Artificial Intelligence

Will artificial intelligence force us
to rethink business in the decades
to come, or will it fall short of all
the hype? Here, a look at what
executives need to know.

10 | IESE Business School Insight | Fall 2018
AI is the new IT.
It means lots of
different things to
different people
A
s artificial intelligence (AI) ex-
pands the boundaries of what
computers are capable of, it also
puts new demands on execu-
tives to understand these evolv-
ing technologies, to find practical applications
for them in their companies, and to resolve the
unique ethical dilemmas arising from machines
with ever more human qualities.
For many, it’s a whole new world. While artificial
intelligence may come to define business trans-
formation in the 21st century, most people are un-
able to describe what AI actually is, or to gauge its
potential impact on their companies and society.
“AI is the new IT. It means lots of different things
to different people,” says Dario Gil, vice pres-
ident for AI and quantum computing at IBM. AI
in its most straightforward definition means the
demonstration of intelligence by machines. The
AI field includes machine learning, neural net-
works and deep learning. But AI has also become
an umbrella term for many other related subjects,
including big data.
“AI now is what Marvin Minsky, co-founder of the
Massachusetts Institute of Technology’s AI labora-
tory, used to call a ‘suitcase word,’ in the sense that
it contains many different meanings. We need to
move from this to the specifics,” says IESE profes-
sor Javier Zamora.
Strategy is not immune to the wave of
change brought on by AI.
In the short term, companies might find
competitive advantage in being early
adopters of AI. In the medium and long
term, however, today’s cutting-edge
technologies will become ubiquitous.
The advantages of having access to
data and algorithms will diminish.
So if the heart of competitive advan-
tage is strategic differentiation, how
then will companies achieve it? The
answer may lie in taking a step back
from the technology and considering
another resource: people.
Algorithms excel at being hyper-
rational: extrapolating and drawing
logical conclusions to maximize
efficiency. People who are rational
thinkers are easy to manage and fit
well into organizations. But hyper-
rationality carries within it a profound
problem when it comes to defining
strategy. Firms that rely on the
same or similar reasonable big-data
analyses and algorithms as everyone
else will struggle to differentiate
themselves strategically.
Human beings, with highly developed
soft skills, can consider the emotional
context and connections of strategic
Competitive
advantage in
an AI world
REPORT Artificial Intelligence, real leadership

Fall 2018 | IESE Business School Insight | 11
AI and its impact on management have been
the focus of a number of forums at IESE in
recent months, gathering industry executives
and academics to consider what it all means for
the future.
Six decades of stops and starts
While companies are investing more in AI than
ever before, it is not a new concept. Artificial in-
telligence was founded as an academic discipline
in 1956, and business felt the first tremors of this
seismic shift in technology in the 1950s, when
early computers gave retailers the power to col-
late sales data on an unprecedented scale.
But AI has historically struggled to find practical ap-
plications that live up to its promise. It did not work
well enough to be useful for most companies until
the last 10 years or so, when lower-cost computa-
tional power and more sophisticated algorithms
intersected with the vast data sets that could be har-
vested from the internet and other sources.
decisions. They can be contrary and
ask difficult, unexpected questions
that result in actionable insights.
They have imagination and can make
intuitive leaps that AI is, at best, many
decades away from replicating.
One of the key roles for human man-
agers in helping companies differen-
tiate themselves will be to go to the
core of exploring what a company
is – to consider its purpose and
identity, to set its goals for the future.
The human capacity for moral and
spiritual discretion will be paramount
to the sustainability of companies.
Good managers also have the ability
to make tradeoffs. They reconcile
different points of view within a
company and manage complex and
unaligned stakeholder demands. They
balance the need to make short-term
profits with the need to take risks
for longer-term growth, despite the
uncertain payoffs.
AI brings us to an inflection point
in strategic decision-making for
competitive advantage. Managing it
successfully will depend on keeping a
clear view of differentiation. We must
harness the power of technological
change without allowing ourselves
to get swept away by its trends,
precisely because everyone else is.
Strategy differentiation will depend on
how firms combine technology with
their internal organizational capabili-
ties and their ability to be more human
– and sometimes unreasonable,
emotional, insightful and creative –
and not more machine-like.
Bruno Cassiman
Professor of Strategic Management and holder
of the Nissan Chair of Corporate Strategy and
International Competitiveness at IESE
Virtual assistants like Siri,
Google Assistant and Alexa are some of
the user-friendly forms of AI

