedicated to a program called Design for Delight. The
centerpiece of the day
was a PowerPoint presentation by Intuit founder Scott Cook,
who realized midway
through that he was no Steve Jobs: The managers listened
dutifully, but there was
little energy in the room. By contrast, a subsequent exercise in
which the participants
worked through a design challenge by creating prototypes,
getting feedback,
iterating, and refining, had them mesmerized. The eventual
result was the creation of
a team of nine design-thinking coaches—“innovation
catalysts”—from across Intuit
who were made available to help any work group create
prototypes, run
experiments, and learn from customers. The process includes a
“painstorm” (to
determine the customer’s greatest pain point), a “sol-jam” (to
generate and then

winnow possible solutions), and a “code-jam” (to write code
“good enough” to take
to customers within two weeks). Design for Delight has enabled
employees
throughout Intuit to move from satisfying customers to
delighting them.
9
From Idea to Initiative
Like most Silicon Valley tech companies, Intuit had user-
interface designers, graphic
designers, and others buried relatively deep in the organization.
Cook turned to a
particularly talented young design director, Kaaren Hanson, and
asked her what she would
do to promote design at Intuit.
Hanson realized that the company needed an organized program
for moving from talking
about D4D to doing it. She persuaded Cook to let her create a
team of design-thinking
coaches—“innovation catalysts”—who could help Intuit
managers work on initiatives
throughout the organization. Hanson selected nine colleagues to
join her in this role. Their
training and deployment was her central agenda for FY 2009.

In selecting the nine, Hanson looked first for people with a
broad perspective on what it
meant to be a designer: Beyond creating a graphic user interface
that was both appealing
and intuitive, it included thinking about whether the software
solved the user’s problem in a
delightful way. She wanted her coaches to be interested in
talking to users and solving
problems with colleagues rather than depending solely on their
own genius. If they were to
successfully coach others in design thinking, they’d need an
outgoing personality and good
people skills.
She invited two direct reports from her own business unit and
seven people from other
units across the company. The group included six women and
four men. They came from a
variety of fields within Intuit—design, research, product
management—and had titles such
as user-interface architect, principal researcher, staff designer,
and product manager.
Hanson chose people who were influential even though they
were all one or two levels

below director, meaning closer to the bottom of the organization
than the top. All nine
signed up enthusiastically.
To begin building design thinking into the DNA of the
company, Cook and Hanson
organized a series of Design for Delight forums. These were
typically attended by more
than 1,000 employees and featured a speaker who’d had
exemplary success in creating
customer delight. Half the featured speakers came from inside
Intuit; the other half included
10
the founding CEO of Flip Video, Facebook’s top data scientist,
and the head of Apple
Stores. The forums also showcased D4D successes to date and
shared best practices.
People who worked together were encouraged to attend together
and were asked as a team
to identify the one thing they would do differently after the
forum.
To ensure that managers who were thinking design didn’t
become too intimidated to

begin the process, or frustrated trying to do something with
which they had little
experience, or delayed by needing to hire an outside design
consultant, Hanson’s innovation
catalysts were available to help any work group create
prototypes, run experiments, and
learn from customers. Of course, there was a risk that this
would stretch the catalysts too
thin, so Hanson placed some constraints on their availability.
They were expected to spend
25% of their time on big-payoff projects for Intuit overall.
Hanson kept in close contact
with general managers who had catalysts working with them to
make sure that the catalysts
were addressing the managers’ biggest problems. She realized
that if design momentum was
to be maintained, her coaches had to be seen as responsible for
three or four visible and
high-impact wins a year.
Some enabling came from the very bottom of the organization.
In 2008 two employees
who had been at Intuit only four months designed an online
social network for the D4D
initiative, which they rolled out the following year with

management’s consent but without
its direct support. In its first year the new platform, named
Brainstorm, generated 32 ideas
that made it to market.
11
From Presentations to Experiments
Traditionally, decisions at Intuit had been made on the basis of
PowerPoint presentations.
Managers would work to produce both (what they saw as) a
great product and a great
presentation for selling the concept to their bosses. Under this
system Intuit managers voted
on ideas and then tried to sell them to customers. A key
component of D4D, therefore, was
shifting the focus away from managerial presentations. It would
be far better, Hanson and
Cook realized, to learn directly from customers through
experiments.
Today D4D innovations begin with what Intuit calls the
painstorm—a process developed
by two innovation catalysts, Rachel Evans and Kim McNealy. It
is aimed at figuring out

customers’ greatest pain point for which Intuit can provide
relief. In a painstorm, team
members talk to and observe customers in their offices or homes
rather than sit in Intuit
offices and imagine what they want. This exercise often shatters
preconceptions. Going into
one painstorm for a sales-oriented product, the team was
convinced that the product
concept should be “Grow your business.” But the painstorm
showed that “Grow your
business” sounded very ambiguous to customers—it could refer
to growing revenues from
their existing customers (not a pain point for them) or to
acquiring similar small businesses
(also not a pain point, but expensive). The true pain point was
acquiring entirely new
customers through organic sales efforts. “Get customers” was a
winning concept that
focused laserlike on that.
Recruiting the Innovation Catalysts
IN 2008 KAAREN HANSON sent this e-mail to some Intuit
colleagues:
Subject: Phase II of Design for Delight—we need YOU
You have been nominated (and your participation has been
approved by your

manager) to help us drive Phase II of Design for Delight at
Intuit. You are a critical
leader who can enable Intuit to become one of the principal
design-thinking
cultures. We have a number of levers at our disposal but we
need your help to
develop even better ideas to drive design thinking deeper into
the organization.
12
Here’s what you’ll be committing to:
• Actively participate in a one-day brainstorm/workshop in early
August to work
through what we (as a force of design thinking and as a larger
company) might do to
take Design for Delight to its next level. Scott will come by and
respond to our
ideas/plan towards the end of the day
• Commit to the execution of initiatives generated through the
August workshop
• Become a more visible Design for Delight leader across Intuit
(e.g., help teach a
Design for Delight 101 session/workshop to FastPath or some
other such leadership
session, contribute to the D4D body of knowledge through
existing and future
contribution systems, be a sounding board for Intuit execs)
• Be a D4D coach/facilitator that the larger company can draw
upon (e.g., coach
key teams across Intuit in brainstorming, design reviews, etc.)
In total, your commitment will be about 2 days/month—and

we’ll be able to
work around your schedule.
Let me know if you are in for FY09—and I’ll get the August
date on everyone’s
calendars. Right now, we’re looking at an in-person workshop
on August 4th, 5th,
or 6th in Mountain View.
Next, within two weeks, the group holds a “sol-jam,” in which
people generate concepts
for as many product or service solutions as possible to address
the pain points they’ve
identified and then weed the concepts down to a short list for
prototyping and testing. In the
early days of prototyping, these high-potential solutions were
integrated into Intuit’s
software development process. But the innovation catalysts
realized that the best way to
maintain momentum would be to get code into users’ hands as
quickly as possible. This
would help determine whether the solution had potential and, if
so, what needed to be done
to enhance it. So the third step became moving immediately to
“code-jam,” with the goal of
writing code that wasn’t airtight but was good enough to take to
customers within two

weeks of the sol-jam. Thus, proceeding from the painstorm to
the first user feedback on a
new product usually takes only four weeks.
Let’s look at a couple of examples. When Intuit’s tax group
began to think about mobile
apps, Carol Howe, a project manager and innovation catalyst,
started with the customer.
Her five-person team went “out in the wild,” she says, to
observe dozens of smart-phone
13
users. It quickly narrowed in on millennials, whose income
range made them likely
candidates for the simplest tax experience. The team created
multiple concepts and iterated
with customers on a weekly basis. They brought customers in
each Friday, distilled what
they’d learned on Monday, brainstormed concepts on Tuesday,
designed them on
Wednesday, and coded them on Thursday, before bringing the
customers in again. Through
these iterations the team uncovered multiple “delighters.” They
launched a pilot in
California in January 2010 and expanded nationwide in January

2011. The resulting
application, SnapTax, has 4.5 stars in both the Apple and
Android stores and a Net
Promoter Score in the high 80s.
An even better example comes from India. In 2008 members of
the India team came up
with an idea remote from tax preparation and other core Intuit
North America products,
none of which were likely to succeed in India. The idea, a
service for poor Indian farmers,
was interesting enough for Intuit to give Deepa Bachu, a
longtime development manager, the
green light to explore it. Bachu and an engineer spent weeks
following subsistence farmers
through their daily lives—in the fields, in their villages, and at
the markets where they sold
their produce. The two came to appreciate the farmers’ greatest
pain point—perishable
inventory that either went unsold or got a suboptimal price. If
Intuit could enable the
farmers to consistently sell their produce before spoilage and at
a decent price, their pain
would be reduced or eliminated.

After the painstorm and the sol-jam, the team went into rapid
experimentation. Within
seven weeks it was running a test of what was eventually
launched as Mobile Bazaar, a
simple text-messaging-based marketplace connecting buyers and
sellers. To get there so
fast, the team had cleverly faked parts of the product that would
have been costly and slow
to code and build. These came to be known as “fako backends.”
What the user saw looked
real, but behind the user interface was a human being—like the
Wizard of Oz behind the
curtain—rather than thousands of lines of code that would have
taken months to write.
The initial trials showed that half the farmers were able to
increase their prices by more
than 10%; some of them earned as much as 50% more. Within a
year of launch, Mobile
Bazaar had 180,000 subscribing farmers, most of them acquired
by word of mouth. They
14
report that, on average, the service boosts their prices by 16%.
15

From Breakthroughs to Culture
Hanson was pleased with the progress of the 10 original
innovation catalysts in their first
year and with the organization’s receptivity, but she knew that
Intuit would have to scale up
to make the transformation complete. Brad Smith, the new CEO,
was raising innovation
expectations for the whole company, focusing particularly on
new arenas that he described
as “mobile, social, and global.” Hanson set a goal for FY 2010
to select, train, and deploy
another 65 catalysts. This meant sourcing from a broader pool
of talent—going deeper into
product management and engineering—and creating a small
dedicated team to support the
catalysts and increase D4D pull from midlevel managers.
She appointed Suzanne Pellican, one of the original 10, to
expand the catalysts’ number
and capabilities. Hanson had learned from the initial work that
the strongest design thinkers
didn’t necessarily make the best catalysts. She says, “We not
only needed people who were

design thinkers—we also needed people with passion to give
D4D away and help others to
do great work, versus coming up with a great idea and bringing
it to others.”
The catalysts also needed mutual support. Hanson’s team had
found that they did their
best work when they worked together. They learned new ideas
and techniques from one
another and provided moral support in tough situations. So as
Pellican scaled up the
catalyst corps, she made sure that each catalyst was part of an
organized “posse” that
typically extended across business units, allowing new methods
to travel quickly from one
end of the organization to the other.
To increase the catalysts’ effectiveness, Hanson established a
second small team—led by
Joseph O’Sullivan, another of the original 10—to help middle
management embrace both
design thinking as a concept and the innovation catalysts as
enablers. For example, after
several catalysts reported encountering resistance at the director
level, Hanson and
O’Sullivan worked to integrate design thinking into Intuit’s

leadership training programs,
applying it directly to problems that leaders faced. In one
training program an IT director
was challenged to lead a team tasked with reducing company
spending on employees’
mobile devices by $500,000. O’Sullivan’s group held a one-day
session on painstorming
16
and sol-jamming for the team. The IT director achieved the
desired saving and won much
appreciation from the members of her team for having made
their task so much easier than
expected. She and the other participants in that leadership
training program became fervent
D4D advocates.
_____________________
Encouraging experimentation rather than PowerPoint has
enabled employees
throughout Intuit to move from satisfying customers to
delighting them. Design for Delight
has stuck because people see that it is an obviously better and
more enjoyable way of

innovating.
Innovation activity has increased dramatically in the
organization. Take TurboTax,
Intuit’s single biggest product. In the 2006 tax year the
TurboTax unit ran just one customer
experiment. In 2010 it ran 600. Experiments in the QuickBooks
unit went from a few each
year to 40 last year. Intuit now seizes new opportunities more
quickly. Brad Smith pushed
for D4D-led innovation in the fast-growing arena of mobile
apps, and within 24 months the
company went from zero to 18, with a number of them,
including SnapTax, off to a very
successful start. Net Promoter Scores are up across the
company, and growth in revenue
and income has increased over the past three years.
Scott Cook may not have been another Steve Jobs, but it turned
out that Intuit didn’t need
one.
Originally published in June 2011. Reprint R1106E
17

Stop the Innovation Wars
by Vijay Govindarajan and Chris Trimble
IT WAS JUST AN INNOCENT COMMENT. While working
with a client at a Fortune 500
company, we proposed the formation of a special group to
execute a new growth strategy.
“For now, let’s just refer to the group as the innovation team,”
we suggested.
The client rolled his eyes. “Let’s call it anything but that,” he
said. “What is this so-
called innovation team going to do? Brainstorm? Sit around
being creative all day? Talk
condescendingly about a superior organizational culture? All of
this while operating with
neither discipline nor accountability? All of this while the rest
of us get the real work
done?”
Wow. All it took was two words: innovation team.
In our experience, innovation teams feel a hostility toward the
people responsible for
day-to-day operations that is just as biting. The rich vocabulary
of disdain includes
bureaucratic, robotic, rigid, ossified, staid, dull, decaying,
controlling, patronizing . . .
and just plain old. Such animosity explains why most executives
believe that any significant

innovation initiative requires a team that is separate and
isolated from the rest of the
company.
But that conventional wisdom is worse than simpleminded. It is
flat wrong. Isolation may
neutralize infighting, but it also neuters innovation.
The reality is that an innovation initiative must be executed by
a partnership that
somehow bridges the hostilities—a partnership between a
dedicated team and what we call
the performance engine, the unit responsible for sustaining
excellence in ongoing
18
operations. Granted, such an arrangement seems, at first glance,
improbable. But to give up
on it is to give up on innovation itself. Almost all innovation
initiatives build directly upon
a company’s existing resources and know-how—brands,
customer relationships,
manufacturing capabilities, technical expertise, and so forth. So
when a large corporation
asks a group to innovate in isolation, it not only ends up

duplicating things it already has but
also forfeits its primary advantage over smaller, nimbler
rivals—its mammoth asset base.
Over the past decade, we have examined dozens of innovation
initiatives and identified
some best practices. In the process we built upon foundational
management theories such as
Jim March’s ideas about balancing exploration with
exploitation, and Paul Lawrence and
Jay Lorsch’s argument that firms need to both integrate and
differentiate corporate units. We
came to the conclusion that the organizational model we
prescribe—a partnership between
a dedicated team and the performance engine—is surprisingly
versatile. It can be adapted
to initiatives that span many innovation categories—sustaining
and disruptive; incremental
and radical; competence enhancing and competence destroying;
new processes, new
products, new businesses, and high-risk new ventures.
This article will show how to make the unlikeliest partnership
work. There are three
steps. First, decide which tasks the performance engine can
handle and which you’ll need

to hand off to a dedicated team. Second, assemble the right
dedicated team. Third,
anticipate and mitigate strains in the partnership. Once you have
taken these steps, you’ll be
in a good position to actually execute on your great ideas.
19
How One Company Organized for Growth
In most law offices, even in the internet era, you’ll find
libraries full of weighty and
majestic-looking books. The books contain rulings from past
cases. With each verdict,
judges contribute to a massive body of precedents that shape
future decisions. (This is the
system, at least, in the United States and many other countries.)
Law students spend
countless hours mastering the intricacies and subtleties of
researching precedents.
Idea in Brief
Special teams dedicated to innovation initiatives inevitably run
into conflict with the
rest of the organization. The people responsible for ongoing
operations view the
innovators as undisciplined upstarts. The innovators dismiss the
operations people

as bureaucratic dinosaurs. It’s natural to separate the two
warring groups. But it’s
also dead wrong, say Tuck Business School’s Govindarajan and
Trimble. Nearly all
innovation initiatives build on a firm’s existing resources and
know-how. When a
group is asked to innovate in isolation, the corporation forfeits
its main advantage
over smaller, nimbler rivals—its mammoth asset base. The best
approach is to set
up a partnership between the dedicated team and the people who
maintain
excellence in ongoing operations, the company’s performance
engine. Such
partnerships were key to the successful launch of new offerings
by legal publisher
Westlaw, Lucent Technologies, and WD-40. There are three
steps to making the
partnership work: First, decide which tasks the performance
engine can handle,
assigning it only those that flow along the same path as ongoing
operations. Next,
assemble a dedicated team to carry out the rest, being careful to
bring in outside
perspectives and create new norms. Last, proactively manage
conflicts. The key here
is having an innovation leader who can collaborate well with
the performance
engine and a senior executive who supports the dedicated team,
prioritizes the
company’s long-term interests, and adjudicates contests for
resources.
West, a 135-year-old business, is one of several publishing
houses whose mission is to

make legal research easier. After it was acquired by the
Thomson Corporation, now
Thomson Reuters, in 1996, West experienced five years of
double-digit growth, as the
industry made a rapid transition from printed books to online
databases. But in 2001 a
major problem arose. Once nearly all of West’s customers had
converted to Westlaw, the
20
company’s online offering, West’s growth plummeted to near
zero.
To restart growth, West set its sights on expanding its product
line. By studying its
customers—law firms, corporate law offices, and law schools,
among others—and how
they worked, West saw that lawyers had no convenient access to
many key sources of
information. For example, to examine legal strategies in past
cases, law firms were sending
runners to courthouses to dig through dusty archives and
photocopy old briefs—documents
written by lawyers for judges, often to summarize their
arguments.

Questions to Ask
When Dividing the Labor
1. Does my company already have the needed skills for all
aspects of the project?
2. What portions of the innovation initiative are consistent with
the existing work
relationships in the performance engine?
When Assembling the Dedicated Team
1. What is the right mix of insiders and outsiders?
2. How should the team be structured differently from the
performance engine?
3. How should the team be measured and incentivized?
As You Manage the Strains on the Partnership
1. Is there a tone of mutual respect?
2. Are resource conflicts resolved proactively?
3. Is the shared staff giving sufficient attention to the
innovation initiative?
Starting with an online database of briefs, West proceeded to
launch a series of new
digital products. By 2007, West had restored its organic growth
to nearly 7% annually—
21

quite an accomplishment since its customer base was growing
much more slowly.
An expansion into databases for different kinds of documents
does not seem, at first
glance, as if it would have been a stretch for West. But Mike
Wilens, then the CEO, and his
head of product development, Erv Barbre, immediately saw that
the briefs project, because
of its size, complexity, and unfamiliarity, was beyond the
capabilities of West’s
performance engine. Some kind of special team was needed. At
the same time, Wilens and
Barbre were confident that portions of the project could be
tackled by West’s existing staff.
They just had to make sure the two groups worked well
together. Ultimately, the new briefs
offering succeeded because Wilens and Barbre built an effective
partnership between a
dedicated team and the performance engine, following the three
steps we’ve outlined.
22
Divide the Labor

Step one in forming the partnership is to define the
responsibilities of each partner.
Naturally, you want to assign as much as you can to the
performance engine. It already
exists and works well. But caution is due. You need to
realistically assess what the
performance engine can handle while it maintains excellence in
ongoing operations.
The proper division of labor can span a wide range—from
10/90, to 50/50, to 90/10
splits. It depends on the nature of the initiative and the
performance engine’s capabilities.
So how do you decide?
The performance engine has two essential limitations. The first
is straightforward. Any
task that is beyond the capabilities of the individuals within the
performance engine must be
assigned to the dedicated team. The second limitation is less
obvious. It involves work
relationships. What Person A and Person B can do together is
not just a function of A’s
skills and B’s skills. It is also a function of the way A and B are
accustomed to working
together. As long as A and B are working inside the

performance engine, their work
relationship is extremely difficult to change. It is reinforced
daily by the demands of
ongoing operations. BMW was confronted with this second
limitation when it designed its
first hybrid vehicle. (See the sidebar “Why BMW Didn’t
Reinvent the Wheel.”)
Therefore, the performance engine should take on only tasks
that flow along the same
path from person to person that ongoing operations do—at the
same pace and with the same
people in charge. To ask more of the performance engine is too
disruptive. It embeds
conflicts between innovation and ongoing operations so deeply
within the performance
engine that they become impossible to manage.
Case Study: Why BMW Didn’t Reinvent the Wheel
AT THE HEART OF EVERY HYBRID automobile lies a
regenerative brake.
Traditional brakes dissipate the energy produced by a vehicle’s
motion, generating
friction and useless heat. Regenerative brakes, by contrast,
capture the energy and
put it back to work. An electrical generator built into the brake
recharges the
23

hybrid’s massive batteries as the car slows.
Chris Bangle, then chief of design at BMW, was discouraged by
slow progress early
in the company’s first effort to design a hybrid vehicle,
launched in 2007. The source
of the problem, Bangle saw, had nothing to do with engineering
prowess; BMW
employed the right experts. The problem lay in the company’s
formal structure and
processes. Under its well-established design procedures, there
was no reason for
battery specialists to speak with brake specialists. There was no
routine work flow
between them.
Bangle ultimately decided to create a dedicated team to enable
the deep
collaboration that was necessary among all component
specialists involved in the
regenerative brake design. He named it the “energy chain” team,
and it succeeded in
moving the project forward quickly. Although a dedicated team
was required for this
one aspect of vehicle design, all other aspects of BMW’s first
hybrid launch—
design, engineering, sales, marketing, distribution, and so
forth—were handled by its
performance engine.
At West, Wilens and Barbre recognized that although product
development staffers had

deep expertise in judicial decisions—such as Miranda v.
Arizona, Brown v. Board of
Education, Roe v. Wade, and thousands more—they had no
experience in gathering briefs,
which were scattered throughout countless courthouses and
were much harder to track and
organize than decisions were. A complicating factor was scale.
There can be dozens of
briefs for every judicial decision. The dedicated team, at the
very least, had to take
responsibility for locating and acquiring the briefs—and would
need a few outside experts
to be effective.
More critically, Wilens and Barbre saw that the briefs project as
a whole would be
inconsistent with the work relationships within West’s product
development group.
Composed of about 50 legal experts, the group worked on a few
dozen small initiatives at a
time. The typical project was an improvement to the Westlaw
database that involved only
two or three people for up to a few weeks. The group was
nonhierarchical, and the
individuals within it did not depend heavily on one another. In
fact, during a given project,

a product developer’s most important work relationship was
likely to be cross-functional,
with a peer in the information technology group.
24
The briefs project was much larger. At its peak, it involved 30
people full-time. The
product developers needed to work together in an unfamiliar
manner. Each needed to take
on a specialized role as part of a tightly structured, close-knit
project team. Asking the
developers to operate in both modes at once would have been
disruptive and confusing for
all involved. Therefore Wilens and Barbre assigned nearly the
entire product development
task to a dedicated team.
However, they assigned the marketing and sales tasks to the
performance engine.
Marketing and selling briefs was not much different from
marketing and selling Westlaw.
The buyers were the same, and the value proposition was easy
to explain. The work could
simply be added to West’s existing marketing and sales

processes. It would flow along the
same path, at the same pace, and with the same people in
charge. The sales and marketing
teams were a component of the performance engine that could
do double duty—a subset we
refer to as the shared staff.
25
Assemble the Dedicated Team
Once the labor has been divided and the required skill sets have
been identified, the
principles for assembling the dedicated team are uncomplicated.
First, choose the best
people you can get, from any source (internal transfers, external
hires, even small
acquisitions). Then, organize the team in a way that makes the
most sense for the task at
hand. Tackle the process as if you were building a new company
from the ground up. This
was the approach Lucent took in launching a new unit that
quickly grew to $2 billion in
annual revenues. (See the sidebar “Why Lucent Engineered a
Service Business from

Scratch.”)
Alas, these principles are easy to state but extremely difficult to
follow. Companies have
a pernicious habit of creating subunits that behave just like the
rest of the company, as
though a genetic code has been passed from parent to offspring.
We think of such subunits as
little performance engines, and they quickly bring innovation
initiatives to a standstill.
Case Study: Why Lucent Engineered a Service Business
from Scratch
IN 2006, LUCENT SIGNED a deal to help a major
telecommunications company
transform its network. Four years earlier, such a huge service
deal would have been
hard to imagine.
Lucent’s historical strength was in products and in making
technological
breakthroughs in telecommunications hardware. But after the
dot-com bust, Lucent
needed a new source of growth and looked to services.
While the company had the necessary technical skills for
services, it hardly had the
organizational DNA. Technologists, not client relationship
managers, held most of
the power. And the pace of service operations was week to
week, a stark departure
from telecom hardware purchasing cycles, which lasted years.

As such, Lucent
recognized that nearly the entire project needed to be executed
by a dedicated team.
Lucent assembled that team as if it were building a new
company. It hired an outside
leader, a veteran of services from EDS, and several experienced
service executives.
26
It adopted new HR policies that mimicked those of service
companies. And it
created a new performance scorecard, one that emphasized
workforce utilization,
not product line ROI. It even tied compensation for service
deliverers directly to
their utilization rates. The result? In four years’ time, Lucent’s
service group was
generating more than $2 billion in revenues.
The most frequent source of the problem is the instinct to
populate dedicated teams
entirely with insiders. This is understandable. It’s natural to
think about who you know
before thinking about skills you need. Insiders are easy to find,
often cheaper to “hire,” and
seem less risky because they’re known commodities. They also
offer a critical benefit:
Because of their familiarity with the organization and

credibility within it, they can help
mitigate conflicts between the dedicated team and the
performance engine.
The trouble is that a dedicated team composed entirely of
insiders is practically
guaranteed to act like a little performance engine. For one
thing, everyone has the same
biases and instincts, grounded in the history of a single
company. Furthermore, work
relationships are sticky. As noted earlier, employees who have
worked together for years
have a hard time altering the way they interact.
Building an effective dedicated team requires breaking down
existing work relationships
and creating new ones. Including some outsiders, even just one
in three, is a powerful
expedient. Outsiders have no existing work relationships to
break down. They must form
new ones from scratch. As a bonus, outsiders naturally
challenge assumptions because their
biases and instincts are rooted in the experiences of other
companies.
Managers can also accelerate the process of breaking down and
re-creating work

relationships by writing new job descriptions, inventing new
and unfamiliar titles, and
explicitly shifting the balance of power within the team.
Shifting that balance is important
because it is rarely the case that a company’s traditional power
center (say, engineering)
should also dominate the dedicated team (if, for example,
customers for the innovation
initiative will care more about a new product’s look than its
performance).
Selecting the right people and forming new work relationships
are the foundational steps
in building an effective dedicated team, but it is also important
to pay attention to other
27
forces that shape behaviors. Beyond new work relationships,
dedicated teams frequently
require performance metrics, incentives, and cultural norms that
differ from those of the
performance engine.
West built a dedicated team that was distinct from its existing
product development staff,

choosing a roughly 50/50 mix of insiders and outsiders. The
company acquired a small
business that had assembled on microfiche a collection of
valuable briefs, including the
very first brief filed before the U.S. Supreme Court. With it,
West brought on board about a
dozen people who knew a great deal about briefs and had no
work relationships with the
West team.
The leader of the briefs effort, Steve Anderson, treated the
process of turning the mix of
insiders and outsiders into a structured team as a zero-based
effort. Rather than drawing on
any of West’s norms for how work gets done (who’s responsible
for what, who has what
decision rights, and so forth), he simply gathered everyone and
said, “Here we are. This is
our task. How should we make it happen?” Of course, the way
to organize the effort was
not obvious on day one. Innovation initiatives are ambiguous.
As Anderson’s team gained
experience, its structure evolved. It’s not essential that the
dedicated team’s structure be

perfectly clear at the outset—only that it be unconstrained by
the parent company’s past.
Working on Anderson’s dedicated team felt much different to
the insiders who were part
of it. They had less autonomy. They had to collaborate with
peers much more closely. And
they knew that if they stumbled, they would be letting down not
just themselves but their
teammates and their company. Some struggled with the
transition and chose to return to the
performance engine. While this may seem unfortunate, it was
actually a mark of Anderson’s
success. As a general rule, if all the insiders on the dedicated
team are comfortable, it must
be a little performance engine. (Note that it’s also important for
insiders to have a clear
path to get back to the performance engine. Innovation
initiatives frequently fail, and the
individuals working on them sometimes don’t succeed in their
assignments. Companies that
create an out for insiders will find that they can more easily
motivate people to join a
dedicated team.)
To further shape his dedicated team, Anderson drew clear

distinctions between its
28
standards and cultural values and those of the performance
engine. West had long
maintained extremely high quality standards. For judicial
decisions—literally, the law
itself—customers demanded infallible information. Therefore,
West had implemented
multistep checks and safeguards in loading documents into its
database, a process that often
started with scanning a physical document. With briefs,
however, West needed to relax,
slightly, from being obsessive about quality to being diligent.
The dauntingly high number of
briefs made exhaustive precautions impractical. Besides,
customers cared more about
convenience and availability than perfection.
29
Anticipate and Mitigate the Strains
Make no mistake, nurturing a healthy partnership is
challenging. Conflicts between

innovation initiatives and ongoing operations are normal and
can easily escalate. Tensions
become rivalries, rivalries become hostilities, and hostilities
become all-out wars in which
the company’s long-term viability is the clearest loser.
Differences between the two groups run deep. Managers of the
performance engine seek
to be efficient, accountable, on time, on budget, and on spec. In
every company, their basic
approach is the same. It is to make every task, process, and
activity as repeatable and
predictable as possible. An innovation initiative, of course, is
exactly the opposite. It is, by
nature, nonroutine and uncertain. These incompatibilities create
a natural us-versus-them
dynamic.
Leaders must counter conflicts by constantly reinforcing a
relationship of mutual respect.
Dedicated team leaders must remember that profits from the
performance engine pay for
innovation, and that their success depends on their ability to
leverage its assets. They must
also remember that pushback from the performance engine does
not arise from laziness or

from an instinctive resistance to change. Quite the contrary—it
arises from the efforts of
good people doing good work, trying to run ongoing operations
as effectively as possible.
For their part, performance engine leaders must recognize that
no performance engine lasts
forever. To dismiss innovation leaders as reckless rebels intent
on undermining discipline
in the pursuit of an esoteric dream is to write off the company’s
future.
For the partnership to work, the leader of the innovation
initiative must set the right tone
—positive and collaborative. Antagonizing the performance
engine is a really bad idea.
The performance engine always wins in an all-out fight. It is,
quite simply, bigger and
stronger.
In fact, for precisely that reason, even the best innovation
leaders need help from high
places. They must be directly supported by an executive senior
enough to act in the long-
term interests of the company, overriding the performance
engine’s short-term demands
when necessary. This typically means that the innovation leader

must report two or more
30
levels higher up than managers with budgets of a similar size.
At WD-40, for example, an
innovation initiative relied on the direct involvement of the
CEO. (See “How WD-40
Minimized Frictions.”)
The senior executive to whom the innovation leader reports
must be careful not to be a
cheerleader only for innovation. He or she must also extol the
virtues and importance of the
performance engine and emphasize that a long-run victory for
the company requires that
both sides win.
Case Study: How WD-40 Minimized Frictions
TO SPUR ORGANIC GROWTH, Garry Ridge, CEO of WD-40,
created a team to
develop breakthrough products. He called it Team Tomorrow. It
included newly
hired research scientists and new outside partners. One of its
first endeavors was
developing the No-Mess Pen, which made it easy to dispense
small quantities of
WD-40 in tight spaces. Though it doesn’t sound like a radical
innovation, the

technological challenges were steep, and the product took
months to develop.
Historically, WD-40’s marketing team had handled product
development, which
generally entailed routine efforts to improve, renew, or
repackage existing products.
Now marketing took on the responsibility of partnering with
Team Tomorrow to
commercialize its offerings.
The sources of conflict in this partnership are not hard to
identify. First, some
marketing staffers felt that the attractive challenge of
developing breakthrough
products should have been theirs. Then there was a resource
constraint. Would the
marketing team expend its limited time and resources on
experimental products or
proven performers? Finally, marketing worried that Team
Tomorrow’s new offerings
would cannibalize existing products.
Graham Milner and Stephanie Barry, the leaders of Team
Tomorrow, overcame these
conflicts by taking a collaborative approach, particularly with
the head of marketing.
They shared information, established an open-door policy, and
coordinated plans
carefully with marketing, anticipating bottlenecks and resource
conflicts. Knowing
that such conflicts could be resolved only by the CEO, Milner
and Barry made sure
Ridge was aware of them early, so that he could set priorities.
When Ridge saw that
it was necessary, he added staff to the marketing team to make

