A full 84% of C-suite executives believe they must leverage artificial
intelligence (AI) to achieve their growth objectives. Nearly all C-suite
executives view AI as an enabler of their strategic priorities. And an
overwhelming majority believe achieving a positive return on AI investments
requires scaling across the organization. Yet 76% acknowledge they
struggle when it comes to scaling it across the business. What’s
more, three out of four C-suite executives believe that if they don’t scale AI
in the next five years, they risk going out of business entirely.
With the stakes higher than ever, what can we learn from companies that are
successfully scaling AI, achieving nearly 3X the return on investments and an
average 32% premium on key financial valuation metrics?
How Crucial Is Scaling AI to Your Business?
Compare to see what respondents in
your country are saying.
Compare to see what global
respondents in your industry are saying.
Nail it, then scale it
To answer that question, Accenture conducted a landmark global study involving
1,500 C-suite executives from organizations across 16 industries. The study
focused on determining the extent to which AI enables the business strategy, the
top characteristics required to scale AI, and the financial results when done
successfully. The aim: Help companies progress on their AI journey, from one-off
AI experimentation to gaining a robust organization-wide capability that acts as
a source of competitive agility and growth.
Three distinct groups of companies with increasing levels of capability required
to successfully scale AI emerged from the research—Proof of Concept Factory,
Strategically Scaling, and Industrialized for Growth.
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
Significant under investment, yielding low returns
In our experience, 80-85% of companies are here
CEO focus with advanced analytics and data team solving big
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
We estimate that 15-20% of companies are here
Digital platform mindset and enterprise
culture of AI democratizing real-time
insights to drive business decisions
Clear enterprise vision, accountability,
metrics, and governance breaking
‘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
Less than 5% of companies have evolved to this point
The Great Divide
Considering that the companies in our study collectively spent US$306 billion
on AI applications in the past three years, the ROI gap amongst them is
significant. How significant? US$110 million between companies in the Proof
of Concept stage and Strategic Scalers.
3X Strategic Scalers achieved nearly triple the return from AI
investments than companies in the Proof of Concept stage of
their AI journey
While the C-suite executives surveyed reported positive ROI on their AI
investments, we wanted to dig deeper. Was there any relationship between
successfully scaling AI across the enterprise and key market valuation metrics?
What was the “premium” for being a leader?
Using survey data combined with publicly available financial data, our team of
data scientists created a model to identify the premium for companies in our
sample that successfully scale AI, controlling for various characteristics of
We discovered a positive correlation between successfully scaling AI and three
key measures of financial valuation with an average lift of 32% on
Enterprise Value/Revenue Ratio, Price/Earnings Ratio, and Price/Sales
How to succeed at scaling
The research revealed three critical success factors that separate the Strategic
Scalers from organizations in the Proof of Concept stage. Strategic Scalers:
01. Drive “intentional” AI
02. Tune out data noise
03. Treat AI as a team sport
01. Drive “intentional” AI
Strategic Scalers pilot and successfully scale more initiatives than their Proof
of Concept counterparts—at a rate of nearly 2:1—and set longer timelines. They
are 65% more likely to report a timeline of one to two years to move from pilot
to scale. And even though they achieve more, Strategic Scalers spend less. At
first glance it may seem paradoxical. But the data indicate that these leaders
are more intentional, with a more realistic expectation in terms of time to
scale—and what it takes to do so responsibly.
To successfully scale, companies need structure and governance in place. And the
Strategic Scalers have both. Nearly three-quarters of them (71%) say they have a
clearly-defined strategy and operating model for scaling AI in place, while only
half of the companies in Proof of Concept report the same.
Strategic Scalers are also far more likely to have defined processes and owners
with clear accountability and established leadership support with dedicated AI
champions. Initiatives not firmly grounded in business strategy and lacking a
governance construct to oversee and manage are slower to progress. Turf wars
break out over who “owns” AI. And, regardless of the AI platforms used, or the
knowhow recruited, misaligned efforts fall flat.
