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Menu
The true value of AI
AI, accelerated
What is AI Maturity
AI, applied
AI, advanced
AI performance
Conclusion
Get the Essentials
THE ART OF AI MATURITY: ADVANCING FROM PRACTICE TO PERFORMANCE
The art of AI maturity Advancing from practice to performance
* LEARN
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* FULL REPORT
* AI MATURITY EXPLORER
* AI CAPABILITY COMPARISON
* CONTACT US
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THE AI OPPORTUNITY
Every time you use a wayfinding app to get from point A to point B, use
dictation to convert speech-to-text, or unlock your phone using face ID…you’re
relying on AI. And companies across industries are also relying on—and investing
in—AI, to improve customer service, increase efficiency, empower employees and
so much more.
In 2021, among executives of the world’s 2,000 largest companies (by market
capitalization), those who discussed AI on their earnings calls were 40% more
likely to see their firms' share prices increase - up from 23% in 2018,
according to analysis by Accenture.
However, when it comes to making the most of AI’s full potential and their own
investments, most organizations are barely scratching the surface.
0%
When leaders mentioned AI on 2021 earnings calls, their share prices were forty
percent more likely to increase
0x
the number of 'Achievers' will more than double by 2024
Our recent research revealed that only 12% of firms have advanced their AI
maturity enough to achieve superior growth and business transformation. These
“AI Achievers” can attribute nearly 30% of their total revenue to AI, on
average. And even in the pre-pandemic era (2019), they enjoyed 50% greater
revenue growth on average, compared with their peers. They also outperform in
customer experience and sustainability.
Our machine learning models suggest that the share of AI Achievers will increase
rapidly and significantly, more than doubling from the current 12% to 27% by
2024.
Advancing AI maturity is no longer a question of “if,” but “when.” It’s an
opportunity facing every industry, every organization and every leader. And as
we confirmed in our research, there is incentive to move quickly.
AI, ACCELERATED
Our survey of over 1,600 C-suite executives and data-science leaders from the
world’s largest organizations found that nearly 75% of companies have already
integrated AI into their business strategies and have reworked their cloud plans
to achieve AI success.
And they’re putting those plans into practice: nearly a third (30%) of all AI
pilot initiatives are subsequently scaled to deliver wide-ranging outcomes, from
accelerating R&D timelines for new products to enhancing customer experiences.
42% said that the return on their AI initiatives exceeded their expectations,
while only 1% said the return didn’t meet expectations.
With early successes building confidence in AI as a value-driver, we estimate
that AI transformation will happen much faster than digital transformation—on
average, 16 months faster.
We project that AI transformation will take less time than digital
transformation
Source: Accenture Research.
Note: Our estimate is derived from a natural language processing analysis of
investor calls of the world’s 2,000 largest companies (by market cap), from 2010
to 2021, that referenced “AI” and “Digital” in tandem with “business
transformation,” respectively. Data was sourced from S&P earnings transcripts.
The incentive to move quickly is strong. We found, for example, that the share
of companies’ revenue that is “AI-influenced” more than doubled between 2018 and
2021 and is expected to roughly triple between 2018 and 2024.
WHAT IS AI MATURITY?
To uncover strategies for AI success, Accenture designed a holistic AI-maturity
framework. Fittingly, our analysis was conducted using AI. We applied machine
learning models to unravel massive survey datasets and uncover drivers of AI
maturity (and therefore, AI performance) that would have been impossible to
detect with more traditional analytical methods.
AI Maturity Defined:
AI maturity measures the degree to which organizations have mastered AI-related
capabilities in the right combination to achieve high performance for customers,
shareholders and employees.
see capability definitions
AI maturity comes down to mastering a set of key capabilities in the right
combinations—not only in data and AI, but also in organizational strategy,
talent and culture.
Our research found that AI maturity comes down to mastering a set of key
capabilities in the right combinations—not only in data and AI, but also in
organizational strategy, talent and culture.
This includes “foundational” AI capabilities—like cloud platforms and tools,
data platforms, architecture and governance—that are required to keep pace with
competitors. It also includes “differentiation” AI capabilities, like AI
strategy and C-suite sponsorship, combined with a culture of innovation that can
set companies apart.
The companies that scored best in both categories are the “AI Achievers” – the
group we mentioned earlier. “AI Builders” show strong foundational capabilities
and average differentiation capabilities, while “AI Innovators” show strong
differentiation capabilities and average foundational capabilities.