sure that it had
31
sufficient bandwidth.
To signal the importance of the long term, Ridge carried a
prototype of the pen with
him wherever he went. This gave Team Tomorrow the attention
it needed, but it also,
at least initially, exacerbated feelings among some in the
marketing team that they
had been left out. Ridge, Milner, and Barry all quickly saw just
how important it was
to celebrate the accomplishments of the core business’s team as
well.
Ridge took other steps that contributed to WD-40’s success. He
made it clear that he
would evaluate all involved employees on their effectiveness in
collaborating
across organizational boundaries. And he was able to lessen the
anxiety about
cannibalization by collecting data and sharing analyses that
showed that the No-
Mess Pen generated purely incremental sales.
Together, the innovation leader and the senior executive must
anticipate and proactively
resolve conflicts. The clashes can be intense, but if the labor is
properly divided between
the performance engine and the dedicated team, they’ll be

manageable. The most common
conflict is over scarce resources. When the sum of activities,
innovation plus ongoing
operations, pushes the performance engine beyond its resource
constraints, choices must be
made.
Sometimes, this competition for scarce resources takes place
through formal budgeting
processes. Innovation leaders often find themselves seeking
explicit commitments from
multiple performance engine leaders. These negotiations are
best resolved through a single
plan and budgeting process for the entire innovation initiative,
with conflicts directly
adjudicated by the senior executive.
In other cases, the competition is for the attention of the shared
staff. It’s tempting for the
innovation leader to think that once the budget for an initiative
is approved, the fight for
resources is over. It is not. Each shared staff member chooses
how much energy to devote
to the new initiative every day. The innovation leader’s powers
of persuasion are critical

but may not be adequate. Some companies create special
incentives and targets for shared
staff members to spur them to keep up with the demands of both
innovation and ongoing
operations. Others charge the innovation initiative for the
shared staff’s time. That way, the
shared staff treats the innovation leader more like a customer
than a distraction.
32
Emotional conflicts must also be managed. Sometimes
resentments are grounded in
substantive business conflicts, like the possibility that the
innovation initiative may
cannibalize the existing business. Senior executives must argue
clearly and consistently that
the innovation initiative is nonetheless in the company’s long-
term best interest and do as
much as possible to allay fears about job security.
At other times, resentments amount to simple jealousy. The
performance engine may feel
disenfranchised if the innovation initiative is viewed as the
company’s most critical

project. Or the dedicated team may feel marginalized as
pursuers of a quirky experiment.
Some companies have countered the effects of envy by making
“ability to work
productively with internal partners” a key assessment in
individual performance reviews.
The briefs project at West faced multiple kinds of conflict but
overcame them. Steve
Anderson provided the right type of leadership. He viewed the
performance engine as his
partner, not his enemy. And he received constant support from
two senior leaders, Mike
Wilens and Erv Barbre.
Wilens and Barbre paid close attention to resource conflicts.
When Barbre asked
members of the shared staff to make contributions to the briefs
effort, he also explicitly
discussed what was on their plates and what could be deferred.
In some cases, he hired
contract labor to help with routine tasks, such as loading
documents, to ensure that the
priorities of both innovation and ongoing operations could be
met.
Meanwhile, Anderson recognized the importance of galvanizing

the shared staff. He and
his team got people on board by, among other things, creating a
skit based on the Perry
Mason television series. It showed what a lawyer’s life was
really like and why a product
like a briefs database would be enormously valuable. Wilens
and Barbre backed up his
effort, in part by creating a special incentive for the sales force
to push the new offering.
All three leaders monitored emotional tensions. As the briefs
project started to show signs
of success, they saw that some in the performance engine felt as
though they had been left
out of a real “glamour project.” The leaders countered by
reinforcing the importance of the
core business and by holding events at which they spread credit
as widely as possible,
within both the dedicated team and the performance engine.
33
The Unlikeliest Partnership Is Manageable
The reception of West’s briefs database exceeded expectations.
In fact, the company was
deluged with queries about how quickly the database would be
expanded to include

additional legal specialties and jurisdictions. The company
followed with many more
initiatives like it, such as new databases of expert testimony and
court dockets.
The organizational formula was not always the same for those
initiatives. For example,
when West pursued a product called PeerMonitor, it assigned
almost the entire job—
development and commercialization—to the dedicated team.
PeerMonitor provided data
that enabled law firms to benchmark their business performance
against that of rivals. West
chose to assign sales and marketing to the dedicated team
because selling PeerMonitor
required a different skill set and a longer cycle. The target
customer was also different:
West sold most of its offerings to law librarians, but
PeerMonitor was sold directly to
managing partners. The PeerMonitor sales force collaborated
with the performance
engine’s sales force to coordinate an overall approach.
West’s example is one worthy of study. The company succeeded
where others have
stumbled because it saw that innovation is not something that

happens either inside or
outside the existing organization, and that innovation does not
require that an upstart fight
the establishment. Instead, innovation requires a partnership
between a newly formed team
and the long-standing one.
While such partnerships are challenging, they are manageable.
And they are
indispensable. Indeed, without them, innovation goes nowhere.
Originally published in July 2010. Reprint R1007F
34
How GE Is Disrupting Itself
by Jeffrey R. Immelt, Vijay Govindarajan, and Chris Trimble
IN MAY 2009, General Electric announced that over the next
six years it would spend $3
billion to create at least 100 health-care innovations that would
substantially lower costs,
increase access, and improve quality. Two products it
highlighted at the time—a $1,000
handheld electrocardiogram device and a portable, PC-based
ultrasound machine that sells

for as little as $15,000—are revolutionary, and not just because
of their small size and low
price. They’re also extraordinary because they originally were
developed for markets in
emerging economies (the ECG device for rural India and the
ultrasound machine for rural
China) and are now being sold in the United States, where
they’re pioneering new uses for
such machines.
We call the process used to develop the two machines and take
them global reverse
innovation, because it’s the opposite of the glocalization
approach that many industrial-
goods manufacturers based in rich countries have employed for
decades. With
glocalization, companies develop great products at home and
then distribute them
worldwide, with some adaptations to local conditions. It allows
multinationals to make the
optimal trade-off between the global scale so crucial to
minimizing costs and the local
customization required to maximize market share. Glocalization
worked fine in an era when
rich countries accounted for the vast majority of the market and
other countries didn’t offer

much opportunity. But those days are over—thanks to the rapid
development of populous
countries like China and India and the slowing growth of
wealthy nations.
GE badly needs innovations like the low-cost ECG and
ultrasound machines, not only to
35
expand beyond high-end segments in places like China and
India but also to preempt local
companies in those countries—the emerging giants—from
creating similar products and
then using them to disrupt GE in rich countries. To put it
bluntly: If GE’s businesses are to
survive and prosper in the next decade, they must become as
adept at reverse innovation as
they are at glocalization. Success in developing countries is a
prerequisite for continued
vitality in developed ones.
The problem is that there are deep conflicts between
glocalization and reverse
innovation. And the company can’t simply replace the first with
the second, because

glocalization will continue to dominate strategy for the
foreseeable future. The two models
need to do more than coexist; they need to cooperate. This is a
heck of a lot easier said than
done since the centralized, product-focused structures and
practices that have made
multinationals so successful at glocalization actually get in the
way of reverse innovation,
which requires a decentralized, local-market focus.
Almost all the people and resources dedicated to reverse
innovation efforts must be
based and managed in the local market. These local growth
teams need to have P&L
responsibility; the power to decide which products to develop
for their markets and how to
make, sell, and service them; and the right to draw from the
company’s global resources.
Once products have proven themselves in emerging markets,
they must be taken global,
which may involve pioneering radically new applications,
establishing lower price points,
and even using the innovations to cannibalize higher-margin
products in rich countries. All
of those approaches are antithetical to the glocalization model.

This article aims to share
what GE has learned in trying to overcome that conflict.
Idea in Brief
For decades, General Electric and other industrial-goods
manufacturers based in
rich countries grew by developing high-end products at home
and distributing them
globally, with some adaptations to local conditions—an
approach known as
glocalization. Now they must do an about-face and learn to
bring low-end products
created specifically for emerging markets into wealthy markets.
That process, called
reverse innovation, isn’t easy to master. It requires a
decentralized, local-market
focus that clashes with the centralized, product-focused
structure that multinationals
36
have evolved for glocalization. In this article, Immelt, GE’s
CEO, and Govindarajan
and Trimble, of Dartmouth’s Tuck School of Business, describe
how GE has dealt
with that challenge. An anomaly within the ultrasound unit of
GE Healthcare
provided the blueprint. Because China’s poorly funded rural
clinics couldn’t afford
the company’s sophisticated ultrasound machines, a local team
built a cheap,
portable ultrasound out of a laptop equipped with special
peripherals and software.

It not only became a hit in China but jump-started growth in the
developed world by
pioneering applications for situations where portability is
critical, such as at
accident sites. The team succeeded because a top executive
championed it and gave
it unprecedented autonomy. GE has since set up more than a
dozen similar operations
in an effort to expand beyond the premium segments in
developing countries—and to
preempt emerging giants from disrupting GE’s sales at home
37
Why Reverse Innovation Is So Important
Glocalization is so dominant today because it has delivered.
Largely because of
glocalization, GE’s revenues outside the United States soared
from $4.8 billion, or 19% of
total revenues, in 1980, to $97 billion, or more than half of the
total, in 2008.
The model came to prominence when opportunities in today’s
emerging markets were
pretty limited—when their economies had yet to take off and
their middle or low-end
customer segments didn’t exist. Therefore, it made sense for
multinational manufacturers to
simply offer them modifications of products for developed

countries. Initially, GE, like
other multinationals, was satisfied with the 15% to 20% growth
rates its businesses
enjoyed in developing countries, thanks to glocalization.
Then in September 2001 one of the coauthors of this piece, Jeff
Immelt, who had just
become GE’s CEO, set a goal: to greatly accelerate organic
growth at the company and
become less dependent on acquisitions. This made people
question many things that had
been taken for granted, including the glocalization strategy,
which limited the company to
skimming the top of emerging markets. A rigorous analysis of
GE’s health-care, power-
generation, and power-distribution businesses showed that if
they took full advantage of
opportunities that glocalization had ignored in heavily
populated places like China and
India, they could grow two to three times faster there. But to do
that, they’d have to develop
innovative new products that met the specific needs and budgets
of customers in those
markets. That realization, in turn, led GE executives to question
two core tenets of

glocalization.
38
Assumption 1: Emerging economies will largely evolve in the
same way that wealthy economies did
The reality is, developing countries aren’t following the same
path and could actually jump
ahead of developed countries because of their greater
willingness to adopt breakthrough
innovations. With far smaller per capita incomes, developing
countries are more than happy
with high-tech solutions that deliver decent performance at an
ultralow cost—a 50%
solution at a 15% price. And they lack many of the legacy
infrastructures of the developed
world, which were built when conditions were very different.
They need communications,
energy, and transportation products that address today’s
challenges and opportunities, such
as unpredictable oil prices and ubiquitous wireless technologies.
Finally, because of their
huge populations, sustainability problems are especially urgent
for countries like China and

India. Because of this, they’re likely to tackle many
environmental issues years or even
decades before the developed world.
All this isn’t theory. It’s already happening. Emerging markets
are becoming centers of
innovation in fields like low-cost health-care devices, carbon
sequestration, solar and wind
power, biofuels, distributed power generation, batteries, water
desalination, microfinance,
electric cars, and even ultra-low-cost homes.
39
Assumption 2: Products that address developing countries’
special needs can’t be sold in developed countries because
they’re not good enough to compete there
The reality here is, these products can create brand-new markets
in the developed world—
by establishing dramatically lower price points or pioneering
new applications.
Consider GE’s health-care business in the United States. It used
to make most of its
money on premium computed tomography (CT) and magnetic
resonance (MR) imaging
machines. But to succeed in the era of broader access and

reduced reimbursement that
President Obama hopes to bring about, the business will
probably need to increase by 50%
the number of products it offers at lower price points. And that
doesn’t mean just cheaper
versions of high-tech products like imaging machines. The
company also must create more
offerings like the heated bassinet it developed for India, which
has great potential in U.S.
inner cities, where infant deaths related to the cold remain high.
And let’s not forget that technology often can be improved until
it satisfies more
demanding customers. The compact ultrasound, which can now
handle imaging applications
that previously required a conventional machine, is one
example. (See the exhibit “Reverse
innovation in practice.”) Another is an aircraft engine that GE
acquired when it bought a
Czech aerospace company for $20 million. GE invested an
additional $25 million to further
develop the engine’s technology and now plans to use it to
challenge Pratt & Whitney’s
dominance of the small turboprop market in developed
countries. GE’s cost position is

probably half of what Pratt’s is.
40
Preempting the Emerging Giants
Before the financial crisis plunged the world into a deep
recession, GE’s leaders had been
looking to emerging markets to help achieve their ambitious
growth objectives. Now
they’re counting on these markets even more because they think
that after the downturn ends,
the developed world will suffer a prolonged period of slow
growth—1% to 3% a year. In
contrast, annual growth in emerging markets could easily reach
two to three times that rate.
Reverse innovation in practice
Ten years ago when GE senior managers discussed the global
marketplace, they talked
about “the U.S., Europe, Japan, and the rest of the world.” Now
they talk about “resource-
rich regions,” such as the Middle East, Brazil, Canada,
Australia, and Russia, and “people-
rich regions,” such as China and India. The “rest of world”
means the U.S., Europe, and

Japan.
To be honest, the company also is embracing reverse innovation
for defensive reasons. If
41
GE doesn’t come up with innovations in poor countries and take
them global, new
competitors from the developing world—like Mindray, Suzlon,
Goldwind, and Haier—
will.
In GE’s markets the Chinese will be bigger players than the
Indians will. The Chinese
have a real plan to become a major global force in
transportation and power generation.
GE Power Generation is already regularly running into Chinese
enterprises as it competes
in Africa, which will be an extremely important region for the
company. One day those
enterprises may compete with GE in its own backyard.
That’s a bracing prospect. GE has tremendous respect for
traditional rivals like Siemens,
Philips, and Rolls-Royce. But it knows how to compete with

them; they will never destroy
GE. By introducing products that create a new price-
performance paradigm, however, the
emerging giants very well could. Reverse innovation isn’t
optional; it’s oxygen.
42
A Clash of Two Models
Glocalization has defined international strategy for three
decades. All the currently
dominant ideas—from Christopher A. Bartlett and Sumantra
Ghoshal’s “transnational”
strategy to Pankaj Ghemawat’s “adaptation-aggregation” trade-
off—fit within the
glocalization framework. Since organization follows strategy,
it’s hardly surprising that
glocalization also has molded the way that multinationals are
structured and run.
GE is a case in point. For the past 30 years, its organization has
evolved to maximize its
effectiveness at glocalization. Power and P&L responsibility
were concentrated in global
business units headquartered in the developed world. The major
business functions—

including R&D, manufacturing, and marketing—were
centralized at headquarters. While
some R&D centers and manufacturing operations were moved
abroad to tap overseas talent
and reduce costs, they focused mainly on products for wealthy
countries.
While this approach has enormous advantages, it makes reverse
innovation impossible.
The experiences of Venkatraman Raja, the head of GE
Healthcare’s business in India,
illustrate why.
GE Healthcare sells an x-ray imaging product called a surgical
C-arm, which is used in
basic surgeries. A high-quality, high-priced product designed
for hospitals in wealthy
countries, it has proven tough to sell in India. Raja saw the
problem and made a proposal in
2005. He wanted to develop, manufacture, and sell a simpler,
easier-to-use, and
substantially cheaper product in India. His proposal made sense,
and yet, to no one’s
surprise, it was not approved.
If you were a leader of a GE operation in a developing country,

as Raja was, here’s what
you were up against: Your formal responsibilities included
neither general management nor
product development. Your responsibility was to sell, distribute,
and service GE’s global
products locally and provide insights into customers’ needs to
help the company adapt its
offerings. You were expected to grow revenues by 15% to 20%
a year and make sure that
costs increased at a much slower rate, so that margins rose. You
were held rigidly
accountable for delivering on plan. Just finding the time for an
extracurricular activity like
43
creating a proposal for a product tailored to the local market
was challenging.
That was nothing, however, compared with the challenge of the
next step: selling your
proposal internally. Doing so required getting the attention of
the general manager at
headquarters in the United States, who sat two or more levels
above your immediate boss

and was far more familiar with a world-renowned medical
center in Boston than a rural
clinic outside Bangalore. Even if you got the meeting, you’d
have limited time to make your
case. (India accounted for just 1% of GE’s revenues at the time
and occupied roughly the
same mindshare of managers with global responsibility.)
If you were extremely persuasive, you might be invited to share
the proposal with others.
But when you visited the head of global manufacturing, you’d
have to counter arguments
that a simple, streamlined global product line was much more
efficient than custom
offerings. When you visited the head of marketing, you’d have
to deal with fears that a
lower-priced product would weaken the GE brand and
cannibalize existing sales. When
you met with the head of finance, you’d have to wrestle with
concerns that lower-priced
products would drag down overall margins. And when you
visited the head of global R&D,
you’d have to explain why the energies of GE’s scientists and
engineers—including those in
technology centers in emerging markets—should be diverted

from projects directed at its
most sophisticated customers, who paid top dollar.
Even if you gained support from each of these executives and
got the proposal off the
ground, you’d still have to compete for capital year after year
against more certain projects
with shorter-term payoffs. Meanwhile, of course, you’d still
have to worry about making
your quarterly numbers for your day job.
It was little wonder that successful efforts to develop radically
new products for poor
countries were extremely rare.
44
Shifting the Center of Gravity
Obviously, changing long-established structures, practices, and
attitudes is an enormous
task. As is the case in any major change program, the
company’s top leaders have to play a
major role.
To do so, they must investigate firsthand the size of the
opportunity and how it could be

exploited and encourage the teams running the corporation’s
businesses to do the same. As
GE’s CEO, Jeff goes to China and India two times a year. When
he’s in, say, China, he’ll
spend a day at GE’s research center in Shanghai and then meet
separately with dozens of
people in the company’s local business operations and just let
them talk about what they’re
working on, what their cost points are, who their competitors
are, and so on. On such visits,
he has realized that there’s a whole realm of technology that the
company should be
applying faster.
While in China, Jeff will also talk with government leaders,
including Premier Wen
Jiabao. Wen has told Jeff about his plans to develop China’s
economy and how making
health care affordable for all citizens fits into that. It takes a
conversation like that to fully
appreciate the opportunities in China.
In India, Jeff will have dinner with the CEOs of Indian
companies. At one dinner Anand
Mahindra talked about how his company, Mahindra &
Mahindra, was making life miserable

for John Deere in India with a tractor that cost half the price of
Deere’s but was still
enormously profitable. Such discussions drive home the point
that you can make a lot of
money in India if you have the right business models.
So the job of the CEO—of any senior business leader, for that
matter—is to connect all
the dots and then act as a catalyst. It’s to give initiatives special
status and funding and
personally monitor them on a monthly or quarterly basis. And
perhaps most important in the
case of reverse innovation, it’s to push your enterprise to come
up with the new
organizational form that will allow product and business-model
innovation to flourish in
emerging markets.
45
A Homegrown Model
To develop that new organizational form, GE did what it has
always done: learn from other
companies’ experiences but also try to find an internal group
that somehow had managed to

overcome the hurdles and achieve success. During their annual
strategy review, the
company’s leaders spotted one in the ultrasound unit of GE
Healthcare.
GE Healthcare’s primary business is high-end medical-imaging
equipment. By the late
1980s it had become clear that a new technology—ultrasound—
had a bright future.
Ultrasound machines, like the other imaging devices, were
typically found in sophisticated
imaging centers in hospitals. While they delivered lower quality
than CT or MR scanners,
they did so at much lower cost. The company aimed to be
number one in ultrasound.
Over the next decade, GE Healthcare expanded its presence in
the market. It built an
R&D facility for developing new ultrasound products near its
headquarters, in Milwaukee,
and made acquisitions and entered into joint ventures around the
world. It competed in all
three of the primary market segments—obstetrics, cardiology,
and general radiology—by
launching premium products that employed cutting-edge
technologies. By 2000, GE

Healthcare had established solid market positions in rich
countries around the world.
The results in developing countries, by contrast, were
disappointing. By 2000, with the
help of a joint venture partner in China, GE saw the problem: In
wealthy countries
performance mattered most, followed by features; in China
price mattered most, followed
by portability and ease of use.
The priorities weren’t the same because the health-care
infrastructure of China was so
different from that of rich countries. More than 90% of China’s
population relied (and still
relies) on poorly funded, low-tech hospitals or basic clinics in
rural villages. These
facilities had no sophisticated imaging centers, and
transportation to urban hospitals was
difficult, especially for the sick. Patients couldn’t come to the
ultrasound machines; the
ultrasound machines, therefore, had to go to the patients.
There was no way that GE could meet that need by simply
scaling down, removing
features from, or otherwise adapting its existing ultrasound

machines, which were large,
46
bulky, expensive, and complex. It needed a revolutionary
product.
In 2002, the company launched its first compact ultrasound,
which combined a regular
laptop computer with sophisticated software. It sold for as low
as $30,000. In late 2007,
GE introduced a model that sold for as low as $15,000, less than
15% of the cost of GE’s
high-end ultrasound machines. Of course, its performance was
not as high, but it was
nonetheless a hit in rural clinics, where doctors used it for
simple applications, such as
spotting enlarged livers and gallbladders and stomach
irregularities. The software-centric
design also made it easy to adjust the machine—for example, to
improve the interfaces—
after observing how doctors worked with it. Today the portable
machine is the growth
engine of GE’s ultrasound business in China.
Even more exciting, the innovation has generated dramatic

growth in the developed
world by pioneering new applications where portability is
critical or space is constrained,
such as at accident sites, where the compacts are used to
diagnose problems like
pericardial effusions (fluid around the heart); in emergency
rooms, where they are
employed to identify conditions such as ectopic pregnancies;
and in operating rooms, where
they aid anesthesiologists in placing needles and catheters.
Six years after their launch, portable ultrasounds were a $278
million global product line
for GE, one that was growing at 50% to 60% a year before the
worldwide recession hit.
Someday every general practitioner may carry both a
stethoscope and a compact ultrasound
device embedded in his or her PDA.
The products owe their successful development to an
organizational anomaly in GE: the
existence of multiple ultrasound business units. Although the
three primary segments of the
ultrasound business are vastly different, GE’s initial instinct
was to follow the glocalization

model when it built the business—that is, to create a single
integrated global organization.
In 1995, however, Omar Ishrak, a newcomer who had been hired
to lead the business, saw
that meshing operations would reduce them to a common
denominator that served nobody
well. He decided to run the business as three independent
business units with their own
P&L responsibility, all reporting to him.
When the compact ultrasound effort began in China, Ishrak saw
that the new business
47
would have little in common with the three units, which were
focused on premium
products. So instead, he created a fourth independent unit,
based in Wuxi, China. It evolved
the local growth team (LGT) model, which is based on five
critical principles.
48
1. Shift power to where the growth is
Without autonomy, the LGTs will become pawns of the global
business and won’t be able

to focus on the problems of customers in emerging markets.
Specifically, they need the
power to develop their own strategies, organizations, and
products. Ishrak understood this
and gave such broad authority to Diana Tang and J.K. Koo, the
leaders of GE’s ultrasound
effort in China. The pair of GE veterans had deep experience in
the ultrasound business,
expertise in biomedical engineering and general management,
and lengthy careers in Asia.
49
2. Build new offerings from the ground up
Given the tremendous gulfs between rich countries and poor
ones in income, infrastructure,
and sustainability needs, reverse innovation must be zero-based.
These wide differences
cannot be spanned by adapting global products.
The compact ultrasound was built from scratch, although it drew
heavily from an existing
R&D effort. In the late 1990s, in a product-development center
in Israel, GE had started to
experiment with a revolutionary new architecture—one that

shifted most of the muscle
inside an ultrasound machine from the hardware to the software.
Instead of a large box full
of custom hardware, the scientists and engineers involved in the
project envisioned a
standard high-performance PC, special peripherals such as an
ultrasound probe, and
sophisticated software.
The concept generated little excitement in GE Healthcare at the
time because it could not
come close to matching the performance of the business’s
premium products. But Ishrak
quickly saw the value of the new architecture in developing
countries. He encouraged the
team in China to pursue the concept further. The resulting
compact ultrasound based on a
laptop computer hit the mark in China.
50
3. Build LGTs from the ground up, like new companies
Zero-based innovation doesn’t happen without zero-based
organizational design. GE’s
organizational “software”—its hiring practices, reporting

structures, titles, job
descriptions, norms for working relationships, and power
balances between functions—all
evolved to support glocalization. LGTs need to rewrite the
software.
Tang and Koo constructed a business unit that managed a
complete value chain: product
development, sourcing, manufacturing, marketing, sales, and
service. By recruiting locally,
they were able to find most of the expertise they needed—
including engineers with deep
knowledge of miniaturization and low-power consumption and a
commercialization team
well versed in health care in rural China.
The LGT also decided that dealers—rather than the direct sales
force used by the
premium ultrasound units—were the only cost-effective way to
reach China’s vast and
fragmented rural markets and third-tier cities. And instead of
relying on GE Healthcare’s
global customer-support and replacement-parts organizations, it
built in-country teams that
could provide quicker and less costly service.

51
4. Customize objectives, targets, and metrics
Innovation endeavors are, by nature, uncertain. It’s more
important to learn quickly by
efficiently testing assumptions than to hit the numbers. So the
relevant metrics and standards
for LGTs—the ones that resolve the critical unknowns—are
rarely the same as those used
by the established businesses.
The ultrasound LGT knew that doctors in rural China were less
familiar with ultrasounds
than doctors in cities. But the team didn’t know how much
experience rural doctors had
with the technology or what features would meet their needs. So
it set out to learn how
doctors reacted to the machines and what the obstacles to their
adoption were. The team
discovered that ease of use, especially in primary-care
screenings, where doctors test for
common local conditions, was even more crucial than
anticipated. In response, the new
business emphasized training, offered online guides, designed
simpler keyboards, created

built-in presets for certain tasks, and tracked customer
satisfaction to gauge success.
Ishrak was careful to use different criteria to evaluate the
performance of the LGT in
China. For example, because the government approval process
for new product releases is
less intricate in China, he set much shorter product-development
cycles than were common
in wealthy countries. He also agreed to allow the size of the
local service organization to
deviate from the GE Healthcare’s global standards. Since
salaries are lower and service is
more demanding in China, a bigger staff relative to the number
of installed machines made
sense.
52
5. Have the LGT report to someone high in the organization
LGTs cannot thrive without strong support from the top. The
executive overseeing the LGT
has three critical roles: mediating conflicts between the team
and the global business,
connecting the team to resources such as global R&D centers,

and helping take the
innovations that the team develops into rich countries. Only a
senior executive in the global
business unit, or even its leader, can accomplish all of that.
Even when it was tiny, the LGT in China reported directly to
Ishrak. Because GE
Healthcare had an ambitious product-development agenda for
rich countries when the
compact project was launched, the LGT’s engineers might
easily have been redirected to
other projects if Ishrak hadn’t shielded the team. He protected
and even expanded the
team’s resources. By 2007 its number of engineers had grown
from 13 to 70 and its total
payroll had increased from 132 to 339. Ishrak also personally
made sure that the team got
the expertise it needed from other parts of GE, such as three
highly respected development
engineers from Israel, Japan, and South Korea. They worked
full-time on the project and
got it extra support from GE’s R&D centers around the world.
Ishrak included the China LGT in the company’s Ultrasound
Council, a group of

ultrasound executives and market and technology experts who
meet for two days three times
a year. At the meeting they share knowledge and insights and
agree on which major projects
to pursue. The council was instrumental in moving knowledge
and technology into China.
Finally, Ishrak played a critical role in building a global market
for the portable
ultrasound. He identified potential new applications in the
developed world and saw to it
that the three units that sold the premium products aggressively
pursued those opportunities.
_____________________
GE now has more than a dozen local growth teams in China and
India. In the midst of a
severe global recession, GE’s businesses in China will grow
25% this year—largely
because of LGTs. It’s way too early to declare victory, however.
Progress has been uneven.
While some businesses—notably, health care and power
generation and distribution—have
taken the ball and run with it, others have been less
enthusiastic. And though GE’s R&D
53

centers in China and India have increased their focus on the
problems of developing
countries, the vast majority of their resources are still devoted
to initiatives for developed
ones. So there is still a long way to go.
It’s still necessary for the company’s top executives to monitor
and protect local efforts
and make sure they get resources. It’s still necessary to
experiment with people transfers,
organizational structures, and processes to see what works. The
biggest experiment is about
to come: To speed progress in India, GE is creating a separate
P&L that will include all GE
businesses in that country and giving the new unit considerable
power to tap GE’s global
R&D resources. It will be headed by a senior vice president who
will report to a vice
chairman. That’s anathema in a company used to a matrix in
which product comes first and
country second. Nonetheless, the company is going to try it and
see if it can create new
markets. GE has to learn how to operate on a different axis.

The resistance to giving India its own P&L reflects what is
perhaps GE’s biggest
challenge: changing the mind-set of managers who’ve spent
their careers excelling at
glocalization. Even the exemplars have a rich-country bias. In a
recent conversation with
Jeff, one such manager—the head of a major business that’s
doing well in India and China
—still seemed preoccupied with problems beyond his control in
the U.S. “I don’t even
want to talk to you about your growth plans for the U.S.,” Jeff
responded. “You’ve got to
triple the size of your Indian business in the next three years.
You’ve got to put more
resources, more people, and more products in there, so you’re
deep in that market and not
just skimming the very top. Let’s figure out how to do it.”
That’s how senior managers have
to think.
Originally published in October 2009. Reprint R0910D
54

The Customer-Centered Innovation Map
by Lance A. Bettencourt and Anthony W. Ulwick
WE ALL KNOW THAT people “hire” products and services to
get a job done. Office
workers hire word-processing software to create documents and
digital recorders to
capture meeting notes. Surgeons hire scalpels to dissect soft
tissue and electrocautery
devices to control patient bleeding. Janitors hire soap
dispensers, paper towels, and
cleansing fluid to help remove grime from their hands.
While all this seems obvious, very few companies use the
perspective of “getting the job
done” to discover opportunities for innovation. In fact, the
innovation journey for many
companies is little more than hopeful wandering through
customer interviews. Such
unsystematic inquiry may occasionally turn up interesting
tidbits of information, but it rarely
uncovers the best ideas or an exhaustive set of opportunities for
growth.
We have developed an efficient yet simple system companies
can use to find new ways
to innovate. Our method, which we call “job mapping,” breaks

down the task the customer
wants done into a series of discrete process steps. By
deconstructing a job from beginning
to end, a company gains a complete view of all the points at
which a customer might desire
more help from a product or service—namely, at each step in
the job. With a job map in
hand, a company can analyze the biggest drawbacks of the
products and services customers
currently use. Job mapping also gives companies a
comprehensive framework with which
to identify the metrics customers themselves use to measure
success in executing a task.
(For a description of these metrics and a discussion about how
to gather and prioritize
them, see Anthony W. Ulwick’s “Turn Customer Input into
Innovation” in HBR’s January
55
2002 issue.)
Job mapping differs substantively from process mapping in that
the goal is to identify
what customers are trying to get done at every step, not what

they are doing currently. For
example, when an anesthesiologist checks a monitor during a
surgical procedure, the action
taken is just a means to an end. Detecting a change in patient
vital signs is the job the
anesthesiologist is trying to get done. By mapping out every
step of the job and locating
opportunities for innovative solutions, companies can discover
new ways to differentiate
their offerings.
56
Anatomy of a Customer Job
Over the past 10 years, we have mapped customer jobs in
dozens of product and service
categories that span professional and consumer services,
durable and consumable goods,
chemicals, software, and many other industries. Our work has
revealed three fundamental
principles about customer jobs.
57
All jobs are processes

Every job, from transplanting a heart to cleaning a floor, has a
distinct beginning, middle,
and end, and comprises a set of process steps along the way.
The starting point for
identifying innovation opportunities is to map out—from the
customer’s perspective—the
steps involved in executing a particular job. Once the steps are
identified, a company can
create value in a number of ways—by improving the execution
of specific job steps;
eliminating the need for particular inputs or outputs; removing
an entire step from the
responsibility of the customer; addressing an overlooked step;
resequencing the steps; or
enabling steps to be completed in new locations or at different
times.
Idea in Brief
We all know that people “hire” products to get jobs done.
Office workers hire word-
processing software to create documents. Surgeons hire scalpels
to dissect soft
tissue. But few companies keep this in mind while searching for
ideas for
breakthrough offerings. Instead, they rely on inquiry methods
(such as customer
interviews) that don’t generate the most promising ideas or
exhaustive sets of
possibilities.