Managing
talent with data
and humanity
The most relevant
competency for managers is
to know what’s possible
Marta Elvira
Professor of Managing People in Organizations
and Strategic Management, and holder of the
Puig Chair of Global Leadership Development
Companies are increasingly using artifi-
cial intelligence to assist decision-mak-
ing on HR strategies. AI tools open up
new possibilities in understanding the
strengths of a company’s workforce as
well as tailoring its talent development
practices to strategic goals.
This is not a peripheral issue. Better
performing companies are those
that foster the adaptation abilities
of their workforce, developing and
compensating professionals who
contribute to innovation. Firms’ long-
term sustainability greatly depends
on their competence in managing
their high potentials and encouraging
creativity through diversity. This
means making sure that human
capital strategies are prioritized
on top management teams’ and
boards’ agendas.
AI can help us move past broad initia-
tives, so we can focus on individuals.
The ability to analyze data and iden-
tify patterns will allow us to pinpoint
with greater accuracy what each
person is likely to be good at, based
on current strengths.
AI can also help managers make
informed decisions on training. As data
sets grow, algorithms will be able to
detect what’s working across organi-
zations and suggest how to implement
best practices in ways tailored to
individual needs. For employees, this
should result in greater job satisfac-
tion, learning and productivity.
At the level of corporate governance, AI
can help boards understand and offer
better guidance on talent issues. For
this to happen, board members must
get informed about data and AI. And
HR managers must get their data house
in order. Most HR departments don’t
gather nearly enough suitable infor-
mation, and they frequently compound
the problem with suboptimal database
management. When companies do
have databases that are large and well
organized enough to deploy AI tools,
they must then consider the objectives
of those decisions. In recruitment, for
example, if the focus is exclusively on
reducing costs instead of identifying
better hires, the long-term outcome
could be worse for everyone.
It is also imperative that companies
guard against the lure of what Wharton’s
Peter Cappelli calls “cool tools.” Today’s
recruitment and people-management
software tools tend to be strong on engi-
neering and weak on psychology. They
have been developed by people who,
in the main, lack significant real-world
management experience. The claims
developers make for them may not lead
to better real-world results.
Fairness is another concern. Algorithms
can be systematically biased, reflecting
hiring managers’ biases regarding any-
thing from gender to race to education.
Biases risk being replicated at scale
across a whole company.
Human oversight of AI decisions will
increase in importance as the power
and ubiquity of the technology grows,
requiring a people-first approach from
managers with emotional intelligence
and integrity.

“What made possible the recent successes of ar-
tificial intelligence and deep learning are basically
two ingredients that were missing some years ago:
access at relatively low cost to very high perfor-
mance computing and of course the availability
of lots of data,” says Ramón López de Mántaras,
director of the Artificial Intelligence Research In-
stitute at the Spanish National Research Council.
“The concepts have been around for a long time.
But actually AI right now is still in its infancy.”
Nonetheless, some are beginning to view AI as a
so-called general purpose technology, capable of
transforming broad sectors of the economy, on par
with developments such as the steam engine, elec-
tricity and the internet. Beyond the large high-tech
companies that have been early adopters, today
AI is penetrating the automotive, telecommunica-
tions and finance sectors. It is being used in retail
and the media, and is seen as having enormous po-
tential in areas such as healthcare and education.
Julian Birkinshaw, a professor at London Business
School, describes the growing ubiquity of AI by
using the analogy of living in a mountainous world
and watching the waters gradually rising. At pres-
ent, he says, AI is about making existing process-
es more efficient. We believe that we are on safe
ground – that there are tasks only humans can
do, requiring cognitive insights that AI will never
reach. But he cautions against complacency. “The
future,” he says, “will be very different.”
Our own cultural attitudes may determine the
success of this adaptation. Tomo Noda, founder
of Shizenkan University in Japan, points out that
Japanese media frequently depicts AI as friendly
and here to help us, citing Doraemon and Astroboy
as examples. He contrasts that with the dystopian
visions presented by Hollywood in movies such
as AI, 2001: A Space Odyssey and The Terminator.
Noda urges us to reframe our engagement with the
new technology by asking more positive questions:
Can we do what we do better with AI? What new
opportunities can we explore? How can we create a
desirable society together with AI?
From narrow to general and back
Current AI technology is “narrow,” capable of work-
ing at superhuman speed on specific tasks in specif-
ic domains – such as language translation, speech
transcription, object detection and face recognition
– given good enough data. But we are already on the
path to “broad” AI, which will be highly disruptive.
To “teach” the algorithms that AI uses, humans
still often need to label the first data sets them-
selves (for example, by identifying objects in pho-
tos). This is an expensive process and, as a con-
sequence, most organizations don’t have enough
labeled data. That is one of the reasons that many
of the companies which have incorporated AI into
their core businesses have been large, digital na-
tives such as Google and Amazon.
Broad AI will be able to transfer some learning
across networks, or use reason to create its own
labels, thus learning more from less data. Narrow
AI needs a new neural network for each task but
broad AI will multitask across multiple domains.

