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Technological progress is a major driver of organizational change. Organizations may flourish
of fail depending on their capability to recognize what technological innovations matter to
them and how they should respond.
This lessons examines the digital revolution, or fourth industrial revolution, a major
technological progress, which in recent decades had major disruptive effects on
organizations.
1. In the First part we will explore the roots, nature and size of the digital revolution,
addressing questions such as what is the digital revolution? why it is happening now?
2. The Second part explores change that the digital revolution is producing in organizations.
3. The Third part explores the main challenges in change in response to disruptive
innovation, by using lessons from past technological shifts.
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Typically, you would point out to your wrist, as if there was a clock….
Yet how many of you have a clock at their wrist now?
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We are so used to the steady flow of new technologies that it may seem a normal
condition in human history
But is this truly the case?
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Lets’ take a moment to contemplate the recent pace of human development,
as described in the book “Sapiens. A brief history of humankind” by Juval
Noah Harari. He wrote:
“A Spanish peasant who had fallen asleep in the year 1000 and woken up 500
years later, to the din of Columbus’ sailors boarding the Niña, Pinta and Santa
Maria, the world would have seemed to him quite familiar. (….) But had one of
Columbus’ sailors fallen into a similar slumber and woken up to the ringtone of
a twenty-first-century iPhone, he would have found himself in a world strange
beyond comprehension.”
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The last 500 years have witnessed a phenomenal and unprecedented growth
in human power.
In the year 1500:
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there were about 500 million Homo sapiens in the entire world. Today,
there are 7 billion
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the total value of goods and services produced by humankind estimated
at $250 billion, in today’s dollars. Nowadays is close to $60 trillion
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humanity consumed about 13 trillion calories of energy per day. Today, we
consume 1,500 trillion calories a day
•
Five modern freighters could have taken onboard all the cargo borne by
the whole world’s merchant Fleets
•
A modern computer could easily store every word and number in all the
codex books and scrolls in every single medieval library with room to
spare
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For many thousands of years, humanity was on a very slow upward trajectory.
Progress was almost invisible. Animals and farms, wars and empires,
philosophies and religions all failed to exert much influence. But just over two
hundred years ago, something sudden and profound arrived and bent the
curve of human history—of population and social development— almost
ninety degrees.
It was the steam engine or, to be more precise, one developed and improved
by by James Watt and colleagues in the second half of the eighteenth century to have
unleashed an unprecedented growth in human power.
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Cotton gin - cotton engine" – is a machine that quickly and easily
separates cotton fibers from their seeds, enabling much greater productivity
than manual cotton separation
The cotton gin, for example, was a pivotal innovation in the textile sector in
early 1800, but not outside of it.
Instead, the steam engine and electrical power impacted all sectors,
generating many more innovations
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Prior to Watt, steam engines were highly inefficient, harnessing 1% of the
energy released by burning coal.
With Watt, steam engines become much more efficient, it massively
increased the amount of power available and had a cascade effect. Watt’s
increased this more than threefold. The steam engine massively increased
the amount of power available to factories and free them from the need to be
located near a stream or river to power the water wheel.
The Industrial Revolution is not only steam power, but this innovation had a
cascade effect, overcoming the limitations of human and animal power,
enabling factories and mass production, railways and mass transportation.
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“The First Industrial Revolution used water and steam power to mechanize
production. The Second used electric power to create mass production. The
Third used electronics and information technology to automate production.
Now a Fourth Industrial Revolution is building on the Third, the digital
revolution that has been occurring since the middle of the last century. It is
characterized by a fusion of technologies that is blurring the lines between the
physical, digital, and biological spheres.” (source: world economic forum)
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There are three reasons why today’s transformations represent not merely a
prolongation of the Third Industrial Revolution but rather the arrival of a Fourth
and distinct one: speed, scope, and systems impact.
The speed of current breakthroughs has no historical precedent. When
compared with previous industrial revolutions, the Fourth is evolving at an
exponential rather than a linear pace. Moreover, it is disrupting almost every
industry in every country. And the breadth and depth of these changes
herald the transformation of entire systems of production, management, and
governance.”