To systematically uncover more—and better—innovative ideas,
Bettencourt and
Ulwick recommend job mapping: Break down a job that
customers want done into
discrete steps. Then brainstorm ways to make steps easier,
faster, or unnecessary.
For example, while cleaning clothes, people don’t notice
stubborn stains until
they’ve taken the clothes from a dryer and started folding them.
If they find a stain,
they must repeat the job. A washer that detects persistent stains
and takes
appropriate action before consumers execute the rest of the job
would have huge
appeal.
When mapping the job of washing clothes, for example, a
company would quickly
discover that the step of “verifying that stains have been
removed” often comes at the end of
the job sequence, after the clothes have been removed from the
washing machine, dried,
folded, and put away—too late to do much of anything about it.
If the washing machine
itself could detect the presence of any remaining stains before
the wash cycle ended—
58

resequence when verification takes place—it could take the
necessary actions at a much
more convenient point in the job. If the machine could be
designed to remove the need for
inputs such as stain removers and bleach, that would be even
better.
59
All jobs have a universal structure
That universal structure, regardless of the customer, has the
following process steps:
defining what the job requires; identifying and locating needed
inputs; preparing the
components and the physical environment; confirming that
everything is ready; executing the
task; monitoring the results and the environment; making
modifications; and concluding the
job. Because problems can occur at many points in the process,
nearly all jobs also require
a problem resolution step.
Idea in Practice
All jobs have the same eight steps. To use job mapping, look for
opportunities to
help customers at every step:

60
61
Some steps are more critical than others, depending on the job,
but each is necessary to
get the job done successfully. For example, when preparing for
the task of replacing a hip
joint, surgeons sterilize their hands, establish a sterile field
between their body and the
patient, prep the patient’s skin for the incision, and properly
position the patient. A janitor
about to clean his hands might prepare by simply rolling up his
sleeves. Innovation
62
possibilities reside within each of the job steps.
63
Jobs are separate from solutions
Customers hire different products or services to get the same

job done. When preparing
income taxes, for example, one person might rely on the
services of a CPA, whereas
another might use tax preparation software. Others might hire
both for different steps in the
process.
Many companies are focused on the product or service they’re
already developing, or on
the one the competition is offering, rather than on the help they
must give the customer to
execute the steps in a job. When the job is the focal point of
value creation, companies not
only can improve their existing offerings but also can target
new, or “blue ocean,” market
space. While other MP3 manufacturers were concentrating on
helping customers listen to
music, for example, Apple reconsidered the entire job of music
management, enabling
customers to acquire, organize, listen to, and share music.
Taken together, these fundamental principles form the
foundation of a company’s search
for opportunities to create customer value.
64

Creating a Job Map
The goal of creating a job map is not to find out how the
customer is executing a job—that
only generates maps of existing activities and solutions. Instead
the aim is to discover what
the customer is trying to get done at different points in
executing a job and what must
happen at each juncture in order for the job to be carried out
successfully. (See the sidebar
“Mapping a Customer Job.”) Let’s look at the steps in detail.
65
1: Define
What aspects of getting the job done must the customer define
up front in order to proceed?
This step includes determining objectives; planning the
approach; assessing which
resources are necessary or available to complete the job; and
selecting resources. A
financial adviser may label this step “assessing the investment
situation,” since she must not
only gauge investment priorities and risk tolerance but also

consider how much money is
available and which types of investments to select. An
anesthesiologist might call it
“formulating the anesthesia plan,” since he must choose which
type of anesthesia to
provide, depending upon case characteristics and the patient’s
medical history.
In this step, a company can look for ways to help customers
understand their objectives,
simplify the resource-planning process, and reduce the amount
of planning needed.
Consider how Weight Watchers assists dieters with the daunting
task of losing weight. The
company offers a core weight-loss plan that helps the dieter
select appropriate foods
without the need to count calories, carbohydrates, or anything
else. In addition, it provides
meal ideas and recipes that fit into its core and points-based
diet plans. For dieters desiring
more flexibility, Weight Watchers offers instant access to point
values for over 27,000
foods and online tools to help dieters gauge the impact of what
they eat.
66

2: Locate
What inputs or items must the customer locate to do the job?
Inputs are both tangible (for
example, the surgical tools a nurse must locate for an operation)
and intangible (say,
business or other requirements that a software developer uses
when writing code). When
tangible materials are involved, a company might consider
streamlining this step by making
the required components easier to gather, ensuring that they are
available when and where
needed, or eliminating the need for some inputs altogether.
Consider how U-Haul helps
customers locate the inputs necessary to complete the job of
moving their physical goods. In
addition to being a one-stop shop for moving supplies, U-Haul
offers customers
prepackaged moving kits that reduce the time it takes to gather
the various boxes and
supplies required for a move. In addition, an online partnership
with eMove helps
customers quickly locate a variety of inputs in the form of
human helpers—packers,

babysitters, cleaners, and painters. Opportunities abound to help
customers assemble
intangible materials as well: for instance, retrieve stored data,
facilitate the collection of
new information, and verify that data are accurate and complete.
Mapping a Customer Job
TO FIND WAYS TO INNOVATE, deconstruct the job a
customer is trying to get
done. By working through the questions here, you can map a
customer job in just a
handful of interviews with customers and internal experts.
Start by understanding the execution step, to establish context
and a frame of
reference. Next, examine each step before execution and then
after, to uncover the
role each plays in getting the job done.
To ensure that you are mapping job steps (what the customer is
trying to accomplish)
rather than process solutions (what is currently being done), ask
yourself the
validating questions below at each step.
Validating Questions
As defined, does the step specify what the customer is trying to
accomplish, or is it
67

only being done to accomplish a more fundamental goal?
Valid step: ascertain patient vital signs
Invalid step: check the monitor
Does the step apply universally for any customer executing the
job, or does it depend
on how a particular customer does the job?
Valid step: place an order
Invalid step: call the supplier to place an order
Defining the Execution Step
What are the most central tasks that must be accomplished in
getting the job done?
• Validate the steps
Defining Pre-Execution Steps
What must happen before the core execution step to ensure the
job is successfully
carried out?
• What must be defined or planned before the execution step?
• What must be located or gathered?
• What must be prepared or set up?
• What must be confirmed before the execution step?
Defining Post-Execution Steps
What must happen after the core execution step to ensure the
job is successfully

carried out?
• What must be monitored or verified after the execution step to
ensure the job is
successfully performed?
• What must be modified or adjusted after the execution step?
• What must be done to properly conclude the job or to prepare
for the next job
cycle?
68
69
3: Prepare
How must the customer prepare the inputs and environment to
do the job? Nearly all
customer jobs involve an element of setting up and organizing
materials. Before cooking
french fries, the fast-food operator must open bags, portion, and
load fries into baskets; the
nurse must set out and organize surgical tools before an
operation can begin. It may also be
necessary to prepare a working surface or some other aspect of
the physical environment.

The dentist readies the surface of a molar prior to restoring the
tooth; the painter cleans the
wall before applying the first coat of paint.
At this stage, companies should consider ways to make setup
less difficult. They might
invent a means to automate the preparation process; make it
easier to organize physical
materials; or create guides and safeguards to ensure the proper
arrangement of the work
area. (For customers dealing with information, companies can
help organize, integrate, and
examine required data.) Bosch learned of one opportunity to
help customers prepare to cut
wood when professional roofers told the company that they
would like a way to speed the
process of setting bevel levels on their saws. Accordingly,
Bosch added adjustable levers
to its CS20 circular saw to accommodate the most common
bevel adjustments such as 30°,
45°, and 60°. This reduced the time needed to cut the wood and
increased the accuracy of
the adjustments.
70

4: Confirm
Once preparation is complete, what does the customer need to
verify before proceeding
with the job to ensure its successful execution? Here, the
customer makes sure that
materials and the working environment have been properly
prepared; validates the quality
and functional capacity of material and informational
components; and confirms priorities
when deciding among execution options. This step is especially
critical for jobs in which a
delay in execution might risk a customer’s money, time, or
safety. For example, after
preparing a patient for an operation, the surgical nurse must
confirm the readiness of the
patient (jewelry removed, vitals in check); of the equipment and
instrumentation (battery
power sufficient, scalpels available); and of the operating room
(materials in place, sterile
field intact).
Uncovering Opportunities for Innovation
WITH A JOB MAP IN HAND, you can begin to look
systematically for

opportunities to create value. The questions below can guide
you in your search and
help you avoid overlooking any possibilities. A great way to
begin is to consider the
biggest drawbacks of current solutions at each step in the map—
in particular,
drawbacks related to speed of execution, variability, and the
quality of output. To
increase the effectiveness of this approach, invite a diverse
team of experts—
marketing, design, engineering, and even some lead
customers—to participate in this
discussion.
Opportunities at the job level
• Can the job be executed in a more efficient or effective
sequence?
• Do some customers struggle more with executing the job than
others (for instance,
novices versus experts, older versus younger)?
• What struggles or inconveniences do customers experience
because they must rely
on multiple solutions to get the job done?
• Is it possible to eliminate the need for particular inputs or
outputs from the job?
71
• Is it necessary that the customers execute all steps for which
they are currently
responsible? Can the burden be automated or shifted to someone

else?
• How may trends affect the way the job is executed in the
future?
• In what contexts do customers most struggle with executing
the job today? Where
else or when else might customers want to execute the job?
Opportunities at the step level
• What causes variability (or unreliability) in executing this
step? What causes
execution to go off track?
• Do some customers struggle more than others with this step?
• What does this step’s ideal output look like (and in what ways
is the current output
less than ideal)?
• Is this step more difficult to execute successfully in some
contexts than others?
• What are the biggest drawbacks of current solutions used to
execute this step?
• What makes executing this step time-consuming or
inconvenient?
A company seeking to differentiate itself at this step could help
customers gain access to
the types of information and feedback they need to confirm
readiness and decide among
execution alternatives. Another approach is to search for ways

to build confirmation into
the locating and preparing steps, since this would allow the
customer to proceed through
the job more quickly and easily. For example, Oracle’s
ProfitLogic merchandising
optimization software removes the responsibility from the
merchandiser for confirming the
optimal timing and level of markdowns by analyzing thousands
of different demand
scenarios at the individual product level and recommending the
scenario for each product
that is likely to yield the highest profit.
72
5: Execute
What must customers do to execute the job successfully?
Whether they’re printing a
document or administering anesthesia, customers consider the
execution step the most
important part of the job. Because execution is also the most
visible step, customers are
especially concerned about avoiding problems and delays, as
well as achieving optimal

results. An office worker who prints out a document wants to
avoid paper jams, running out
of toner, and long print queues. She also wants to improve the
quality of printed output. An
anesthesiologist wants to prevent negative patient reactions and
to ensure that the patient is
unable to feel pain.
Here, innovating companies can apply their technological
knowhow to provide
customers with real-time feedback or to automatically correct
execution problems.
Companies can also think about ways to keep performance
consistent in different contexts.
Kimberly-Clark’s Patient Warming System is a good example of
value added in this way.
The system relies on a control unit that automatically circulates
heated water through
thermal pads placed on the patient to avoid temperature spikes
during surgery. The system
can maintain normal patient temperature with only 20% of the
patient body covered, which
means the device performs consistently and efficiently in a
variety of complex surgical
procedures.

73
6: Monitor
What does the customer need to monitor to ensure that the job is
successfully executed?
Customers must keep an eye on the results or output during
execution, especially to
determine whether they have to make adjustments to get the task
back on track in the event
of a problem. For some jobs, customers must also monitor
environmental factors to see
whether and when adjustments are necessary. A network
administrator, for example,
monitors Web traffic to avoid system overload.
While some monitoring activities are passive (like the way a
pacemaker monitors
heartbeats), others can often be time-consuming and demanding
for customers. When the
costs of poor execution are significant, as when operating on a
patient, solutions that call
attention to problems or relevant changes in the environment are
especially valuable.

Solution
s that link monitoring with improved job execution or that
provide diagnostic
feedback offer considerable value as well. Consider how Nike
helps runners monitor their
workouts using the Nike+iPod Sport Kit. A sensor placed in
Nike shoes communicates with
an iPod being worn by the runner, providing ongoing audio
feedback about time, distance,
pace, and calories burned. When the runner notices he is
flagging, he can select his “power
song” to reinvigorate himself. The kit also allows runners to
track progress against
predefined goals.
74

7: Modify
What might the customer need to alter for the job to be
completed successfully? When there
are changes in inputs or in the environment, or if the execution
is problematic, the customer
may need help with updates, adjustments, or maintenance. At
this step, customers need help
deciding what should be adjusted as well as determining when,
how, and where to make
changes. Like monitoring, searching for the right adjustment
can be both time-consuming
and costly. Companies can help by offering ways to get
execution back on track when there
are problems. They can also provide avenues for reducing the
time needed make updates

and the number of adjustments the customer has to make to
achieve desired results. (In
addition, solutions that target the location and preparation steps
can be designed to
eliminate modifications.) Many software programs perform well
at supporting this step.
Microsoft, for example, assists customers with the job of
modifying their computer to
protect against security threats. Automatic updates of its
operating system remove the hassle
of determining which updates are necessary, finding them, and
ensuring that fixes are
compatible with various elements of the operating system.
75
8: Conclude

What must the customer do to finish the job? With some simple
jobs such as hand washing,
the conclusion is self-evident. Complex jobs, on the other hand,
may involve some
concluding process steps. The office worker has to retrieve a
document from the printer and
possibly collate, bind, and store it. An anesthesiologist must
document surgery details, as
well as wake and oversee transfer of the patient to a
postoperative recovery area.
Customers often think of concluding steps as burdensome
because the core job has
already been completed, so companies need to help them
simplify the process. Also, the
conclusion of one job cycle is often the start of another or may
affect the next one’s
beginning. When a job is cyclical, companies can help

customers make sure that concluding
activities are closely connected to the starting point of a new
job cycle.
One way to help customers finish the job is to design benefits
sought at the conclusion
into an earlier step in the process. 3M’s Coban Self-Adherent
Wrap, for example, offers a
convenient way for medical personnel to secure wound
dressings at the end of treatment,
because it is made of a material that stretches and adheres only
to itself. This self-
adherence property makes the wrap easy to remove, because it
doesn’t stick to patient skin
or the wound. 3M designed the product in such a way that
putting on the wrap anticipates
the act of taking it off.

76
Ancillary Step: Troubleshooting
What problems must the customer troubleshoot and resolve in
the course of performing the
job? Even in the simplest jobs, things occasionally go wrong—
orders are late, printers jam,
surgical tools are misplaced, and software test cases fail. When
that happens, the customer
must disengage from the core job process and enter into a
distinct ancillary job of
troubleshooting and resolving the problem at hand. What
customers want at that point is a
speedy resolution—which is a function of how clearly the
problem is understood. If the
printer jams, for example, how should the office worker remove
the damaged paper? If a

nurse gets cut when a surgeon hands him a scalpel, what steps
must the nurse take to avoid
being infected with a blood-borne organism?
When a problem arises, customers need resources, tools, and
diagnostics to help them
determine a resolution quickly, protect themselves and
resources that might be affected, and
know when the problem is fixed. They also want solutions that
prevent problems at each
job step. Consider how MasterCard helps customers with the job
of paying for products
and services when problems occur. In addition to its zero-
liability coverage policy,
MasterCard provides downloadable contact numbers so that
customers who lose a card
while traveling know exactly how to contact the company to

report the loss. Then
MasterCard can send out emergency cash advances and a
replacement card within 48
hours.
_____________________
To identify opportunities for innovation, some companies focus
on product leadership,
some on operational excellence, and some on customer
intimacy. Some offer services;
others offer goods. Regardless of which business model a
company chooses, the
fundamental basis for identifying opportunities for growth is the
same. When companies
understand that customers hire products, services, software, and
ideas to get jobs done, they
can dissect those jobs to discover the innovation opportunities

that are the key to growth.
Originally published in May 2008. Reprint R0805H
77
Is It Real? Can We Win? Is It Worth Doing?
Managing Risk and Reward in an Innovation Portfolio. by
George S. Day
MINOR INNOVATIONS MAKE up 85% to 90% of companies’
development portfolios, on
average, but they rarely generate the growth companies seek. At
a time when companies
should be taking bigger—but smart—innovation risks, their bias
is in the other direction.
From 1990 to 2004 the percentage of major innovations in
development portfolios dropped
from 20.4 to 11.5—even as the number of growth initiatives

rose.1 The result is internal
traffic jams of safe, incremental innovations that delay all
projects, stress organizations,
and fail to achieve revenue goals.
These small projects, which I call “little i” innovations, are
necessary for continuous
improvement, but they don’t give companies a competitive edge
or contribute much to
profitability. It’s the risky “Big I” projects—new to the
company or new to the world—that
push the firm into adjacent markets or novel technologies and
can generate the profits
needed to close the gap between revenue forecasts and growth
goals. (According to one
study, only 14% of new-product launches were substantial
innovations, but they accounted

for 61% of all profit from innovations among the companies
examined.)2
The aversion to Big I projects stems from a belief that they are
too risky and their
rewards (if any) will accrue too far in the future. Certainly the
probability of failure rises
sharply when a company ventures beyond incremental
initiatives within familiar markets.
But avoiding risky projects altogether can strangle growth. The
solution is to pursue a
disciplined, systematic process that will distribute your
innovations more evenly across the
78
spectrum of risk.
Two tools, used in tandem, can help companies do this. The

first, the risk matrix, will
graphically reveal risk exposure across an entire innovation
portfolio. The second, the R-
W-W (“real, win, worth it”) screen, originated by Dominick
(“Don”) M. Schrello, of Long
Beach, California, can be used to evaluate individual projects.
Versions of the screen have
been circulating since the 1980s, and since then a growing
roster of companies, including
General Electric, Honeywell, Novartis, Millipore, and 3M, have
used them to assess
business potential and risk exposure in their innovation
portfolios; 3M has used R-W-W for
more than 1,500 projects. I have expanded the screen and used
it to evaluate dozens of
projects at four global companies, and I have taught executives
and Wharton students how

to use it as well.
Although both tools, and the steps within them, are presented
sequentially here, their
actual use is not always linear. The information derived from
each one can often be
reapplied in later stages of development, and the two tools may
inform each other. Usually,
development teams quickly discover when and how to improvise
on the tools’ structured
approach in order to maximize learning and value.
79
The Risk Matrix
To balance its innovation portfolio, a company needs a clear
picture of how its projects fall

on the spectrum of risk. The risk matrix employs a unique
scoring system and calibration of
risk to help estimate the probability of success or failure for
each project based on how big
a stretch it is for the firm: The less familiar the intended market
(x axis) and the product or
technology (y axis), the higher the risk. (See the exhibit
“Assessing risk across an
innovation portfolio.”)
Idea in Brief
Incremental innovations (small, safe changes to your firm’s
offerings) make up 85%–
90% of companies’ development portfolios. But “little i”
projects rarely produce
competitive advantage. For that, you need “Big I” innovations—
offerings new to
your organization or the world. Yes, they’re risky. But avoid
them, and you may
strangle your company’s growth.
Day recommends a solution: Increase the proportion of major
innovations in your

portfolio while carefully managing their risks. Two tools can
help:
• A risk matrix enables you to estimate each project’s
probability of success or
failure based on how big a stretch it is for your firm. The less
familiar the intended
market and the product or technology, the higher the risk.
• The R-W-W (“real,” “win,” “worth it”) screen helps you
evaluate projects’
feasibility. The first step in using this tool—asking “Is it real”
questions—helps you
determine whether customers want your innovation and, if so,
whether you can build
it.
A project’s position on the matrix is determined by its score on
a range of factors, such
as how closely the behavior of targeted customers will match
that of the company’s current
customers, how relevant the company’s brand is to the intended
market, and how applicable

its technology capabilities are to the new product.
A portfolio review team—typically consisting of senior
managers with strategic
oversight and authority over development budgets and
allocations—conducts the
80
evaluation, with the support of each project’s development
team. Team members rate each
project independently and then explain their rationale. They
discuss reasons for any
differences of opinion and seek consensus. The resulting scores
serve as a project’s
coordinates on the risk matrix.
The determination of each score requires deep insights. When

McDonald’s attempted to
offer pizza, for example, it assumed that the new offering was
closely adjacent to its
existing ones, and thus targeted its usual customers. Under that
assumption, pizza would be
a familiar product for the present market and would appear in
the bottom left of the risk
matrix. But the project failed, and a postmortem showed that the
launch had been fraught
with risk: Because no one could figure out how to make and
serve a pizza in 30 seconds or
less, orders caused long backups, violating the McDonald’s
service-delivery model. The
postmortem also revealed that the company’s brand didn’t give
“permission” to offer pizza.
Even though its core fast-food customers were demographically
similar to pizza lovers,

their expectations about the McDonald’s experience didn’t
include pizza.
Idea in Practice
Using the Risk Matrix
Assemble a team to assess each innovation project’s potential
risk using these
criteria:
• How closely target customers’ behavior will match current
customers’
• How relevant the company’s brand is to the intended market
• How applicable your capabilities are to the new product
Neglect to assess risk, and you may make a major misstep.
Example: When McDonald’s started offering pizza, it assumed
the new product was
closely adjacent to existing ones. So it targeted its usual
customers. But employees
couldn’t make and serve a pizza within 30 seconds—which

violated McDonald’s
service-delivery model. And the company’s brand didn’t give
“permission” to offer
pizza. The project failed.
81
Using the R-W-W screen
Used throughout a product’s development, the R-W-W screen
exposes faulty
assumptions, knowledge gaps, sources of risk, and problems
suggesting termination.
To employ this tool, repeatedly test each project’s viability
according to these
criteria:
Is it real?
Can we win?
Is it worth doing?
82

Assessing risk across an innovation portfolio
The risk matrix*
This tool will reveal the distribution of risk across a company’s
innovation
portfolio. Each innovation can be positioned on the matrix by
determining its
score on two dimensions—how familiar to the company the
intended market is (x
axis) and how familiar the product or technology is (y axis)—
using the grid
“Positioning projects on the matrix.” Familiar products aimed at
the company’s
current markets will fall in the bottom left of the matrix,
indicating a low
probability of failure. New products aimed at unfamiliar
markets will fall in the
upper right, revealing a high probability of failure.
83

Risk and revenue
Each dot on this risk matrix stands for one innovation in an
imaginary company’s
portfolio. The size of each dot is proportional to the project’s
estimated revenue.
(Companies may choose to illustrate estimated development
investment or some
other financial measure instead.) This portfolio, dominated by
relatively low-risk,
low-reward projects, is typical in its distribution.
84
Positioning projects on the matrix
Position each innovation product or concept by completing each
statement in the
left-hand column with one of the options offered across the top
to arrive at a score
from 1 to 5. Add the six scores in the “Intended market” section
to determine the
project’s x-axis coordinate on the risk matrix. Add the seven
scores in the
“Product/technology” section to determine its y-axis coordinate.

85
*This risk matrix was developed from many sources, including
long-buried consulting reports by A.T. Kearney and other
firms, the extensive literature on the economic performance of
acquisitions and alliances, and numerous audits of product
and service innovations. It broadly defines “failure” as
significantly missing the objectives that were used to justify the
investment in the growth initiative. Estimates of the probability
of failure have been thoroughly validated in dozens of
interviews with consultants and senior managers involved in
innovation initiatives and are consistent with recent surveys
that place the overall failure rate of new products close to 40%.
The ranges in probabilities take into account some of the
variability in organizations’ definitions of failure and in what
constitutes a new market or technology for a given company.
The probabilities do not apply to fast-moving consumer goods
(where incremental innovations have high long-run failure
rates) or ethical pharmaceuticals, and don’t distinguish whether
“new to the company” is also “new to the world.”
86

(Although these are distinct categories, in my experience most
major new-to-the-company innovations are also new to the
world; for the purposes of this article, they’re considered to be
broadly overlapping.) “Market” refers to customers, not
geographies.
Once the risk matrix has been completed, it typically reveals
two things: that a company
has more projects than it can manage well, and that the
distribution of Big I and little i
innovations is lopsided. Most companies will find that the
majority of their projects cluster
in the bottom left quadrant of the matrix, and a minority skew
toward the upper right.
This imbalance is unhealthy if unsurprising. Discounted cash
flow analysis and other
financial yardsticks for evaluating development projects are

usually biased against the
delayed payoffs and uncertainty inherent in Big I innovations.
What’s more, little i projects
tend to drain R&D budgets as companies struggle to keep up
with customers’ and
salespeople’s demands for a continuous flow of incrementally
improved products. The risk
matrix creates a visual starting point for an ongoing dialogue
about the company’s mix of
projects and their fit with strategy and risk tolerance. The next
step is to look closely at
each project’s prospects in the marketplace.
87
Screening with R-W-W
The R-W-W screen is a simple but powerful tool built on a

series of questions about the
innovation concept or product, its potential market, and the
company’s capabilities and
competition (see the exhibit “Screening for success”). It is not
an algorithm for making
go/no-go decisions but, rather, a disciplined process that can be
employed at multiple
stages of product development to expose faulty assumptions,
gaps in knowledge, and
potential sources of risk, and to ensure that every avenue for
improvement has been
explored. The R-W-W screen can be used to identify and help
fix problems that are miring
a project, to contain risk, and to expose problems that can’t be
fixed and therefore should
lead to termination.

Innovation is inherently messy, nonlinear, and iterative. For
simplicity, this article
focuses on using the R-W-W screen in the early stages to test
the viability of product
concepts. In reality, however, a given product would be
screened repeatedly during
development—at the concept stage, during prototyping, and
early in the launch planning.
Repeated assessment allows screeners to incorporate
increasingly detailed product,
market, and financial analyses into the evaluation, yielding ever
more accurate answers to
the screening questions.
R-W-W guides a development team to dig deeply for the
answers to six fundamental
questions: Is the market real? Is the product real? Can the
product be competitive? Can

our company be competitive? Will the product be profitable at
an acceptable risk? Does
launching the product make strategic sense?
The development team answers these queries by exploring an
even deeper set of
supporting questions. The team determines where the answer to
each question falls on a
continuum ranging from definitely yes to definitely no. A
definite no to any of the first five
fundamental questions typically leads to termination of the
project, for obvious reasons. For
example, if the consensus answer to Can the product be
competitive? is a definite no, and
the team can imagine no way to change it to a yes (or even a
maybe), continuing with
development is irrational. When a project has passed all other
tests in the screen, however,
88

and thus is a very good business bet, companies are sometimes
more forgiving of a no to the
sixth question, Does launching the product make strategic
sense?
Screening for success
Each product concept in your company’s innovation portfolio
should be assessed
by its development team using the R-W-W screen below. A
definite yes or no
answer to the first-column questions Is it real?, Can we win?,
and Is it worth
doing? requires digging deeply for robust answers to the
supporting questions in
the second and third columns. Often a team will answer maybe;
its goal should be
to investigate all possible avenues to converting no or maybe
into yes. A definite
no to any second-column question typically leads to termination
of the project,
since failure is all but certain. A definite no to any third-column

question argues
strongly against proceeding with development. (The full set of
questions in
columns two and three of the screen come from evaluations of
more than 50
product failures within two companies I worked with by teams
of auditors who
asked, “What questions, properly answered, might have
prevented the failure?”)
89
The Screening Team
PROJECT SCREENING TEAMS vary by company, type of
initiative, and stage of
development. Over the course of R-W-W screening, teams
typically involve
members from across functions, including R&D, marketing, and
manufacturing. They
should also work with senior managers who are familiar with
the screen and have
the expertise and the instincts to push dispassionately for
accurate answers,

90
particularly at each decision point during development. At the
same time, however,
these managers should be sympathetic and willing to provide
the team with the
resources to fill information gaps.
A critical job in managing the R-W-W process is preventing
teams from regarding
the screen as an obstacle to be overcome or circumvented. It’s
also important that the
team not regard the screen as simply a go/no-go tool imposed by
management—a
potential threat to a favorite project. Such a misperception will
subvert proper use
of the screen as a learning tool for revealing dubious
assumptions and identifying
problems and solutions.
Because the members of the development team are both
evaluators and advocates,

the screen is vulnerable to misuse and manipulation. Team
members’ convictions
about the merits of the project may lead them to make cursory
evaluations if they fear
that a deep assessment, including a frank voicing of doubts,
might imperil the
project. One way to avoid this pitfall is to enlist a credible
outside facilitator,
perhaps someone from another part of the company who has a
solid new-product
track record and no stake in the outcome. This person’s job
should be to unearth all
the key uncertainties, information gaps, and differences of
opinion and help resolve
them.
This article will delineate the screening process and
demonstrate the depth of probing
needed to arrive at valid answers. What follows is not, of
course, a comprehensive guide to
all the issues that might be raised by each question.
Development teams can probe more or

less deeply, as needed, at each decision point. (For more on
team process, see the sidebar
“The Screening Team.”)
91
Is It Real?
Figuring out whether a market exists and whether a product can
be made to satisfy that
market are the first steps in screening a product concept. Those
steps will indicate the
degree of opportunity for any firm considering the potential
market, so the inquiring
company can assess how competitive the environment might be
right from the start.
One might think that asking if the envisioned product is even a
possibility should come

before investigating the potential market. But establishing that
the market is real takes
precedence for two reasons: First, the robustness of a market is
almost always less certain
than the technological ability to make something. This is one of
the messages of the risk
matrix, which shows that the probability of a product failure
becomes greater when the
market is unfamiliar to the company than when the product or
technology is unfamiliar. A
company’s ability to crystallize the market concept—the target
segment and how the
product can do a better job of meeting its needs—is far more
important than how well the
company fields a fundamentally new product or technology. In
fact, research by Procter &
Gamble suggests that 70% of product failures across most
categories occur because

companies misconstrue the market. New Coke is a classic
market-concept failure; Netflix
got the market concept right. In each case the outcome was
determined by the company’s
understanding of the market, not its facility with the enabling
technologies.
Second, establishing the nature of the market can head off a
costly “technology push.”
This syndrome often afflicts companies that emphasize how to
solve a problem rather than
what problem should be solved or what customer desires need to
be satisfied. Segway,
with its Personal Transporter, and Motorola, with its Iridium
satellite phone, both
succumbed to technology push. Segway’s PT was an ingenious
way to gyroscopically

stabilize a two-wheeled platform, but it didn’t solve the
mobility problems of any target
market. The reasons for Iridium’s demise are much debated, but
one possibility is that
mobile satellite services proved less able than terrestrial
wireless roaming services to
cost-effectively meet the needs of most travelers.
Whether the market and the product are real should dominate
the screening dialogue
92
early in the development process, especially for Big I
innovations. In the case of little i
innovations, a close alternative will already be on the market,
which has been proved to be
real.