Beyond broad AI, “general” AI, with full cross-do-
main learning and autonomous reasoning, will be
truly revolutionary. But machines capable of rea-
soning and understanding the way humans do are
decades away, and fears of “singularity” – the mo-
ment when AI surpasses human intelligence and
evolves beyond our control – are premature. Still,
those concerns will one day have to be addressed.
New management competencies
And even narrow AI requires new knowledge and
abilities of management. Studies have shown that
AI is most successfully deployed in companies
where it is prioritized by the highest-level execu-
tives. Executives must have a basic understanding
of data and its potential uses, to have a sense of
which tasks are best handled by AI and which are
best left to humans, and of how to enable their
teams with these new technologies.
That is not to say that executives must be data sci-
entists orcomputerprogrammers. But theydo need
to be able to ask the right questions. “The most rel-
evant competency for managers is to know what’s
possible,” says INSEAD Dean Ilian Mihov. “Algo-
rithms will find patterns in the data but they can’t
interpret them. Managers have to think about what
might be happening. Then ask themselves how they
can create an experiment to test if it’s true.”
For IESE’s Zamora, putting AI into practice be-
gins with a series of basic questions that exec-
utives must ask themselves: Is AI relevant to
the problem that I’m dealing with? Do I have
enough high-quality data to train the system to
produce solutions? Are there tools such as soft-
ware and algorithms available that can be ap-
plied to this problem? Can I trust the results of
the algorithms?
Data readiness must go beyond the management
level. Creating a data-fluent corporate culture is
essential. Nico Rose, vice president of employer
branding and talent acquisition at Bertelsmann,
recounts how his company hired brilliant data
scientists from big tech companies to implement
advanced AI solutions. But they had little success.
Switching tack, Bertelsmann began focusing on
building an organizational ecosystem that supports
data readiness. Hiring is part of that, but so is con-
tinuous education and learning for all employees.
Today’s university students face the prospect
of forging their entire careers amid the changes
AI will bring – and they need to be prepared.
Over 1,000 Stanford students enrolled in an
introduction to machine learning class this past
academic year. Professor Jeffrey Pfeffer explains:
“They don’t want to be computer scientists; they
just know it’s an important tool for everything.”
The humans in the mix
There are also limits. Wharton’s Peter Cappel-
li calls the corporate world’s new obsession with
data “the revenge of the engineers.” He says that
management is in part to blame for this in areas
such as human resources because it has devel-
oped so many theories that have failed. But rather
than turn people divisions over to data scientists,
executives should reconsider how they manage,
evaluate and motivate people.
For the World Economic Forum’s Anne Marie En-
gtoft Larsen, those skills are not all about technol-
ogy. “I think there is a huge need for us to go back
to some of these more social, human sciences,
such as complex problem solving, critical think-
ing, creativity, people management, coordination
with others, and emotional intelligence.”
And executives should reflect on what they do
best and focus on how their strengths differ from
those of AI. There are some areas of strategic de-
cision-making where humans will always have an
advantage. These includes injecting creativity, orig-
inality and emotion into issues, and making con-
cessions. The most insightful strategies, LBS’s Bir-
kinshaw asserts, are often taken from metaphors
from very different spheres. “That’s a very human
quality,” he says, “to see a pattern in something