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Digital progress is doing for mental power, what the steam engine and its
descendants did for muscle power. It allows to overcome its traditional
limitations.
While organizations have been using computers for decades, yet - just as it
took generations to improve the steam engine to the point that it could power
the Industrial Revolution, it’s also taking time to refine our digital engines.
In the last decades we achieved a turning point, and the pace of technological
impact is now surprising even experts
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Several technological breakthrough went from being the stuff of
science fiction to on-the-road reality in a few short years
For example:
In 2002 the US Defense Advanced Research Projects Agency (DARPA),
launched a Grand Challenge to build a completely autonomous vehicle
that could complete a 150-mile course. The race took place on 2004, with
15 participants. The “winning” car- from Carnegie Mellon university covered only 7.4 miles before getting stuck. In October 2010 Google
announces a completely autonomous cars had been driving successfully,
in traffic, on American roads and highways. TESLA cars on the market
have auto pilot.
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In 2004, experts admitted that “Human-level speech recognition is an elusive
goal,” but .. in October of 2016, a team from Microsoft Research
announced that a neural network they had built had achieved “human
parity in conversational speech recognition,” … being more accurate than
professional human transcriptionists.
Since 2012 Forbes corporate earnings previews that appear on the website
are generated by algorithms without human involvement.
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To understand why it’s unfolding now, we need to understand the nature of technological
progress in the era of digital hardware, software, and networks, namely three key
characteristics: exponential, digital, and combinatorial.
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The game of chess originated in present-day India during the sixth century BC. The inventor
traveled to the capital city to presented the game to the emperor. The ruler was so impressed
by the beautiful game that he invited the inventor to set his reward.
The inventor proposed to place one single grain of rice on the first square of the board, two on
the second, four on the third, and so on, so that each square receives twice as many grains
as the previous.”
The emperor agreed, impressed by the inventor’s apparent modesty.
At half of the chessboard (thirty-two squares), the emperor had given the inventor about 4
billion grains of rice. That’s about one large field’s worth—and the emperor did start to take
notice.
However, in the second half of the chessboard numbers mount into trillions, quadrillions, and
quintillions—we also lose sense of how quickly numbers like these appear as exponential
growth continues.
64 instances of doubling yields more than eighteen quintillion grains, which is more rice than
has been produced in the history of the world.
The anecdote illustrates the power of sustained exponential growth and highlights the point at
which the numbers start to become so big they are inconceivable.
Source: Brynjolfsson, E., & McAfee, A. 2014 citing Kurzweil 2000 The Age of Spiritual Machines:
When Computers Exceed Human Intelligence
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Moore’s law and the second half of the chessboard
In a 1965, Intel cofounder Gordon Moore observed that over the 10-years
history of integrated circuits industry the amount of computing power you
could buy for one dollar doubled each year. He predicted this would continue
for at least another ten years- which meant a 500 more powerful potential in
1975 than in 1965. After five decades the law is still true, as well as for digital
progress in other areas as well. Moore’s Law is not a laws of physics, of
course, but it signals the constant successful efforts of the computer
industry’s engineers and scientists.
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Exponential growth leads to staggeringly big numbers.
It explains why progress with digital technologies has been so much faster these days and
why we’ve seen so many recent technological breakthroughs.
“It’s because we’re now in a different regime of computing: we’re now in the second half of
the chessboard. Cars that drive themselves, auto-generated news stories; cheap, flexible
factory robots—have all appeared since 2006. Things get weird in the second half of the
chessboard. And like the emperor, most of us have trouble keeping up.” (Brynjolfsonn and
McAfee 2012)
The BEA first noted “information technology” as a distinct corporate investment category in
1958. If we take that year as the starting point for when Moore’s Law entered the business
world, and used eighteen months as the doubling period. After thirty-two of these doublings,
U.S. businesses entered the second half of the chessboard when it comes to the use of digital
gear. That was in 2006.
