AI Research & Innovation

Why Only 12% of Companies Have Achieved ‘AI Maturity’

Why Only 12% of Companies Have Achieved ‘AI Maturity’

A recent Accenture report called “The Art of AI Maturity: Advancing from Practice to Performance” went into detail about the journey some companies have taken from just testing the waters of AI to achieving a level of sophistication that is having massive positive impacts on their business. 

Precisely, “some companies” refers to the 12% of firms that, according to the report, “have advanced their AI maturity enough to achieve superior performance and growth.” The report also refers to these companies as “AI Achievers.” Another 25% of firms are “somewhat advanced in their level of AI maturity,” the report states, while the remaining 63% are still in the early stages.

So, what exactly is AI maturity and why does it matter? Accenture defines AI maturity as “the degree to which organizations have mastered AI-related capabilities in the right combination to achieve high performance for customers, shareholders, and employees.” It’s become increasingly clear in recent years that harnessing the power of AI is crucial for businesses to have a competitive advantage. In fact, among executives of the world’s 2,000 largest companies, those who discussed AI on their earnings calls were 40% more likely to see their firms’ share prices increase.

If most organizations are racing to embrace AI, why are some seeing more value than others? AI Achievers, according to the report, are not defined by the sophistication of any one capability, but by their ability to combine strengths across strategy, processes, and people. Here are five key success factors for Achievers:

  1. Their top leaders champion AI as a strategic priority for the entire organization.
    Companies can create strong AI strategies, but unless those strategies receive enthusiastic support from the CEO and the rest of the C-suite, they’re likely to flounder. When it comes to Achievers, 83% of these companies have CEO and senior sponsorship.

  2. They invest heavily in talent to get more from their AI investments.
    This senior sponsorship allows organizations to invest heavily in creating data and AI fluency across their workforces. 78% of Achievers have mandatory AI trainings for most employees, from product development engineers to C-suite executives.
  3. They industrialize AI tools and teams to create a strong AI core.
    An AI core is an operational data and AI platform that taps into companies’ talent, technology, and data ecosystems, allowing firms to balance experimentation and execution.
  1. They design AI responsibly, from the start.
    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. Achievers are 53% more likely than other companies 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 scale AI with confidence.
  2. They prioritize long- and short-term AI investments.
    One reason Achievers get more out of AI is simply because they invest more in it. Achievers also understand that their AI investment journey doesn’t have a finish line and that there is 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.

Echoing the report’s findings, Arthur’s customers self-identify as AI Experimenters (63%) or AI Innovators (13%), with longer-term aspirations of evolving into the AI Builder or Achiever categories. Advancing from practice to performance is a roadmap typically spanning a two-year time horizon for most enterprises. 

Maturity isn’t a one-size-fits-all path, either. While organizations may be farther along in ML/AI development and management maturity, we’ve discovered these same organizations are still in infancy for model monitoring and validation maturity. They’re still logging inferences, manually juggling Python notebooks, and facing problems from fragmented or restricted data stacks for day-to-day model data science workflows.

The report says it best: “Advancing AI maturity is no longer a choice. It’s an opportunity facing every industry, every organization, and every leader.” The good news is that the share of AI Achievers will increase rapidly and significantly in the next few years, more than doubling from the current 12% to 27% by 2024. Will your organization be one of them?