“Without defining values
we cannot deal effectively
with problem solving”
Tomo Noda / Shizenkan University
“We need to have a
discussion around
what values are
going to guide how
we develop these
technologies”
THOUGHTS
FROM
AROUND
THEGLOBE
Anne Marie Engtoft Larsen
World Economic Forum
“If you look at the
evidence of what
creates long-term
value for the firm,
it is customer
asset building
and brand asset
building, which
require that
holistic view.
And so far, I have
never seen a
computer build
a brand”
Dominique Hanssens
University of California,
Los Angeles

The next major phase in the evolution
of artificial intelligence has already
begun. Managers need to be aware
of what’s possible, not only now but
also around the corner, and the impli-
cations it will have for how we work.
Today’s AI falls mainly into the cate-
gory of so-called “narrow” AI, with
enough computing power to deliver
superhuman accuracy and speed
when performing single tasks in single
domains. Hence, despite its raw pow-
er, its applicability remains relatively
limited. However, “broad” AI has the
potential to be much more disruptive,
once it is able to operate in a multi-
task, multi-domain, multi-modal
manner. In the near future, broad AI
will impact risk modeling, prediction
making and even strategic planning.
In this world where AI can make
faster, better-informed decisions
than humans, what will the role of
managers be? The answer to that
can be found in some of AI’s inherent
limitations, which even broad AI is
unlikely to overcome.
For all their power, algorithms don’t
solve everything and will only ever
be as good as the data fed into them.
Self-reinforcing biases are a real
risk, as was demonstrated by an
MIT research project in 2018, which
discovered that leading AI-powered
Thinking outside
the black box
There is a huge need
to go back to some
of the more social,
human sciences
unrelated and link it back to theworld inwhichwe’re
working. I can’t see how computers can do that.”
He believes it is worthwhile to go back to basics
and ask: What is a strategy? Citing American pro-
fessor and author Michael Porter, he defines strat-
egy in two dimensions: as choices that lead to
actions, and – in the case of successful strategies
– as being differentiated. A good strategy, in other
words, is different to that of one’s competitors.
If companies rely on similar data sources and
algorithms for AI-supported strategic deci-
sion-making, differentiation could be lost. Purely
quantitative decisions can be too sterile; emo-
tional conviction still adds value.
“Planning, budgeting and organizing can be done
by AI,” says Shizenkan’s Noda. “But establishing
vision, aligning people and motivating them re-
quires people.”
Executives also must reflect on why firms exist,
in order to clarify their own roles within them
in a changing AI world. A key reason is to craft
a sense of identity or purpose. Successful com-
panies understand this and know who they are.
A second reason firms exist is because they are
good at managing complex tradeoffs, even over
time and in the face of shareholder pressure. AI
is not good at this and is unlikely to become so.