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In 1996 the Accelerated Strategic Computing Initiative (ASCI Red) funded
with 55 million $ by the US government was the world’s fastest
supercomputer. It occupied nearly a tennis court of space. ASCI Red was the
first computer to score above one teraflop—one trillion floating point
operations* per second, using the energy that would power 800 homes.
In 2006 the same performance was met by Sony PlayStation 3, for 500 $
dollars and 200 watts.
In 10 years, exponential digital progress brought teraflop calculating power
from a single government lab to all around the world.
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Digitalization: turning information—text, sounds, photos, video, data from
instruments and sensors, into the ones and zeroes that are the language of
computers.
Digital information has two key properties:
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it is non-rival, namely it is not consumed or extinguished when it gets
used by someone. For instance, an apple is a rival good, a news article
is a website is not
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it has close to zero marginal cost of reproduction, namely it is
extremely cheap to make another copy
These traits of digital information open new opportunities and technological
developments. Let’s consider a few examples of new devices and products
which are possible thanks to digitalization.
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GPS-based app that provide driving directions tell you not only what route
to your destination is best in general, but what route is best in that specific
moment– considering traffic jams, accidents, road closures, etc.
They make use of subscribers’ smartphones, which upload constantly to the
company’s servers their location and speed information.
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When translating from language X to language Y, Google Translate scans
all the documents it available in both languages, looking for a close match,
then returns the corresponding text. Namely what is does is statistical pattern
matching over huge pools of digital content.
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Internet is a tremendous source of user-generated information. A team of US
used information on housing search to predict house price evolution, and
the results outcompeted the predictions published by the experts at the
National Association of Realtors.
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Sensors like microphones, cameras, and accelerometers have moved from
the analog world to the digital one and became subject to the exponential
improvement of Moore’s Law.
A Google autonomous car incorporates several sensors - the most important
‘eye’ being a LIDAR (a combination of “LIght” and “raDAR”) mounted on the
roof. The first commercial LIDAR systems costed around $35.000.000 in year
2000; in year 2019 they cost 4.000$ *
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Most innovation are not completely new but derive from recombining things
that already exist, namely they result from combinatorial innovation. For
example:
• The sewing machine was enabled by the push for interchangeable parts
in the late XVIII century munitions industry
• Johannes Gutenberg applied the technology of the screw press that was
designed for making wine and reconfigured it with metal type to create the
printing press
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“To create is to combine existing bits of insight, knowledge, ideas, and
memories into new material and new interpretations of the world, to connect
the seemingly dissociated, to see patterns where others see chaos.”*
The more we have access to a pool of information and ideas that are diverse,
cross-disciplinary, and wide-ranging, the more we can put them together in
new combinations for new insights and ideas.
Using a metaphor, “if you only have a few LEGO pieces of the same shape
and color, what you can create is limited, while if you have many LEGOs
made up of various sizes, shapes, and colors, you can create an almost
infinite number of interesting combinations.”*
*https://www.smithsonianmag.com/innovation/combinatorial-creativity-and-the-myth-oforiginality-114843098/?no-ist
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Today, we have Internet components–software, protocols, languages, etc.—
that are being combined to create totally new innovations. Such component
parts are all bits. That means you can reproduce them, duplicate them,
spread them around the world, and you can have thousands and tens of
thousands of innovators combining or recombining the same component parts
to create new innovation, and so we get a tremendous burst of innovation.
The self-driving car is a current example of a whole lot of different
technologies—digital mapping, GPS, machine learning, developments in laser
and infrared sensor technology–coming together to create something truly
innovative.

The speed of combinatorial innovation may continue to accelerate in the
digital age where new pieces of software can be sent around the world in
a matter of seconds

innovators everywhere can combine and recombine this software with
other components to make new innovation. Innovators everywhere can
combine and recombine then software with other components to make new
innovations

On top of that, through the internet and digitalization, we can access an
unprecedented amount and variety of information
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Digitalization is producing three major organizational transformations, by
changing the balance between machine and minds, product and platform,
core and crowd.
Organization need to reflect on whether and how to implement such
transformations.
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Artificial intelligence is the “science and engineering of making intelligent
machines.” The first conference on the topic was held in 1956 and a few
years later the field divided into two approaches.