Achievers, Builders and Innovators collectively represent just 37% of surveyed
organizations—Achievers accounted for 12%, Builders for 12% and Innovators for
13%.
A fourth group we’re calling “AI Experimenters”—those with average capabilities
in both categories—make up the majority (63%) of those surveyed. (See chart
below.)
ONLY 12% OF COMPANIES ARE AI ACHIEVERS
Discover the varying levels of AI Maturity across different industries, company
sizes and geographies using the filters below. Click reset to return to the
global view.
Filter by Industry Company size Region
Filter by
Industry
Company size
Region
Select Aerospace & Defense (46)*Automotive (89)*Banking & Capital markets
(69)*Chemicals (75)*Communication & Media (77)*Consumer Goods & Services
(71)*Energy (72)*Healthcare (76)*Industrial (75)*Insurance (54)*Life Sciences
(65)*Natural Resources (84)*Public Services (93)*Retail (75)*Tech (108)*Travel
(76)*Utilities (81)*
*No. of companies in category
Select
Aerospace & Defense (46)*
Automotive (89)*
Banking & Capital markets (69)*
Chemicals (75)*
Communication & Media (77)*
Consumer Goods & Services (71)*
Energy (72)*
Healthcare (76)*
Industrial (75)*
Insurance (54)*
Life Sciences (65)*
Natural Resources (84)*
Public Services (93)*
Retail (75)*
Tech (108)*
Travel (76)*
Utilities (81)*
Select $1-$4.9 billion (705)*$5-$9.9 billion (214)*$10-$19.9 billion
(163)*$20-$49.9 billion (124)*$50 billion or more (80)*
*No. of companies in category
Select
$1-$4.9 billion (705)*
$5-$9.9 billion (214)*
$10-$19.9 billion (163)*
$20-$49.9 billion (124)*
$50 billion or more (80)*
Select Europe (422)*Growth market (491)*North America (373)*
*No. of companies in category
Select
Europe (422)*
Growth market (491)*
North America (373)*
Reset
AI DIFFERENTIATION
AI capabilities identified as key drivers to
achieve at least 30% AI influenced revenue
LOW HIGH
LOW HIGH
AI FOUNDATION
AI capabilities identified as key drivers to achieve at least 10% AI influenced
revenue
AI INNOVATORS
13%
Companies that have mature AI strategies but struggle to operationalize
AI ACHIEVERS
12%
Companies that have differentiated AI strategies and the ability to
operationalize for value
AI EXPERIMENTERS
63%
Companies that lack mature AI strategies and the capabilities to operationalize
AI BUILDERS
12%
Companies that have mature foundational capabilities that exceed their AI
strategies
0%
of companies are
AI Achievers
0%
of companies are
AI Experimenters
KEY CAPABILITIES
Strategy and
Sponsorship
1. Senior Sponsorship: Organizations have an AI strategy that is developed by
the Chief Analytics Officer, Chief Data Officer, Chief Digital Officer or an
equivalent. The CEO and the Board actively sponsor and share accountability for
the strategy and associated AI initiatives.
2. AI Strategy: Organizations not only have a core AI strategy aligned to the
overall business strategy, but they also dedicate tools and tactics to execute
it and continuously track their performance against that strategy.
3. Proactive vs. Reactive: Organizations have the resources (such as technology,
talent, and patents) to proactively define and demonstrate how AI can create
value vs. apply AI as a reaction to a need. They’re first-movers instead of fast
followers in terms of applying AI for business value.
4. Readily Available AI and ML tools: Organizations work with an ecosystem of
technology partners to access machine learning models and tools to help innovate
new products and services.
5. Readily Available Developer Networks: Organizations tap into an ecosystem of
technology partners to access developer networks that support the development of
new products and services.
--------------------------------------------------------------------------------
Data and
AI Core
6. Build vs. Buy: Organizations develop custom-built AI applications or work
with a partner who offers solutions as-a-service, vs. purchase “off-the-shelf”
AI solutions with little-to-no customization.
7. Platform and Technology: Organizations apply the necessary cloud, data and AI
infrastructure, software, self-serve capabilities and industry best practices,
and they adopt the latest tools available from platform and technology partners.
8. Experimentation Data—Change: Organizations improved their use of
experimentation data between 2018 and 2021, effectively translating into a
higher data and AI maturity. Experimentation data is the use of internal and
external data to design new models and generate new insights. To do that,
organizations use enterprise-grade cloud platforms to keep data clean and
trustworthy, and to support decision making at greater speed and scale.
