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The art of AI maturity: advancing from practice to performance

The art of AI maturity Advancing from practice to performance

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.


When leaders mentioned AI on 2021 earnings calls, their share prices were forty percent more likely to increase


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.



AI capabilities identified as key drivers to
achieve at least 30% AI influenced revenue



AI capabilities identified as key drivers to achieve at least 10% AI influenced revenue



Companies that have mature AI strategies but struggle to operationalize



Companies that have differentiated AI strategies and the ability to operationalize for value



Companies that lack mature AI strategies and the capabilities to operationalize



Companies that have mature foundational capabilities that exceed their AI strategies


of companies are
AI Achievers


of companies are
AI Experimenters

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
Mandatory training
Employee competency in AI-related skills
Innovation culture embedded
Innovation culture encouraged
AI talent strategy
Achievers Builders Innovators Experimenters                                        
Responsible AI by design
Responsible data & AI strategy - Change
Achievers Builders Innovators Experimenters                


  Strategy and Sponsorship Data and AI Core Talent & Culture Responsible AI

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.

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).​

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:

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.

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.

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.

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.

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.


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.

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The art of AI Maturity – Authors

Global authors

Sanjeev Vohra

Sanjeev Vohra

Global Lead - Applied Intelligence

Ajay Vasal

Ajay Vasal

Growth & Strategy Lead and Centre for Data & Insights Lead – Applied Intelligence

Philippe Roussiere

Philippe Roussiere

Accenture Research Innovation and AI Global Lead

Philippe Roussiere

Praveen Tanguturi, PhD

Thought Leadership Research Principal Director

Lan Guan

Lan Guan

Lead, Cloud First – Data & AI

Markets authors

Arnab Chakraborty

Arnab Chakraborty

Senior Managing Director, North America Lead, Applied Intelligence

Joseph Depa

Joseph Depa

Senior Managing Director, Europe, Applied Intelligence

Senthil Ramani

Senthil Ramani

Senior Managing Director, Growth Markets, Applied Intelligence