An example is useful to understand the two approaches’ basic premises.
Children learn a language by listening, their brain operates on statistical
principles to discern the patterns in language, and they do not need to learn
rules to learn a language.
Instead, adults’ brain are different. When learning a second language they
rely much more on learning explicitly rules.
AI community split into two similarly differentiated camps. One pursued socalled rule-based, or “symbolic,” artificial intelligence, while the other built
statistical pattern recognition systems.
At first, it looked like the symbolic approach would dominate. Examples
include programs to play chess or to solve theorems. However, other
challenges proved impossible, such as in speech recognition, image
classification, language translation, by the late 1980s major corporate and
governmental sources of research funding dried up.
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Two major obstacles explains the failure of symbolic approaches.
First, there are a too many rules for certain tasks. Attempts to codify all of
them have failed (e.g., in complex things like languages or image recognition).
The worlds we inhabit follows several and inconsistent set of rules. For
instance, in English two negatives can make a positive (“she is never not
cheerful”), but two positives can never make a negative.
Our human common sense allows us to live in the world’s complexity and
inconsistency, but this knowledge cannot be codified. This leads to the
second main obstacle, namely that commonsense reasoning processes are
largely unavailable to introspection, we don’t and can’t know what rules we
ourselves are using. This is known as the Polany’s paradox, namely that we
know more than we can tell.
In such cases, a rule-based system cannot be developed.
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The other camp of artificial intelligence has tried to overcome Polanyi’s
Paradox by building systems that learn tasks the way a young child learns
language: by experience,d repetition, and feedback.
They’ve created the field of “machine learning”. For a long time, progress in
this camp also remained very slow, until recent incredible achievements –
e.g., in language recognition, image analysis, etc.
These breakthroughs have become possible primarily because of the Moore’s
law and related astonishing increase in computer power and the phenomenon
of “big data”, namely the explosion of digital text, pictures, sounds, videos,
etc.
Like young children, who hear a lot of words and sentences to learn
language, machine “learns” exposed to many examples and provided by
computing power to classify and make order in them.
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The balance between machine and humans in work has changed drastically
over the last two centuries.
Steam, electric power, combustion engines freed humans and animals from
most tasks requiring pure strength.
Since the third industrial revolution (ICT), a shift of intellectual tasks has
occurred too.
Algorithms take decisions and make forecast – for example judging credit
worthiness. Many decisions are nowadays made by algorithms, and we do
not even notice.
What tasks will be left for humans in the future?
This has clearly huge consequences on how work will be organized in the
future.
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In 2004, Frank Levy and Richard Murnane wrote The New Division of Labor,
focusing on the labor division between humans and computers, assuming that
people should focus on tasks where they have a comparative advantage over
computers.
The put information processing tasks on a spectrum.
On the one side are tasks that require only the application of well-understood
rules, like arithmetic. Since computers excel at following rules, they should do
arithmetic and similar tasks. For example, a person’s credit score is a good
general Predictor of whether they’ll pay back their mortgage as promised. So,
the decision about whether to give someone a mortgage can be boiled down
to a rule and computers can take up this task.
In turn, machines would take care of basic math, record keeping, and data
transmission. This would free up people to make decisions, exercise
judgment, use their creativity and intuition, and interact with each other to
solve problems and take care of customers.
At the other side lie information processing tasks that cannot be boiled down
to rules or algorithms.
For example, while driving a person faces a constant flow of diverse and
complex information and stimuli, produced by other cars, drivers, pedestrians,
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they might have to choose between formal and informal rules according to
different contexts, and so forth. The same occurs with speech recognition or
walking in the street. These tasks rely on human capability to take information
via our multiple senses and recognizing patterns. Humans are very good at
this, although we are often not able to describe and explain how are doing
such task (which is know as Michael Polanyi’s paradox “We know more than
we can tell”). According to Levy and Murnane, these tasks cannot be
computerized and will remain in the domain of human workers.