93
Is the market real?
A market opportunity is real only when four conditions are
satisfied: The proposed product
will clearly meet a need or solve a problem better than available
alternatives; customers
are able to buy it; the potential market is big enough to be
worth pursuing; and customers
are willing to buy the product.
Is there a need or desire for the product? Unmet or poorly
satisfied needs must be
surfaced through market research using observational,
ethnographic, and other tools to
explore customers’ behaviors, desires, motivations, and
frustrations. Segway’s poor

showing is partly a market-research failure; the company didn’t
establish at the outset that
consumers actually had a need for a self-balancing two-wheeled
transporter.
Once a need has been identified, the next question is, Can the
customer buy it? Even if
the proposed product would satisfy a need and offer superior
value, the market isn’t real
when there are objective barriers to purchasing it. Will
budgetary constraints prevent
customers from buying? (Teachers and school boards, for
example, are always eager to
invest in educational technologies but often can’t find the
funding.) Are there regulatory
requirements that the new product may not meet? Are customers
bound by contracts that
would prevent them from switching to a new product? Could
manufacturing or distribution

problems prevent them from obtaining it?
The team next needs to ask, Is the size of the potential market
adequate? It’s dangerous
to venture into a “trombone oil” market, where the product may
provide distinctive value
that satisfies a need, but the need is minuscule. A market
opportunity isn’t real unless there
are enough potential buyers to warrant developing the product.
Finally, having established customers’ need and ability to buy,
the team must ask, Will the
customer buy the product? Are there subjective barriers to
purchasing it? If alternatives to
the product exist, customers will evaluate them and consider,
among other things, whether
the new product delivers greater value in terms of features,
capabilities, or cost. Improved
value doesn’t necessarily mean more capabilities, of course.
Many Big I innovations, such

as the Nintendo Wii, home defibrillators, and Salesforce.com’s
CRM software as a service,
have prevailed by outperforming the incumbents on a few
measures while being merely
94
adequate on others. By the same token, some Big I innovations
have stumbled because
although they had novel capabilities, customers didn’t find them
superior to the incumbents.
Even when customers have a clear need or desire, old habits, the
perception that a
switch is too much trouble, or a belief that the purchase is risky
can inhibit them. One
company encountered just such a problem during the launch of a
promising new epoxy for

repairing machine parts during routine maintenance. Although
the product could prevent
costly shutdowns and thus offered unique value, the plant
engineers and production
managers at whom it was targeted vetoed its use. The engineers
wanted more proof of the
product’s efficacy, while the production managers feared that it
would damage equipment.
Both groups were risk avoiders. A postmortem of the troubled
launch revealed that
maintenance people, unlike plant engineers and production
managers, like to try new
solutions. What’s more, they could buy the product
independently out of their own budgets,
circumventing potential vetoes from higher up. The product was
relaunched targeting
maintenance and went on to become successful, but the delay

was expensive and could
have been avoided with better screening.
Customers may also be inhibited by a belief that the product
will fail to deliver on its
promise or that a better alternative might soon become
available. Addressing this
reluctance requires foresight into the possibilities of
improvement among competitors. The
prospects of third-generation (3G) mobile phones were
dampened by enhancements in 2.5G
phones, such as high-sensitivity antennae that made the
incumbent technology perform much
better.
95

Is the product real?
Once a company has established the reality of the market, it
should look closely at the
product concept and expand its examination of the intended
market.
Is there a clear concept? Before development begins, the
technology and performance
requirements of the concept are usually poorly defined, and
team members often have
diverging ideas about the product’s precise characteristics. This
is the time to expose those
ideas and identify exactly what is to be developed. As the
project progresses and the team
becomes immersed in market realities, the requirements should
be clarified. This entails
not only nailing down technical specifications but also
evaluating the concept’s legal,
social, and environmental acceptability.

Can the product be made? If the concept is solid, the team must
next explore whether a
viable product is feasible. Could it be created with available
technology and materials, or
would it require a breakthrough of some sort? If the product can
be made, can it be
produced and delivered cost-effectively, or would it be so
expensive that potential
customers would shun it? Feasibility also requires either that a
value chain for the
proposed product exists or that it can be easily and affordably
developed, and that de facto
technology standards (such as those ensuring compatibility
among products) can be met.
Some years ago the R-W-W screen was used to evaluate a
radical proposal to build
nuclear power–generating stations on enormous floating

platforms moored offshore. Power
companies were drawn to the idea, because it solved both
cooling and not-in-my-backyard
problems. But the team addressing the Is the product real? stage
of the process found that
the inevitable flexing of the giant platforms would lead to metal
fatigue and joint wear in
pumps and turbines. Since this problem was deemed
insurmountable, the team concluded
that absent some technological breakthrough, the no answer to
the feasibility question could
never become even a maybe, and development was halted.
Will the final product satisfy the market? During development,
trade-offs are made in
performance attributes; unforeseen technical, manufacturing, or
systems problems arise; and
features are modified. At each such turn in the road, a product
designed to meet customer

expectations may lose some of its potential appeal. Failure to
monitor these shifts can result
96
in the launch of an offering that looked great on the drawing
board but falls flat in the
marketplace.
Consider the ongoing disappointment of e-books. Even though
the newest entrant, the
Sony Reader, boasts a huge memory and breakthrough display
technology, using it doesn’t
begin to compare with the experience of reading conventional
books. The promised black-
on-white effect is closer to dark gray on light gray. Meanwhile,
the Reader’s unique
features, such as the ability to store many volumes and to search

text, are for many
consumers insufficiently attractive to offset the near $300 price
tag. Perhaps most
important, consumers are well satisfied with ordinary books. By
July of 2007 the entire e-
book category had reached only $30 million in sales for the
year.
97
Can We Win?
After determining that the market and the product are both real,
the project team must assess
the company’s ability to gain and hold an adequate share of the
market. Simply finding a
real opportunity doesn’t guarantee success: The more real the
opportunity, the more likely it

is that hungry competitors are eyeing it. And if the market is
already established,
incumbents will defend their positions by copying or
leapfrogging any innovations.
Two of the top three reasons for new-product failures, as
revealed by audits, would have
been exposed by the Can we win? analysis: Either the new
product didn’t achieve its
market-share goals, or prices dropped much faster than
expected. (The third reason is that
the market was smaller, or grew more slowly, than expected.)
The questions at this stage of the R-W-W screening carefully
distinguish between the
offering’s ability to succeed in the marketplace and the
company’s capacity—through
resources and management talent—to help it do so.
98

Can the product be competitive?
Customers will choose one product over alternatives if it’s
perceived as delivering
superior value with some combination of benefits such as better
features, lower life-cycle
cost, and reduced risk. The team must assess all sources of
perceived value for a given
product and consider the question Does it have a competitive
advantage? (Here the
customer research that informed the team’s evaluation of
whether the market and the
product were real should be drawn on and extended as needed.)
Can someone else’s
offering provide customers with the same results or benefits?
One company’s promising
laminate technology, for instance, had intrigued technical

experts, but the launch failed
because the customers’ manufacturing people had found other,
cheaper ways to achieve the
same improvement. The team should also consider whether the
product offers additional
tangible advantages—such as lifetime cost savings, greater
safety, higher quality, and lower
maintenance or support needs—or intangible benefits, such as
greater social acceptability
(think of hybrid cars and synthetic-fur coats) and the promise of
reduced risk that is implicit
in a trusted brand name.
Can the advantage be sustained? Competitive advantage is only
as good as the
company’s ability to keep imitators at bay. The first line of
defense is patents. The project
team should evaluate the relevance of its existing patents to the

product in development and
decide what additional patents may be needed to protect related
intellectual property. It
should ask whether a competitor could reverse engineer the
product or otherwise
circumvent patents that are essential to the product’s success. If
maintaining advantage lies
in tacit organizational knowledge, can that knowledge be
protected? For example, how can
the company ensure that the people who have it will stay? What
other barriers to imitation
are possible? Can the company lock up scarce resources or enter
into exclusive supply
contracts?
Consider the case of 3M’s computer privacy screen. Although
the company’s

microlouver technology promised unique privacy benefits, its
high price threatened to limit
sales to a small market niche, making the project’s status
uncertain. An R-W-W screening,
however, revealed that the technology was aggressively
patented, so no competitor could
99
imitate its performance. It also clarified an opportunity in
adjacent markets for antiglare
filters for computers. Armed with these insights, 3M used the
technology to launch a full
line of privacy and antiglare screens while leveraging its brand
equity and sales presence
in the office-products market. Five years later the product line
formed the basis of one of
3M’s fastest-growing businesses.

How will competitors respond? Assuming that patent protection
is (or will be) in place,
the project team needs to investigate competitive threats that
patents can’t deflect. A good
place to start is a “red team” exercise: If we were going to
attack our own product, what
vulnerabilities would we find? How can we reduce them? A
common error companies
make is to assume that competitors will stand still while the
new entrant fine-tunes its
product prior to launch. Thus the team must consider what
competing products will look
like when the offering is introduced, how competitors may react
after the launch, and how
the company could respond. Finally, the team should examine
the possible effects of this
competitive interplay on prices. Would the product survive a

sustained price war?
100
Can our company be competitive?
After establishing that the offering can win, the team must
determine whether or not the
company’s resources, management, and market insight are
better than those of the
competition. If not, it may be impossible to sustain advantage,
no matter how good the
product.
Do we have superior resources? The odds of success increase
markedly when a
company has or can get resources that both enhance customers’
perception of the new
product’s value and surpass those of competitors. Superior
engineering, service delivery,

logistics, or brand equity can give a new product an edge by
better meeting customers’
expectations. The European no-frills airline easyJet, for
example, has successfully
expanded into cruises and car rentals by leveraging its ability to
blend convenience, low
cost, and market-appropriate branding to appeal to small-
business people and other price-
sensitive travelers.
If the company doesn’t have superior resources, addressing the
deficiency is often
straightforward. When the U.S. market leader for high-
efficiency lighting products wanted
to expand into the local-government market, for example, it
recognized two barriers: The
company was unknown to the buyers, and it had no experience

with the competitive bidding
process they used. It overcame these problems by hiring people
who were skilled at
analyzing competitors, anticipating their likely bids, and
writing proposals. Some of these
people came from the competition, which put the company’s
rivals at a disadvantage.
Sometimes, though, deficiencies are more difficult to overcome,
as is the case with brand
equity. As part of its inquiry into resources, the project team
must ask whether the
company’s brand provides—or denies—permission to enter the
market. The 3M name gave
a big boost to the privacy screen because it is strongly
associated with high-quality,
innovative office supplies—whereas the McDonald’s name
couldn’t stretch to include

pizza. Had the company’s management asked whether its brand
equity was both relevant
and superior to that of the competition—such as Papa Gino’s—
the answer would have been
equivocal at best.
Do we have appropriate management? Here the team must
examine whether the
101
organization has direct or related experience with the market,
whether its development-
process skills are appropriate for the scale and complexity of
the project, and whether the
project both fits company culture and has a suitable champion.
Success requires a
passionate cheerleader who will energize the team, sell the

vision to senior management,
and overcome skepticism or adversity along the way. But
because enthusiasm can blind
champions to potentially crippling faults and lead to a biased
search for evidence that
confirms a project’s viability, their advocacy must be
constructively challenged throughout
the screening process.
Can we understand and respond to the market? Successful
product development
requires a mastery of market-research tools, an openness to
customer insights, and the
ability to share them with development-team members.
Repeatedly seeking the feedback of
potential customers to refine concepts, prototypes, and pricing
ensures that products won’t
have to be recycled through the development process to fix

deficiencies.
Most companies wait until after development to figure out how
to price the new product
—and then sometimes discover that customers won’t pay.
Procter & Gamble avoids this
problem by including pricing research early in the development
process. It also asks
customers to actually buy products in development. Their
answers to whether they would
buy are not always reliable predictors of future purchasing
behavior.
102
Is It Worth Doing?
Just because a project can pass the tests up to this point doesn’t
mean it is worth pursuing.
The final stage of the screening provides a more rigorous

analysis of financial and strategic
value.
103
Will the product be profitable at an acceptable risk?
Few products launch unless top management is persuaded that
the answer to Are forecasted
returns greater than costs? is definitely yes. This requires
projecting the timing and
amount of capital outlays, marketing expenses, costs, and
margins; applying time to
breakeven, cash flow, net present value, and other standard
financial-performance
measures; and estimating the profitability and cash flow from
both aggressive and cautious
launch plans. Financial projections should also include the cost
of product extensions and

enhancements needed to keep ahead of the competition.
Forecasts of financial returns from new products are notoriously
unreliable. Project
managers know they are competing with other worthy projects
for scarce resources and
don’t want theirs to be at a disadvantage. So it is not surprising
that project teams’ financial
reports usually meet upper management’s financial-performance
requirements. Given the
susceptibility of financial forecasts to manipulation,
overconfidence, and bias, executives
should depend on rigorous answers to the prior questions in the
screen for their conclusions
about profitability.
Are the risks acceptable? A forecast’s riskiness can be initially
assessed with a standard
sensitivity test: How will small changes in price, market share,

and launch timing affect
cash flows and breakeven points? A big change in financial
results stemming from a small
one in input assumptions indicates a high degree of risk. The
financial analysis should
consider opportunity costs: Committing resources to one project
may hamper the
development of others.
To understand risk at a deeper level, consider all the potential
causes of product failure
that have been unearthed by the R-W-W screen and devise ways
to mitigate them—such as
partnering with a company that has market or technology
expertise your firm lacks.
104

Does launching the product make strategic sense?
Even when a market and a concept are real, the product and the
company could win, and the
project would be profitable, it may not make strategic sense to
launch. To evaluate the
strategic rationale for development, the project team should ask
two more questions.
Does the product fit our overall growth strategy? In other
words, will it enhance the
company’s capabilities by, for example, driving the expansion
of manufacturing, logistics,
or other functions? Will it have a positive or a negative impact
on brand equity? Will it
cannibalize or improve sales of the company’s existing
products? (If the former, is it better
to cannibalize one’s own products than to lose sales to
competitors?) Will it enhance or

harm relationships with stakeholders—dealers, distributors,
regulators, and so forth? Does
the project create opportunities for follow-on business or new
markets that would not be
possible otherwise? (Such an opportunity helped 3M decide to
launch its privacy screen:
The product had only a modest market on its own, but the
launch opened up a much bigger
market for antiglare filters.) These questions can serve as a
starting point for what must be
a thorough evaluation of the product’s strategic fit. A
discouraging answer to just one of
them shouldn’t kill a project outright, but if the overall results
suggest that a project makes
little strategic sense, the launch is probably ill-advised.
Will top management support it? It’s certainly encouraging for a
development team

when management commits to the initial concept. But the
ultimate success of a project is
better assured if management signs on because the project’s
assumptions can withstand the
rigorous challenges of the R-W-W screen.
105
Notes
1. Robert G. Cooper, “Your NPD Portfolio May Be Harmful to
Your Business Health,” PDMA Visions, April 2005.
2. W. Chan Kim and Renée Mauborgne, “Strategy, Value
Innovation, and the Knowledge Economy,” Sloan
Management Review, Spring 1999.
Originally published in December 2007. Reprint R0712S
106

Six Myths of Product Development
The Fallacies That Cause Delays, Undermine Quality, and Raise
Costs. by Stefan Thomke and Donald Reinertsen
MOST PRODUCT-DEVELOPMENT MANAGERS are always
struggling to bring in
projects on time and on budget. They never have enough
resources to get the job done, and
their bosses demand predictable schedules and deliverables. So
the managers push their
teams to be more parsimonious, to write more-detailed plans,
and to minimize schedule
variations and waste. But that approach, which may work well
in turning around
underperforming factories, can actually hurt product-
development efforts.
Although many companies treat product development as if it
were similar to

manufacturing, the two are profoundly different. In the world of
manufacturing physical
objects, tasks are repetitive, activities are reasonably
predictable, and the items being
created can be in only one place at a time. In product
development many tasks are unique,
project requirements constantly change, and the output—thanks,
in part, to the widespread
use of advanced computer-aided design and simulation and the
incorporation of software in
physical products—is information, which can reside in multiple
places at the same time.
The failure to appreciate those critical differences has given
rise to several fallacies that
undermine the planning, execution, and evaluation of product-
development projects.

Together, we have spent more than 50 years studying and
advising companies on product-
development efforts, and we have encountered these
misconceptions—as well as others
that arise for different reasons—in a wide range of industries,
including semiconductors,
autos, consumer electronics, medical devices, software, and
financial services. In this
107
article we’ll expose them and offer ways to overcome the
problems they create.
108
Fallacy 1: High Utilization of Resources Will Improve
Performance

In both our research and our consulting work, we’ve seen that
the vast majority of
companies strive to fully employ their product-development
resources. (One of us, Donald,
through surveys conducted in executive courses at the
California Institute of Technology,
has found that the average product-development manager keeps
capacity utilization above
98%.) The logic seems obvious: Projects take longer when
people are not working 100%
of the time—and therefore, a busy development organization
will be faster and more
efficient than one that is not as good at utilizing its people.
But in practice that logic doesn’t hold up. We have seen that
projects’ speed, efficiency,
and output quality inevitably decrease when managers
completely fill the plates of their

product-development employees—no matter how skilled those
managers may be. High
utilization has serious negative side effects, which managers
underestimate for three
reasons.
109
They don’t take into full account the intrinsic variability of
development work
Many aspects of product development are unpredictable: when
projects will arrive, what
individual tasks they’ll require, and how long it will take
workers who’ve never tackled
such tasks before to do them. Companies, however, are most
familiar with repetitive
processes like manufacturing and transaction processing, where

the work doesn’t change
much and surprises are few and far between. Such processes
behave in an orderly manner
as the utilization of resources increases. Add 5% more work,
and it will take 5% more time
to complete.
Idea in Brief
Many companies approach product development as if it were
manufacturing, trying
to control costs and improve quality by applying zero-defect,
efficiency-focused
techniques. While this tactic can boost the performance of
factories, it generally
backfires with product development. The process of designing
products is
profoundly different from the process of making them, and the
failure of executives
to appreciate the differences leads to several fallacies that
actually hurt product-
development efforts.

In this article, the authors, an HBS professor and a consultant,
expose these
misperceptions and others. They look at six dangerous myths:
1. High utilization of resources will make the department more
efficient.
2. Processing work in large batches will be more economical.
3. Teams need to faithfully follow their development plan,
minimizing any
deviations from it.
4. The sooner a project is started, the sooner it will be finished.
5. The more features a product has, the better customers will
like it.
6. Projects will be more successful if teams “get them right the
first time.”
The authors explain the negative effects these “principles” have
when applied to
product development, offer practical guidelines on overcoming
them, and walk

readers through a visual tool that will help them keep projects
on track.
110
Processes with high variability behave very differently. As
utilization increases, delays
lengthen dramatically. (See the exhibit “High utilization leads
to delays.”) Add 5% more
work, and completing it may take 100% longer. But few people
understand this effect. In
our experience with hundreds of product-development teams, we
have found that most were
significantly overcommitted. To complete all projects on time
and on budget, some
organizations we worked with would have needed at least 50%
more resources than they

had.
High utilization leads to delays
The curve below is calculated using Queuing Theory, the
mathematical study of
waiting lines. It shows that with variable processes, the amount
of time projects
spend on hold, waiting to be worked on, rises steeply as
utilization of resources
increases. Though the curve changes slightly depending on the
project work, it
always turns sharply upward as utilization nears 100%.
111
It is true that some variability is the result of a lack of
discipline, and that some product-
development tasks (like designing components for an airplane
prototype or performing
clinical trials) include more-repetitive work. But even if some
of the work is predictable,

when it’s combined with other unpredictable work, you will see
queuing problems.
112
They don’t understand how queues affect economic
performance
High utilization of resources inevitably creates queues of
projects. When partially
completed work sits idle, waiting for capacity to become
available, the duration of the
overall project will grow. Queues also delay feedback, causing
developers to follow
unproductive paths longer. They make it hard for companies to
adjust to evolving market
needs and to detect weaknesses in their product before it’s too
late. Ironically, these

problems are precisely the ones that managers think high
utilization will allow their teams
to avoid.
Even when managers know that they’re creating queues, they
rarely realize the economic
cost. Although that cost can be quantified, we’ve found that the
vast majority of companies
don’t calculate it. Managers need to weigh queue costs against
the costs of underutilized
capacity in order to strike the right balance.
113
In product development, work-in-process inventory is
predominantly invisible
Manufacturing queues consist of physical things, and when
inventory in a factory doubles,

it’s obvious. That’s not the case in product development, where
inventory largely consists
of information, such as design documentation, test procedures
and results, and instructions
for building prototypes. When inventory doubles in an
engineering process, there are no
physical signs. Moreover, because accounting standards require
most R&D inventory to be
carried at zero value, financial statements give no indication of
serious inventory excesses
in product development.
It is very difficult to fight a problem that you can’t see or
measure. Consider the situation
at a major pharmaceutical firm. Several years ago its newly
appointed head of drug
discovery faced a managerial dilemma. Like other senior
executives who run large R&D

organizations, he was trying to find ways to make his scientists
more innovative. He wanted
them to experiment more with new chemical compounds that
could generate promising new
drugs and, at the same time, to eliminate unpromising
candidates as early as possible.
Experiments with living organisms, however, were the
responsibility of animal testing, a
department that was not under his control and was run as a cost
center. It was evaluated by
how efficiently it used testing resources, which naturally led to
high utilization.
Consequently, the scientists in drug discovery had to wait three
to four months for the
results of tests that took a little more than a week to perform.
The “well-managed” testing

organization impeded the discovery unit’s progress.
The obvious solution to such problems is to provide a capacity
buffer in processes that
are highly variable. Some companies have long understood this.
For decades, 3M has
scheduled product developers at 85% of their capacity. And
Google is famous for its “20%
time” (allowing engineers to work one day a week on anything
they want—a practice that
means extra capacity is available if a project falls behind
schedule). However, in our
experience this kind of solution is quite hard to implement. As
we will discuss, few
organizations can resist the temptation to use every last bit of
available capacity. Managers
reflexively start more work whenever they see idle time.
114

But there are other viable solutions:
Change the management-control systems. For the
pharmaceutical company, this might
involve taking steps to align the objectives of the animal-testing
unit with those of the
discovery unit. The company could, for example, reward animal
testing for prompt
responses (measuring time from request to completion of test)
rather than resource
utilization.
Selectively increase capacity. Adding extra resources to the
areas where the utilization
rates are 70% or higher can significantly reduce waiting time. If
the pharmaceutical
company did this in animal testing, it would obtain feedback on
new chemical

compounds far faster. In settings where testing is conducted
with computer modeling and
simulation, increasing capacity is often relatively inexpensive,
since it just involves
buying additional computer equipment and software licenses.
Limit the number of active projects. If the pharmaceutical firm
couldn’t increase
animal testing’s capacity, it could still lower the utilization rate
by reducing the number
of active projects exploring new chemical compounds. The
discipline of putting a hard
limit on what goes into a product-development pipeline often
results in sharper focus and
clearer priorities.
Make the work-in-process inventory easier to see. One method
is to use visual
control boards. These can take a number of forms, but the key is

to have some sort of
physical token, such as a Post-it note, represent the
development work (see the exhibit
“Typical work-in-process control board”). A control board
should display all active
work and show what state each part of the project is in. It
should be at the center of the
team’s management process. Teams can hold 15-minute daily
stand-up meetings around
such boards to coordinate efforts and keep work moving.
115
Fallacy 2: Processing Work in Large Batches Improves the
Economics of the Development Process
A second cause of queues in product development is batch size.
Let’s say a new product is

composed of 200 components. You could choose to design and
build all 200 parts before
you test any of them. If you instead designed and built only 20
components before you began
testing, the batch size would be 90% smaller. That would have a
profound effect on queue
time, because the average queue in a process is directly
proportional to batch size.
The reduction of batch sizes is a critical principle of lean
manufacturing. Small batches
allow manufacturers to slash work in process and accelerate
feedback, which, in turn,
improves cycle times, quality, and efficiency. Small batches
have even greater utility in
product development, but few developers realize the power of
this method.
One reason is the nature of their work flow. Again, because the

information they’re
producing is mostly invisible to them, the batch sizes are too.
Second, developers seem to
have an inherent bias to use large batches—possibly because
they incorrectly believe that
large batches produce economies of scale.
In a well-managed process, the batch size will balance
transaction and holding costs (see
the exhibit “How to determine optimal batch size”). It’s similar
to buying eggs at the
grocery store. If you buy a 12-month supply on a single trip,
your transaction cost is low,
but most of the eggs will spoil, increasing your holding cost. If
you buy a one-day supply at
a time, your spoilage will be low, but your transaction costs
will be high. Intuitively, you

try to strike a balance between the two.
How to determine optimal batch size
Changes in batch size affect two primary costs: the transaction
cost and the
holding cost. As batch sizes become larger, average inventory
levels rise, which
raises holding costs. But at the same time, transaction costs
decrease, because it
takes fewer transactions to service demand.
The optimal batch size is the point where the total cost
(combined holding and
transaction cost) is lowest. When a company operates near this
point, small
116
deviations have little impact. For example, if a company
operates at less than
20% above or below the optimal batch size, total costs increase
less than 3%. So
even rough estimates permit a company to capture large

economic benefits.
The companies that understand how this works have exploited
IT advances to reduce
batch sizes, often with astonishing results. Some software
companies that used to test large
batches of code every 90 days are now testing much smaller
batches several times a day. A
manufacturer of computer peripherals that used a similar
approach with its software group
reduced cycle time in software testing by 95% (from 48 months
to 2.5 months), improved
efficiency by 220%, and decreased defects by 33%. The cost
savings were twice as high as
the company had expected. Although those results were
exceptional, we have found that
reducing batch size improves most development projects
significantly. Similarly,

computerized modeling and simulation tools have dramatically
lowered the optimal batch
size of experimentation and testing in companies that develop
physical products.
117
Fallacy 3: Our Development Plan Is Great; We Just Need to
Stick to It
In all our consulting work and research, we’ve never come
across a single product-
development project whose requirements remained stable
throughout the design process.
Yet many organizations place inordinate faith in their plans.
They attribute any deviations to
poor management and execution and, to minimize them,
carefully track every step against
intermediate targets and milestones. Such thinking is fine for

highly repetitive activities in
established manufacturing processes. But it can lead to poor
results in product innovation,
where new insights are generated daily and conditions are
constantly changing.
A classic study of technical problem solving done by Thomas
Allen of MIT highlights the
fluid nature of development work. He found that engineers who
were developing an
aerospace subsystem conceived of and evaluated a number of
design alternatives before
selecting one that they judged to be the best. Along the way
their preferences changed
frequently, as they tested and refined competing technical
solutions. This is typical in
innovation projects: Testing and experimentation reveal what
does and doesn’t work, and

initial assumptions about costs and value may be disproved.
Defining customers’ needs can also be hard to do at the outset
of a product-development
project. When you think about it, that’s not surprising: It isn’t
easy for customers to
accurately specify their needs for solutions that don’t yet exist.
In fact, familiarity with
existing product attributes can interfere with an individual’s
ability to express his or her
need for a novel product. Customers’ preferences can also shift
abruptly during the course
of a development project, as competitors introduce new
offerings and new trends emerge.
For all those reasons, sticking to the original plan—no matter
how excellent its
conception and how skillful its execution—can be a recipe for

disaster. This is not to
suggest that we don’t believe in planning. Product development
is a set of complex
activities that require careful coordination and attention to the
smallest detail. However, the
plan should be treated as an initial hypothesis that is constantly
revised as the evidence
unfolds, economic assumptions change, and the opportunity is
reassessed. (See “The Value
118
Captor’s Process,” by Rita Gunther McGrath and Thomas Keil,
HBR May 2007.)
119

Fallacy 4: The Sooner the Project Is Started, the Sooner It
Will Be Finished
As we discussed earlier, idle time is anathema to managers.
They tend to exploit any
downtime by starting a new project. Even if the task cannot be
completed because people
have to return to another project, managers reason that anything
accomplished on the new
project is work that won’t have to be done later. Such thinking
leads companies to start
more projects than they can vigorously pursue, diluting
resources.
This dilution is dangerous. If a company embarks on a project
before it has the resources
to move ahead, it will stumble slowly through the development
process. That’s problematic
because product-development work is highly perishable:
Assumptions about technologies

and the market can quickly become obsolete. The slower a
project progresses, the greater
the likelihood it will have to be redirected. Indeed, one branch
of the military discovered
that its cost and schedule overruns were exponentially
proportional (to the fourth power) to
a project’s duration. In other words, when the original schedule
of a project doubled, the
cost and schedule overruns increased by a factor of 16.
The importance of reducing the amount of work in process is
evident when we look at
one of the classic formulas of queuing theory: Little’s Law. It
simply states that, on average,
cycle time is proportional to the size of the queue divided by
the processing rate. Thus, if
20 people are ahead of you in line at Starbucks and the barista

is serving five people a
minute, you will be served in four minutes. You can shorten the
cycle time by raising the
processing rate or by reducing the number of jobs under way. In
most settings the latter is
the only practical choice.
Typical work-in-process control board
Control boards make invisible work visible by showing the
precise stage that each
work item is in. In designing the boards, most teams limit the
number of tasks at
each stage to prevent delays. This simple board contains
features that might be
found on a software project that involves a team composed of
six to 10 people.
120
For some product developers the solution has been to rigorously

control the rate at which
they start work. They match it to the rate at which work is
actually completed; carefully
manage the number of projects in process; make sure that once a
project is launched, it is
adequately staffed until it is completed; and resist the
temptation to steal resources from an
ongoing project to squeeze in new ones.
121
Fallacy 5: The More Features We Put into a Product, the
More Customers Will Like It
Product-development teams seem to believe that adding features
creates value for
customers and subtracting them destroys value. This attitude
explains why products are so

complicated: Remote controls seem impossible to use,
computers take hours to set up, cars
have so many switches and knobs that they resemble airplane
cockpits, and even the humble
toaster now comes with a manual and LCD displays.
Companies that challenge the belief that more is better create
products that are elegant in
their simplicity. Bang & Olufsen, the Danish manufacturer of
audio products, televisions,
and telephones, understands that customers don’t necessarily
want to fiddle with the
equalizer, balance, and other controls to find the optimum
combination of settings for
listening to music. Its high-end speakers automatically make the
adjustments needed to
reproduce a song with as much fidelity to the original as
possible. All that’s left for users to

select is the volume.
Getting companies to buy into and implement the principle that
less can be more is hard
because it requires extra effort in two areas of product
development.
122
Defining the problem
Articulating the problem that developers will try to solve is the
most underrated part of the
innovation process. Too many companies devote far too little
time to it. But this phase is
important because it’s where teams develop a clear
understanding of what their goals are
and generate hypotheses that can be tested and refined through
experiments. The quality of a

problem statement makes all the difference in a team’s ability to
focus on the few features
that really matter.
When Walt Disney was planning Disneyland, he didn’t rush to
add more features (rides,
kinds of food, amount of parking) than other amusement parks
had. Rather, he began by
asking a much larger question: How could Disneyland provide
visitors with a magical
customer experience? Surely, the answer didn’t come overnight;
it required painstakingly
detailed research, constant experimentation, and deep insights
into what “magical” meant to
Disney and its customers. IDEO and other companies have
dedicated phases in which they
completely immerse themselves in the context in which the

envisioned product or service
will be used. Their developers read everything of interest about
the markets, observe and
interview future users, research offerings that will compete with
the new product, and
synthesize everything that they have learned into pictures,
models, and diagrams. The result
is deep insights into customers that are tested, improved, or
abandoned throughout the
iterative development process.
123
Determining what to hide or omit
Teams are often tempted to show off by producing brilliant
technical solutions that amaze
their peers and management. But often customers would prefer a

product that just works
effortlessly. From a customer’s point of view, the best solutions
solve a problem in the
simplest way and hide the work that developers are so proud of.
One company that has understood this is Apple. It is known for
many things—innovative
products, stylish designs, and savvy marketing—but perhaps its
greatest strength is its
ability to get to the heart of a problem. (See “The Real
Leadership Lessons of Steve Jobs,”
by Walter Isaacson, in our April issue.) As the late Steve Jobs
once explained, “When you
start looking at a problem and it seems really simple, you don’t
really understand the
complexity of the problem. And your solutions are way too
oversimplified. Then you get

into the problem, and you see it’s really complicated. And you
come up with all these
convoluted solutions. . . . That’s where most people stop.” Not
Apple. It keeps on plugging
away. “The really great person will keep on going,” said Jobs,
“and find . . . the key
underlying principle of the problem and come up with a
beautiful, elegant solution that
works.”
Determining which features to omit is just as important as—and
perhaps more important
than—figuring out which ones to include. Unfortunately, many
companies, in an effort to be
innovative, throw in every possible bell and whistle without
fully considering important
factors such as the value to customers and ease of use. When
such companies do omit some

planned functionality, it’s typically because they need to cut
costs or have fallen behind
schedule or because the team has failed in some other way.
Instead, managers should focus on figuring out whether the
deletion of any proposed
feature might improve a particular product and allow the team
to concentrate on things that
truly heighten the overall customer experience. This can be
determined by treating each
alleged requirement as a hypothesis and testing it in small,
quick experiments with
prospective customers.
Development teams often assume that their products are done
when no more features can
be added. Perhaps their logic should be the reverse: Products
get closer to perfection when

124
no more features can be eliminated. As Leonardo da Vinci once
said, “Simplicity is the
ultimate sophistication.”
125
Fallacy 6: We Will Be More Successful If We Get It Right
the First Time
Many product-development projects fail to meet their objectives
for budgets, schedules,
and technical performance. Undoubtedly, poor planning, rigid
processes, and weak
leadership all play a role. But another cause that’s often
overlooked is managers’ demand
that their teams “get it right the first time.” Requiring success

on the first pass biases teams
toward the least-risky solutions, even if customers don’t
consider them much of an
improvement over what’s already available. Worse yet, teams
have little incentive to
pursue innovative solutions to customers’ problems.
To avoid making mistakes, teams follow a linear process in
which each stage (specify,
design, build, test, scale, launch) is carefully monitored at
review “gates.” Work on the next
stage cannot begin until the project passes through the gate. As
the project moves down the
line, significant commitments are made and the cost of
responding to new insights increases
by orders of magnitude. Successful tests in late stages are
celebrated, and surprises, no

matter how valuable they are, are considered setbacks.
Unfortunately, such a linear process
flow can cause project overruns because test feedback is
delayed, teams cling to bad ideas
longer than they should, and problems aren’t unearthed until it’s
expensive to solve them.
Practical guidelines for overcoming common fallacies
A checklist for today’s product-development managers
1. Make queues and information flows visible.
2. Quantify the cost of delays and factor it into your decisions.
3. Introduce resource slack where utilization is highest.
4. Shift the focus of control systems from efficiency to response
time.
5. Reduce transaction costs to enable smaller batch sizes and
faster feedback.
6. Experiment with smaller batches; you can easily revert to
large batches if this
doesn’t work.
7. Treat the development plan as a hypothesis that will evolve
as new information

becomes available.
8. Start projects only when you are ready to make a full
commitment.
126
9. Aim for simplicity: Ask what features can be deleted, not just
what can be
added.
10. Experiment early, rapidly, and frequently, with computer
models and physical
prototypes, in controlled and real-life customer environments.
11. Emphasize overlapping and iterative—not linear—process
designs.
12. Focus on quick feedback instead of first-pass success.
A tolerance for “getting it wrong the first time” can be the
better strategy as long as
people iterate rapidly and frequently and learn quickly from

their failures. Advances in
simulation and rapid-prototyping technologies have made
operating in this fashion vastly
easier and less expensive.
Consider what we found in a study of 391 teams that designed
custom integrated circuits.
Teams that followed an iterative approach and conducted early
and frequent tests made
more errors along the way. But because they used low-cost
prototyping technologies, they
outperformed (in terms of the time and effort required) teams
that tried to get their design
right the first time. The teams that faced high prototyping costs
invested more effort on
specification, development, and verification. And because they
did their iterations later in

the process—and did far fewer of them—they delayed the
discovery of critical problems.
Experimenting with many diverse ideas is crucial to innovation
projects. When people
experiment rapidly and frequently, many novel concepts will
fail, of course. But such early
failures can be desirable because they allow teams to eliminate
poor options quickly and
focus on more-promising alternatives. A crash test that shows
that a car design is unsafe, a
drug candidate that proves to be toxic, or a software user
interface that confuses customers
can all be desirable outcomes—provided that they occur early in
a process, when few
resources have been committed, designs are still very flexible,
and other solutions can be
tried.