face-recognition systems were “racist”
– incorrectly determining the gender
of dark-skinned females because the
algorithms had been trained primarily
with images of white males. Another
system used by U.S. courts was found
to be prone to misjudge the likely re-
cidivism of black defendants because
of biased data. Human managers will
need to be trained to be on the alert for
such potential biases in order to pro-
vide a safeguard against blind accep-
tance of AI-generated decisions.
This “decision curation” is especially im-
portant in the face of another AI charac-
teristic – “black-box” decision-making.
This is the idea that we can understand
what goes into AI systems (data) and
what comes out (useful information), but
not what goes on inside. As AI becomes
more sophisticated, the math it uses is
becoming so complex that it is some-
times beyond human comprehension
– even for the people who wrote the
algorithms. As a result, we can’t “show
the working” and explain how AI reaches
its conclusions.
This has implications for transparency
and accountability when the system’s
output is used to make important –
perhaps even life or death – decisions.
Managers, therefore, must under-
stand the context in which black-box
AI decisions are to be implemented,
and consider their consequences. To
do this requires a (thus far) uniquely
human quality: imagination. Compa-
nies often pay lip service to the idea of
thinking outside the box; in the future,
this will be a literal necessity. The abil-
ity to ask “what if?” – to look before
leaping – is paramount to effectively
and ethically harness the power of AI.
AI
over
time Nor is it good at building processes for reconcil-
ing diverse points of view.
Bringing ethics to the table
AI raises the spectre of machines that we create
but that mayone daysurpass us, of computers that
we program but that may take decisions we do not
fully understand, of robots that assist us but that
may make mistakes. All of this generates a host of
new ethical quandaries, particularly around issues
of fairness, accountability and transparency.
The fairness debate, in part, centers around the
danger of perpetuating biases. While data sets
are ostensibly objective, they actually reflect in-
grained biases and prejudices. If data is then fed
into algorithms, how can we ensure that those bi-
ases are not embedded into the future?
Accountability has to do with deciding who is ulti-
mately responsible for systems, particularly when
something goes wrong. As machines become
more autonomous, there are increasing questions
about where responsibility lies among those who
design and deploy the systems, and the systems
themselves. Who is responsible if a cognitive ma-
chine takes the wrong decision?
Transparency and explainability become issues as
AI-assisted decisions become less easy to under-
stand. How can decisions be interpreted and under-
stood if we don’t understand how they are arrived
at? As AI algorithms become so complex that even
their authors don’t fully understand them, how can
we explain and justify the decisions they make?
These are unique dilemmas, although ethicists
also warn against exceptionalism in the AI debate.
Previous ethical debates, such as those surround-
ing medicine or nuclear energy, have much to
contribute to the conversation.
Sandra Sieber
Professor and head of the Information Systems
Department at IESE
Government funding
and AI programs with
practical applications
spark renewed
interest in AI
mid
1980s
SECOND WAVE
More powerful
computers, better
algorithms and more
data cause AI to
flourish
mid
1990s to
present
THIRD WAVE
Term “artificial
intelligence” used for
first time in 1956, amid
a surge in interest in
this new field
mid
1950s
to early
1970s
FIRST WAVE

For most companies, the ethical questions will re-
late more to AI’s application than its technological
development. Even narrow AI is affecting society
at large, rapidly making some jobs and industries
obsolete while creating new ones, which require
completely new skill sets.
While businesses adapt to the AI-fueled Fourth
Industrial Revolution, millions of people around
the world have failed to benefit from the advanc-
es of the First, the Second, or the Third.
The implications of AI go well beyond the business
sphere, involving policymakers and governments.
Stanford’s Pfeffer believes that governments lack
the skills they need to manage the employment
changes AI will bring. “Advanced, industrialized
countries have done a bad job of managing the
move from manufacturing to services,” he says.
“They will struggle here too, especially with age-
ing and sometimes shrinking populations, budget
deficits and rising social costs for healthcare. How
are they going to find the resources to reskill or
upskill large numbers of people?” Reflecting on
the possible political and social implications, he
adds: “We have to do better this time.”
Business leaders in the AI era will need to consider
how their use of technology affects society. They
will need to think of the human consequences
and ethical dimensions of their AI-derived de-
cision-making. And ultimately they will have to
manage businesses by relying on what sets them
apart from machines.