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This is counterintuitive. We usually think of sophisticated tasks as those
related to human rationality as a unique skills compared to animals. Instead,
“The main lesson of thirty-five years of AI research is that the hard problems
are easy and the easy problems are hard”*. Or, as in the Moravec
paradox that “contrary to traditional assumptions, high-level reasoning
requires very little computation, but (…) it is very difficult or impossible to give
them the skills of a one-year-old when it comes to perception and mobility”.
* quoting Stephen Pinker
Several “easy” tasks will remain for long in human’s realm, namely “As the
new generation of intelligent devices appears, it will be the stock analysts and
petrochemical engineers and parole board members who are in danger of
being replaced by machines. The gardeners, receptionists, and cooks are
secure in their jobs for decades to come.”* quoting Stephen Pinker
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Actually not even the “easy” are easy..
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Another common belief is that as computers take care of routine tasks,
people will be freed to make the more sophisticated decisions, to exercise
their judgement and make decisions.
This is clearly a core aspect of organizations, namely what is the role of
machines and minds in taking decisions? From very simple to more complex
ones?
However, decades of research in cognition have revealed several
limitations of humans’ decision making process. Most prominently,
1. We are easily overloaded by information, and in these situation with filter
out useful and important information
2. We need meaning so much that if information if lacking or inconsistent, we
see patterns and meanings fed by our own assumptions and beliefs, or we fill
the gap base on our memory – which is also itself selective and limited
3. When pushed to act fact we tend to we jump to often flawed conclusions
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Therefore, human judgment can often be replaced by machines with
better results.
For example, decisions on whether someone should receive a loan or not
may be affected by prejudices, as well incomplete or inconsistent use of the
information available to the decision makers. As a matter of fact, this is one of
the first case of automated decision making in the era of corporate computing,
through the development of score of creditworthiness.
Progress in digital data and computing make more and more decisions
managed by computers alone.
For example, companies like Google run some of the world’s largest data
centers, which are extremely energy consuming. Humans managed the
equipment that keep data centers at the right temperature. Then, Google took
years of historical data on data centers’ computing load, sensor readings, and
environmental factors like temperature and humidity and used this information
to train their algorithms to achieve better energy efficiency – which led to 40%
less energy consumption.
Automated decisions nowadays include giving recommendations for each
client online, and all sorts of transactions that happens with no human
involvement.
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In some cases, instead of having machines provide data as an input to
human judgment, human judgment serve as an input to an algorithm.
For example, job interviews often confirm what someone already think of a
candidate rather than truly assessing the candidate. Google, now rely heavily
on structured interviews, a set of predefined questions where the interviewer
indicates how did the candidate perform, but they’re quantified and used to
assign a numeric score to job applicants.
In other cases, humans have an important role of making the judgement
effective.
For example, in the field of medical diagnosis, computers outperform humans’
assessment in most specialties—radiology, pathology, oncology, and so on—
thanks to the new availability of digitalized data (e.g. images, screening, etc.).
In such an areas the computer make better assessments. Most patients,
however, want to get their diagnosis not from a computed but from a person
that helps them understand and accept it.
In some cases Humans complement computers.
For example, humans still hold an important role in decisions thanks to their
openness to a diversity of information that we don’t preselect. While this
creates confusion as we have difficulties in selecting the most useful signals,
computers have the opposite problem, namely they only gather and analyze
the data their programmers allowed to see.
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This can create severe failures. For example, UBER automatically adapt
prices based on the demand in a local area. In 2014, following a terrorist
attach in Sydney, the prices in the area of the incident automatically and
suddenly spiked and UBER was heavily criticized for being cynical. Hence,
the decision-making process was changed, enabling human supervision to
prevent similar problems.
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The iPhone was launched on the market in 2007. It was a groundbreaking
product in terms of design and features, like touch screen, accelerometer,
GPS, etc. and many attributes of computers. For example, like computers
have programs, iPhone had applications, called “apps”.
The then leader of Apple, Steve Jobs, was known to hold a tight control on
companies’ products and intended to maintain control over the apps as well–
which could not be introduced from external developers.
