The art of AI maturity: advancing from practice to performance

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.