9. Data Management and Governance: Organizations scale their data management and
governance practices to increase data quality, trust, and ethics across entities
—e.g., by implementing master data management and ensuring security, compliance
and interoperability.
10. Data Management and Governance—Change: Organizations improved their data
management and governance practices between 2018 and 2021, effectively
translating into a higher data and AI maturity.
--------------------------------------------------------------------------------
Talent & Culture
11. Mandatory AI Training: Organizations enforce AI-specific training programs
to improve AI fluency, which are tailored for senior leadership and specific
functions, e.g., salesforce, product engineers, etc. They also create deliberate
opportunities for employees to learn and apply AI in their roles.
12. Employee Competency in AI-Related Skills: Organizations regularly measure
the competency level of employees to determine where further training is needed
to improve overall acumen. They measure and build acumen in critical areas like
coding, data processing and exploration, business analytics, domain and business
expertise, ML, visualization and more.
13. Innovation Culture Embedded: Organizations ensure innovation is part of the
day-to-day work environment. They encourage mindsets, behaviors and routines
that all serve as a vehicle for experimentation, collaboration and learning from
ideation to product development to market launch.
14. Innovation Culture Encouraged: Organizations promote and reward innovative
mindsets and behaviors including entrepreneurship, collaboration and thoughtful
risk-taking.
15. AI Talent Strategy: Organizations have an AI talent strategy - hiring,
acquiring, retention - that evolves to keep pace with market or business needs.
They also have an AI talent “roadmap” for hiring diverse AI-related roles,
beyond “just” ML engineers—such as behavioral scientists, social scientists, and
ethicists.
--------------------------------------------------------------------------------
Responsible AI
16. Responsible AI: Organizations have an industrialized, responsible approach
to data and AI across the complete lifecycle of their AI models—an approach that
can meet changing regulatory requirements, mitigate risks, and support
sustainable, trustworthy AI.
17. Responsible AI—Change: Organizations have improved their responsible data
and AI practices between 2018 and 2021, effectively translating into a higher
data and AI maturity.
AI, APPLIED
While industries like tech are currently far ahead in their respective AI
maturity, the gap will likely narrow considerably by 2024. (See chart below.)
Automotive is betting on a big surge in sales of AI-powered self-driving
vehicles. Aerospace and defense firms anticipate continued demand for AI-enabled
remote systems. And the life sciences industry will expand its use of AI in
efficient drug development. Still, there is enormous room for growth in AI
adoption across all industries and an enormous opportunity for those companies
that choose to seize it.
* One food delivery service uses deep learning to guide drivers to the best
delivery routes. AI models analyze more than 2,000 variables, from the latest
food ordering trends to traffic conditions, to make real-time
recommendations.
* A Middle East-based telco uses AI-driven virtual assistants— which can
communicate in different Arab dialects as well as in English— to deftly
handle some 1.65 million customer calls each month.
* A large Australian telco deployed AI to quantify the effectiveness of its
individual marketing initiatives. The firm was able to measure some 4,000
different marketing metrics—and, in the process, they have created a
world-class marketing performance insights capability, with a range of
strategic and tactical applications. They are using insights gained from
Marketing Mix Modelling (MMM) to optimize the allocation of marketing spend,
messaging and media.
* A leading solar-panel installer is using satellite photos and deep-learning
algorithms to create fully automated rooftop-installation plans and price
estimates. In addition to offering end customers an industry-first ability to
self-design their systems, the company expects its AI-led design efforts to
ultimately lower the firm’s sales costs by 25%.
* In the public sector, Metro de Madrid, one of the world’s oldest urban rail
systems, deployed AI algorithms to sift through mountains of data—on
everything from air temperature at individual stations, to train frequency
and passenger patterns, to electricity prices—to reduce its annual energy
intake by 25%.
* A major US beverage bottler used AI to consolidate data sources and measure
the effect of promotions on different retailers and markets, boosting the
bottler’s annual sales by 3%.
For industry laggards like financial services and healthcare, a range of factors
may be contributing to their relatively low AI maturity—including legal and
regulatory challenges, inadequate AI infrastructure and a shortage of AI-trained
workers.
Levels of AI maturity by industry, 2021 and 2024*
Source: Accenture Research
Note: *2024 = estimated scores. Industries’ AI maturity scores represent the
arithmetic average of their respective Foundational and Differentiation index.
There is enormous room for growth in AI adoption across all industries and an
enormous opportunity for those companies that choose to seize it.