Artificial intelligence promises to be a
powerful driver of change in ways we
cannot yet forecast. Even so, it’s worth
considering how management should
prepare their companies for some of
the changes that we can already see.
Data will be the most important com-
modity of the future. As such, a top-to-
bottom company culture of readiness
– in terms of data governance, archi-
tecture, traceability, accessibility and
visualization – will be a prerequisite to
survive and thrive in an AI world.
In my research on top management
teams, I have found that the ones
that operate at peak performance
are those that exhibit high cognitive
flexibility – meaning they are open
to new ideas, take a lot of different
perspectives into account, and are
able to change their opinions in light
of incoming information. Cognitive
flexibility increases the creativity with
which information is interpreted and
alternatives are generated.
Top management teams may not
need coding skills or deep technical
knowledge of AI, but they do need a
robust understanding of its possibili-
ties in order to ask the right questions
and consider all the angles, without
prejudice or fear. Shaping an organi-
zational culture that encourages this
open, agile, multidisciplinary culture
is crucial.
Usually, any cultural change should
start by educating those at the top.
Again, my research has shown that
what goes on in the C-suite has an
influential effect on the rest of the
organization. So, the more the top
management team is able to model
the behavior it seeks, the more mid-
dle managers and other employees
will start to align their own behavior
and get excited about the possibilities
of AI. In an attempt to be the change
they seek, many leading companies
are actively recruiting MBAs who are
further along the AI learning curve
than senior management, so their
knowledge and enthusiasm can per-
meate the entire organization.
AI will increasingly automate business
processes, which implies fewer people
will be involved in operations. At the
same time, AI will increasingly free
talent from the constraints of location.
Management structures will become
much more fluid. Managers from any-
where in the world can use AI to make
better-informed decisions.
Advances in automatic translation
and virtual assistants will allow the
brightest talent to manage more easily
and effectively in international and
multicultural settings, even if working
remotely. AI will help geographically
dispersed managers get the best from
individual team members, wherev-
er they are. Multidisciplinary teams
in diverse locations will be linked by
AI-derived best practices, tailored to
their own needs. The more participa-
tive, integrative and cooperative the
leadership is, the more motivational it
is for employees. For all AI’s disrup-
tive effects, it’s worth sticking to the
foundations upon which successful
organizational cultures are built.
Data readiness
starts at the top
Anneloes Raes
Professor of Managing People in
Organizations at IESE

THEKEY
WORDS
04MACHINE LEARNING
When computers are able
to detect patterns and make
predictions and recommendations
(prescriptive) based on
systems that learn from
experience (data)
Artificial intelligence in
recent years has found
some of the practical
business applications
that had eluded it for
decades.
But while the successes are many
and growing, most experts agree that
AI is decades away from reaching
human-level intelligence – if, indeed,
that will ever be reached. Here, a
primer of key AI concepts from Javier
Zamora, senior lecturer in IESE’s
Department of Information Systems.
03NARROW ARTIFICIAL
INTELLIGENCE
Artificial intelligence that
is applied to a single,
well-defined task such as
language translation or
facial recognition
02SINGULARITY
The possible moment
when machines, powered
by artificial intelligence,
will be able to surpass all
human intelligence
01GENERAL ARTIFICIAL
INTELLIGENCE
The theoretical potential of
artificial intelligence to
simulate human-level cognition,
taking complex decisions in
varying contexts

12NATURAL LANGUAGE
PROCESSING
The part of AI dedicated
to making computers
understand and process
human language
09TRANSFER
LEARNING
A focus on storing knowledge
gained while solving one
problem, and applying it to
a different but related
problem
08REINFORCED
LEARNING
When the machine is told
only the goal of a given task,
and through trial and error
tries to maximize
that goal
07BLACK BOX
The ability of computers
to reach conclusions in
ways that humans cannot
necessarily explain or
understand
06DEEP (and
SHALLOW) LEARNING
Deep learning involves neural
networks with many layers,
while shallow learning
involves few layers
05NEURAL NETWORKS
A set of nodes connected
in layers, in which each
layer provides input to the
next through forward
propagation and back
propagation
10SUPERVISED
(and UNSUPERVISED)
LEARNING
The two main types of machine
learning algorithms. With supervised
learning, data scientists must label
data and guide outputs.
With unsupervised learning,
the computer identifies
patterns on its own
11ROBOTICS
A branch of technology
dealing with devices –
often robots – that can
sense and react to their
surroundings

It is
imperative
for leaders
to separate
AI reality
from hype
Darío Gil
Chief operating officer of IBM Research and vice
president of AI and quantum computing at the
technology giant. PhD in Electrical Engineering
and Computer Science from MIT.

