Gartner's recent survey, conducted in the fourth quarter of 2024, has highlighted significant disparities in artificial intelligence (AI) adoption and maturity among organisations across the United States, United Kingdom, France, Germany, India, and Japan.

The survey, which included 432 respondents, utilised the Gartner AI Maturity Model to assess organisations on a scale from Level 1 ('planning/beginning') to Level 5 ('leadership').

The findings reveal that high-maturity organisations scored between 4.2 and 4.5, while low-maturity organisations scored between 1.6 and 2.2. A notable 45% of high-maturity organisations have managed to keep their AI projects operational for three years or more, in contrast to only 20% of low-maturity organisations achieving the same longevity.

Metrics to evaluate AI project success are implemented by 63% of high-maturity organisations. These metrics include financial analysis on risk factors, return on investment (ROI) analysis, and customer impact measurement. In addition, 91% of high-maturity organisations have appointed dedicated AI leaders to steer their AI initiatives.

Data availability and quality remain top challenges in AI implementation, as identified by 34% of low-maturity and 29% of high-maturity organisations. Security threats are perceived as a significant barrier by 48% of leaders in high-maturity organisations. Meanwhile, 37% of leaders in low-maturity organisations cited finding the right use case as a primary challenge.

High-maturity organisations are prioritising the fostering of AI innovation, delivering AI infrastructure, building AI teams, and designing AI architecture. Nearly 60% of these organisations have centralised their AI strategy, governance, data, and infrastructure capabilities.

Senior Director Analyst at Gartner, Birgi Tamersoy, said, "Trust is one of the differentiators between success and failure for an AI or GenAI initiative... Building trust in AI and GenAI solutions fundamentally drives adoption, and since adoption is the first step in generating value, it significantly influences success.

"This reflects a strategic approach to managing AI resources and initiatives, which requires dedicated AI teams."