After a year, Jobs changed his mind. Nowadays we could not conceive a
smartphone without thousands available apps created by independent
developers. The apps act as a so-called “complements” to a smartphone:
when I buy an iPhone, I also benefit from the related apps, which increase the
value of the product, and hence how many people will buy it.
In turn, iPhone also works as a platform for developing apps, and by turning
to an open platform it greatly increased the value of the product. Of course,
open platforms need to be curated, controls and boundaries are set by the
owner of the platform, but in the current digital economy the potential of open
platform is often much bigger than closed ones.
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Platforms can be defined as online environments that take advantage of the
characteristics of information in a digital environment. Namely, once
information is produced, it is free, perfect, and instant to replicate – hence, a
platform is digital environment characterized by near-zero marginal cost* of
access, reproduction, and distribution.
Digital platforms facilitate economic activity, they represent a new way to
match product and client. They can create a new market or affect existing
markets to a different extent.
* Marginal cost means the cost of an additional unit
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Digital platforms facilitate economic activity, they represent a new way to
match product and client. They can create a new market or affect existing
markets to a different extent. For example:
In the case of the iPhone, the platform not only acts as a complement to the
product, but also connects users to producers of apps
Facebook is a product - platform and connects users and companies through
ads
Blockbuster was a store where to rent movies in VHS/DVD and it has been
gradually replaced by illegal platforms (e.g. Torrent, etc.) and legal ones
(e.g. Netflix)
Amazon disrupted shops and malls
Uber enables car rides, increasing the competition to traditional taxis
Airbnb enables many more suppliers, and mostly affect the lower end of the
market
BlaBlaCar matches people driving from one place to another with passengers
who want to make the same trip. It has small effect on other suppliers
(e.g. train)
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Platforms are particularly good at affecting existing organizations/providers
when there is little difference among their offerings.
For example, getting a ride from place A to B is a largely undifferentiated
experience, as a traveler basically wants to go somewhere quickly, safely, and
cheaply.
Travelers' accommodations are not undifferentiated, there are much
differences and that is the reason why hotels have not been displaced by
Airbnb- their offerings are somehow different one from another.
This is important to consider for activities that do not want to be
displaced by platforms.
question
1.
Can platforms affect the Higher Education sector? How?
2.
How can a university tackle the competition of platforms? Or use
platforms?
For example, some universities that have created their own platforms of
courses for distant education, potentially threatening universities in distant
places. However, Higher education is not an undifferentiated good, so the
impact of online courses have been so far limited. At the same time, if
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universities do not want to be displaced in the future, they must preserve the
distinctiveness of what they offer.
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Platforms that connects users and suppliers strongly benefit from network
effects, namely the more the users and suppliers join the platform the more
the advantages on both sides.
Such network effects are so strong that in a competition between platforms,
the bigger may outcompete the others largely based on such effect, in a selfreinforcing cycle that leads the winner to take the whole market.
As a matter of fact, the most renown platforms have quasi-monopolistic
positions in their respective markets – e.g., Airbnb, Uber, Amazon, Alibaba,
Booking.
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In late 1999 Jimmy Wales and Larry Sanger started to build a free universally
accessible online encyclopedia: Nupedia. In order to contribute writing an
entry, one had to be a true expert in the field and possess a Ph.D. After
eighteen months and $250,000 of spending, Nupedia had only twelve
completed articles.
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In mid-January 2001 a new website was launched. It was based on a different
approach in which any user could make a contribution, edit someone else’s
contribution, or undo any previous edit. By the end of January, it contained
617 articles. By the end of 2001 there were 19,000. By 2016 there were 36
million articles across 291 languages.
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Wikipedia, unlike Nupedia, activated contribution not from a closed group of
experts (i.e. the Core) but potentially from any external person, i.e. the
crowd. It did so by adopting the principles of openness and noncredentialism,
meaning that everyone was invited to contribute and did not have to possess
specific credentials, and of self-organization, namely everyone decided what
to contribute to.
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Modern organizations face complex environments and new problems. In order
solve a specific problem, every organization has a set of competences,
knowledge, expertise among their members: i.e., the Core.