A
rtificial intelligence (AI) is evolving faster by
the day and its application impacts almost
every industry. But it also affects the way
corporations work. IBM’s Darío Gil knows
firsthand how this technology is creating
new challenges for leadership and management. Here, Gil
talks about the present and future of AI as well as his vision of
quantum computing.
What do you mean when you say that AI is the new IT?
As happened with IT, many companies are viewing the cre-
ation and adoption of AI as an essential part of their suc-
cess. There are parallels in today’s efforts with the previ-
ous IT experiences: corporations are creating internal AI
departments and aggressively recruiting AI experts, trying
to determine how this technology might be used. Further-
more, the hype around AI is increasing every day. The term
has become a buzzword which is replacing other tech terms
such as IT, automation and data analysis; many times, in
ways that aren’t really about AI.
There are usually two visions of AI: a utopia of machines
improving our world and a dystopia where humans are
superfluous. Which is the real one?
I would say that neither of these is the reality of AI. We have
seen its rise being accompanied by both these perspectives:
boundless enthusiasm for its ability to transform our lives,
and fear that it could potentially harm and displace us. At
IBM, we are guided by the use of artificial intelligence to aug-
ment human work and decision-making. Our aim is to build
practical AI applications that assist people with well-defined
tasks. There’s no question that the arrival of artificial intelli-
gence will impact certain jobs, but we believe people working
collaboratively with AI systems is the future of expertise.
Then how will AI change employment?
History suggests that, even in the face of technological trans-
formation, employment continues to grow. It is the tasks
that are automated and reorganized where the transforma-
tion occurs, and workers will need new skills for these trans-
formed tasks. Some new jobs will emerge: “trainers” will be
required to teach AI systems how to perform, and another
category of “explainers” will be needed to help convey how
these intelligent devices have arrived at a decision. In some
areas, a shortage of qualified professionals may exist and the
emergence of AI systems further highlights the need for in-
dividuals such as cybersecurity professionals.
Do we need to understand algorithms to be able to work
with them?
I don’t believe that it is mandatory for every AI system to
be understandable at a very precise level, but I expect us to
reach a foundation of trust through our testing methodol-
ogies and experiences. In some cases, users of AI systems,
such as doctors and clinicians, will need to justify recom-
mendations produced by one of these systems.
How can business leaders adapt to the new AI landscape?
It is imperative for CEOs, senior leaders and managers to
become knowledgeable about AI, to separate reality from
hype and to start developing a strategy on how they might
use AI systems within their business. The nature of lead-
ership will likely change in many aspects of the “hard” el-
ements, such as data-driven decision-making, and leaders
will need to embrace “softer” aspects of leadership, such as
credit: IBM

helping their employees work together. They will also have
to identify ways to enable the partnership between their
workers and the AI systems.
What organizational challenges will companies face?
One major challenge is determining whether their compa-
ny will drive the AI strategy and capabilities internally, or
whether they will work with external organizations to get
support. Many considerations impact this decision, includ-
ing the ability of the company to attract, develop and retain
key AI talent. I also believe that it is critical for companies to
have a senior leader focused on AI strategy and execution for
their business. It could be achieved through the creation of a
new role such as the chief AI officer, but it could also become
part of the responsibilities of existing leaders, like the chief
technology officer or the chief data officer.
How do you envision the impact of quantum technologies?
IBM is focused on advancing quantum information science
and looking for practical applications for quantum comput-
ing. In September 2017, our team pioneered a new approach
to simulate chemistry on our quantum computers. Now, we
expect the first promising applications: the discovery of new
algorithms that can run on quantum computers and untangle
the complexity of molecular and chemical interactions, tackle
complex optimization problems and address artificial intelli-
gence challenges. These advances could open the door to new
scientific achievements in areas such as material design, drug
discovery, portfolio optimization, scenario analysis, logistics
and machine learning.
What other steps have to be taken in the coming years
regarding AI and quantum technologies?
Business leaders and managers must take the first steps in
being informed about AI, quantum computing and the po-
tential impacts and benefits to their companies. We have to
move from a narrow to a broader form of artificial intelligence
that enables the application of AI systems from one task to
another. Leaders must begin to define a strategy for the devel-
opment and use of it for their specific business, and then their
style of leadership will evolve to embrace the value that this
brings in decision-making. For quantum computing, we en-
courage organizational leaders to become “quantum ready”:
to get educated on quantum computing, identify applicable
use cases and explore the development of algorithms that
may lead to demonstrations of quantum advantage.
“Business leaders
and managers
must take the first
steps in being
informed about
AI and quantum
computing”
Creating the quantum computers of tomorrow.
An IBM cryostat wired for a 50 qubit system.
credit: IBM
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