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.

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.

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.)

0%

of companies are
AI Achievers

0%

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.

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.

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.

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?

Get the essentials

10 min read

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

Lan Guan

Lan Guan

Lead, Cloud First – Data & AI