A classic example of the advantages of the “fail early, fail
often” approach is Team New
Zealand’s surprising victory in the 1995 America’s Cup. To test
ideas for improving the
keel design, the team used two nearly identical boats: one boat
that was modified during the
course of the project and a “control” boat that was not. On a
daily basis, the team simulated
127
hypotheses on a local graphics workstation, applied those that
looked promising to the one
boat, raced it against the control, and analyzed the results. In
contrast, its competitor, Team
Dennis Conner, which had access to more-powerful computers
(supercomputers at Boeing),

ran large batches of simulations every few weeks and then
tested possible solutions on one
boat. The result: Team New Zealand completed many more
learning cycles, eliminated
unpromising ideas more rapidly, and ended up beating Team
Dennis Conner’s boat Young
America.
What we hope is becoming clear by now is that experiments
resulting in failures are not
necessarily failed experiments. They generate new information
that an innovator was
unable to foresee. The faster the experimentation cycle, the
more feedback can be gathered
and incorporated into new rounds of experiments with novel and
potentially risky ideas. In
such an environment employees tend to persevere when times
get tough, engage in more-

challenging work, and outperform their risk-averse peers.
But creating this kind of environment isn’t easy—a topic that
Amy C. Edmondson of
Harvard Business School explored in “Strategies for Learning
from Failure” (HBR April
2011). Failure can lead to embarrassment and expose gaps in
knowledge, which can
undermine individuals’ self-esteem and standing in an
organization. After all, how often are
managers promoted and teams rewarded for the early exposure
of failures that lead a
project to be killed—even though the early redeployment of
precious resources benefits the
company? This is especially true in organizations that have built
a “zero tolerance for
failure” or “error-free” (Six Sigma) environment.

Thomas Alva Edison understood all this. He organized his
famous laboratories around
the concept of rapid experimentation, locating machine shops
for building models close to
the rooms where experiments were conducted so that machinists
could cooperate closely
with researchers. The labs had libraries containing a vast
number of volumes so that
information could be found quickly; nearby storerooms with
ample quantities of supplies;
and a diverse workforce of craftsmen, scientists, and engineers.
Edison wanted to make
sure that when he or his people had an idea, it could be
immediately turned into a working
model or prototype. “The real measure of success is the number
of experiments that can be
128

crowded into 24 hours,” he said.
_____________________
Advances in information technology, such as computer-aided
design,
modeling, and simulation, have already allowed companies to
make great strides in
developing better products in less time and at a lower cost.
Many companies, however,
have not tapped the full potential of these tools, because their
management thinking has not
evolved as quickly as the technology: They still approach the
highly variable information-
generating work of product development as if it were like
manufacturing. As advances in IT
continue, the opportunity to improve the product-development

process will become even
greater. But so will the risks for companies that fail to
recognize that product development
is profoundly different from manufacturing.
Originally published in May 2012. Reprint R1205E
129
Innovation: The Classic Traps
by Rosabeth Moss Kanter
INNOVATION IS BACK AT the top of the corporate agenda.
Never a fad, but always in or
out of fashion, innovation gets rediscovered as a growth enabler
every half-dozen years
(about the length of a managerial generation). Too often,
however, grand declarations about

innovation are followed by mediocre execution that produces
anemic results, and
innovation groups are quietly disbanded in cost-cutting drives.
Each generation embarks on
the same enthusiastic quest for the next new thing and faces the
same challenge of
overcoming innovation stiflers. Over the past 25 years, I have
conducted research and
advised companies during at least four major waves of
competitive challenges that led to
widespread enthusiasm for innovation.
The first was the dawn of the global information age in the late
1970s and early 1980s,
an era that introduced new industries and threatened to topple
old ones. Entrepreneurs and
foreign competitors imperiled established companies on their
own turf. Information

technology was beginning to evolve from the clunky mainframe
to a consumer and desktop
product, and companies such as Apple Computer made Silicon
Valley garages the new base
for product innovation in the United States. IBM emulated
Apple’s model by developing its
PC in dingy surroundings in Boca Raton, Florida, freed from
many corporate constraints.
High-quality Japanese products, such as the Sony Walkman and
Toyota cars, reflected not
just good product design but also innovations in manufacturing
processes that forced
American giants to create their own programs to generate new
ideas faster. “Total quality
management” became a passion.
130

The second wave was the pressure to restructure during the
takeover scare of the late
1980s. Buyout groups were attacking traditional companies,
seeking to unlock the value of
underutilized assets; “shareholder value” became a rallying cry.
In Europe, restructuring
was associated with the privatization of state-owned enterprises
now exposed to the
pressures of capital markets. Software was emerging as a major
force behind innovation,
and the strategic value of IT was touted, with American
Airlines’ Sabre reservations system
widely cited as an example of a process innovation that
succeeded as a separate business.
Companies created new-venture departments to make sure they

captured the value of their
own ideas and inventions, rather than allowing a behemoth like
Microsoft to arise outside
the firm. Financial innovations were the rage: leveraged and
management buyouts,
derivatives and other forms of financial engineering, or
financial supermarkets combining
banks and nearly everything else. The restructuring era also
favored products that could be
instantly global: After defeating a hostile takeover bid in the
late 1980s, Gillette boldly and
successfully launched Sensor Excel shaving systems in the early
1990s, in identical form
worldwide, with a single advertising message.
Third was the digital mania of the 1990s. The promise (and
threat) of the World Wide

Web drove many established companies to seek radical new
business models. Brick-and-
mortar companies were at risk for extinction; many rushed to
create stand-alone Web
ventures, often unconnected to the core business and sometimes
in conflict with it. Eyes
were on the capital markets rather than on customers, and
companies got rich without
profits or revenues. AOL bought Time Warner, put its name
first, and proceeded to destroy
value rather than create innovation.
The current wave of innovation began in a more sober mood,
following the dot-com
crash and belt-tightening of the global recession. Having
recognized the limits of
acquisitions and become skeptical about technology hype,
companies refocused on organic

growth. Surviving giants such as General Electric and IBM have
adopted innovation as a
corporate theme. GE, for instance, is committed to double-digit
growth from within. For its
part, IBM is seeking innovation by tackling difficult social
problems that require—and
showcase—its technology solutions. A good example is World
Community Grid, a
131
nonprofit IBM created that aggregates unused computer power
from numerous partners to
give AIDS researchers and other scientists the ability to work
with unusually large data
sets. This wave’s central focus is on new products designed to
offer users new features and

functionality to meet emerging needs. Customers and consumer
markets have returned to
center stage, after having been temporarily crowded out by
other obsessions. Companies
are seeking new categories to enrich their existing businesses
rather than grand new
ventures that will take them into totally different realms.
Signature innovations in this era
include Apple’s iPod and Procter & Gamble’s Swiffer.
Idea in Brief
Most companies fuel growth by creating new products and
services. Yet too many
firms repeat the same growth-sapping mistakes in their efforts
to innovate.
For example, some companies adopt the wrong strategy:
investing only in ideas they
think will become blockbusters. Result? Small ideas that could
have generated big
profits get rejected. For years, Time, Inc. didn’t develop new

publications: managers
wanted any start-up to succeed on the same scale as the
enormously popular People
magazine. Only after Time decided to gamble on a large number
of new publications
did revenues rise.
Other companies err on the side of process-strangling
innovations by subjecting them
to the strict performance criteria their existing businesses must
follow. At
AlliedSignal, new Internet-based products and services had to
satisfy the same
financial metrics as established businesses. Budgets contained
no funds for
investment—so managers working on innovations had to find
their own funding. The
consequences? Retrofitted versions of old ideas.
To avoid such traps, Kanter advocates applying lessons from
past failures to your
innovation efforts. For instance, augment potential “big bets”
with promising
midrange ideas and incremental innovations. And add flexibility
to your innovation

planning, budgeting, and reviews.
Your reward? Better odds that the new ideas percolating in your
company today will
score profitable successes in the market tomorrow.
Idea in Practice
132
To innovate successfully, replace common mistakes with potent
remedies.
Strategy Mistakes
• Rejecting opportunities that at first glance appear too small.
• Assuming that only new products count—not new services or
improved processes.
• Launching too many minor product extensions that confuse
customers and increase
internal complexity.
Remedy: Widen your search and broaden your scope. Support a

few big bets at
the top that represent clear directions for the future and receive
the lion’s share of
investment. Also create a portfolio of promising midrange
ideas. And fund a broad
base of early stage ideas or incremental innovations.
Process Mistakes
• Strangling innovation with the same tight planning, budgeting,
and reviews applied
to existing businesses.
• Rewarding managers for doing only what they committed to
do—and discouraging
them from making changes as circumstances warrant.
Remedy: Add flexibility to planning and control systems. For
instance, reserve
special funds for unexpected opportunities.
Example: After executives at the struggling UK television
network BBC set aside
funds in a corporate account to support innovation proposals, a
new recruit used
money originally allocated for a new BBC training film to make

a pilot for The
Office. The show became the BBC’s biggest hit comedy in
decades.
Structure Mistakes
• Isolating fledgling and established enterprises in separate
silos.
• Creating two classes of corporate citizens—those who have all
the fun
(innovators) and those who must make the money (mainstream
business managers).
Remedy: Tighten the human connections between innovators
and others
throughout your organization. Convene frequent conversations
between innovators
and mainstream business managers to promote mutual learning
and integration of
new businesses into the organization. Create overlapping
relationships—by having
133

representatives from mainstream businesses rotate through
innovation groups or
innovation advisory boards. Identify people who lead informal
networks that span
innovation and mainstream groups, and encourage them to
strengthen those
connections.
Skills Mistakes
• Allowing innovators to rotate out of teams so quickly that
team chemistry can’t
gel.
• Assuming that innovation teams should be led by the best
technical people.
Remedy: Select innovation leaders with strong interpersonal
skills. They’ll keep
the innovation team intact, help innovation teams embrace
collective goals, leverage
one another’s different strengths, and share hard-to-document
knowledge while
innovations are under development.

Example: When Williams-Sonoma launched its ultimately
successful e-commerce
group, it put a manager in charge who wasn’t a technology
expert but who could
assemble the right team. He chose a mixture of employees from
other units who
could be ambassadors to their former groups and new hires that
brought diverse
skills.
Each wave brought new concepts. For example, the rise of
biotechnology, characterized
by complicated licensing arrangements, helped legitimize the
idea that established firms
could outsource R&D and learn from entrepreneurial partners or
that consumer products
companies could turn to external idea shops, as well as their
own labs, to invent new
products. Approaches to innovation also reflected changing
economic conditions and

geopolitical events. And, of course, innovation has covered a
wide spectrum, including
technologies, products, processes, and complete business
ventures, each with its own
requirements.
Still, despite changes to the environment and differences among
types of innovation, each
wave of enthusiasm has encountered similar dilemmas. Most of
these stem from the
tensions between protecting revenue streams from existing
businesses critical to current
success and supporting new concepts that may be crucial to
future success. These tensions
134
are exacerbated by the long-known phenomenon that important

innovations often arise from
outside an industry and beyond the established players, creating
extra pressure for
companies to find the next big concept quickly. Consequently, a
large body of knowledge
about innovation dilemmas has arisen.
Books such as Tom Peters and Bob Waterman’s In Search of
Excellence, my own The
Change Masters, and Gifford Pinchot’s Intrapreneuring
supported the 1980s innovation
wave by pointing to the importance of relieving potential
innovators of bureaucratic
constraints so they could run with their ideas. This was
followed by a body of work
documenting the difficulty of exploring the new while
exploiting the old, reflected in
Michael Tushman and Charles O’Reilly’s call for more
ambidextrous organizations in

Winning Through Innovation; my work on managing the
tensions between the powerful
organizational mainstream and fragile new streams produced by
innovation groups in When
Giants Learn to Dance; and Clayton Christensen’s more recent
finding, in The Innovator’s
Dilemma, that listening to current customers can inhibit
breakthrough innovation.
Yet despite all the research and literature, I still observe
executives exhibiting the same
lack of courage or knowledge that undercut previous waves of
innovation. They declare
that they want more innovation but then ask, “Who else is doing
it?” They claim to seek
new ideas but shoot down every one brought to them. And,
repeatedly, companies make the
same mistakes as their predecessors. For example, a 1983 HBR
article by Harvard

Business School professor Malcolm Salter, et al., “When
Corporate Venture Capital
Doesn’t Work,” provided warnings that companies failed to
heed about exactly the same
dilemmas they face today: With a few notable exceptions, such
as Intel and Reuters,
companies’ venture-capital departments rarely create significant
value for the core
business.
It’s inevitable that historical memory will fade—but not
inevitable that we lose the
lessons. Here’s a chance to collect some of what is known about
innovation traps and how
to avoid them.
135

Strategy Mistakes: Hurdles Too High, Scope Too Narrow
The potential for premium prices and high margins lures
executives to seek blockbuster
innovations—the next iPod, Viagra, or Toyota Production
System. Along the way, they
expend enormous resources, though big hits are rare and
unpredictable. Meanwhile, in
seeking the killer app, managers may reject opportunities that at
first glance appear too
small, and people who aren’t involved in the big projects may
feel marginalized.
For years, large consumer products companies typically
screened out ideas that couldn’t
result in revenues of several hundred million dollars within two
years. This screen
discouraged investments in ideas that couldn’t be tested and
measured using conventional

market research, or that weren’t grounded in experience, in
favor of ideas that were close
to current practice and hardly innovative. In the 1980s and
1990s, Pillsbury, Quaker Oats,
and even Procter & Gamble (an innovation powerhouse today)
were vulnerable to smaller
companies that could quickly roll out new products, thus
eroding the giants’ market share.
P&G, for example, lamented not having introduced a new toilet
bowl cleaner before a
competitor did, despite P&G labs’ having developed similar
technology. The rival, of
course, gained dominant market share by being a first mover.
Likewise, Pillsbury and
Quaker lagged the competition in bringing new concepts to
market and, as underperformers,

were eventually acquired.
Time Incorporated, the magazine wing of Time Warner, for a
long time was slow to
develop new publications because managers wanted any start-up
to have the potential to
grow into another People or Sports Illustrated, two of the
company’s legendary successes.
During the period before Don Logan took the helm in 1992,
almost no new magazines were
launched. After Logan brought a different innovation strategy to
the magazine group, Time
developed (or bought) about 100 magazines, which dramatically
increased the company’s
revenues, cash flow, and profits. Not every offering was a
blockbuster, but Time had
learned what successful innovators know: To get more
successes, you have to be willing to

risk more failures.
136
The Lessons of Innovation
INNOVATION GOES IN OR OUT of fashion as a strategic
driver of corporate
growth, but with every wave of enthusiasm, executives make the
same mistakes.
Most of the time, they stumble in their R&D efforts because
they are engaged in a
difficult balancing act: They need to protect existing revenue
streams while coaxing
along new ones. But “corporate entrepreneurship” doesn’t have
to be an oxymoron.
Innovation can flourish if executives heed business lessons from
the past.
Strategy Lessons
• Not every innovation idea has to be a blockbuster. Sufficient
numbers of small or
incremental innovations can lead to big profits.

• Don’t just focus on new product development: Transformative
ideas can come
from any function—for instance, marketing, production,
finance, or distribution.
• Successful innovators use an “innovation pyramid,” with
several big bets at the
top that get most of the investment; a portfolio of promising
midrange ideas in test
stage; and a broad base of early stage ideas or incremental
innovations. Ideas and
influence can flow up or down the pyramid.
Process Lessons
• Tight controls strangle innovation. The planning, budgeting,
and reviews applied
to existing businesses will squeeze the life out of an innovation
effort.
• Companies should expect deviations from plan: If employees
are rewarded simply
for doing what they committed to do, rather than acting as
circumstances would
suggest, their employers will stifle and drive out innovation.

Structure Lessons
• While loosening formal controls, companies should tighten
interpersonal
connections between innovation efforts and the rest of the
business.
• Game-changing innovations often cut across established
channels or combine
elements of existing capacity in new ways.
137
• If companies create two classes of corporate citizens—
supplying the innovators
with more perks, privileges, and prestige—those in the existing
business will make
every effort to crush the innovation.
Skills Lessons
• Even the most technical of innovations requires strong leaders
with great
relationship and communication skills.

• Members of successful innovation teams stick together
through the development of
an idea, even if the company’s approach to career timing
requires faster job
rotation.
• Because innovations need connectors—people who know how
to find partners in
the mainstream business or outside world—they flourish in
cultures that encourage
collaboration.
A related mistake is to act as if only products count, even
though transformative new
ideas can come from a range of functions, such as production
and marketing. For instance, a
fabric company that made complicated woven materials had a
long-standing problem: yarn
breakage during production, which was reflected in the cost of
the company’s products and
represented a competitive disadvantage. But the top team at the

fabric maker continued to
talk about the company’s search for really big product
innovations, such as totally new
materials. A new executive, who believed in opening the search
for innovation to all
employees, joined the company. After a meeting discussing the
need for change, a veteran
factory worker, who had joined as a young immigrant and still
spoke with a heavy accent,
tentatively approached the new executive with an idea for
ending the breakage. The
company tried it, and it worked. When asked how long he had
had that idea, the worker
replied, “Thirty-two years.”
Similarly, because managers at Quaker Oats in the 1990s were
too busy tweaking

product formulas in minor ways, the company missed numerous
opportunities in other
arenas, such as distribution—for instance, taking advantage of
the smaller, health-oriented
outlets used by its Snapple beverage acquisition. And in a
packaging coup, Ocean Spray,
138
the cranberry juice company, stole a march on America’s largest
juice purveyors (then
including P&G and Coca-Cola) by getting an 18-month
exclusive license for the
introduction of Tetra Pak’s paper bottles to the U.S. market.
Ocean Spray boasted a more
eclectic innovation strategy than that of its rivals, including
idea forums to explore

innovations in any domain and open to any employee. Paper
bottles were an instant hit with
children (and parents packing their lunches), and Ocean Spray’s
market share shot way up.
Early in its history, the U.S. auto industry gained a
breakthrough innovation from its
financial function: Consumer financing opened mass markets
for products that previously
only the affluent could afford. One Intel breakthrough was in
marketing: It treated computer
chips like potato chips. As a technology company, Intel could
have left innovation to its
R&D folks. But by marketing a component directly to
consumers, Intel gained enormous
power with computer manufacturers, which had little choice but
to put an Intel Inside label
on every machine.

Similarly, Cemex, the global cement company based in Mexico,
has used widespread
brainstorming to generate innovations that create other sources
of value for a product that
could easily become a commodity. Those innovations include
branded, bagged cement and
technology-enabled delivery methods to get cement to
customers as fast as if it were a
pizza. And while P&G is getting attention for its product
innovations, such as the Swiffer
and Crest Whitestrips, its innovations in new media, such as
interactive Web sites for the
soap operas it sponsors, may prove even more valuable for the
company’s future.
When a company is both too product centric and too revenue
impatient, an additional

problem can arise. The organization’s innovation energy can
dissipate across a raft of tiny
me-too projects chasing immediate revenue. Perversely, such
projects may raise costs in
the long run. While a failure to encourage small wins can mean
missed opportunities, too
many trivial projects are like seeds sown on stony ground—they
might sprout, but they do
not take root and grow into anything useful. If new ideas take
the form not of distinctive
innovations but of modest product variations, the resulting
proliferation can dilute the
brand, confuse customers, and increase internal complexity—
such as offering a dozen sizes
and flavors of crackers rather than a new and different snack
food, a problem Kraft
139

currently faces.
140
Process Mistakes: Controls Too Tight
A second set of classic mistakes lies in process; specifically,
the impulse to strangle
innovation with tight controls—the same planning, budgeting,
and reviews applied to
existing businesses. The inherent uncertainty of the innovation
process makes sidetracks or
unexpected turns inevitable. The reason upstart Ocean Spray
could grab the paper-bottle
opportunity from large U.S. juice makers is that the big
companies’ funds had already been
allocated for the year, and they wanted committees to study the

packaging option before
making commitments that would deviate from their plans.
AlliedSignal (now Honeywell) in 2000 sought new Internet-
based products and services
using established strategic-planning and budgeting processes
through existing business
units. The CEO asked the divisions to bring their best ideas for
Internet-related innovations
to the quarterly budget reviews. Although designated as a
priority, these innovation projects
were subjected to the same financial metrics the established
businesses were. Budgets
contained no additional funds for investment; managers working
on innovations had to find
their own sources of funding through savings or internal
transfers. What emerged were often

retrofitted versions of ideas that had been in the pipeline
anyway.
Performance reviews, and their associated metrics, are another
danger zone for
innovations. Established companies don’t just want plans; they
want managers to stick to
those plans. They often reward people for doing what they
committed to do and discourage
them from making changes as circumstances warrant. At a large
defense contractor, for
instance, people got low marks for not delivering exactly what
they had promised, even if
they delivered something better—which led people to
underpromise, eventually reducing
employees’ aspirations and driving out innovation.
In the early 1990s, Bank of Boston (now part of Bank of
America) set up an innovative

unit called First Community Bank (FCB), the first
comprehensive banking initiative to focus
on inner-city markets. FCB struggled to convince mainstream
managers in Bank of Boston’s
retail-banking group that the usual performance metrics, such as
transaction time and
profitability per customer, were not appropriate for this
market—which required customer
141
education, among other things—or for a new venture that still
needed investment.
Mainstream managers argued that “underperforming” branches
should be closed. In order to
save the innovation, FCB leaders had to invent their own
metrics, based on customer

satisfaction and loyalty, and find creative ways to show results
by clusters of branches. The
venture later proved both profitable and important to the parent
bank as it embarked on a
series of acquisitions.
142
Structure Mistakes: Connections Too Loose, Separations Too
Sharp
While holding fledgling enterprises to the same processes as
established businesses is
dangerous, companies must be careful how they structure the
two entities, to avoid a clash
of cultures or conflicting agendas.
The more dramatic approach is to create a unit apart from the
mainstream business,

which must still serve its embedded base. This was the logic
behind the launch of Saturn as
an autonomous subsidiary of General Motors. GM’s rules were
suspended, and the Saturn
team was encouraged to innovate in every aspect of vehicle
design, production, marketing,
sales, and customer service. The hope was that the best ideas
would be incorporated back
at the parent company, but instead, after a successful launch,
Saturn was reintegrated into
GM, and many of its innovations disappeared.
In the time it took for Saturn to hit its stride, Toyota—which
favored continuous
improvement over blockbusters or greenfield initiatives like
Saturn—was still ahead of
GM in quality, customer satisfaction, and market share growth.

Similarly, U.S. charter
schools were freed from the rules of public school systems so
they could innovate and thus
serve as models for improved education. They’ve employed
many innovative practices,
including longer school days and focused curricula, but there is
little evidence that charter
schools have influenced changes in the rest of their school
districts.
The problem in both cases can be attributed to poor connections
between the greenfield
and the mainstream. Indeed, when people operate in silos,
companies may miss innovation
opportunities altogether. Game-changing innovations often cut
across established channels
or combine elements of existing capacity in new ways. CBS was
once the world’s largest

broadcaster and owned the world’s largest record company, yet
it failed to invent music
video, losing this opportunity to MTV. In the late 1990s,
Gillette had a toothbrush unit (Oral
B), an appliance unit (Braun), and a battery unit (Duracell), but
lagged in introducing a
battery-powered toothbrush.
The likelihood that companies will miss or stifle innovations
increases when the
143
potential innovations involve expertise from different industries
or knowledge of different
technologies. Managers at established organizations may both
fail to understand the nature

of a new idea and feel threatened by it.
AT&T Worldnet, the Internet access venture of the venerable
long-distance telephone
company, faced this lethal mix in the mid-1990s. Managers in
the traditional consumer
services and business services units participated in a series of
debates over whether to
manage Worldnet as a distinct business unit, with its own P&L,
or to include it in the
existing business units focused primarily on the consumer
sector. While consumer services
managers were reluctant to let go of anything, they eventually
agreed to a carve-out
intended to protect the embryonic venture from being crushed
by the bureaucracy and to
keep it from being measured against more-mature businesses
that were generating

significant cash flow rather than requiring investment. They
weren’t all that concerned,
because they believed an Internet service provider would never
generate significant
revenue and profitability.
But as Worldnet gained momentum, it attracted more attention.
The people in consumer
services began to view the innovation’s possible expansion to
provide voice over Internet
protocol (VoIP) services as a threat that could cannibalize
existing business. Consumer
services managers grabbed control of Worldnet and proceeded
to starve it. They used it as
a platform to sell core land-based long-distance services and
started applying the same
metrics to the Internet business that were used for consumer

long-distance. Pricing was an
immediate problem. Worldnet’s services had been priced low to
fuel growth, to get the
scale and network effects of a large group of subscribers, but
the mainstream unit did not
want to incur losses on any line of business. So it raised prices,
and Worldnet’s growth
stalled. Consumer services managers could then treat Worldnet
as a trivial, slow-growing
business, not worthy of large investment. They did not allocate
sufficient resources to
develop Internet access and VoIP technology, restraining
important telecom innovations in
which AT&T could have been the pioneer.
Cultural clashes exacerbated tensions at AT&T. Mainstream
managers had long tenures

in the Bell system. The Internet group, however, hired external
tech professionals who
144
spoke the language of computers, not telephony.
Even when a new venture is launched within an existing
business, culture clashes
become class warfare if there are two classes of corporate
citizens—those who have all
the fun and those who make all the money. The designated
innovators, whether an R&D
group or a new-venture unit, are identified as creators of the
future. They are free of rules
or revenue demands and are allowed to play with ideas that
don’t yet work. Their
colleagues are expected to follow rules, meet demands, and
make money while feeling like

grinds and sometimes being told they are dinosaurs whose
business models will soon be
obsolete.
In the early 2000s, Arrow Electronics’ attempt at an Internet
venture, Arrow.com, was
given space in the same facility as the traditional sales force.
The similarities stopped
there. The Internet group was composed of new hires, often
young, from a different
background, who dressed in a completely different style. It
spent money on cushy furniture,
including a big expenditure on a new kitchen—justified, it was
said, because the
Arrow.com team worked 24/7. The traditional sales force,
already anxious about the threat
Internet-enabled sales posed to its commissions and now aware

of its dingier offices,
became overtly angry. Relations between the groups grew so
acrimonious that a brick wall
was erected to separate the two sides of the building. Both
teams wasted time battling,
endangering customer relationships when the two groups fought
over the same customers—
after all, Arrow.comwas just another distribution channel. The
CEO had to intervene and
find structures to connect them.
145
Skills Mistakes: Leadership Too Weak, Communication Too
Poor
Undervaluing and underinvesting in the human side of
innovation is another common

mistake. Top managers frequently put the best technical people
in charge, not the best
leaders. These technically oriented managers, in turn,
mistakenly assume that ideas will
speak for themselves if they are any good, so they neglect
external communication. Or they
emphasize tasks over relationships, missing opportunities to
enhance the team chemistry
necessary to turn undeveloped concepts into useful innovations.
Groups that are convened without attention to interpersonal
skills find it difficult to
embrace collective goals, take advantage of the different
strengths various members bring,
or communicate well enough to share the tacit knowledge that is
still unformed and hard to
document while an innovation is under development. It takes
time to build the trust and

interplay among team members that will spark great ideas. MIT
researchers have found that
for R&D team members to be truly productive, they have to
have been on board for at least
two years. At one point, Pillsbury realized that the average
length of time the company took
to go from new product idea to successful commercialization
was 24 to 26 months, but the
average length of time people spent on product teams was 18
months. No wonder the
company was falling behind in innovation.
Changes in team composition that result from companies’
preferences for the frequency
with which individuals make career moves can make it hard for
new ventures to deal with
difficult challenges, prompting them to settle for quick, easy,

conventional solutions. At
Honeywell in the 1980s, leaders of new-venture teams were
often promoted out of them
before the work had been completed. Because promotions were
take-it-or-leave-it offers
and pay was tied to size of assets controlled (small by definition
in new ventures) rather
than difficulty of task, even dedicated innovators saw the
virtues of leaving their projects
midstream. Honeywell was undermining its own innovation
efforts. An executive review of
why new ventures failed uncovered this problem, but a
technology bias made it hard for
old-school managers of that era to increase their appreciation
for the value of team bonding
146

and continuity.
Innovation efforts also bog down when communication and
relationship building outside
the team are neglected. When Gap Incorporated was struggling
in the late 1990s, the
company mounted several cross-unit projects to find
innovations in products, retail
concepts, and operations. Some of the project teams quickly
became closed environments,
and members cut themselves off from their former peers. By
failing to tap others’ ideas,
they produced lackluster recommendations; and by failing to
keep peers informed, they
missed getting buy-in for even their weak proposals.
Innovators cannot work in isolation if they want their concepts

to catch on. They must
build coalitions of supporters who will provide air cover for the
project, speak up for them
in meetings they don’t attend, or sponsor the embryonic
innovation as it moves into the next
stages of diffusion and use. To establish the foundation for
successful reception of an
innovation, groups must be able to present the radical so it can
be understood in familiar
terms and to cushion disruptive innovations with assurances that
the disruption will be
manageable. When technical experts mystify their audiences
rather than enlighten them, they
lose support—and “no” is always an easier answer than “yes.”
Groups that work in secret
and then present their ideas full-blown at the end face
unexpected objections that sometimes

kill the project.
Such inattention to relationships and communication with
mainstream business managers
doomed the launch of Timberland’s promising TravelGear line.
Developed by an R&D
group called the Invention Factory, which was independent of
the company’s mainstream
businesses, TravelGear allowed a user to travel with a single
pair of shoes, adding or
subtracting components suitable for a range of outdoor
activities. The concept won a design
award from Business-Week in 2005. But some existing business
teams had not been
included in the Invention Factory’s developments, and the
traditional sales force refused to
sell TravelGear products.

