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.
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.)
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 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: