Forty-five percent of leaders in organisations with high AI maturity said their AI initiatives remain in production for three years or more to ensure sustained impact and value, according to a survey by Gartner, Inc. This compares to only 20% in low-maturity organisations.
The survey revealed that choosing AI projects based on business value and technical feasibility, along with establishing robust governance structures and engineering practices, ensures the longevity of AI projects in high-maturity organisations.
The survey was conducted in the fourth quarter of 2024 to understand how organisations adopt AI and GenAI. A total of 432 respondents from organisations in the U.S., the U.K., France, Germany, India, and Japan participated in the survey. Gartner assessed an organisation’s AI maturity with a seven-question survey based on Gartner AI Maturity Model, a structured framework to evaluate and enhance an organisation’s capabilities in leveraging AI. Each area was rated from Level 1 (“planning/beginning”) to Level 5 (“leadership”). High-maturity organisations scored on average 4.2–4.5, while low-maturity organisations averaged.
“Trust is one of the differentiators between success and failure for an AI or GenAI initiative,” said Birgi Tamersoy,Sr. Director Analyst at Gartner.
The survey found that in 57% of high-maturity organisations, business units trust and are ready to use new AI solutions compared with only 14% of low-maturity organisations. “Building trust in AI and GenAI solutions fundamentally drives adoption, and since adoption is the first step in generating value, it significantly influences success,” said Tamersoy.
Data Availability and Quality Challenge AI Implementation
Regardless of AI maturity, data availability and quality are among the top challenges in AI implementation, as identified by 34% of leaders from low-maturity and 29% from high-maturity organisations, respectively (see Figure 1). For high-maturity organisations, 48% of leaders identified security threats as one of their top three implementation barriers, while 37% of leaders in low-maturity organisations said finding the right use case was a top barrier.
Implement Metrics for Optimal AI Results
The survey also revealed that creating metrics contributes to AI efficacy. High-maturity organisations can 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. The survey found that 63% of leaders from high-maturity organisations run financial analysis on risk factors, conduct ROI analysis and concretely measure customer impact, which in return help them sustain AI success.
Appoint a Dedicated AI Leader to Foster AI Innovation and Develop AI Infrastructure
Ninety-one percent of leaders from high-maturity organisations said they have already appointed dedicated AI leaders. As part of their role, AI leaders prioritise fostering AI innovation (65%), delivering AI infrastructure (56%), building AI organisations and teams (50%), and designing AI architecture (48%).
In high-maturity organisations, almost 60% of leaders said they have centralised their AI strategy, governance, data and infrastructure capabilities to increase consistency and efficiency within their organisation. “This reflects a strategic approach to managing AI resources and initiatives, which requires dedicated AI teams,” said Tamersoy.
