AI: Built to Scale

Provocativethinking, transformativeinsights,
tangibleoutcomes
AI:BUILT
TOSCALEFrom experimental toexponential
Achievecompetitiveagility

SCALINGAI:
KEYTOSURVIVAL
ANDGROWTH
Copyright © 2019 Accenture. All rights reserved. 2
75%84% 76%
of executives agree that if
they don’t scale AI in the
next five years, they risk
going out of business.
of executives say they
must scale AI to achieve
their growth objectives.
of executives acknowledge
they know how to pilot, but
struggle to scale AI across
the business.

Evolutionary paths to growth
THE JOURNEY TO SCALE
ProofofConcept
Factory
In our experience, most companies
(80-85%) are stuck on this path.
• Analytics buried deep and not
a CEO focus
• Siloed operating model typically
IT-led
• Unable to extract value from
their data
• Struggle to scale as unrealistic
expectations on time required
• Significant under investment,
yielding low returns.
01
Strategically Scaling
Only 15-20% of companies have made
this leap.
• CEO focus with advanced analytics
and data team solving big rock
problems
• Multi-disciplinary teams of 200+
specialists championed by Chief AI,
Data or Analytics Officer
• Able to tune out data noise and
focus on essentials
• Intelligent automation and
predictive reporting
• Catch up on digital / AI / data
asset debt
• Experimental mindset achieving
scale and returns
02
Industrialized
for Growth
Very few (<5%) companies have
progressed to this point on their
AI journey.
• Digital platform mindset and
enterprise culture of AI
democratizing real-time insights to
drive business decisions
• Clear enterprise vision,
accountability, metrics, and
governance breaking
down silos
• ‘What if’ analysis enabling improved
acquisition, service and satisfaction
• Responsible business practices
enhancing brand perception and
trust
• Competitive differentiator and value
creator driving higher P/E multiples
03

PAYINGDIVIDENDS
We uncovered a positive correlation between successfully scaling AI
and three key measures of financial valuation.
+35% Enterprise value /
revenue ratio
+33% Price / earnings
(P/E) ratio
+28% Price / sales
(P/S) ratio

THEGREATDIVIDE
“Strategic Scalers” report a much higher success rate and return on AI
investments compared to companies pursuing siloed proof of concepts.
The difference in return on AI investments between companies in the
Proof of Concept stage and Strategic Scalers equates to an average of US$110 million.

WHATSETSSTRATEGIC
SCALERSAPART?
Three critical success factors separate the Strategic Scalers
from companies in the Proof of Concept stage. These companies:
01
Drive “intentional” AI. To successfully scale, companies need
structure and governance in place. Strategic Scalers have both.
02
Tune out data noise. Strategic Scalers recognize the importance of
business-critical data and are more adept at structuring and managing data.
03
Treat AI as a team sport. Strategic Scalers leverage multi-disciplinary teams
throughout the organization—with clear sponsorship from the top ensuring
alignment with the C-suite vision.

BENEFITSEXTEND
ACROSSTHEBUSINESS
Strategic Scalers are more likely to achieve a wide range of benefits.

STRATEGICSCALERS
DRIVE“INTENTIONAL”AI
They have a clear strategy and operating model for AI in place
that is aligned and integrated with the overall C-suite objectives.
In place for the majority of Artificial Intelligence scaling projects:
Proof of Concept Strategic Scalers

STRATEGICSCALERS
TUNEOUTDATANOISE
They recognize the importance of managing
data and have a solid data foundation in place.
Proof of Concept Strategic Scalers

STRATEGICSCALERS
TREATAIASATEAMSPORT
They establish dedicated multi-disciplinary teams,
provide formal training, experience and clear
accountability.
1.5x
Formal
training:
2x
Fully understand
how AI applies
to role:
1.7x
Understand &
implement
responsible AI:
92%
of Strategic
Scalers leverage
multi-disciplinary
teams

Focus on the ‘I’ in ROI
The C-suite views AI
investment as the cost of
doing business. They earmark
budget for AI recognizing its
criticality for future growth
and spend without the need
to prove ROI in advance to
justify the investment.
Adopt a digital platform
mindset to scale
The two main objectives of
platforms are acceleration and
extended value. Publishing
data on a platform once for
products to consume through
APIs and microservices is more
cost effective. It also drives scale
by breaking down silos and
democratizing data and insights
enabling greater collaboration
and innovation across the
enterprise and broader
ecosystem of partners.
Build trust through
Responsible AI
Responsible AI entails creating a
framework that ensures the
ethical, transparent and
accountable use of technologies in
a manner consistent with user
expectations, organizational
values and societal laws and
norms. Responsible AI can guard
against the use of biased data or
algorithms, ensure that automated
decisions are justified and
explainable, and help maintain
user trust and individual privacy.
REALIZINGTHEPOTENTIAL
Industrializing for growth is a dynamic destination

It’s not just about SPEED
It’s about moving deliberately,
in the right direction.
It’s not just about MORE DATA
It’s about investing in your data,
deliberately yet pragmatically,
to drive the right insights.
It’s not just about MONEY
It’s about aligning your investments
to the right places with the intention
of driving large-scale change.
It’s not just about a SINGLE LEADER
It’s about building multi-disciplinary teams that
bring the right capabilities.
SCALINGTONEWHEIGHTS
There are reams of information on the “what” of AI. But scaling new
heights of competitiveness with AI requires understanding the “how.”
And at times eschewing conventional wisdom that continues to emerge as AI evolves:

CALLTOACTION
Read the full report

ABOUTTHE
RESEARCH
Our research involved 1,500 C-suite
executives from companies with a minimum
revenue of US$1 billion in 12 countries around
the world across 16 industries, with the aim to
uncover the success factors for scaling AI.
Our research, and that of our partners in our
ecosystem, employs ethical and responsible
research methods. Respondents reveal their
identities voluntarily, we anonymize all data
from companies in our data set, and report
results in aggregate. We commit to not using
the data collected to personally identify the
respondents and/or contact therespondents.
Roles
Chief AI Officer (93)
Chief Data and/or Analytics Officer (113)
Chief Digital Officer (136)
Chief Financial Officer (231)
Chief Information Officer (441)
Chief Innovation Officer (114)
Chief Operating Officer (215)
Chief Strategy Officer (59)
VP/SVP of AI/ Data/Analytics (98)
16industries
Banking + Capital Markets (100)
Chemicals (100)
Communications (100)
Consumer Goods & Services (100)
Energy (Oil & Gas) (100)
Healthcare (Payers) (100)
High Tech (100)
Industrial Equipment (100)
Insurance (100)
Life Sciences
(Pharma & Biotech) (100)
Metals and Mining (100)
Retail (100)
Software & Platforms (100)
Travel & Transport
(Hotels & Passenger) (100)
Utilities (100)
12Countries
Brazil (115)
Canada (113)
China (139)
France (105)
Germany (116)
India (126)
Italy (113)
Japan (117)
Singapore (101)
Spain (106)
United Kingdom (116)
United States (233)
Revenue (n=1,500)

AI: Built to Scale

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