Sizing up the situation
The “smaller” companies in our study generated revenues between US$1 and 5
billion a year. The largest had revenues of more than US$30 billion. When it
comes to scaling AI, are there any major differences between these two
groups of companies? Do the largest companies face lower scaling success
rates due to their organizational complexity? Or, quite the opposite, do
they achieve higher returns as they untap greater value potential?
When we grouped the surveyed companies by size, we found no significant
differences in scaling success rate or return on AI investments. So, size is
not a factor. It’s all about instilling the right AI capabilities and
mindset in the organization.
02. Tune out data noise
My organization recognizes the
importance of our core data as the foundation for scaling AI.
vs 37% Strategic ScalersProof of Concept
Ninety percent of the data in the world was created in just the past 10 years.
One-hundred and seventy-five zettabytes of data will be created by 2025. Yet
after years of collecting, storing, analyzing, and reconfiguring troves of
information, most organizations struggle with the sheer volume of data and how
to cleanse, manage, maintain, and consume it.
Strategic Scalers tune out “the noise” surrounding data. They recognize the
importance of business-critical data, identifying financial, marketing,
consumer, and master data as priority domains. And Strategic Scalers are more
adept at structuring and managing data. The research shows they are much more
likely to wield a larger, more accurate data set (61% versus 38% of respondents
in Proof of Concept). And 67% of Strategic Scalers integrate both internal and
external data sets as a standard practice compared to 56% of their Proof of
What’s more, they use the right AI tools—things like cloud-based data lakes, data
engineering/data science workbenches, and data and analytics search—to manage
the data (60% compared with 47%) for their applications. From creation to
custodianship to consumption. Strategic Scalers understand the importance of
using more diverse datasets to support initiatives.
From creation to custodianship to
consumption, Strategic Scalers focus on data assets that underpin their
03. Treat AI as a team sport
The effort of scaling calls for embedding multi-disciplinary teams throughout the
organization—teams with clear sponsorship from the top ensuring alignment with
the C-suite vision. For Strategic Scalers, these teams are most often headed by
the Chief AI, Data or Analytics Officer. They’re comprised of data scientists;
data modelers; machine learning, data and AI engineers; visualization experts;
data quality, training and communications, and other specialists.
It’s a lesson Strategic Scalers have learned well. In fact, a full 92% of them
leverage multidisciplinary teams. Embedding them across the organization is not
only a powerful signal about the strategic intent of the scaling effort, it also
enables faster culture and behavior changes. In contrast, those still in Proof
of Concept are more likely to rely on a lone champion within the technology
organization to drive AI efforts.
92% of Strategic Scalers leverage multi-disciplinary teams
Industrializing for Growth is a dynamic
From our experience, we know of three additional variables that speed companies
along their journey to the ultimate destination: A data-driven culture where AI
is driving exponential returns.
Focus on the ‘I’ in ROI
Adopt a digital platform
mindset to scale
Build trust through
Scaling to new heights of competitiveness
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:
It’s not just about SPEED
It’s about moving
deliberately, in the right direction.
It’s not just about MONEY
It’s about aligning your
investments to the right places with the intention of driving
It’s not just about MORE DATA
It’s about investing in your
data, deliberately yet pragmatically, to drive the right
It’s not just about a SINGLE LEADER
It’s about building
multidisciplinary teams that bring the right capabilities.
Scaling the exponential power of AI across the enterprise is a journey. Those
that learn the lessons on each path will reach a place where the business is
seamlessly fused with intelligence that boosts productivity and effectiveness.
The result: industrialized growth through unassailable competitive strength in
everything from organizational effectiveness to brand perception and trust.
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 success factors for scaling AI.
Managing Director – Accenture Strategy, CFO &
Athena guides teams in developing actionable
strategies to create more value through analytics and define the optimal
Managing Director – North America South Region MU
Lead, Applied Intelligence
Joe is the Applied Intelligence Lead for the
Accenture South market unit.
Senior Managing Director – Technology Strategy & Advisory (Retired)
Greg helps clients achieve high performance
through profitable growth, accelerated innovation and organizational