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*