By contrast, Dr. Craig Feied’s success in developing a state-of-
the-art digital network
for Washington Hospital Center and its parent, MedStar Health,
was a testimony to
investment in the human dimension. A small group of
programmers designed a user-friendly
147
information system in the emergency department, not the IT
department, so they were
already close to users. Dr. Feied and his partner, Dr. Mark
Smith, made a point of sitting on
numerous hospital committees so they would have a wide base
of relationships. Their
investment in people and their contributions toward shared
hospital goals had a positive
effect: Feied and Smith’s actions helped create good word of

mouth and support among
other departments for their information system (now called
Azyxxi), which resulted in
saved time and lives.
The climate for relationships within an innovation group is
shaped by the climate outside
it. Having a negative instead of a positive culture can cost a
company real money. During
Seagate Technology’s troubled period in the mid-to-late 1990s,
the company, a large
manufacturer of disk drives for personal computers, had seven
different design centers
working on innovation, yet it had the lowest R&D productivity
in the industry because the
centers competed rather than cooperated. Attempts to bring
them together merely led people

to advocate for their own groups rather than find common
ground. Not only did Seagate’s
engineers and managers lack positive norms for group
interaction, but they had the opposite
in place: People who yelled in executive meetings received
“Dog’s Head” awards for the
worst conduct. Lack of product and process innovation was
reflected in loss of market
share, disgruntled customers, and declining sales. Seagate, with
its dwindling PC sales and
fading customer base, was threatening to become a commodity
producer in a changing
technology environment.
Under a new CEO and COO, Steve Luczo and Bill Watkins, who
operated as partners,
Seagate developed new norms for how people should treat one
another, starting with the

executive group. Their raised consciousness led to a systemic
process for forming and
running “core teams” (cross-functional innovation groups), and
Seagate employees were
trained in common methodologies for team building, both in
conventional training programs
and through participation in difficult outdoor activities in New
Zealand and other remote
locations. To lead core teams, Seagate promoted people who
were known for strong
relationship skills above others with greater technical skills.
Unlike the antagonistic
committees convened during the years of decline, the core teams
created dramatic process
148

and product innovations that brought the company back to
market leadership. The new
Seagate was able to create innovations embedded in a wide
range of new electronic
devices, such as iPods and cell phones.
149
Innovation Remedies
The quest for breakthrough ideas, products, and services can get
derailed in any or all of
the ways described earlier. Fortunately, however, history also
shows how innovation
succeeds. “Corporate entrepreneurship” need not be an
oxymoron. Here are four ways to
win.
150

Strategy remedy: Widen the search, broaden the scope
Companies can develop an innovation strategy that works at the
three levels of what I call
the “innovation pyramid”: a few big bets at the top that
represent clear directions for the
future and receive the lion’s share of investment; a portfolio of
promising midrange ideas
pursued by designated teams that develop and test them; and a
broad base of early stage
ideas or incremental innovations permitting continuous
improvement. Influence flows down
the pyramid, as the big bets encourage small wins heading in
the same direction, but it also
can flow up, because big innovations sometimes begin life as
small bits of tinkering—as in

the famously accidental development of 3M’s Post-it Notes.
Thinking of innovation in terms of this pyramid gives senior
managers a tool for
assessing current efforts, making adjustments as ideas prove
their value and require further
support, and ensuring that there is activity at all levels. A
culture of innovation grows
because everyone can play. While dedicated groups pursue the
big projects and temporary
teams develop midrange ideas, everyone else in the company
can be invited to contribute
ideas. Every employee can be a potential idea scout and project
initiator, as IBM is
demonstrating. This past July, the company held a three-day
InnovationJam on the Web,
during which about 140,000 employees and clients—
representing 104 countries—

contributed about 37,000 ideas and ranked them, giving the
company an enormous pool of
raw ideas, some of them big, most of them small. Indeed, an
organization is more likely to
get bigger ideas if it has a wide funnel into which numerous
small ideas can be poured. One
of the secrets of success for companies that demonstrate high
rates of innovation is that they
simply try more things.
Gillette adopted the pyramid model as part of its push to
accelerate innovation in 2003
and 2004. The result was a stream of innovations in every
function and business unit that
raised revenues and profits. They included new products such as
a battery-powered
toothbrush; new concepts in the R&D pipeline, such as the 2006

five-blade, battery-
powered Fusion shaving system; innovative marketing
campaigns that neutralized the
competition, such as the campaign for the Mach3Turbo, which
outshone Schick’s
introduction of its Quattro razor; and new technology in HR. At
the first Gillette innovation
151
fair in March 2004, every unit showcased its best ideas of the
year in a creative way. The
legal department promoted its novel online ethics course with a
joke: distributing “get out
of jail free” cards like those in Monopoly. Having the legal
department embrace innovation
was a plus for a company in which innovators needed speedy
service to file patent

applications or help to clear regulatory hurdles.
An innovation strategy that includes incremental innovations
and continuous
improvement can help to liberate minds throughout the
company, making people more
receptive to change when big breakthroughs occur.
152
Process remedy: Add flexibility to planning and control
systems
One way to encourage innovation to flourish outside the normal
planning cycles is to
reserve pools of special funds for unexpected opportunities.
That way, promising ideas do
not have to wait for the next budget cycle, and innovators do
not have to beg for funds from

mainstream managers who are measured on current revenues
and profits. In the mid-to-late
1990s, autocratic management and rigid controls caused the
BBC to slip in program
innovation and, consequently, audience share. Budgets were
tight, and, once they were set,
expenditures were confined to predetermined categories. In
2000, a new CEO and his CFO
relaxed the rules and began setting aside funds in a corporate
account to support proposals
for innovation, making it clear that bureaucratic rules should
not stand in the way of
creative ideas. The BBC’s biggest hit comedy in decades, The
Office, was an accident,
made possible when a new recruit took the initiative to use
money originally allocated for a
BBC training film to make the pilot.

IBM is building such flexibility directly into its infrastructure.
The company established
a $100 million innovation fund to support the best ideas arising
from its InnovationJam,
independent of the normal planning and budgeting processes, to
allow bottom-up ideas to
flourish. “No one has ever before brought together such a global
and diverse set of business
thought leaders on this scale to discuss the most pressing issues
and opportunities of our
age,” says Nick Donofrio, IBM’s executive vice president of
innovation and technology.
“We have companies literally knocking at the door and saying,
‘Give us your best and
brightest ideas, and let’s work together to make them a reality.’
It’s a golden opportunity to
create entirely new markets and partnerships.”

Besides needing different funding models and development
partnerships, the innovation
process requires exemption from some corporate requirements;
after all, there are
numerous differences between established businesses and new
ventures. For example, the
knowledge that innovations could move forward through rapid
prototyping—learning from
a series of fast trials—might mean that certain milestones
triggering review and additional
funding would occur faster than they would for established
businesses, following the
153
rhythm of the project rather than a fixed quarterly or annual
calendar. For other kinds of

projects, greater patience might be required—for instance, when
an innovation group
encounters unexpected obstacles and needs to rethink its model.
The key is flexible,
customized treatment.
154
Structure remedy: Facilitate close connections between
innovators and mainstream businesses
While loosening the formal controls that would otherwise stifle
innovations, companies
should tighten the human connections between those pursuing
innovation efforts and others
throughout the rest of the business. Productive conversations
should take place regularly
between innovators and mainstream business managers.
Innovation teams should be

charged with external communication as part of their
responsibility, but senior leaders
should also convene discussions to encourage mutual respect
rather than tensions and
antagonism. Such conversations should be aimed at mutual
learning, to minimize
cannibalization and to maximize effective reintegration of
innovations that become new
businesses. In addition to formal meetings, companies can
facilitate informal conversations
—as Steelcase did by building a design center that would force
people to bump into one
another—or identify the people who lead informal cross-unit
networks and encourage their
efforts at making connections.
Innovation groups can be told at the outset that they have a

responsibility to serve the
mainstream while also seeking bigger innovations to start new
businesses. This can be built
into their charters and reinforced by overlapping relationships—
whether it involves
representatives from mainstream businesses rotating through
innovation groups or advisory
boards overseeing innovation efforts. After its first great idea
flopped, Timberland’s
Invention Factory learned to work closely with mainstream
teams to meet their needs for
immediate innovations, such as recreational shoes lined with
SmartWool, and to seek game-
changing breakthroughs. Turner Broadcasting’s new-products
group mixes project types:
stand-alone developments, enhancements for current channels,
external partnerships, and

venture capital investments. PNC Financial Services Group
recently established a new-
products group to oversee mainstream developments, such as
pricing and product
enhancements, as well as growth engines in new capabilities,
such as technology-enabled
services and back-office services for investment funds. The
company’s sales of emerging
products were up 21% in 2005, accounting for 46% of all sales.
Flexible organizational structures, in which teams across
functions or disciplines
155
organize around solutions, can facilitate good connections.
Media conglomerate Publicis
has “holistic communication” teams, which combine people

across its ad agencies (Saatchi
& Saatchi, Leo Burnett, Publicis Worldwide, and so on) and
technology groups to focus on
customers and brands. Novartis has organized around diseases,
with R&D more closely
connected to markets and customers; this has helped the
company introduce pathbreaking
innovations faster, such as its cancer drug Gleevec. The success
of Seagate’s companywide
Factory of the Future team at introducing seemingly miraculous
process innovations led to
widespread use of its core-teams model.
Would-be innovators at AlliedSignal discovered that tackling
promising opportunities
required outreach across silos. For example, the aerospace
division was organized into

groups that were dedicated to large commercial airlines, small
commercial airlines, and
general aviation (private and charter planes), but the best new
idea involved differentiating
customers by whether they performed their own maintenance or
contracted it to others. The
division needed to create new connections across previously
divided territories in order to
begin the innovation process.
The success of Williams-Sonoma as a multichannel retailer
innovating in e-commerce
can be attributed to the ways its Web pioneers connected their
developments to the rest of
the company. From the very beginning, CEO Howard Lester
refused to consider Internet
ventures that were independent of other company operations.
The first main Web

development was a bridal registry to create new functionality
for the mainstream business.
When this pilot project proved its value, an e-commerce
department was launched and
housed in its own building. But rather than standing apart and
pursuing its own direction,
that department sought to enhance existing channels, not
compete with them. It measured its
success not only according to e-commerce sales but also
according to incremental sales
through other channels that the Web had facilitated. To further
its close connections with the
mainstream business, the department offered free training to the
rest of the company.
156

Skills remedy: Select for leadership and interpersonal skills,
and surround innovators with a supportive culture of
collaboration
Companies that cultivate leadership skills are more likely to net
successful innovations.
One reason Williams-Sonoma could succeed in e-commerce
quickly and profitably was its
careful attention to the human dimension. Shelley Nandkeolyar,
the first manager of
Williams-Sonoma’s e-commerce group, was not the most
knowledgeable about the
technology, but he was a leader who could assemble the right
team. He valued
relationships, so he chose a mixture of current employees from
other units, who could be
ambassadors to their former groups, and new hires that brought
new skills. He added cross-

company teams to advise and link to the e-commerce team. He
invented an integrator role to
better connect operations groups and chose Patricia Skerritt,
known for being relationship
oriented, to fill it.
Similarly, Gail Snowden was able to steer Bank of Boston’s
First Community Bank
through the minefields of middle-manager antagonism toward a
successful innovation that
produced other innovations (new products and services) because
of her leadership skills,
not her banking skills. She built a close-knit team of talented
people who bonded with one
another and felt passion for the mission. Soon her group became
one of the parent bank’s
most desirable places to work. She developed strong
relationships with senior executives

who helped her deal with tensions in the middle, and she
communicated well and often
about why her unit needed to be different. Her creativity,
vision, teamwork, and persistence
helped this group succeed and become a national role model,
while other banks’ efforts
faltered.
IBM’s big innovations, such as demonstrating grid computing
through World Community
Grid, are possible only because the company’s culture
encourages people to collaborate.
CEO Sam Palmisano has engaged hundreds of thousands of
IBMers in a Web-based
discussion of company values, and Nick Donofrio, IBM’s
executive vice president for
innovation and technology, works to make 90,000 technical

people around the world feel
part of one innovationseeking community. The corporate
champion of World Community
157
Grid, IBM vice president Stanley Litow, sought out partners in
its business units and
geographies to move the innovation forward.
Established companies can avoid falling into the classic traps
that stifle innovation by
widening the search for new ideas, loosening overly tight
controls and rigid structures,
forging better connections between innovators and mainstream
operations, and cultivating
communication and collaboration skills.
Innovation involves ideas that create the future. But the quest

for innovation is doomed
unless the managers who seek it take time to learn from the
past. Getting the balance right
between exploiting (getting the highest returns from current
activities) and exploring
(seeking the new) requires organizational flexibility and a great
deal of attention to
relationships. It always has, and it always will.
Originally published in November 2006. Reprint R0611C
158
Discovery-Driven Planning
by Rita Gunther McGrath and Ian C. MacMillan*
BUSINESS LORE IS FULL of stories about smart companies
that incur huge losses when

they enter unknown territory—new alliances, new markets, new
products, new
technologies. The Walt Disney Company’s 1992 foray into
Europe with its theme park had
accumulated losses of more than $1 billion by 1994. Zapmail, a
fax product, cost Federal
Express Corporation $600 million before it was dropped.
Polaroid lost $200 million when
it ventured into instant movies. Why do such efforts often
defeat even experienced, smart
companies? One obvious answer is that strategic ventures are
inherently risky: The
probability of failure simply comes with the territory. But many
failures could be prevented
or their cost contained if senior managers approached
innovative ventures with the right
planning and control tools.

Discovery-driven planning is a practical tool that acknowledges
the difference between
planning for a new venture and planning for a more
conventional line of business.
Conventional planning operates on the premise that managers
can extrapolate future results
from a well-understood and predictable platform of past
experience. One expects
predictions to be accurate because they are based on solid
knowledge rather than on
assumptions. In platform-based planning, a venture’s deviations
from plan are a bad thing.
The platform-based approach may make sense for ongoing
businesses, but it is sheer
folly when applied to new ventures. By definition, new ventures
call for a company to

envision what is unknown, uncertain, and not yet obvious to the
competition. The safe,
reliable, predictable knowledge of the well-understood business
has not yet emerged.
159
Instead, managers must make do with assumptions about the
possible futures on which new
businesses are based. New ventures are undertaken with a high
ratio of assumption to
knowledge. With ongoing businesses, one expects the ratio to be
the exact opposite.
Because assumptions about the unknown generally turn out to
be wrong, new ventures
inevitably experience deviations—often huge ones—from their
original planned targets.

Indeed, new ventures frequently require fundamental
redirection.
Rather than trying to force startups into the planning
methodologies for existing
predictable and well-understood businesses, discovery-driven
planning acknowledges that
at the start of a new venture, little is known and much is
assumed. When platform-based
planning is used, assumptions underlying a plan are treated as
facts—givens to be baked
into the plan—rather than as best-guess estimates to be tested
and questioned. Companies
then forge ahead on the basis of those buried assumptions. In
contrast, discovery-driven
planning systematically converts assumptions into knowledge as
a strategic venture unfolds.
When new data are uncovered, they are incorporated into the

evolving plan. The real
potential of the venture is discovered as it develops—hence the
term discovery-driven
planning. The approach imposes disciplines different from, but
no less precise than, the
disciplines used in conventional planning.
160
Euro Disney and the Platform-Based Approach
Even the best companies can run into serious trouble if they
don’t recognize the
assumptions buried in their plans. The Walt Disney Company, a
49% owner of Euro Disney
(now called Disneyland Paris), is known as an astute manager of
theme parks. Its success
has not been confined to the United States: Tokyo Disneyland

has been a financial and
public relations success almost from its opening in 1983. Euro
Disney is another story,
however. By 1993, attendance approached 1 million visitors
each month, making the park
Europe’s most popular paid tourist destination. Then why did it
lose so much money?
Idea in Brief
You’re weighing a major strategic venture—a first-time
alliance, a new market, an
innovative product. Beware: business history is littered with
stories about smart
companies that hemorrhaged multiple millions from ventures
gone bad.
Why such massive losses? Too many firms use conventional
planning to manage their
ventures, say McGrath and MacMillan. They make predictions
about a venture’s
potential based on their established businesses. And they treat
the assumptions

underlying those predictions—“The product will sell itself,”
“We’ll have no
competitors”—as facts. By the time they realize a key
assumption was flawed, it’s
too late to stanch the bleeding.
How to avoid this scenario? As your venture unfolds, use a
disciplined process to
systematically uncover, test, and (if necessary) revise the
assumptions behind your
venture’s plan. You’ll expose the make-or-break uncertainties
common to ventures.
And you’ll address those uncertainties at the lowest possible
cost—so you don’t set
your venture on the path to ruin.
In planning Euro Disney in 1986, Disney made projections that
drew on its experience
from its other parks. The company expected half of the revenue
to come from admissions,
the other half from hotels, food, and merchandise. Although by
1993, Euro Disney had

succeeded in reaching its target of 11 million admissions, to do
so it had been forced to
drop adult ticket prices drastically. The average spending per
visit was far below plan and
added to the red ink.
161
The point is not to play Monday-morning quarterback with
Disney’s experience but to
demonstrate an approach that could have revealed flawed
assumptions and mitigated the
resulting losses. The discipline of systematically identifying
key assumptions would have
highlighted the business plan’s vulnerabilities. Let us look at
each source of revenue in turn.
162

Admissions price
In Japan and the United States, Disney found its price by raising
it over time, letting early
visitors go back home and talk up the park to their neighbors.
But the planners of Euro
Disney assumed that they could hit their target number of
visitors even if they started out
with an admission price of more than $40 per adult. A major
recession in Europe and the
determination of the French government to keep the franc strong
exacerbated the problem
and led to low attendance. Although companies cannot control
macroeconomic events, they
can highlight and test their pricing assumptions. Euro Disney’s
prices were very high

compared with those of other theme attractions in Europe, such
as the aqua palaces, which
charged low entry fees and allowed visitors to build their own
menus by paying for each
attraction individually. By 1993, Euro Disney not only had been
forced to make a sharp
price reduction to secure its target visitors, it had also lost the
benefits of early-stage word
of mouth. The talking-up phenomenon is especially important in
Europe, as Disney could
have gauged from the way word of mouth had benefited Club
Med.
Idea in Practice
McGrath and MacMillan suggest this five-step process for
successful venture
planning:
1. Bake Profitability into Your Venture’s Plan
Instead of estimating the venture’s revenues and then assuming

profits will come,
create a “reverse income statement” for the project: Determine
the profit required to
make the venture worthwhile—it should be at least 10%. Then
calculate the
revenues needed to deliver that profit.
2. Calculate Allowable Costs
Lay out all the activities required to produce, sell, service, and
deliver the new
product or service to customers. Together, these activities
comprise the venture’s
allowable costs. Ask, “If we subtract allowable costs from
required revenues, will
the venture deliver significant returns?” If not, it may not be
worth the risk.
163
3. Identify Your Assumptions
If you still think the venture is worth the risk, work with other
managers on the
venture team to list all the assumptions behind your profit,

revenue, and allowable
costs calculations. Use disagreement over assumptions to
trigger discussion, and be
open to adjusting your list.
Example: A company has determined that it needs to sell 250
million units of a
proposed new product at a particular price to generate the
revenue required to
meet the venture’s profit goal. It decides how many orders it’ll
need to sell the 250
million units, how many sales calls it’ll take to secure those
orders, how many
salespeople will be required to make those calls, and how much
this will cost in
sales-force compensation.
4. Determine If the Venture Still Makes Sense
Check your assumptions against your reverse income statement
for the venture. Can
you still make the required profit, given your latest estimates of
revenues and
allowable costs? If not, the venture should be scrapped.
5. Test Assumptions at Milestones

If you’ve decided to move ahead with the venture, use milestone
events to test—and,
if necessary, further update—your assumptions. Postpone major
commitments of
resources until evidence from a previous milestone signals that
taking the next step is
justified.
164
Hotel accommodations
Based on its experience in other markets, Disney assumed that
people would stay an
average of four days in the park’s five hotels. The average stay
in 1993 was only two days.
Had the assumption been highlighted, it might have been
challenged: Since Euro Disney
opened with only 15 rides, compared with 45 at Disney World,
people could do them all in

a single day.
165
Food
Park visitors in the United States and Japan “graze” all day. At
Euro Disney, the buried
assumption was that Europeans would do the same. Euro
Disney’s restaurants, therefore,
were designed for all-day streams of grazers. When floods of
visitors tried to follow the
European custom of dining at noon, Disney was unable to seat
them. Angry visitors left the
park to eat, and they conveyed their anger to their friends and
neighbors back home.
166

Merchandise
Although Disney did forecast lower sales per visitor in Europe
than in the United States
and Japan, the company assumed that Europeans would buy a
similar mix of cloth goods
and print items. Instead, Euro Disney fell short of plan when
visitors bought a far smaller
proportion of high-margin items such as T-shirts and hats than
expected. Disney could have
tested the buried assumption before forecasting sales: Disney’s
retail stores in European
cities sell many fewer of the high-margin cloth items and far
more of the low-margin print
items.
Disney is not alone. Other companies have paid a significant
price for pursuing platform-

based ventures built on implicit assumptions that turn out to be
faulty. Such ventures are
usually undertaken without careful up-front identification and
validation of those
assumptions, which often are unconscious. We have repeatedly
observed that the following
four planning errors are characteristic of this approach.
Companies don’t have hard data but, once a few key decisions
are made, proceed as
though their assumptions were facts. Euro Disney’s implicit
assumptions regarding the
way visitors would use hotels and restaurants are good
examples.
Companies have all the hard data they need to check
assumptions but fail to see the
implications. After making assumptions based on a subset of the
available data, they
proceed without ever testing those assumptions. Federal Express
based Zapmail on the

assumption that there would be a substantial demand for four-
hour delivery of documents
faxed from FedEx center to FedEx center. What went
unchallenged was the implicit
assumption that customers would not be able to afford their own
fax machines before long.
If that assumption had been unearthed, FedEx would have been
more likely to take into
account the plunging prices and increasing sales of fax
machines for the office and, later,
for the home.
Companies possess all the data necessary to determine that a
real opportunity exists
but make implicit and inappropriate assumptions about their
ability to implement their
plan. Exxon lost $200 million on its office automation business
by implicitly assuming that
it could build a direct sales and service support capability to
compete head-to-head with

167
IBM and Xerox.
Companies start off with the right data, but they implicitly
assume a static
environment and thus fail to notice until too late that a key
variable has changed.
Polaroid lost $200 million from Polavision instant movies by
assuming that a three minute
cassette costing $7 would compete effectively against a half-
hour videotape costing $20.
Polaroid implicitly assumed that the high cost of equipment for
videotaping and playback
would remain prohibitive for most consumers. Meanwhile,
companies pursuing those
technologies steadily drove down costs. (See the exhibit “Some
dangerous implicit

assumptions.”)
Some dangerous implicit assumptions
1. Customers will buy our product because we think it’s a good
product.
2. Customers will buy our product because it’s technically
superior.
3. Customers will agree with our perception that the product is
“great.”
4. Customers run no risk in buying from us instead of
continuing to buy from their
past suppliers.
5. The product will sell itself.
6. Distributors are desperate to stock and service the product.
7. We can develop the product on time and on budget.
8. We will have no trouble attracting the right staff.
9. Competitors will respond rationally.
10. We can insulate our product from competition.
11. We will be able to hold down prices while gaining share
rapidly.
12. The rest of our company will gladly support our strategy
and provide help as

needed.
168
Discovery-Driven Planning: An Illustrative Case
Discovery-driven planning offers a systematic way to uncover
the dangerous implicit
assumptions that would otherwise slip unnoticed and thus
unchallenged into the plan. The
process imposes a strict discipline that is captured in four
related documents: a reverse
income statement, which models the basic economics of the
business; pro forma
operations specs, which lay out the operations needed to run the
business; a key
assumptions checklist, which is used to ensure that assumptions
are checked; and a
milestone planning chart, which specifies the assumptions to be
tested at each project
milestone. As the venture unfolds and new data are uncovered,
each of the documents is

updated.
To demonstrate how this tool works, we will apply it
retrospectively to Kao
Corporation’s highly successful entry into the floppy disk
business in 1988. We deliberately
draw on no inside information about Kao or its planning process
but instead use the kind of
limited public knowledge that often is all that any company
would have at the start of a new
venture.
169
The company
Japan’s Kao Corporation was a successful supplier of
surfactants to the magnetic-media

(floppy disk) industry. In 1981, the company began to study the
potential for becoming a
player in floppy disks by leveraging the surfactant technology it
had developed in its core
businesses, soap and cosmetics. Kao’s managers realized that
they had learned enough
process knowledge from their floppy disk customers to
supplement their own skills in
surface chemistry. They believed they could produce floppy
disks at a much lower cost and
higher quality than other companies offered at that time. Kao’s
surfactant competencies
were particularly valuable because the quality of the floppy
disk’s surface is crucial for its
reliability. For a company in a mature industry, the opportunity
to move current product into
a growth industry was highly attractive.

170
The market
By the end of 1986, the demand for floppy disks was 500
million in the United States, 100
million in Europe, and 50 million in Japan, with growth
estimated at 40% per year,
compounded. This meant that by 1993, the global market would
be approaching 3 billion
disks, of which about a third would be in the original equipment
manufacturer (OEM)
market, namely such big-volume purchasers of disks as IBM,
Apple, and Microsoft, which
use disks to distribute their software. OEM industry prices were
expected to be about 180
yen per disk by 1993. Quality and reliability have always been

important product
characteristics for OEMs such as software houses because
defective disks have a
devastating impact on customers’ perceptions of the company’s
overall quality.
171
The reverse income statement
Discovery-driven planning starts with the bottom line. For Kao,
back when it began to
consider its options, the question was whether the floppy disk
venture had the potential to
enhance the company’s competitive position and financial
performance significantly. If not,
why should Kao incur the risk and uncertainty of a major
strategic venture?

Here, we impose the first discipline, which is to plan the
venture using a reverse income
statement, which runs from the bottom line up. (See the exhibit,
“First, start with a reverse
income statement.” The four exhibits referred to in the article
are located in the sidebar,
“How Kao Might Have Tackled Its New Venture.”) Instead of
starting with estimates of
revenues and working down the income statement to derive
profits, we start with required
profits. We then work our way up the profit and loss to
determine how much revenue it will
take to deliver the level of profits we require and how much
cost can be allowed. The
underlying philosophy is to impose revenue and cost disciplines
by baking profitability into
the plan at the outset: Required profits equal necessary revenues
minus allowable costs.

At Kao in 1988, management might have started with these
figures: net sales, about 500
billion yen; income before taxes, about 40 billion yen; and
return on sales (ROS), 7.5%.
Given such figures, how big must the floppy disk opportunity be
to justify Kao’s attention?
Every company will set its own hurdles. We believe that a
strategic venture should have the
potential to enhance total profits by at least 10%. Moreover, to
compensate for the
increased risk, it should deliver greater profitability than
reinvesting in the existing
businesses would. Again, for purposes of illustration, assume
that Kao demands a risk
premium of 33% greater profitability. Since Kao’s return on
sales is 7.5%, it will require
10%.

If we use the Kao data, we find that the required profit for the
floppy disk venture would
be 4 billion yen (10% × 40 billion). To deliver 4 billion yen in
profit with a 10% return on
sales implies a business with 40 billion yen in sales.
Assuming that, despite its superior quality, Kao will have to
price competitively to gain
share as a new entrant, it should set a target price of 160 yen
per disk. That translates into
unit sales of 250 million disks (40 billion yen in sales divided
by 160 yen per disk). By
172
imposing these simple performance measures at the start (1988),
we quickly establish both
the scale and scope of the venture: Kao would need to capture

25% of the total world OEM
market (25% of 1 billion disks) by 1993. Given what is known
about the size of the market,
Kao clearly must be prepared to compete globally from the
outset, making major
commitments not only to manufacturing but also to selling.
How Kao Might Have Tackled Its New Venture:
Discovery-Driven Planning in Action
THE GOAL HERE is to determine the value of success quickly.
If the venture can’t
deliver significant returns, it may not be worth the risk.
First, start with a reverse income statement
Total figures
Required profits to add 10% to total profits = 4 billion yen
Necessary revenues to
deliver 10% sales margin = 40 billion yen Allowable costs to
deliver 10% sales
margin = 36 billion yen

Per unit figures
Required unit sales at 160 yen per unit = 250 million units
Necessary percentage of
world market share of OEM unit sales = 25% Allowable costs
per unit for 10%
sales margin = 144 yen
Second, lay out all the activities needed to run the venture
Pro forma operations specs
1. Sales
Required disk sales = 250 million disks
Average order size (Assumption 8) = 10,000 disks
173
Orders required (250 million/10,000) = 25,000
Number of calls to make a sale (Assumption 9) = 4
Sales calls required (4 x 25,000) = 100,000 per year
Calls per day per salesperson (Assumption 10) = 2
Annual salesperson days (100,000/2) = 50,000

Sales force for 250 days per year (Assumption 11) 50,000
salesperson days/250 =
200 people
Salary per salesperson = 10 million yen (Assumption 12) Total
sales-force salary
cost (10 million yen x 200) = 2 billion yen
2. Manufacturing
Quality specification of disk surface: 50% fewer flaws than best
competitor
(Assumption 15)
Annual production capacity per line = 25 per minute x 1440
minutes per day x 348
days (Assumption 16) = 12.5 million disks
Production lines needed (250 million disks/12.5 million disks
per line) = 20 lines
Production staffing (30 per line [Assumption 17] x 20 lines) =
600 workers
Salary per worker = 5 million yen (Assumption 18)
Total production salaries (600 x 5 million yen) = 3 billion yen

Materials costs per disk = 20 yen (Assumption 19)
Total materials cost (20 x 250 million disks) = 5 billion yen
Packaging per 10 disks = 40 yen (Assumption 20)
Total packaging costs (40 x 25 million packages) =1 billion yen
3. Shipping
Containers needed per order of 10,000 disks = 1 (Assumption
13)
Shipping cost per container = 100,000 yen (Assumption 14)
Total shipping costs (25,000 orders x 100,000 yen) = 2.5 billion
yen
4. Equipment and depreciation
Fixed asset investment to sales = 1:1 (Assumption 5) = 40
billion yen
Equipment life = 3 years (Assumption 7)
Annual depreciation (40 billion yen/3 years) = 13.3 billion yen
Keeping a checklist is an important discipline to ensure that
each assumption is
174

flagged and tested as a venture unfolds.
Third, track all assumptions
Now, with better data, one can see if the entire business
proposition hangs together.
Fourth, revise the reverse income statement
175
Finally, plan to test assumptions at milestones
Continuing up the profit and loss, we next calculate allowable
costs: If Kao is to capture
10% margin on a price of 160 yen per disk, the total cost to
manufacture, sell, and
distribute the disks worldwide cannot exceed 144 yen per disk.
The reverse income

176
statement makes clear immediately that the challenge for the
floppy disk venture will be to
keep a lid on expenses.
177
The pro forma operations specs and the assumptions
checklist
The second discipline in the process is to construct pro forma
operations specs laying out
the activities required to produce, sell, service, and deliver the
product or service to the
customer. Together, those activities comprise the venture’s
allowable costs. At first, the
operations specs can be modeled on a simple spreadsheet

without investing in more than a
few telephone calls or on-line searches to get basic data. If an
idea holds together, it is
possible to identify and test underlying assumptions, constantly
fleshing out and correcting
the model in light of new information. When a company uses
this cumulative approach,
major flaws in the business concept soon become obvious, and
poor concepts can be
abandoned long before significant investments are made.
We believe it is essential to use industry standards for building
a realistic picture of what
the business has to look like to be competitive. Every industry
has its own pressures—
which determine normal rates of return in that industry—as well
as standard performance

measures such as asset-to-sales ratios, industry profit margins,
plant utilization, and so on.
In a globally competitive environment, no sane manager should
expect to escape the
competitive discipline that is captured and measured in industry
standards. These standards
are readily available from investment analysts and business
information services. In
countries with information sources that are less well developed
than those in the United
States, key industry parameters are still used by investment
bankers and, more specifically,
by those commercial bankers who specialize in loans to the
particular industry. For those
getting into a new industry, the best approach is to adapt
standards from similar industries.
Note that we do not begin with an elaborate analysis of product

or service attributes or
an in-depth market study. That comes later. Initially, we are
simply trying to capture the
venture’s embedded assumptions. The basic discipline is to
spell out clearly and
realistically where the venture will have to match existing
industry standards and in what
one or two places managers expect to excel and how they expect
to do so.
Kao’s managers in 1988 might have considered performance
standards for the floppy
disk industry. Because there would be no reason to believe that
Kao could use standard
178
production equipment any better than established competitors
could, it would want to plan

to match industry performance on measures relating to
equipment use. Kao would ascertain,
for example, that the effective production capacity per line was
25 disks per minute in the
industry; and the effective life of production equipment was
three years. Kao’s advantage
was in surface chemistry and surface physics, which could
improve quality and reduce the
cost of materials, thus improving margins. When Kao planned
its materials cost, it would
want to turn that advantage into a specific challenge for
manufacturing: Beat the industry
standard for materials cost by 25%. The formal framing of
operational challenges is an
important step in discovery-driven planning. In our experience,
people who are good in

design and operations can be galvanized by clearly articulated
challenges. That was the
case at Canon, for example, when Keizo Yamaji challenged the
engineers to develop a
personal copier that required minimal service and cost less than
$1,000, and the Canon
engineers rose to the occasion.
A company can test the initial assumptions against experience
with similar situations, the
advice of experts in the industry, or published information
sources. The point is not to
demand the highest degree of accuracy but to build a reasonable
model of the economics
and logistics of the venture and to assess the order of magnitude
of the challenges. Later, the
company can analyze where the plan is most sensitive to wrong
assumptions and do more

formal checks. Consultants to the industry—bankers, suppliers,
potential customers, and
distributors—often can provide low-cost and surprisingly
accurate information.
The company must build a picture of the activities that are
needed to carry out the
business and the costs. Hence in the pro forma operations specs,
we ask how many orders
are needed to deliver 250 million units in sales; then how many
sales calls it will take to
secure those orders; then how many salespeople it will take to
make the sales calls, given
the fact that they are selling to a global OEM market; then how
much it will cost in sales-
force compensation. (See the exhibit “Second, lay out all the
activities needed to run the

venture” located on the sidebar “How Kao Might Have Tackled
Its New Venture.”) Each
assumption can be checked, at first somewhat roughly and then
with increasing precision.
Readers might disagree with our first-cut estimates. That is
fine—so might Kao
179
Corporation. Reasonable disagreement triggers discussion and,
perhaps, adjustments to the
spreadsheet. The evolving document is doing its job if it
becomes the catalyst for such
discussion.
The third discipline of discovery-driven planning is to compile
an assumption checklist
to ensure that each assumption is flagged, discussed, and
checked as the venture unfolds.