AI, ADVANCED
AI Achievers are deploying AI solutions to solve problems, spot opportunities
and outperform their peers. They’ve taken their AI agenda beyond cost savings to
drive growth and innovation. In fact, they’re 3.5 times more likely than
Experimenters to see their “AI-influenced” revenue surpass 30% of their total
revenues.
When compared with all other groups, AI Achievers are also more likely to…
* Demonstrate high performance across a combination of capabilities. They are
not defined by the sophistication of any one individual capability, but by
their ability to combine strengths across strategy, processes and people.
* Consistently turn pilots into production. They move past experimenting and
apply AI to solve critical business problems. Achievers are more likely to
scale AI pilots across the enterprise compared with Experimenters.
* Focus beyond financial metrics. They outperform other groups on ESG and
customer metrics. They’re more likely than other groups to rigorously measure
and reduce their greenhouse gas emissions, consume natural resources
economically and use AI responsibly. They’re also more likely to develop
strong relationships with customers—building trust, reducing churn and
boosting the quality and safety of offerings.
AI ACHIEVERS OUTPERFORM IN NEARLY ALL CAPABILITIES
Explore more below to better understand the AI capabilities and what sets each
group apart.
Achievers Builders Innovators Experimenters
Strategy and Sponsorship
Senior sponsorship
AI Strategy
Proactive vs. Reactive
Readily available AI and ML tools
Readily available developer networks
Achievers Builders Innovators Experimenters
Data and
AI Core
Build vs. Buy
Platform and technology
Experimentation data - Change
Data management and governance
Data management and governance - Change
Achievers Builders Innovators Experimenters
Talent and
Culture
Mandatory training
Employee competency in AI-related skills
Innovation culture embedded
Innovation culture encouraged
AI talent strategy
Achievers Builders Innovators Experimenters
Responsible
AI
Responsible AI by design
Responsible data & AI strategy - Change
Achievers Builders Innovators Experimenters
OVERVIEW
Strategy and Sponsorship Data and AI Core Talent & Culture Responsible AI
Achievers
Builders
Innovators
Experimenters
AI maturity is enabled by multitasking. While Builders and Innovators often show
distributed strengths across 4 and 3 categories, respectively, Achievers are the
only group to show above-average performance across almost all capabilities in
all categories.
All AI-mature companies have elements of an AI strategy. However, AI Achievers
possess a complete set of capabilities to rally their organizations around AI,
define their strategy and quickly create value. They can deliver that value
because they have strong data foundations, workplace cultures where innovation
is deeply embedded, and talent with advanced skills and competencies. AI
Achievers are also consciously applying responsible AI with greater urgency than
their peers. No other group holds this unique combination of strengths.
AI Builders show strong foundational capabilities and average differentiation
capabilities. While this group has strong tech proficiency and excels at
creating data and AI platforms, they’re less successful at cultivating AI talent
and culture capabilities within their organizations. This prevents the broad AI
adoption needed to advance their AI maturity.
AI Innovators show strong differentiation capabilities and average foundational
capabilities. They excel at securing senior sponsorship and show intent to
embrace AI across their organization and culture through mandatory trainings for
all employees. However, they lack foundational capabilities within the data and
AI core that would allow them to scale AI and truly embed it throughout their
organization.
Experimenters show strengths in neither foundational nor differentiation
capabilities. They don’t have the technical proficiency to build a strong AI and
/ or data foundation, nor do they do they have the strategic vision to help
encourage innovation, be it via senior sponsorship or talent strategy and
training.
SENIOR SPONSORSHIP
Organizations have an AI strategy that is developed by the Chief Analytics
Officer, Chief Data Officer, Chief Digital Officer or an equivalent. The CEO and
the Board actively sponsor and share accountability for the strategy and
associated AI initiatives.
AI STRATEGY
Organizations not only have a core AI strategy aligned to the overall business
strategy, but they also dedicate tools and tactics to execute it and
continuously track their performance against that strategy.
PROACTIVE VS. REACTIVE
Organizations have the resources (such as technology, talent, and patents) to
proactively define and demonstrate how AI can create value vs. apply AI as a
reaction to a need. They’re first-movers instead of fast followers in terms of
applying AI for business value.
READILY AVAILABLE AI AND ML TOOLS
Organizations work with an ecosystem of technology partners to access machine
learning models and tools to help innovate new products and services.
READILY AVAILABLE DEVELOPER NETWORKS
Organizations tap into an ecosystem of technology partners to access developer
networks that support the development of new products and services.
