FINDINGS FROM THE 2017 ARTIFICIAL INTELLIGENCE
GLOBAL EXECUTIVE STUDY AND RESEARCH PROJECT
#MITSMRREPORT
REPRINT NUMBER 59181
13%
Neither
83%
STRATEGIC OPPORTUNITY
4%
Risk
only
50%
Opportunity
only
33%
Both opportunity
and risk
37%
STRATEGIC
RISKAI as strategic opportunity and risk
Do you perceive AI as a strategic opportunity or risk to your organization
Reshaping
Business With
Artificial
Intelligence
Closing the Gap Between
Ambition and Action
FALL 2017
RESEARCH
REPORT
By Sam Ransbotham, David Kiron, Philipp Gerbert,
and Martin Reeves
In collaboration with
RESEARCH REPORT RESHAPING BUSINESS WITH ARTIFICIAL INTELLIGENCE
CopyrightMIT, 2017. All rights reserved.
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AUTHORS
CONTRIBUTORS
SAM RANSBOTHAM is an associate professor in the
information systems department at the Carroll
School of Business at Boston College, as well as guest
editor for
MIT Sloan Management Review
’s Artificial
Intelligence Big Ideas Initiative. He can be reached
on Twitter @ransbotham.
DAVID KIRON is the executive editor of
MIT Sloan
Management Review
, which brings ideas from the
world of thinkers to the executives and managers
who use them.
PHILIPP GERBERT is a senior partner and managing
director at The Boston Consulting Group’s Munich,
Germany, office. He is BCG’s global topic leader for
digital strategy and a BCG Henderson Institute Fellow
for the Impact of Artificial Intelligence on Business.
He can be reached at gerbert.philipp@bcg.
MARTIN REEVES is a senior partner and managing
director at The Boston Consulting Group and the di-
rector of the BCG Henderson Institute, which brings
ideas and inspiration to forward-looking leaders.
Sebastian Steinhuser, principal and member of the AI core team, BCG
Patrick Ruwolt, consultant and member of the AI core team, BCG
Allison Ryder, senior project editor,
MIT Sloan Management Review
To cite this report, please use:
S. Ransbotham, D. Kiron, P. Gerbert, and M. Reeves, “Reshaping Business With Artificial Intelligence,”
MIT Sloan Management Review
and The Boston Consulting Group, September 2017.
The research and analysis for this report was conducted under the direction of
the authors as part of an
MIT Sloan Management Review
research initiative in
collaboration with and sponsored by The Boston Consulting Group.
RESHAPING BUSINESS WITH ARTIFICIAL INTELLIGENCEMIT SLOAN MANAGEMENT REVIEW i
CONTENTS
RESEARCH
REPORT
FALL 2017
1 / Executive Summary
2/ About the Research
2 / AI at Work
3 / High Expectations
Amid Diverse
Applications
5 / Disparity in Adoption
and Understanding
7 / The Need for Data,
Training, and Algorithms
11 / Beyond Technology:
Management
Challenges
13 / What to Do Next
14 / The Way Forward:
Implications for
the Future
16 / Appendix: Work in the
Longer Term
17 / Acknowledgments
RESHAPING BUSINESS WITH ARTIFICIAL INTELLIGENCEMIT SLOAN MANAGEMENT REVIEW 1
Reshaping
Business With
Artificial
Intelligence
Executive Summary
E
xpectations for artificial intelligence (AI) are sky-high, but what are businesses actu-
ally doing now The goal of this report is to present a realistic baseline that allows
companies to compare their AI ambitions and efforts. Building on data rather than
conjecture, the research is based on a global survey of more than 3,000 executives,
managers, and analysts across industries and in-depth interviews with more than 30
technology experts and executives. (See “About the Research,” page 2.)
The gap between ambition and execution is large at most companies. Three-quarters of executives
believe AI will enable their companies to move into new businesses. Almost 85% believe AI will
allow their companies to obtain or sustain a competitive advantage. But only about one in five com-
panies has incorporated AI in
some
offerings or processes. Only one in 20 companies has
extensively
incorporated AI in offerings or processes. Less than 39% of all companies have an AI strategy in
place. The largest companies — those with at least 100,000 employees — are the most likely to have
an AI strategy, but only half have one.
Our research reveals large gaps between today’s leaders — companies that already understand and
have adopted AI — and laggards. One sizeable difference is their approach to data. AI algorithms are
not natively “intelligent.” They learn inductively by analyzing data. While most leaders are invest-
ing in AI talent and have built robust information infrastructures, other companies lack analytics
expertise and easy access to their data. Our research surfaced several misunderstandings about the
resources needed to train AI. The leaders not only have a much deeper appreciation about what’s
required to produce AI than laggards, they are also more likely to have senior leadership support and
have developed a business case for AI initiatives.
2 MIT SLOAN MANAGEMENT REVIEWTHE BOSTON CONSULTING GROUP
RESEARCH REPORT RESHAPING BUSINESS WITH ARTIFICIAL INTELLIGENCE
AI has implications for management and organiza-
tional practices. While there are already multiple
models for organizing for AI, organizational flexibil-
ity is a centerpiece of all of them. For large companies,
the culture change required to implement AI will
be daunting, according to several executives with
whom we spoke.
Our survey respondents and interviewees are more
sanguine than conventional wisdom on job loss.
Most managers we surveyed do not expect that AI
will lead to staff reductions at their organization
within the next five years. Rather, they hope that
AI will take over some of their more boring and un-
pleasant current tasks.
As Airbus started to ramp up production of its new
A350 aircraft, the company faced a multibillion-
euro challenge. In the words of Matthew Evans, vice
president of digital transformation at the Toulouse,
France-based company, “Our plan was to increase
the production rate of that aircraft faster than ever
before. To do that, we needed to address issues like
responding quickly to disruptions in the factory. Be-
cause they will happen.”
Airbus turned to artificial intelligence. It combined
data from past production programs, continuing
input from the A350 program, fuzzy matching, and
a self-learning algorithm to identify patterns in pro-
duction problems. In some areas, the system matches
about 70% of the production disruptions to solutions
used previously — in near real time. Evans describes
how AI enables the entire Airbus production line to
learn quickly and meet its business challenge:
What the system does is essentially look at a
problem description, taking in all of the contex-
tual information, and then it matches that with
the description of the issue itself and gives the
person on the floor an immediate recommen-
dation. The problem might be new to them, but
in fact, we’ve seen something very similar in the
production line the weekend before, or on a dif-
ferent shift, or on a different section of the line.
This has allowed us to shorten the amount of
time it takes us to deal with disruptions by more