(See the exhibit “Third, track all assumptions.”)
The entire process is looped back into a revised reverse income
statement, in which one
can see if the entire business proposition hangs together. (See
the exhibit, “Fourth, revise
the reverse income statement.”) If it doesn’t, the process must
be repeated until the
performance requirements and industry standards can be met;
otherwise, the venture should
be scrapped.
180
Milestone planning
Conventional planning approaches tend to focus managers on
meeting plan, usually an

impossible goal for a venture rife with assumptions. It is also
counterproductive—
insistence on meeting plan actually prevents learning. Managers
can formally plan to learn
by using milestone events to test assumptions.
Milestone planning is by now a familiar technique for
monitoring the progress of new
ventures. The basic idea, as described by Zenas Block and Ian
C. MacMillan in the book
Corporate Venturing (Harvard Business School Press, 1993), is
to postpone major
commitments of resources until the evidence from the previous
milestone event signals that
the risk of taking the next step is justified. What we are
proposing here is an expanded use
of the tool to support the discipline of transforming assumptions
into knowledge.

Going back to what Kao might have been thinking in 1988,
recall that the floppy disk
venture would require a 40-billion-yen investment in fixed
assets alone. Before investing
such a large sum, Kao would certainly have wanted to find ways
to test the most critical
assumptions underlying the three major challenges of the
venture:
• capturing 25% global market share with a 20-yen-per-disk
discount and superior
quality;
• maintaining at least the same asset productivity as the average
competitor and
producing a floppy disk at 90% of the estimated total costs of
existing competitors; and
• using superior raw materials and applied surface technology to
produce superior-

quality disks for 20 yen per unit instead of the industry standard
of 27 yen per unit.
For serious challenges like those, it may be worth spending
resources to create specific
milestone events to test the assumptions before launching a 40-
billion-yen venture. For
instance, Kao might subcontract prototype production so that
sophisticated OEM customers
could conduct technical tests on the proposed disk. If the
prototypes survive the tests, then,
rather than rest on the assumption that it can capture significant
business at the target price,
Kao might subcontract production of a large batch of floppy
disks for resale to customers. It
could thus test the appetite of the OEM market for price
discounting from a newcomer.
181

Similarly, for testing its ability to cope with the second and
third challenges once the
Kao prototype has been developed, it might be worthwhile to
buy out a small existing
floppy disk manufacturer and apply the technology in an
established plant rather than try to
start up a greenfield operation. Once Kao can demonstrate its
ability to produce disks at the
required quality and cost in the small plant, it can move ahead
with its own full-scale
plants.
Deliberate assumption-testing milestones are depicted in the
exhibit “Finally, Plan to
Test Assumptions at Milestones,” which also shows some of the
other typical milestones

that occur in most major ventures. The assumptions that should
be tested at each milestone
are listed with appropriate numbers from the assumption
checklist.
In practice, it is wise to designate a keeper of the assumptions—
someone whose formal
task is to ensure that assumptions are checked and updated as
each milestone is reached and
that the revised assumptions are incorporated into successive
iterations of the four
discovery-driven planning documents. Without a specific person
dedicated to following up,
it is highly unlikely that individuals, up to their armpits in
project pressures, will be able to
coordinate the updating independently.
Discovery-driven planning is a powerful tool for any significant
strategic undertaking

that is fraught with uncertainty—new-product or market
ventures, technology development,
joint ventures, strategic alliances, even major systems
redevelopment. Unlike platform-
based planning, in which much is known, discovery-driven
planning forces managers to
articulate what they don’t know, and it forces a discipline for
learning. As a planning tool,
it thus raises the visibility of the make-or-break uncertainties
common to new ventures and
helps managers address them at the lowest possible cost.
182
Note
* The authors wish to thank Shiuchi Matsuda of Waseda
University’s Entrepreneurial Research Unit for providing case

material on Kao’s floppy disk venture.
Originally published in July 1995. Reprint 95406
183
The Discipline of Innovation
by Peter F. Drucker
DESPITE MUCH DISCUSSION THESE days of the
“entrepreneurial personality,” few of
the entrepreneurs with whom I have worked during the past 30
years had such personalities.
But I have known many people—salespeople, surgeons,
journalists, scholars, even
musicians—who did have them without being the least bit
entrepreneurial. What all the
successful entrepreneurs I have met have in common is not a

certain kind of personality but
a commitment to the systematic practice of innovation.
Innovation is the specific function of entrepreneurship, whether
in an existing business, a
public service institution, or a new venture started by a lone
individual in the family
kitchen. It is the means by which the entrepreneur either creates
new wealth-producing
resources or endows existing resources with enhanced potential
for creating wealth.
Today, much confusion exists about the proper definition of
entrepreneurship. Some
observers use the term to refer to all small businesses; others, to
all new businesses. In
practice, however, a great many well-established businesses
engage in highly successful

entrepreneurship. The term, then, refers not to an enterprise’s
size or age but to a certain
kind of activity. At the heart of that activity is innovation: the
effort to create purposeful,
focused change in an enterprise’s economic or social potential.
184
Sources of Innovation
There are, of course, innovations that spring from a flash of
genius. Most innovations,
however, especially the successful ones, result from a
conscious, purposeful search for
innovation opportunities, which are found only in a few
situations. Four such areas of
opportunity exist within a company or industry: unexpected
occurrences, incongruities,

process needs, and industry and market changes.
Three additional sources of opportunity exist outside a company
in its social and
intellectual environment: demographic changes, changes in
perception, and new knowledge.
True, these sources overlap, different as they may be in the
nature of their risk, difficulty,
and complexity, and the potential for innovation may well lie in
more than one area at a
time. But together, they account for the great majority of all
innovation opportunities.
185
1. Unexpected occurrences
Consider, first, the easiest and simplest source of innovation
opportunity: the unexpected. In

the early 1930s, IBM developed the first modern accounting
machine, which was designed
for banks. But banks in 1933 did not buy new equipment. What
saved the company—
according to a story that Thomas Watson, Sr., the company’s
founder and long-term CEO,
often told—was its exploitation of an unexpected success: The
New York Public Library
wanted to buy a machine. Unlike the banks, libraries in those
early New Deal days had
money, and Watson sold more than a hundred of his otherwise
unsalable machines to
libraries.
Fifteen years later, when everyone believed that computers were
designed for advanced
scientific work, business unexpectedly showed an interest in a
machine that could do

payroll. Univac, which had the most advanced machine, spurned
business applications. But
IBM immediately realized it faced a possible unexpected
success, redesigned what was
basically Univac’s machine for such mundane applications as
payroll, and within five years
became a leader in the computer industry, a position it has
maintained to this day.
Idea in Brief
In the hypercompetition for breakthrough solutions, managers
worry too much about
characteristics and personality—“Am I smart enough? Do I have
the right
temperament?”—and not enough about process. A commitment
to the systematic
search for imaginative and useful ideas is what successful
entrepreneurs share—not
some special genius or trait. What’s more, entrepreneurship can
occur in a business
of any size or age because, at heart, it has to do with a certain

kind of activity:
innovation, the disciplined effort to improve a business’s
potential.
Most innovations result from a conscious, purposeful search for
opportunities—
within the company and the industry as well as the larger social
and intellectual
environment. A successful innovation may come from pulling
together different
strands of knowledge, recognizing an underlying theme in
public perception, or
extracting new insights from failure.
The key is to know where to look.
186
The unexpected failure may be an equally important source of
innovation opportunities.
Everyone knows about the Ford Edsel as the biggest new-car
failure in automotive history.

What very few people seem to know, however, is that the
Edsel’s failure was the
foundation for much of the company’s later success. Ford
planned the Edsel, the most
carefully designed car to that point in American automotive
history, to give the company a
full product line with which to compete with General Motors.
When it bombed, despite all
the planning, market research, and design that had gone into it,
Ford realized that something
was happening in the automobile market that ran counter to the
basic assumptions on which
GM and everyone else had been designing and marketing cars.
No longer was the market
segmented primarily by income groups; the new principle of
segmentation was what we

now call “lifestyles.” Ford’s response was the Mustang, a car
that gave the company a
distinct personality and reestablished it as an industry leader.
Unexpected successes and failures are such productive sources
of innovation
opportunities because most businesses dismiss them, disregard
them, and even resent them.
The German scientist who around 1905 synthesized novocaine,
the first nonaddictive
narcotic, had intended it to be used in major surgical procedures
like amputation. Surgeons,
however, preferred total anesthesia for such procedures; they
still do. Instead, novocaine
found a ready appeal among dentists. Its inventor spent the
remaining years of his life
traveling from dental school to dental school making speeches
that forbade dentists from

“misusing” his noble invention in applications for which he had
not intended it.
Idea in Practice
Successful entrepreneurs don’t wait for innovative ideas to
strike like a lightning
bolt. They go out looking for innovation opportunities in seven
key areas:
1. Unexpected occurrences. These often include failures. Few
people know, for
instance, that the failure of the Edsel led Ford to realize that the
auto market was
now segmented by lifestyle instead of by income group. Ford’s
response was the
Mustang, and an auto legend was born.
2. Incongruities. By the 1960s, cataract removal had become
high-tech, except for
cutting a ligament, an “old-fashioned” step that was
uncomfortable for eye surgeons.
187

Alcon Laboratories responded by modifying an enzyme that
dissolved the ligament.
Surgeons immediately accepted the new product, giving Alcon a
monopoly.
3. Process needs. Two process innovations developed around
1890 created “the
media” as we know it today: linotype made it possible to
produce newspapers
quickly, and advertising made it possible to distribute news
practically free of
charge.
4. Industry and market changes. The brokerage firm Donaldson,
Lufkin & Jenrette
achieved fabulous success because its founders recognized that
the emerging market
for institutional investors would one day predominate in the
industry.
5. Demographic changes. Why are the Japanese ahead in
robotics? Around 1970,
everyone knew that there was both a baby bust and an education

explosion, such that
the number of blue-collar manufacturing workers would decline.
Everyone knew—
but only the Japanese took action.
6. Changes in perception. Such changes don’t alter the facts, but
can dramatically
change their meaning. Americans’ health has never been
better—yet we’re obsessed
with preventing disease and staying fit. Innovators who
understand our perception
of health have launched magazines, introduced health foods, and
started exercise
classes.
7. New knowledge. Knowledge-based innovations require long
lead times and the
convergence of different kinds of knowledge. The computer
required knowledge
that was available by 1918, but the first operational digital
computer did not appear
until 1946.
Purposeful innovation begins with looking, asking, and
listening. Talent and expert

knowledge help, but don’t be deluded by all the stories about
flashes of insight. The
key task is to work out analytically what the innovation has to
be in order to satisfy a
particular opportunity.
This is a caricature, to be sure, but it illustrates the attitude
managers often take to the
unexpected: “It should not have happened.” Corporate reporting
systems further ingrain this
reaction, for they draw attention away from unanticipated
possibilities. The typical monthly
or quarterly report has on its first page a list of problems—that
is, the areas where results
fall short of expectations. Such information is needed, of
course, to help prevent
deterioration of performance. But it also suppresses the
recognition of new opportunities.
188

The first acknowledgment of a possible opportunity usually
applies to an area in which a
company does better than budgeted. Thus genuinely
entrepreneurial businesses have two
“first pages”—a problem page and an opportunity page—and
managers spend equal time
on both.
189
2. Incongruities
Alcon Laboratories was one of the success stories of the 1960s
because Bill Conner, the
company’s cofounder, exploited an incongruity in medical
technology. The cataract
operation is the world’s third or fourth most common surgical
procedure. During the past

300 years, doctors systematized it to the point that the only
“old-fashioned” step left was
the cutting of a ligament. Eye surgeons had learned to cut the
ligament with complete
success, but it was so different a procedure from the rest of the
operation, and so
incompatible with it, that they often dreaded it. It was
incongruous.
Doctors had known for 50 years about an enzyme that could
dissolve the ligament
without cutting. All Conner did was to add a preservative to this
enzyme that gave it a few
months’ shelf life. Eye surgeons immediately accepted the new
compound, and Alcon found
itself with a worldwide monopoly. Fifteen years later, Nestlé
bought the company for a

fancy price.
Such an incongruity within the logic or rhythm of a process is
only one possibility out of
which innovation opportunities may arise. Another source is
incongruity between economic
realities. For instance, whenever an industry has a steadily
growing market but falling
profit margins—as, say, in the steel industries of developed
countries between 1950 and
1970—an incongruity exists. The innovative response:
minimills.
An incongruity between expectations and results can also open
up possibilities for
innovation. For 50 years after the turn of the century,
shipbuilders and shipping companies
worked hard both to make ships faster and to lower their fuel
consumption. Even so, the

more successful they were in boosting speed and trimming their
fuel needs, the worse the
economics of ocean freighters became. By 1950 or so, the ocean
freighter was dying, if not
already dead.
All that was wrong, however, was an incongruity between the
industry’s assumptions and
its realities. The real costs did not come from doing work (that
is, being at sea) but from
not doing work (that is, sitting idle in port). Once managers
understood where costs truly
lay, the innovations were obvious: the roll-on and roll-off ship
and the container ship.
These solutions, which involved old technology, simply applied
to the ocean freighter what
190

railroads and truckers had been using for 30 years. A shift in
viewpoint, not in technology,
totally changed the economics of ocean shipping and turned it
into one of the major growth
industries of the last 20 to 30 years.
191
3. Process needs
Anyone who has ever driven in Japan knows that the country
has no modern highway
system. Its roads still follow the paths laid down for—or by—
oxcarts in the tenth century.
What makes the system work for automobiles and trucks is an
adaptation of the reflector
used on American highways since the early 1930s. The reflector
lets each car see which

other cars are approaching from any one of a half-dozen
directions. This minor invention,
which enables traffic to move smoothly and with a minimum of
accidents, exploited a
process need.
What we now call the media had its origin in two innovations
developed around 1890 in
response to process needs. One was Ottmar Mergenthaler’s
Linotype, which made it
possible to produce newspapers quickly and in large volume.
The other was a social
innovation, modern advertising, invented by the first true
newspaper publishers, Adolph
Ochs of the New York Times, Joseph Pulitzer of the New York
World, and William Randolph
Hearst. Advertising made it possible for them to distribute news
practically free of charge,

with the profit coming from marketing.
192
4. Industry and market changes
Managers may believe that industry structures are ordained by
the good Lord, but these
structures can—and often do—change overnight. Such change
creates tremendous
opportunity for innovation.
One of American business’s great success stories in recent
decades is the brokerage firm
of Donaldson, Lufkin & Jenrette, recently acquired by the
Equitable Life Assurance
Society. DL&J was founded in 1960 by three young men, all
graduates of the Harvard

Business School, who realized that the structure of the financial
industry was changing as
institutional investors became dominant. These young men had
practically no capital and no
connections. Still, within a few years, their firm had become a
leader in the move to
negotiated commissions and one of Wall Street’s stellar
performers. It was the first to be
incorporated and go public.
In a similar fashion, changes in industry structure have created
massive innovation
opportunities for American health care providers. During the
past ten or 15 years,
independent surgical and psychiatric clinics, emergency centers,
and HMOs have opened
throughout the country. Comparable opportunities in
telecommunications followed industry

upheavals—in transmission (with the emergence of MCI and
Sprint in long-distance
service) and in equipment (with the emergence of such
companies as Rolm in the
manufacturing of private branch exchanges).
When an industry grows quickly—the critical figure seems to be
in the neighborhood of
40% growth in ten years or less—its structure changes.
Established companies,
concentrating on defending what they already have, tend not to
counterattack when a
newcomer challenges them. Indeed, when market or industry
structures change, traditional
industry leaders again and again neglect the fastest growing
market segments. New
opportunities rarely fit the way the industry has always

approached the market, defined it,
or organized to serve it. Innovators therefore have a good
chance of being left alone for a
long time.
193
5. Demographic changes
Of the outside sources of innovation opportunities,
demographics are the most reliable.
Demographic events have known lead times; for instance, every
person who will be in the
American labor force by the year 2000 has already been born.
Yet because policy makers
often neglect demographics, those who watch them and exploit
them can reap great
rewards.

The Japanese are ahead in robotics because they paid attention
to demographics.
Everyone in the developed countries around 1970 or so knew
that there was both a baby
bust and an education explosion going on; about half or more of
the young people were
staying in school beyond high school. Consequently, the number
of people available for
traditional blue-collar work in manufacturing was bound to
decrease and become
inadequate by 1990. Everyone knew this, but only the Japanese
acted on it, and they now
have a ten-year lead in robotics.
Much the same is true of Club Mediterranee’s success in the
travel and resort business.
By 1970, thoughtful observers could have seen the emergence of

large numbers of affluent
and educated young adults in Europe and the United States. Not
comfortable with the kind
of vacations their working-class parents had enjoyed—the
summer weeks at Brighton or
Atlantic City—these young people were ideal customers for a
new and exotic version of
the “hangout” of their teen years.
Managers have known for a long time that demographics matter,
but they have always
believed that population statistics change slowly. In this
century, however, they don’t.
Indeed, the innovation opportunities made possible by changes
in the numbers of people—
and in their age distribution, education, occupations, and
geographic location—are among

the most rewarding and least risky of entrepreneurial pursuits.
194
6. Changes in perception
“The glass is half full” and “The glass is half empty” are
descriptions of the same
phenomenon but have vastly different meanings. Changing a
manager’s perception of a glass
from half full to half empty opens up big innovation
opportunities.
All factual evidence indicates, for instance, that in the last 20
years, Americans’ health
has improved with unprecedented speed—whether measured by
mortality rates for the
newborn, survival rates for the very old, the incidence of
cancers (other than lung cancer),

cancer cure rates, or other factors. Even so, collective
hypochondria grips the nation.
Never before has there been so much concern with or fear about
health. Suddenly,
everything seems to cause cancer or degenerative heart disease
or premature loss of
memory. The glass is clearly half empty.
Rather than rejoicing in great improvements in health,
Americans seem to be emphasizing
how far away they still are from immortality. This view of
things has created many
opportunities for innovations: markets for new health care
magazines, for exercise classes
and jogging equipment, and for all kinds of health foods. The
fastest growing new U.S.
business in 1983 was a company that makes indoor exercise
equipment.

A change in perception does not alter facts. It changes their
meaning, though—and very
quickly. It took less than two years for the computer to change
from being perceived as a
threat and as something only big businesses would use to
something one buys for doing
income tax. Economics do not necessarily dictate such a change;
in fact, they may be
irrelevant. What determines whether people see a glass as half
full or half empty is mood
rather than fact, and a change in mood often defies
quantification. But it is not exotic. It is
concrete. It can be defined. It can be tested. And it can be
exploited for innovation
opportunity.
195

7. New knowledge
Among history-making innovations, those that are based on new
knowledge—whether
scientific, technical, or social—rank high. They are the
superstars of entrepreneurship; they
get the publicity and the money. They are what people usually
mean when they talk of
innovation, although not all innovations based on knowledge are
important.
Knowledge-based innovations differ from all others in the time
they take, in their
casualty rates, and in their predictability, as well as in the
challenges they pose to
entrepreneurs. Like most superstars, they can be temperamental,
capricious, and hard to

direct. They have, for instance, the longest lead time of all
innovations. There is a
protracted span between the emergence of new knowledge and
its distillation into usable
technology. Then there is another long period before this new
technology appears in the
marketplace in products, processes, or services. Overall, the
lead time involved is
something like 50 years, a figure that has not shortened
appreciably throughout history.
To become effective, innovation of this sort usually demands
not one kind of knowledge
but many. Consider one of the most potent knowledge-based
innovations: modern banking.
The theory of the entrepreneurial bank—that is, of the
purposeful use of capital to generate
economic development—was formulated by the Comte de Saint-

Simon during the era of
Napoleon. Despite Saint-Simon’s extraordinary prominence, it
was not until 30 years after
his death in 1825 that two of his disciples, the brothers Jacob
and Isaac Pereire,
established the first entrepreneurial bank, the Credit Mobilier,
and ushered in what we now
call finance capitalism.
The Pereires, however, did not know modern commercial
banking, which developed at
about the same time across the channel in England. The Credit
Mobilier failed
ignominiously. A few years later, two young men—one an
American, J.P. Morgan, and one
a German, Georg Siemens—put together the French theory of
entrepreneurial banking and

the English theory of commercial banking to create the first
successful modern banks: J.P.
Morgan & Company in New York, and the Deutsche Bank in
Berlin. Ten years later, a
young Japanese, Shibusawa Eiichi, adapted Siemens’s concept
to his country and thereby
laid the foundation of Japan’s modern economy. This is how
knowledge-based innovation
196
always works.
The computer, to cite another example, required no fewer than
six separate strands of
knowledge:
• binary arithmetic;
• Charles Babbage’s conception of a calculating machine, in the

first half of the
nineteenth century;
• the punch card, invented by Herman Hollerith for the U.S.
census of 1890;
• the audion tube, an electronic switch invented in 1906;
• symbolic logic, which was developed between 1910 and 1913
by Bertrand Russell and
Alfred North Whitehead;
• and concepts of programming and feedback that came out of
abortive attempts during
World War I to develop effective anti-aircraft guns.
Although all the necessary knowledge was available by 1918,
the first operational
digital computer did not appear until 1946.
Long lead times and the need for convergence among different

kinds of knowledge
explain the peculiar rhythm of knowledge-based innovation, its
attractions, and its dangers.
During a long gestation period, there is a lot of talk and little
action. Then, when all the
elements suddenly converge, there is tremendous excitement
and activity and an enormous
amount of speculation. Between 1880 and 1890, for example,
almost 1,000 electric-
apparatus companies were founded in developed countries.
Then, as always, there was a
crash and a shakeout. By 1914, only 25 were still alive. In the
early 1920s, 300 to 500
automobile companies existed in the United States; by 1960,
only four of them remained.
It may be difficult, but knowledge-based innovation can be
managed. Success requires

careful analysis of the various kinds of knowledge needed to
make an innovation possible.
Both J.P. Morgan and Georg Siemens did this when they
established their banking ventures.
The Wright brothers did this when they developed the first
operational airplane.
Careful analysis of the needs—and, above all, the capabilities—
of the intended user is
197
also essential. It may seem paradoxical, but knowledge-based
innovation is more market
dependent than any other kind of innovation. De Havilland, a
British company, designed
and built the first passenger jet, but it did not analyze what the
market needed and therefore

did not identify two key factors. One was configuration—that
is, the right size with the right
payload for the routes on which a jet would give an airline the
greatest advantage. The
other was equally mundane: How could the airlines finance the
purchase of such an
expensive plane? Because de Havilland failed to do an adequate
user analysis, two
American companies, Boeing and Douglas, took over the
commercial jet-aircraft industry.
198
Principles of Innovation
Purposeful, systematic innovation begins with the analysis of
the sources of new
opportunities. Depending on the context, sources will have
different importance at different

times. Demographics, for instance, may be of little concern to
innovators of fundamental
industrial processes like steelmaking, although the Linotype
machine became successful
primarily because there were not enough skilled typesetters
available to satisfy a mass
market. By the same token, new knowledge may be of little
relevance to someone
innovating a social instrument to satisfy a need that changing
demographics or tax laws
have created. But whatever the situation, innovators must
analyze all opportunity sources.
Because innovation is both conceptual and perceptual, would-be
innovators must also go
out and look, ask, and listen. Successful innovators use both the
right and left sides of their

brains. They work out analytically what the innovation has to be
to satisfy an opportunity.
Then they go out and look at potential users to study their
expectations, their values, and
their needs.
To be effective, an innovation has to be simple, and it has to be
focused. It should do
only one thing; otherwise it confuses people. Indeed, the
greatest praise an innovation can
receive is for people to say, “This is obvious! Why didn’t I
think of it? It’s so simple!”
Even the innovation that creates new users and new markets
should be directed toward a
specific, clear, and carefully designed application.
Effective innovations start small. They are not grandiose. It may
be to enable a moving

vehicle to draw electric power while it runs along rails, the
innovation that made possible
the electric streetcar. Or it may be the elementary idea of
putting the same number of
matches into a matchbox (it used to be 50). This simple notion
made possible the automatic
filling of matchboxes and gave the Swedes a world monopoly on
matches for half a century.
By contrast, grandiose ideas for things that will “revolutionize
an industry” are unlikely to
work.
In fact, no one can foretell whether a given innovation will end
up a big business or a
modest achievement. But even if the results are modest, the
successful innovation aims from
199

the beginning to become the standard setter, to determine the
direction of a new technology
or a new industry, to create the business that is—and remains—
ahead of the pack. If an
innovation does not aim at leadership from the beginning, it is
unlikely to be innovative
enough.
Above all, innovation is work rather than genius. It requires
knowledge. It often requires
ingenuity. And it requires focus. There are clearly people who
are more talented innovators
than others, but their talents lie in well-defined areas. Indeed,
innovators rarely work in
more than one area. For all his systematic innovative
accomplishments, Thomas Edison

worked only in the electrical field. An innovator in financial
areas, Citibank for example, is
not likely to embark on innovations in health care.
In innovation, as in any other endeavor, there is talent, there is
ingenuity, and there is
knowledge. But when all is said and done, what innovation
requires is hard, focused,
purposeful work. If diligence, persistence, and commitment are
lacking, talent, ingenuity,
and knowledge are of no avail.
There is, of course, far more to entrepreneurship than
systematic innovation—distinct
entrepreneurial strategies, for example, and the principles of
entrepreneurial management,
which are needed equally in the established enterprise, the
public service organization, and

the new venture. But the very foundation of entrepreneurship is
the practice of systematic
innovation.
Originally published in May 1985. Reprint 3480
200
Innovation Killers
How Financial Tools Destroy Your Capacity to Do New Things.
by Clayton M. Christensen, Stephen P. Kaufman, and Willy C.
Shih
FOR YEARS WE’VE BEEN puzzling about why so many smart,
hardworking managers in
well-run companies find it impossible to innovate successfully.
Our investigations have
uncovered a number of culprits, which we’ve discussed in
earlier books and articles.

