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Here’s what businesses can learn from the small group of organizations that already use artificial (AI) to their competitive advantage. 

If the world’s largest companies were people, most would be in their teenage years when it comes to using Artificial Intelligence (AI).

According to new research from Accenture on AI maturity, 63% of 1,200 companies were identified as “Experimenters,” or companies that are stuck in the experimentation phase of their AI lives. They have yet to leverage the technology’s full potential to innovate and transform their business, and they risk leaving money on the table. 

This is money that the most AI-mature organizations are already pocketing. While the “AI adults” (dubbed Achievers in the research) are only a small group — representing 12% of companies — they are reaping big rewards: By outperforming their peers on AI, they are increasing their revenue growth by 50% on average. How? Because they master key capabilities in the right combination by having command of the technology itself — including data, AI and cloud — as well as their organizational strategy, responsible use of AI, C-suite sponsorship, talent and culture. 

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Unlike people, companies don’t necessarily grow up and graduate into adulthood in a relatively fixed period. Instead, they hold their development in their own hands. This makes it crucial to understand what keeps adolescent AI users from reaching their maturity. They typically share the five following characteristics:

1. Their C-suite has not bought into AI’s ability to spur growth

Only 56% of Experimenters have CEO and senior sponsorship — compared to 83% of Achievers — signaling that AI maturity starts with leadership buy-in. What’s more, Achievers are four times more likely than Experimenters to implement platforms that encourage idea sharing and easily posing questions internally. In one example of innovation emboldened by leadership, a global digital platform is harnessing AI and generative design to create autonomous buildings that fit together like pieces of a LEGO set.

2. They are not investing in their team members

Experimenters are hampered by a shortage of AI-skilled workers. Furthermore, they have yet to invest in training that helps their employees reach AI literacy. While more than three-quarters of Achievers (78%) have mandatory AI trainings for its engineers to C-suite executives, the same can be said for only 51% of Experimenters. 

To succeed with AI, Experimenters should reskill current team members in the technology. For example, a leading Southeast Asian oil and gas firm built a gamified platform to expand its employees’ digital fluency. It later created a cloud-based performance reviewer that assessed a decade’s worth of employee data to make recommendations for filling various digital roles. This reduced the time needed to fill positions and helped close the digital skills gap. 

3. Their AI use is not integrated across the enterprise

While 75% of all companies analyzed have incorporated AI into their business strategies and cloud plans, they lack a foundational AI core. To achieve AI maturity, they must integrate AI across the enterprise while also knowing when to tap external resources. 

Achievers are 32% more likely than Experimenters to develop custom-built machine learning applications or work with a partner to extract value from their data. For instance, one major U.S. credit card company created an innovative AI ecosystem by partnering with a technical university to create a dedicated analytics laboratory. The lab helped it stay on top of science and engineering breakthroughs.

4. They are designing AI without considering its implications

Scaling AI effectively relies on building responsibly from the start. With an increase in AI regulation, organizations that can demonstrate high-quality, trustworthy technology systems that are “regulation ready” will have a significant advantage in the marketplace. In fact, Achievers are already 53% more likely than their peers to develop and deploy AI responsibly. 

Otherwise, companies risk destroying trust with customers, employees, businesses and society. To combat this, a European-based pharmaceutical company created accountability mechanisms and risk management controls to ensure its AI-powered operations and services aligned with its core values. 

5. They wrongly believe AI has already plateaued

Companies that do not aggressively increase their AI spending risk being left behind. To successfully generate business value with AI, leaders know this is just the beginning, which is why in the last year alone, 46% of CEOs mentioned the technology in their earnings calls.

By 2024, we project nearly half of companies (49%) will devote at least 30% of their technology budgets to AI, up from 19% in 2021. These organizations know the quality of their investments matters just as much as the quantity, and they are dedicated to simultaneously expanding AI’s scope while better integrating its solutions.  

AI means lifelong learning

Environments shape people, especially in their teenage years. It’s not so different with companies and the industries they are rooted in. Tech firms with little legacy technology have a natural AI advantage. Most insurance companies, on the other hand, are both hampered by this legacy and face a much higher degree of regulation. Not surprisingly, these are the sectors where AI maturity is highest and lowest, respectively. Still, most industries have their Achievers, and across the board, all are expected to mature further. By 2024, the overall share of Achievers will increase from the current rate of 12% to 27%. 

But even these “adults” will need to continue learning as technology is transforming every part of a business, sometimes leading to total enterprise reinvention. There’s plenty of room for growth around AI for everyone. 

Sanjeev Vohra leads Accenture’s data and AI service Applied Intelligence and is a member of Accenture’s Global Management Committee. 

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