BUILD VS. BUY
Organizations develop custom-built AI applications or work with a partner who
offers solutions as-a-service, vs. purchase “off-the-shelf” AI solutions with
little-to-no customization.
PLATFORM AND TECHNOLOGY
Organizations apply the necessary cloud, data and AI infrastructure, software,
self-serve capabilities and industry best practices, and they adopt the latest
tools available from platform and technology partners.
EXPERIMENTATION DATA — CHANGE
Organizations improved their use of experimentation data between 2018 and 2021,
effectively translating into a higher data and AI maturity. Experimentation data
is the use of internal and external data to design new models and generate new
insights. To do that, organizations use enterprise-grade cloud platforms to keep
data clean and trustworthy, and to support decision making at greater speed and
scale.
DATA MANAGEMENT AND GOVERNANCE
Organizations scale their data management and governance practices to increase
data quality, trust, and ethics across entities —e.g., by implementing master
data management and ensuring security, compliance and interoperability.
DATA MANAGEMENT AND GOVERNANCE — CHANGE
Organizations improved their data management and governance practices between
2018 and 2021, effectively translating into a higher data and AI maturity.
MANDATORY AI TRAINING
Organizations enforce AI-specific training programs to improve AI fluency, which
are tailored for senior leadership and specific functions, e.g., salesforce,
product engineers, etc. They also create deliberate opportunities for employees
to learn and apply AI in their roles.
EMPLOYEE COMPETENCY IN AI-RELATED SKILLS
Organizations regularly measure the competency level of employees to determine
where further training is needed to improve overall acumen. They measure and
build acumen in critical areas like coding, data processing and exploration,
business analytics, domain and business expertise, ML, visualization and more.
INNOVATION CULTURE EMBEDDED
Organizations ensure innovation is part of the day-to-day work environment. They
encourage mindsets, behaviors and routines that all serve as a vehicle for
experimentation, collaboration and learning from ideation to product development
to market launch.
INNOVATION CULTURE ENCOURAGED
Organizations promote and reward innovative mindsets and behaviors including
entrepreneurship, collaboration and thoughtful risk-taking.
AI TALENT STRATEGY
Organizations have an AI talent strategy - hiring, acquiring, retention - that
evolves to keep pace with market or business needs. They also have an AI talent
“roadmap” for hiring diverse AI-related roles, beyond “just” ML engineers—such
as behavioral scientists, social scientists, and ethicists.
RESPONSIBLE AI
Organizations have an industrialized, responsible approach to data and AI across
the complete lifecycle of their AI models—an approach that can meet changing
regulatory requirements, mitigate risks, and support sustainable, trustworthy
AI.
RESPONSIBLE AI—CHANGE
Organizations have improved their responsible data and AI practices between 2018
and 2021, effectively translating into a higher data and AI maturity.
Note: Each square represents one of the 17 key capabilities. The square is
filled in where the AI Maturity profile is out-performing against peers (higher
than the average across all companies in terms of % of companies reaching the
mature level).
Out-performing
Under-performing
Download
AI Maturity graph (PDF)
Deep Dive: The Elements of AI Maturity
MASTERING THE CRAFT—5 SUCCESS FACTORS FOR AI PERFORMANCE
Advancing to the rank of “AI Achiever” requires focus and commitment. Here’s
what we can learn from these high performers who have advanced their AI maturity
beyond the rest:
1. CHAMPION AI AS A STRATEGIC PRIORITY FOR THE ENTIRE
ORGANIZATION, WITH FULL SPONSORSHIP FROM LEADERSHIP
Achievers are more likely to have formal senior sponsorship for their AI
strategies: we found that 83% of Achievers have such sponsorship, while only 67%
of Builders and just 56% of Experimenters have it.
Our research also suggests that the best AI strategies tend to be bold, even
when they have modest beginnings.
Bold AI strategies, in turn, help spur innovation. And for the CEOs of
Achievers, creating a culture of innovation is itself a deliberate, strategic
move—one that is used as a vehicle for experimentation and learning across the
organization. In fact, 48% of Achievers embed innovation in their organizational
strategies, while just 33% of Experimenters do.
1. INVEST HEAVILY IN TALENT TO GET MORE FROM AI
INVESTMENTS
With a clear AI strategy and strong CEO sponsorship, organizations are more
likely to invest heavily in creating data and AI fluency across their
workforces. While AI proficiency must start at the top, it can’t end there.