When missing specific expertise and knowledge, organizations can also rely
on external consultants or opinion polls, which still represent a kind of semifixed set of expertise.
Digitalization enables access to a much wider pool of knowledge, external to
the organizational boundaries and often for free, the so-called ‘Crowd’.
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The crowd has an immense problem-solving potential.
For example, innovation scholars Karim Lakhani, Kevin Boudreau organized
more than 700 challenges involving non-experts to solve problems of
organization such as NASA, medical school, companies, and the solutions
such groups found was always similar in quality or better that the internal
solutions.
WHY IS THE CROWD MORE EFFECTIVE THAN THE CORE IN
ADDRESSING MANY PROBLEMS?
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There are two main reasons explaining why the crowd so-often outperform
the Core in finding a solution to a problem.
1. Expertise in the Core is necessarily limited and suffers from obsolescence
2. In solving problems, it is important to have different perspectives, multiple
dissimilar backgrounds, educations, approaches, etc.
First, an organization can face so many different and changing problems that
the core will never have the expertise to address all of them. Moreover, its
expertise tend to become gradually out of date. Instead, the crowd is so big
and constantly renewing that won’t have such limit.
Second, in order to solve problems, it is important to have different
perspectives, people with multiple dissimilar backgrounds, educations,
approaches, etc. This is a trait of the crowd, whereas organizations can only
afford to hire experts whose skills are close to the problems that they typically
met.
For example, innovation scholars Lakhani and Jeppesen studied 166
scientific challenges posted on InnoCentive and found that the ones most
likely to be successfully solved were those that attracted people who were
technically or socially “far away” from the organization that posted the
challenge.
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The key organizational consequence of this process is to rethink the
role of Core and the Crowd, to reflect upon the opportunities of Crowd
and on ways to access the knowledge of the Crowd
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Until the twentieth century, factories were powered by a single big steam
engine that was typically located in the basements of factories. At the time,
electricity appeared as a viable alternative. Once they were made electric,
power sources could spread throughout a building and there could be several
power sources instead of one huge one.
Today it is unconceivable to do differently than this, but the concept of several
powers sources was first met with skepticism.
Most successful incumbent companies did not survive the transition from
steam to electric power which occurred one century ago. Electrification was
one of the most disruptive technologies ever, it caused something close to a
mass extinction in US manufacturing industries.
As a matter of fact, many organizations fail to adapt technological change.
Why does this happen?
There are three main challenges to adapt to a disruptive change, namely to 1.
recognize that a relevant change is occurring, 2. decide that it is necessary to
act, and 3. in implementing technological transformation.
Let’ analyze these challenges more in detail.
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The first step to change an organization in response to technological change
is arguably being able to recognize that a relevant change is happening.
Recognizing that a relevant change is happening is more difficult that it might appear
at first glance.
Disruptive innovations typically starts in marginal or new markets segments
and grow from the ‘periphery’ to the core of the market and change the way
customers perceive the product.
Because they happen initially in a marginal market segment, these
innovations only produce ‘weak signals’ that an incumbent organization (i.e.,
an organization which is among the market leaders) may be unable to detect
or to believe in its importance.
Part of the problem is that organizations tend to focus on their current
customers’ needs. However, focusing too heavily on current customers’ needs
can blind the company to a change which is happening in less important
market segments.
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Disruptive innovations make something so much simpler and more affordable,
so that a new population of customers get pulled into the market. At the
beginning, however, the performance of the disruptive technology is not as
good as the performance of the established products that are sold by the
incumbents.
For example, in mid 80s offices typically had photocopy centers for printing.
Xerox was the market leader producing high-speed, fully featured machines in
such photocopy centers. Then, Canon introduced in the market smaller table
copiers which could only do three or four copies a minute, with no possibility
to collate, enlarge or reduce or do grayscale. Xerox customers were fully
satisfied so the company did not get a clear signal that the Canon product
could become important.
However, Canon had its niche because for the simple things, the little Canon
around the corner was much better. This granted Canon the time and
resources to gradually improve until an entirely new market was created.
Xerox missed this opportunity, paradoxically, because they listened to their
customers.
