Almost half (45 percent) of leaders in organizations with high artificial intelligence (AI) maturity say their AI initiatives remain in production for three years or more to ensure sustained impact and value, according to a new survey by Gartner, Inc. This compares to just 20 percent in low-maturity organizations.
The survey of 432 global organizations also found that selecting AI projects based on business value and technical feasibility – combined with strong governance structures and sound engineering practices – helps ensure the long-term success of AI initiatives in high-maturity organizations.
What is AI maturity?
AI maturity refers to the degree to which an organization effectively leverages AI technologies and integrates them into its operations, culture and decision-making processes.
Gartner assessed an organization’s AI maturity with a seven-question survey based on Gartner AI Maturity Model, a structured framework to evaluate and enhance an organization’s capabilities in leveraging AI. Each area was rated from Level 1 (“planning/beginning”) to Level 5 (“leadership”). High-maturity organizations scored on average 4.2-4.5, while low-maturity organizations averaged 1.6-2.2.
Trust drives successful AI adoption
The survey found that business units trust and are ready to use new AI solutions in 57 percent of high-maturity organizations, compared with only 14 percent of low-maturity organizations.
“Trust is one of the differentiators between success and failure for an AI or generative AI initiative,” said Birgi Tamersoy, senior director analyst at Gartner. “Building trust in AI and generative AI solutions fundamentally drives adoption, and since adoption is the first step in generating value, it significantly influences success.”
Meanwhile, 91 percent of leaders from high-maturity organizations have already appointed dedicated AI leaders responsible for fostering AI innovation (65 percent), delivering AI infrastructure (56 percent), building AI organizations and teams (50 percent) and designing AI architecture (48 percent).
Furthermore, almost 60 percent of leaders in high-maturity organizations have centralized their AI strategy, governance, data and infrastructure capabilities to increase consistency and efficiency within their organization. “This reflects a strategic approach to managing AI resources and initiatives, which requires dedicated AI teams,” said Tamersoy.
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Data availability and quality hamper AI adoption
Regardless of AI maturity, data availability and quality are among the top challenges in AI implementation, identified by 34 percent of leaders from low-maturity and 29 percent from high-maturity organizations, respectively. Security threats are the leading AI adoption barrier for high-maturity organizations (48 percent), while finding the right use case is the top barrier for low-maturity organizations (37 percent).
Creating metrics contributes to AI efficacy, according to Gartner. High-maturity organizations deliver high-level impacts on their AI projects over time because they regularly quantify the benefits of their AI initiatives and evaluate the success through multiple metrics. Almost two thirds (63 percent) of leaders from high-maturity organizations run financial analysis on risk factors, conduct ROI analysis and concretely measure customer impact, which helps them sustain AI success.
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