We found, for example, that 78% of Achievers—compared with just 56% of Builders
and 51% of Experimenters—have mandatory AI trainings for most employees, from
product development engineers to C-suite executives.
Because Achievers prioritize efforts to build AI literacy in their workforces,
it’s no surprise that their employees are also more proficient in AI-related
skills. This makes it much easier to scale human and AI collaboration and ensure
that AI permeates the organization.
Nearly half (44%) of Achievers have employees with consistently high AI skills
competencies. Innovators (33%) and Experimenters (30%) have significantly fewer
such employees, on average.
1. INDUSTRIALIZE AI TOOLS AND TEAMS TO CREATE AN
AI CORE
Another priority for Achievers involves building an AI “core,” an operational
data and AI platform that taps into companies’ talent, technology and data
ecosystems.
An AI core also works across the cloud continuum (e.g., migration, integration,
growth and innovation), provides end-to-end data capabilities (foundation,
management and governance), manages the machine learning lifecycle (workflow,
model training, model deployment) and provides self-service capabilities.
AI cores are, in turn, managed by dedicated interdisciplinary teams of machine
learning engineers, data scientists, data-domain experts and systems engineers.
1. USE AI RESPONSIBLY, FROM THE START
As companies deploy AI for a growing range of tasks, adhering to laws,
regulations and ethical norms is critical to building a sound data and AI
foundation. The potential for regulatory changes in many countries makes the
challenge even more daunting.
Achievers are consciously applying “responsible” AI with greater urgency than
their peers. Achievers are 53% more likely, on average, than Builders and
Innovators to be “responsible by design”: designing, developing and deploying AI
with good intention to empower employees and businesses, and to fairly impact
customers and society—allowing companies to engender trust and scaling to scale
with confidence.
1. PRIORITIZE LONG- AND SHORT-TERM AI INVESTMENTS
To avoid being left behind, most companies need to aggressively increase their
spending on AI. One reason Achievers get more out of AI is simply because they
invest more in it.
We found that in 2018, Achievers devoted 14% of their total technology budgets
to AI, while in 2021 they devoted 28%. In 2024, they expect to devote 34%.
Achievers also understand that their AI investment journey doesn’t have a finish
line. There is, they frequently note, no “peak AI.” These companies know they
have only scratched the surface of their AI transformations and that the quality
of their investments matters just as much as the quantity. For Achievers,
continued investment involves expanding the scope of AI to deliver maximum
impact while “cross-pollinating” AI solutions and redeploying resources.
Practice makes progress
PRACTICE MAKES PROGRESS
CONCLUSION
The concept of using AI to solve business problems isn’t new. By 2019, there was
evidence that scaling AI beyond proofs of concept had a significant impact on
ROI. Then the pandemic hit. For many organizations, enterprise-wide
transformation was an urgent means of survival. For others, it quickly became a
catalyst to thrive.
AI Achievers are thriving. Across industries, they’ve moved past cloud migration
to innovation. They’ve capitalized on cloud’s scale and computing power to tap
into new data sources and AI technologies that are widely available. But AI
isn’t their secret to superior performance. It’s how they’re approaching AI that
makes them different. They’ve established that AI maturity is as much about
people as it is about technology. As much about strategy as it is about
implementation. As much about responsibility as it is about agility.
While Achievers are advanced relative to their peers, they’ll set new standards
for high performance as their own maturity evolves.
As AI technologies become more prevalent, the future of all businesses is going
to look very different – some will lead the change, and some will be subjected
to it. Those who transform will be the ones whose teams master the art of AI
maturity, using cloud as the enabler, data as the driver and AI as the
differentiator.
How can AI help you differentiate?
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THE ART OF AI MATURITY – AUTHORS
GLOBAL AUTHORS
SANJEEV VOHRA
Global Lead - Applied Intelligence
AJAY VASAL
Growth & Strategy Lead and Centre for Data & Insights Lead – Applied
Intelligence
PHILIPPE ROUSSIERE
Accenture Research Innovation and AI Global Lead
PRAVEEN TANGUTURI, PHD
Thought Leadership Research Principal Director
LAN GUAN
Lead, Cloud First – Data & AI
MARKETS AUTHORS
ARNAB CHAKRABORTY
Senior Managing Director, North America Lead, Applied Intelligence
JOSEPH DEPA
Senior Managing Director, Europe, Applied Intelligence
SENTHIL RAMANI
Senior Managing Director, Growth Markets, Applied Intelligence
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