How could Xerox have picked up the weak signal and understand that the
small copier was going to be important?
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How could Xerox have picked up the weak signal and understand that the
small copier was going to be important?
Clayton Christensen – a major expert on the topic of disruptive technological
innovations – argues that the attention should rather be on the job and in
watching customers working. In this case, they would have seen that in many
cases the workers did not have the time to walk to the corporate copy center
and preferred to do the little additional work with the (initially) slow and lowquality printer.
The questions driving organizations’ attention should be whether there is
someone who can’t do something because the solution is just too expensive
and too inconvenient.
Outstanding companies can do everything "right" and yet still lose their
market leadership – or even fail – as new, unexpected competitors rise and
take over the market. There are two key parts to this dilemma.
Value to innovation is an S-curve: Improving a product takes time and many
iterations. The first of these iterations provide minimal value to the customer
but once the base is created then each iteration is drastically better. At some
point the most valuable improvements are complete and the value per
iteration is minimal again. So, in the middle is the most value, at the beginning
and end the value is minimal. By the time the new product becomes
interesting to the incumbent's customers it is too late.
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Even when recognizing the importance of an ongoing technological change,
organizations may not be willing to adapt.
Because incumbents are so proficient in what they do, they are victim of a
status quo bias, or knowledge curse, as they are reluctant to abandon their
business model or technology for what is initially only a marginally superior
and less profitable option.
Moreover…
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Consider this excerpt from an interview to Clayton Christensen:
“You could imagine that, in a doctor’s office somewhere, a nurse observed the
gross dissatisfaction among the customers and saw this great opportunity. (..)
But the doctor’s office is structured to make money (..) What they need is a
way to be reimbursed $175 per visit (..) and the nurse comes at them with this
$40 per visit idea. It just doesn’t make sense in the business the way it’s
structured. That’s why disruptions are very hard to deploy within an
established business.”
What kind of trap is the incumbent organization in?
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Intel recognized the change, but it did not feel necessary to adapt because it
was focused on profitability and their most important clients.
However, they later recognized that such a niche could become a serious
threat in a near future.
What was happening at Intel? Why they decide to act?
Intel recognized the change, but it did not feel necessary to adapt because it
was focused on profitability and their most important clients. However, they
later recognized that such a niche could become a serious threat in a near
future.
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Some general lessons can be drawn from the case regarding organizational change and
more specifically in response to technological innovations.
Resistance due to different implicit interpretations of the organization’s priorities
 reasons for change need to be fully understood throughout the top and middle
management
in the short term, the dominant logic of profitability often clashes with the need to invest in a
different course of action
 a strong leadership need to invest time and effort in spreading a vision, a common
understanding and language – beyond a simple slogan
Resistance or lack of commitment is not necessarily driven by self-interest, but rather by a
different interpretation of the events and of the organization’s priorities
Hence, implementing change requires in the first place that the reasons underpinning the
organizational need to change are fully understood throughout the middle management
Responding to technological change is particularly tricky for incumbent organizations because
the dominant logic of profitability clashes in the short term with the need to invest in a
different course of action
It is therefore essential a strong leadership, which invests time and effort in spreading a
vision, a common the understanding and language – which go beyond a simple slogan
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As for Intel, the case of Kodak highlights the importance of a shared vision on the appropriate
course of change. The new leadership had a traditional focus on profitability, they did not
understand the necessity for change, they reversed the course of action and Kodak failed.
Moreover, it provides further insights.
First, a major challenge is that the organization must excel in two different technologies at the
same time, which is very expensive. Think, as a further example, to car companies shifting to
electric; for a long period of time, they will have to go on investing in people and
infrastructures dedicated to traditional internal combustion engines, and at the same time
open a completely new development path towards electric vehicles.
Second, the new and old products or the traditional way of doing things are often underpinned
by different logics, which hardly co-exist. In the case of Kodak, film was profitable and in the
high end of the market, while digital was low profit and in the low end of the market. From an
organizational perspective, the only solution was to create distinct divisions to enable different
visions to succeed.
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