These include paying too much attention to the company’s most
profitable customers
(thereby leaving less-demanding customers at risk) and creating
new products that don’t
help customers do the jobs they want to do. Now we’d like to
name the misguided
application of three financial-analysis tools as an accomplice in
the conspiracy against
successful innovation. We allege crimes against these suspects:
• The use of discounted cash flow (DCF) and net present value
(NPV) to evaluate
investment opportunities causes managers to underestimate the
real returns and benefits
of proceeding with investments in innovation.
• The way that fixed and sunk costs are considered when
evaluating future investments

confers an unfair advantage on challengers and shackles
incumbent firms that attempt to
respond to an attack.
• The emphasis on earnings per share as the primary driver of
share price and hence of
shareholder value creation, to the exclusion of almost
everything else, diverts resources
away from investments whose payoff lies beyond the immediate
horizon.
201
These are not bad tools and concepts, we hasten to add. But the
way they are commonly
wielded in evaluating investments creates a systematic bias
against innovation. We will
recommend alternative methods that, in our experience, can help

managers innovate with a
much more astute eye for future value. Our primary aim,
though, is simply to bring these
concerns to light in the hope that others with deeper expertise
may be inspired to examine
and resolve them.
202
Misapplying Discounted Cash Flow and Net Present Value
The first of the misleading and misapplied tools of financial
analysis is the method of
discounting cash flow to calculate the net present value of an
initiative. Discounting a future
stream of cash flows into a “present value” assumes that a
rational investor would be
indifferent to having a dollar today or to receiving some years

from now a dollar plus the
interest or return that could be earned by investing that dollar
for those years. With that as
an operating principle, it makes perfect sense to assess
investments by dividing the money
to be received in future years by (1 + r)n, where r is the
discount rate—the annual return
from investing that money—and n is the number of years during
which the investment could
be earning that return.
While the mathematics of discounting is logically impeccable,
analysts commonly
commit two errors that create an anti-innovation bias. The first
error is to assume that the
base case of not investing in the innovation—the do-nothing
scenario against which cash
flows from the innovation are compared—is that the present
health of the company will

persist indefinitely into the future if the investment is not made.
As shown in the exhibit
“The DCF trap,” the mathematics considers the investment in
isolation and compares the
present value of the innovation’s cash stream less project costs
with the cash stream in the
absence of the investment, which is assumed to be unchanging.
In most situations, however,
competitors’ sustaining and disruptive investments over time
result in price and margin
pressure, technology changes, market share losses, sales volume
decreases, and a declining
stock price. As Eileen Rudden at Boston Consulting Group
pointed out, the most likely
stream of cash for the company in the do-nothing scenario is not
a continuation of the status

quo. It is a nonlinear decline in performance.
Idea in Brief
Most companies aren’t half as innovative as their senior
executives want them to be
(or as their marketing claims suggest they are). What’s stifling
innovation? There are
plenty of usual suspects, but the authors finger three financial
tools as key
accomplices. Discounted cash flow and net present value, as
commonly used,
underestimate the real returns and benefits of proceeding with
an investment. Most
203
executives compare the cash flows from innovation against the
default scenario of
doing nothing, assuming—incorrectly—that the present health
of the company will
persist indefinitely if the investment is not made. In most
situations, however,
competitors’ sustaining and disruptive investments over time

result in deterioration
of financial performance. Fixed- and sunk-cost conventional
wisdom confers an
unfair advantage on challengers and shackles incumbent firms
that attempt to respond
to an attack. Executives in established companies, bemoaning
the expense of
building new brands and developing new sales and distribution
channels, seek
instead to leverage their existing brands and structures.
Entrants, in contrast, simply
create new ones. The problem for the incumbent isn’t that the
challenger can spend
more; it’s that the challenger is spared the dilemma of having to
choose between full-
cost and marginal-cost options. The emphasis on short-term
earnings per share as the
primary driver of share price, and hence shareholder value
creation, acts to restrict
investments in innovative long-term growth opportunities.
These are not bad tools
and concepts in and of themselves, but the way they are used to
evaluate investments
creates a systematic bias against successful innovation. The
authors recommend

alternative methods that can help managers innovate with a
much more astute eye for
future value.
It’s tempting but wrong to assess the value of a proposed
investment by measuring
whether it will make us better off than we are now. It’s wrong
because, if things are
deteriorating on their own, we might be worse off than we are
now after we make the
proposed investment but better off than we would have been
without it. Philip Bobbitt calls
this logic Parmenides’ Fallacy, after the ancient Greek logician
who claimed to have
proved that conditions in the real world must necessarily be
unchanging. Analysts who
attempt to distill the value of an innovation into one simple
number that they can compare

with other simple numbers are generally trapped by Parmenides’
Fallacy.
The DCF trap
Most executives compare the cash flows from innovation
against the default
scenario of doing nothing, assuming—incorrectly—that the
present health of the
company will persist indefinitely if the investment is not made.
For a better
assessment of the innovation’s value, the comparison should be
between its
projected discounted cash flow and the more likely scenario of a
decline in
204
performance in the absence of innovation investment.
It’s hard to accurately forecast the stream of cash from an
investment in innovation. It is
even more difficult to forecast the extent to which a firm’s

financial performance may
deteriorate in the absence of the investment. But this analysis
must be done. Remember the
response that good economists are taught to offer to the
question “How are you?” It is
“Relative to what?” This is a crucial question. Answering it
entails assessing the projected
value of the innovation against a range of scenarios, the most
realistic of which is often a
deteriorating competitive and financial future.
The second set of problems with discounted cash flow
calculations relates to errors of
estimation. Future cash flows, especially those generated by
disruptive investments, are
difficult to predict. Numbers for the “out years” can be a
complete shot in the dark. To cope

with what cannot be known, analysts often project a year-by-
year stream of numbers for
three to five years and then “punt” by calculating a terminal
value to account for everything
thereafter. The logic, of course, is that the year-to-year
estimates for distant years are so
imprecise as to be no more accurate than a terminal value. To
calculate a terminal value,
analysts divide the cash to be generated in the last year for
which they’ve done a specific
estimate by (r–g), the discount rate minus the projected growth
rate in cash flows from that
time on. They then discount that single number back to the
present. In our experience,
assumed terminal values often account for more than half of a
project’s total NPV.
Terminal value numbers, based as they are on estimates for
preceding years, tend to

205
amplify errors contained in early-year assumptions. More
worrisome still, terminal value
doesn’t allow for the scenario testing that we described above—
contrasting the result of
this investment with the deterioration in performance that is the
most likely result of doing
nothing. And yet, because of market inertia, competitors’
development cycles, and the
typical pace of disruption, it is often in the fifth year or
beyond—the point at which
terminal value factors in—that the decline of the enterprise in
the do-nothing scenario
begins to accelerate.
Arguably, a root cause of companies’ persistent

underinvestment in the innovations
required to sustain long-term success is the indiscriminate and
oversimplified use of NPV
as an analytical tool. Still, we understand the desire to quantify
streams of cash that defy
quantification and then to distill those streams into a single
number that can be compared
with other single numbers: It is an attempt to translate
cacophonous articulations of the
future into a language—numbers—that everyone can read and
compare. We hope to show
that numbers are not the only language into which the value of
future investments can be
translated—and that there are, in fact, other, better languages
that all members of a
management team can understand.

206
Using Fixed and Sunk Costs Unwisely
The second widely misapplied paradigm of financial decision
making relates to fixed and
sunk costs. When evaluating a future course of action, the
argument goes, managers should
consider only the future or marginal cash outlays (either capital
or expense) that are
required for an innovation investment, subtract those outlays
from the marginal cash that is
likely to flow in, and discount the resulting net flow to the
present. As with the paradigm of
DCF and NPV, there is nothing wrong with the mathematics of
this principle—as long as the
capabilities required for yesterday’s success are adequate for
tomorrow’s as well. When

new capabilities are required for future success, however, this
margining on fixed and sunk
costs biases managers toward leveraging assets and capabilities
that are likely to become
obsolete.
For the purposes of this discussion we’ll define fixed costs as
those whose level is
independent of the level of output. Typical fixed costs include
general and administrative
costs: salaries and benefits, insurance, taxes, and so on.
(Variable costs include things like
raw materials, commissions, and pay to temporary workers.)
Sunk costs are those portions
of fixed costs that are irrevocably committed, typically
including investments in buildings
and capital equipment and R&D costs.

An example from the steel industry illustrates how fixed and
sunk costs make it difficult
for companies that can and should invest in new capabilities
actually to do so. In the late
1960s, steel minimills such as Nucor and Chaparral began
disrupting integrated
steelmakers such as U.S. Steel (USX), picking off customers in
the least-demanding product
tiers of each market and then moving relentlessly upmarket,
using their 20% cost advantage
to capture first the rebar market and then the bar and rod, angle
iron, and structural beam
markets. By 1988 the minimills had driven the higher-cost
integrated mills out of lower-tier
products, and Nucor had begun building its first minimill to roll
sheet steel in

Crawfordsville, Indiana. Nucor estimated that for an investment
of $260 million it could
sell 800,000 tons of steel annually at a price of $350 per ton.
The cash cost to produce a
ton of sheet steel in the Crawfordsville mill would be $270.
When the timing of cash flows
207
was taken into account, the internal rate of return to Nucor on
this investment was over 20%
—substantially higher than Nucor’s weighted average cost of
capital.
Incumbent USX recognized that the minimills constituted a
grave threat. Using a new
technology called continuous strip production, Nucor had now
entered the sheet steel

market, albeit with an inferior-quality product, at a significantly
lower cost per ton. And
Nucor’s track record of vigilant improvement meant that the
quality of its sheet steel would
improve with production experience. Despite this
understanding, USX engineers did not
even consider building a greenfield minimill like the one Nucor
built. The reason? It
seemed more profitable to leverage the old technology than to
create the new. USX’s
existing mills, which used traditional technology, had 30%
excess capacity, and the
marginal cash cost of producing an extra ton of steel by
leveraging that excess capacity was
less than $50 per ton. When USX’s financial analysts contrasted
the marginal cash flow of
$300 ($350 revenue minus the $50 marginal cost) with the

average cash flow of $80 per
ton in a greenfield mill, investment in a new low-cost minimill
made no sense. What’s
more, USX’s plants were depreciated, so the marginal cash flow
of $300 on a low asset
base looked very attractive.
And therein lies the rub. Nucor, the attacker, had no fixed or
sunk cost investments on
which to do a marginal cost calculation. To Nucor, the full cost
was the marginal cost.
Crawfordsville was the only choice on its menu—and because
the IRR was attractive, the
decision was simple. USX, in contrast, had two choices on its
menu: It could build a
greenfield plant like Nucor’s with a lower average cost per ton
or it could utilize more

fully its existing facility.
So what happened? Nucor has continued to improve its process,
move upmarket, and
gain market share with more efficient continuous strip
production capabilities, while USX
has relied on the capabilities that had been built to succeed in
the past. USX’s strategy to
maximize marginal profit, in other words, caused the company
not to minimize long-term
average costs. As a result, the company is locked into an
escalating cycle of commitment to
a failing strategy.
The attractiveness of any investment can be completely assessed
only when it is
208

compared with the attractiveness of the right alternatives on a
menu of investments. When a
company is looking at adding capacity that is identical to
existing capacity, it makes sense
to compare the marginal cost of leveraging the old with the full
cost of creating the new. But
when new technologies or capabilities are required for future
competitiveness, margining
on the past will send you down the wrong path. The argument
that investment decisions
should be based on marginal costs is always correct. But when
creating new capabilities is
the issue, the relevant marginal cost is actually the full cost of
creating the new.
When we look at fixed and sunk costs from this perspective,
several anomalies we have
observed in our studies of innovation are explained. Executives

in established companies
bemoan how expensive it is to build new brands and develop
new sales and distribution
channels—so they seek instead to leverage their existing brands
and structures. Entrants, in
contrast, simply create new ones. The problem for the
incumbent isn’t that the challenger
can outspend it; it’s that the challenger is spared the dilemma of
having to choose between
full-cost and marginal-cost options. We have repeatedly
observed leading, established
companies misapply fixed-and-sunk-cost doctrine and rely on
assets and capabilities that
were forged in the past to succeed in the future. In doing so,
they fail to make the same
investments that entrants and attackers find to be profitable.

A related misused financial practice that biases managers
against investment in needed
future capabilities is that of using a capital asset’s estimated
usable lifetime as the period
over which it should be depreciated. This causes problems when
the asset’s usable lifetime
is longer than its competitive lifetime. Managers who depreciate
assets according to the
more gradual schedule of usable life often face massive write-
offs when those assets
become competitively obsolete and need to be replaced with
newer-technology assets. This
was the situation confronting the integrated steelmakers. When
building new capabilities
entails writing off the old, incumbents face a hit to quarterly
earnings that disruptive
entrants to the industry do not. Knowing that the equity markets
will punish them for a

write-off, managers may stall in adopting new technology.
This may be part of the reason for the dramatic increase in
private equity buyouts over
the past decade and the recent surge of interest in technology-
oriented industries. As
209
disruptions continue to shorten the competitive lifetime of
major investments made only
three to five years ago, more companies find themselves
needing to take asset write-downs
or to significantly restructure their business models. These are
wrenching changes that are
often made more easily and comfortably outside the glare of the
public markets.
What’s the solution to this dilemma? Michael Mauboussin at
Legg Mason Capital

Management suggests it is to value strategies, not projects.
When an attacker is gaining
ground, executives at the incumbent companies need to do their
investment analyses in the
same way the attackers do—by focusing on the strategies that
will ensure long-term
competitiveness. This is the only way they can see the world as
the attackers see it and the
only way they can predict the consequences of not investing.
No manager would consciously decide to destroy a company by
leveraging the
competencies of the past while ignoring those required for the
future. Yet this is precisely
what many of them do. They do it because strategy and finance
were taught as separate
topics in business school. Their professors of financial
modeling alluded to the importance

of strategy, and their strategy professors occasionally referred
to value creation, but little
time was spent on a thoughtful integration of the two. This
bifurcation persists in most
companies, where responsibilities for strategy and finance
reside in the realms of different
vice presidents. Because a firm’s actual strategy is defined by
the stream of projects in
which it does or doesn’t invest, finance and strategy need to be
studied and practiced in an
integrated way.
210
Focusing Myopically on Earnings per Share
A third financial paradigm that leads established companies to
underinvest in innovation is

the emphasis on earnings per share as the primary driver of
share price and hence of
shareholder value creation. Managers are under so much
pressure, from various directions,
to focus on short-term stock performance that they pay less
attention to the company’s long-
term health than they might—to the point where they’re
reluctant to invest in innovations
that don’t pay off immediately.
Where’s the pressure coming from? To answer that question, we
need to look briefly at
the principal-agent theory—the doctrine that the interests of
shareholders (principals)
aren’t aligned with those of managers (agents). Without
powerful financial incentives to
focus the interests of principals and agents on maximizing

shareholder value, the thinking
goes, agents will pursue other agendas—and in the process, may
neglect to pay enough
attention to efficiencies or squander capital investments on pet
projects—at the expense of
profits that ought to accrue to the principals.
That conflict of incentives has been taught so aggressively that
the compensation of most
senior executives in publicly traded companies is now heavily
weighted away from
salaries and toward packages that reward improvements in share
price. That in turn has led
to an almost singular focus on earnings per share and EPS
growth as the metric for
corporate performance. While we all recognize the importance
of other indicators such as
market position, brands, intellectual capital, and long-term

competitiveness, the bias is
toward using a simple quantitative indicator that is easily
compared period to period and
across companies. And because EPS growth is an important
driver of near-term share price
improvement, managers are biased against investments that will
compromise near-term
EPS. Many decide instead to use the excess cash on the balance
sheet to buy back the
company’s stock under the guise of “returning money to
shareholders.” But although
contracting the number of shares pumps up earnings per share,
sometimes quite
dramatically, it does nothing to enhance the underlying value of
the enterprise and may even
damage it by restricting the flow of cash available for
investment in potentially disruptive

211
products and business models. Indeed, some have fingered
share-price-based incentive
compensation packages as a key driver of the share price
manipulation that captured so
many business headlines in the early 2000s.
The myopic focus on EPS is not just about the money. CEOs
and corporate managers
who are more concerned with their reputations than with
amassing more wealth also focus
on stock price and short-term performance measures such as
quarterly earnings. They know
that, to a large extent, others’ perception of their success is tied
up in those numbers,

leading to a self-reinforcing cycle of obsession. This behavior
cycle is amplified when
there is an “earnings surprise.” Equity prices over the short term
respond positively to
upside earnings surprises (and negatively to downside
surprises), so investors have no
incentive to look at rational measures of long-term performance.
To the contrary, they are
rewarded for going with the market’s short-term model.
The active leveraged buyout market has further reinforced the
focus on EPS. Companies
that are viewed as having failed to maximize value, as
evidenced by a lagging share price,
are vulnerable to overtures from outsiders, including corporate
raiders or hedge funds that
seek to increase their near-term stock price by putting a
company into play or by replacing

the CEO. Thus, while the past two decades have witnessed a
dramatic increase in the
proportion of CEO compensation tied to stock price—and a
breathtaking increase in CEO
compensation overall—they have witnessed a concomitant
decrease in the average tenure
of CEOs. Whether you believe that CEOs are most motivated by
the carrot (major increases
in compensation and wealth) or the stick (the threat of the
company being sold or of being
replaced), you should not be surprised to find so many CEOs
focused on current earnings
per share as the best predictor of stock price, sometimes to the
exclusion of anything else.
One study even showed that senior executives were routinely
willing to sacrifice long-term

shareholder value to meet earnings expectations or to smooth
reported earnings.
We suspect that the principal-agent theory is misapplied. Most
traditional principals—by
which we mean shareholders—don’t themselves have incentives
to watch out for the long-
term health of a company. Over 90% of the shares of publicly
traded companies in the
United States are held in the portfolios of mutual funds, pension
funds, and hedge funds. The
212
average holding period for stocks in these portfolios is less than
10 months—leading us to
prefer the term “share owner” as a more accurate description
than “shareholder.” As for
agents, we believe that most executives work tirelessly,

throwing their hearts and minds
into their jobs, not because they are paid an incentive to do so
but because they love what
they do. Tying executive compensation to stock prices,
therefore, does not affect the
intensity or energy or intelligence with which executives
perform. But it does direct their
efforts toward activities whose impact can be felt within the
holding horizon of the typical
share owner and within the measurement horizon of the
incentive—both of which are less
than one year.
Ironically, most so-called principals today are themselves
agents—agents of other
people’s mutual funds, investment portfolios, endowments, and
retirement programs. For

these agents, the enterprise in which they are investing has no
inherent interest or value
beyond providing a platform for improving the short-term
financial metric by which their
fund’s performance is measured and their own compensation is
determined. And, in a final
grand but sad irony, the real principals (the people who put
their money into mutual funds
and pension plans, sometimes through yet another layer of
agents) are frequently the very
individuals whose long-term employment is jeopardized when
the focus on short-term EPS
acts to restrict investments in innovative growth opportunities.
We suggest that the
principal-agent theory is obsolete in this context. What we
really have is an agent-agent
problem, where the desires and goals of the agent for the share
owners compete with the

desires and goals of the agents running the company. The
incentives are still misaligned, but
managers should not capitulate on the basis of an obsolete
paradigm.
213
Processes That Support (or Sabotage) Innovation
As we have seen, managers in established corporations use
analytical methods that make
innovation investments extremely difficult to justify. As it
happens, the most common
system for green-lighting investment projects only reinforces
the flaws inherent in the tools
and dogmas discussed earlier.
214

Stage-gate innovation
Most established companies start by considering a broad range
of possible innovations;
they winnow out the less viable ideas, step by step, until only
the most promising ones
remain. Most such processes include three stages: feasibility,
development, and launch. The
stages are separated by stage gates: review meetings at which
project teams report to
senior managers what they’ve accomplished. On the basis of
this progress and the project’s
potential, the gatekeepers approve the passage of the initiative
into the next phase, return it
to the previous stage for more work, or kill it.
Many marketers and engineers regard the stage-gate
development process with disdain.

Why? Because the key decision criteria at each gate are the size
of projected revenues and
profits from the product and the associated risks. Revenues
from products that
incrementally improve upon those the company is currently
selling can be credibly
quantified. But proposals to create growth by exploiting
potentially disruptive technologies,
products, or business models can’t be bolstered by hard
numbers. Their markets are
initially small, and substantial revenues generally don’t
materialize for several years. When
these projects are pitted against incremental sustaining
innovations in the battle for funding,
the incremental ones sail through while the seemingly riskier
ones get delayed or die.

The process itself has two serious drawbacks. First, project
teams generally know how
good the projections (such as NPV) need to look in order to win
funding, and it takes only
nanoseconds to tweak an assumption and run another full
scenario to get a faltering project
over the hurdle rate. If, as is often the case, there are eight to 10
assumptions underpinning
the financial model, changing only a few of them by a mere 2%
or 3% each may do the
trick. It is then difficult for the senior managers who sit as
gatekeepers to even discern
which are the salient assumptions, let alone judge whether they
are realistic.
The second drawback is that the stage-gate system assumes that
the proposed strategy is
the right strategy. Once an innovation has been approved,

developed, and launched, all that
remains is skillful execution. If, after launch, a product falls
seriously short of the
projections (and 75% of them do), it is canceled. The problem is
that, except in the case of
incremental innovations, the right strategy—especially which
job the customer wants done
215
—cannot be completely known in advance. It must emerge and
then be refined.
The stage-gate system is not suited to the task of assessing
innovations whose purpose is
to build new growth businesses, but most companies continue to
follow it simply because
they see no alternative.

216
Discovery-driven planning
Happily, though, there are alternative systems specifically
designed to support intelligent
investments in future growth. One such process, which Rita
Gunther McGrath and Ian
MacMillan call discovery-driven planning, has the potential to
greatly improve the success
rate. Discovery-driven planning essentially reverses the
sequence of some of the steps in
the stage-gate process. Its logic is elegantly simple. If the
project teams all know how good
the numbers need to look in order to win funding, why go
through the charade of making and
revising assumptions in order to fabricate an acceptable set of
numbers? Why not just put

the minimally acceptable revenue, income, and cash flow
statement as the standard first
page of the gate documents? The second page can then raise the
critical issues: “Okay. So
we all know this is how good the numbers need to look. What
set of assumptions must
prove true in order for these numbers to materialize?” The
project team creates from that
analysis an assumptions checklist—a list of things that need to
prove true for the project to
succeed. The items on the checklist are rank-ordered, with the
deal killers and the
assumptions that can be tested with little expense toward the
top. McGrath and MacMillan
call this a “reverse income statement.”
When a project enters a new stage, the assumptions checklist is
used as the basis of the

project plan for that stage. This is not a plan to execute,
however. It is a plan to learn—to
test as quickly and at as low a cost as possible whether the
assumptions upon which
success is predicated are actually valid. If a critical assumption
proves not to be valid, the
project team must revise its strategy until the assumptions upon
which it is built are all
plausible. If no set of plausible assumptions will support the
case for success, the project is
killed.
Traditional stage-gate planning obfuscates the assumptions and
shines the light on the
financial projections. But there is no need to focus the
analytical spotlight on the numbers,
because the desirability of attractive numbers has never been
the question. Discovery-

driven planning shines a spotlight on the place where senior
management needs illumination
—the assumptions that constitute the key uncertainties. More
often than not, failure in
innovation is rooted in not having asked an important question,
rather than in having arrived
217
at an incorrect answer.
Today, processes like discovery-driven planning are more
commonly used in
entrepreneurial settings than in the large corporations that
desperately need them. We hope
that by recounting the strengths of one such system we’ll
persuade established corporations
to reassess how they make decisions about investment projects.

_____________________
We keep rediscovering that the root reason for established
companies’ failure to innovate is
that managers don’t have good tools to help them understand
markets, build brands, find
customers, select employees, organize teams, and develop
strategy. Some of the tools
typically used for financial analysis, and decision making about
investments, distort the
value, importance, and likelihood of success of investments in
innovation. There’s a better
way for management teams to grow their companies. But they
will need the courage to
challenge some of the paradigms of financial analysis and the
willingness to develop
alternative methodologies.

Originally published in January 2008. Reprint R0801F
218
About the Contributors
LANCE A. BETTENCOURT is a founding partner at Service
360 Partners.
CLAYTON M. CHRISTENSEN is the Kim B. Clark Professor of
Business
Administration at Harvard Business School and the world’s
foremost authority on
disruptive innovation.
GEORGE S. DAY is the Geoffrey T. Boisi Professor and a
codirector of the Mack
Center for Technological Innovation at the University of
Pennsylvania’s Wharton

School.
PETER F. DRUCKER the was the Marie Rankin Clarke
Professor of Social Science
and Management at Claremont Graduate University.
VIJAY GOVINDARAJAN is the Earl C. Daum 1924 Professor
of International
Business at the Tuck School of Business at Dartmouth.
JEFFREY R. IMMELT, chairman and CEO of GE, heads
President Obama’s Council
on Jobs and Competitiveness.
ROSABETH MOSS KANTER is the Ernest L. Arbuckle
Professor of Business
Administration at Harvard Business School.
STEPHEN P. KAUFMAN, a senior lecturer at Harvard Business
School, is the retired

chairman and CEO of Arrow Electronics.
IAN C. MACMILLAN the Dhirubhai Ambani Professor of
Innovation and
Entrepreneurship at the University of Pennsylvania’s Wharton
School.
ROGER L. MARTIN is dean of the University of Toronto’s
Rotman School of
Management.
219
RITA GUNTHER MCGRATH is a professor at Columbia
University’s Graduate
School of Business.
DONALD REINERTSEN is president of Reinertsen &
Associates, a consulting firm in

Redondo Beach, California.
WILLY C. SHIH is a professor of management practice at
Harvard Business School.
He held executive positions at IBM, Silicon Graphics, and
Kodak.
STEFAN THOMKE is the William Barclay Harding Professor of
Business
Administration at Harvard Business School.
CHRIS TRIMBLE is on the faculty at the Tuck School of
Business at Dartmouth.
ANTHONY W. ULWICK is the founder and Chief Innovation
Officer of Strategyn.
220
Index
Africa, 33

agent-agent problem, 168
Alcon Laboratories, 147–148
AlliedSignal, 103, 111, 122
allowable costs, 128
Apple, 1, 4, 8, 48, 95, 101
Arrow.com, 114–115
asset depreciation, 164–165
assumptions about new ventures, 127–131, 135–137
checklist, 135–136, 138–140, 170
testing, 131, 136–137, 140–142
AT&T Worldnet, 113–114
banking, 152–153
Bank of Boston, 111–112, 123
batch size, in product development, 89–91
biotechnology, 104
BMW, 16
breakthrough innovations, 30
buyouts, 102, 165, 167
capital assets, 164–165
cash flow predictions, 161
Cemex, 110

charter schools, 112
China, 28, 30, 33, 35–42
“code-jam,” at Intuit, 3, 7
collaboration
culture of, 123–124
for innovation, 11–25
communication, 115–117
competition, global, 33
competitive advantage, 63, 75–79
221
computers, 153
control boards, 93
Cook, Scott, 1–5
corporate culture, 117–118
corporate reporting systems, 146–147
cross-functional teams, 122, 123
culture
clashes, 114

of collaboration, 123–124
corporate, 117–118
of innovation, 118–119
customer-centered innovation, 43–58
customer jobs, 43–58
customer needs, 72–73, 91
demographic changes, as sources of innovation, 147, 150–151,
154
design-driven innovation, 1–10
Design for Delight (D4D) program, at Intuit, 2–10
design simplicity, 94–96
developed countries, 31
developing countries. See emerging economies
development plans, 91–92
digital era, 102
discounted cash flow (DCF), 157–161
discovery-driven planning, 125–126, 131–142, 170–171
Disney, Walt, 95
Donaldson, Lufkin & Jenrette, 146, 149
earnings per share, 157–158, 165–168
economies of scale, 89
emerging economies, 27, 28, 30–31

growth in, 31–33
innovation in, 30–31
local growth team (LGT) model in, 38–42
multinational corporations in, 27–42
entrepreneurship, 143, 145–146, 156
222
Euro Disney, 126–130
experimentation, 91, 98–99
“fail early, fail often” approach, 97–99
failure
learning from, 97–99
risk of, 125
feature creep, 94–96
FedEx, 130
financial-analysis tools, 157–168
financial innovations, 102
First Community Bank (FCB), 111–112, 123

fixed costs, 157, 162–165
flexibility, 119–121
Ford Motor Company, 145–146
funding models, 119–120
Gap, 116
GE Healthcare, 34–41
General Electric (GE), 27–42, 102–103
General Motors (GM), 112, 145
Gillette, 119
global information age, 101
glocalization, 27–30, 33–35
growth strategy, 63, 80–81
Hanson, Kaaren, 3–6, 8–9
health care, 31, 34–38, 149–150
holding costs, 90
Honeywell, 115–116
IBM, 101–103, 119, 120, 123–124, 144
implicit assumptions about new ventures, 127–131
incongruities, as sources of innovation, 146, 147–148
incremental innovation, 59, 61, 65, 119
India, 28, 30, 36, 42

223
industry changes, as sources of innovation, 146, 149–150
information age, 101
information technology, 99–102
innovation
breakthrough, 30
catalysts, 1–10
customer-centered, 43–58
design-driven, 1–10
discipline of, 143–156
in emerging economies, 30–42
financial, 102
financial killers of, 157–171
at GE, 27–42
incremental, 59, 65, 119
knowledge-based, 147, 152–155
leaders, 21–25, 105
lessons from, 108–109
major, 59–61, 65
mistakes, 103–105, 107–118
opportunities for, 53, 57–58, 154–155

partnerships for, 11–25
principles of, 154–156
process, 102
processes that support, 168–171
reception of, 116
remedies, 118–124
reverse, 27–42
risk and reward assessment for, 59–81
R-W-W screen for, 60–62, 68–81
sources of, 144–154
stage-gate, 169–170, 171
waves of, 101–106
zero-based, 39–40
innovation pyramid, 108, 118–119
innovation teams, 11–25, 121–123
224
insiders, on innovation teams, 18–19
interpersonal skills, 115, 123–124
Intuit, 1–10
iterative development, 97–99

job mapping, 43–58
Kao Corporation, 132–142
key assumptions checklist, 131, 135–136, 138–140
knowledge-based innovations, 147, 152–154
leaders, innovation, 21–25, 105
leadership skills, 123–124
lean manufacturing, 89
Little’s Law, 92–93
local growth team (LGT) model, 38–42
Lucent Technologies, 13, 17, 18
mainstream business, connections between innovators and, 11–
25, 121–
123
major innovations, 59–61, 65
management, 78
management flexibility, 119–121
manufacturing, 83–84, 89, 100
market, for new product, 62, 71–72
market changes, as sources of innovation, 146, 149–150
market research, 78–79

Microsoft, 47, 56, 102, 132
milestone planning, 129, 131, 136–137, 140–142
minor innovations, 59, 65
mistakes
avoiding, 96–97
in innovation, 103–105, 107–118
Mobile Bazaar, 8
multinational corporations
glocalization and, 27–30, 33–35
225
reverse innovation and, 27–42
net present value, 158–161
Net Promoter Score, 1–2, 3, 8, 10
new-product failures, 75
new ventures, planning for, 125–142
Nucor, 162–164
Ocean Spray, 109–110

ongoing operations, innovation teams and, 11–25, 121–123
opportunities, for innovation, 53, 57–58, 144–155
organic growth, 102–103
organizational structures, 122
original equipment manufacturers (OEMs), 132
outsiders, and positive effect on innovation team, 19
“painstorm,” at Intuit, 3, 5–9
Parmenides’ Fallacy, 160
partnerships, for innovation, 11–25, 121–123
PeerMonitor, 25
perceptual changes, as sources of innovation, 147, 151–152
performance engine, 12, 15–18, 21, 24–25
performance reviews, 111
planning
discovery-driven, 125–126, 131–142, 170–171
milestones, 129, 131, 136–137, 140–142
platform-based, 125–131
planning and control systems, 119–121
platform-based planning, 125–131
portfolio review team, 61
present value, 158
price-performance paradigm, 33

pricing decisions, 127–128
principal-agent theory, 166–168
privatization, 102
problem definition, 94–95
226
process innovation, 102
process mistakes, 104, 111–112
process needs, as sources of innovation, 146, 149
Procter & Gamble (P&G), 107, 110
product concept, 71–74
product development
adding features in, 94–96
batch size in, 89–91
capacity buffer in, 88
“fail early, fail often” approach to, 97–99
first-time success in, 96–97
vs. manufacturing, 83–84, 100
myths of, 83–100
process remedy for, 119–121
queues in, 86–87, 89, 92–93

requirements changes during, 91–92
resource utilization in, 84, 88
time management in, 92–94
variability of, 84–86
work-in-process inventory in, 87–89
product development teams, 115–116
product innovations
competitiveness of, 63, 75–79
market for, 62, 71–73
profitability of, 63, 79–80
real, 62, 71–72, 74
resource utilization in, 86
R-W-W screening of, 68–81
strategic fit of, 63, 80–81
value of, 79–81
profitability, 63, 79–80, 128, 133
pro forma operations specs, 131, 134–135, 138–140
project screening teams, 70
Quaker Oats, 109
227

queues, of projects, 86–87, 89
queuing theory, 86, 92–93
relationships, importance of, on innovation teams, 115–117, 123
resource conflicts, 23–24
resource utilization, in product development, 84, 86, 88
restructuring, 102
reverse income statement, 131, 133–134, 136, 138, 170
reverse innovation, 27–42
risk assessment, 60–68
risk matrix, 60–68
R-W-W screen, 60, 61–63, 68–81
Seagate Technology, 117–118, 122
shared staff, 17, 23
shareholder value, 102, 157–158, 166
simplicity, in design, 94–96, 155
skills mistakes in innovation, 105, 115–118
remedy for, 123–124
software testing, 90–91
“sol-jam,” at Intuit, 3, 7–9

solutions, as separate from jobs consumers want done, 48
stage-gate innovation, 169–170, 171
stock prices, 166–167
strategy mistakes in innovation, 104, 107–110
structure mistakes in innovation, 104–105, 112–115
sunk costs, 157, 162–165
sustainability issues, 30
teams
cross-functional, 122
innovation, 11–25
testing, 90–91, 97–98, 129
Timberland, 116–117, 121
228
Time Warner, 107
total quality management, 102

transaction costs, 90
troubleshooting, 57
TurboTax, 10
ultrasound machine, compact, 37–41
unexpected occurrences, as sources of innovation, 144–147
U.S. Steel, 162–164
venture capital, 106
Walt Disney Company, 126–131
WD-40, 21, 22–23
West (publishing house), 13–17, 19–20, 24–25
Williams-Sonoma, 122–123
work-in-process control board, 93
work-in-process inventory, 87–89
work relationships, 15, 18–19
World Community Grid, 103, 123
World Wide Web, 102
zero-based innovation, 39–40
229

230
231
Title PageCopyrightContentsThe Innovation CatalystsStop the
Innovation WarsHow GE Is Disrupting ItselfThe Customer-
Centered Innovation MapIs It Real? Can We Win? Is It Worth
Doing?Six Myths of Product DevelopmentInnovation: The
Classic TrapsDiscovery-Driven PlanningThe Discipline of
InnovationInnovation KillersAbout the ContributorsIndexBack
Cover

Test Your Innovation IQ Holly Green, Contributor Origina.docx

More Related Content

Similar to Test Your Innovation IQ Holly Green, Contributor Origina.docx (20)

More from todd191 (20)

Recently uploaded (20)

Test Your Innovation IQ Holly Green, Contributor Origina.docx