AI Ambitions Are Outpacing Cloud Maturity at Most Organizations

Cloud maturity is quietly becoming the defining factor in who wins the AI race, and IT leaders are at the center of that divide. New research shows that while AI ambitions are surging, only 14% of organizations have the cloud expertise needed to turn those ambitions into real, scalable impact.

According to a new report from NTT Data, 99% of organizations acknowledge that AI is driving a sharp increase in demand for cloud capabilities. In fact, 88% say their current cloud spending levels are actively putting AI initiatives, cloud‑native development, and broader modernization efforts at risk.

While cloud adoption has been widespread for years, only a small proportion of organizations can be considered truly “cloud‑evolved.” Most companies still use the cloud mainly for hosting systems rather than as a strategic driver of innovation. This maturity gap limits their ability to scale AI successfully and translate cloud spending into measurable growth.

Cloud maturity drives stronger business performance

Organizations that have reached advanced levels of cloud maturity outperform their peers. These “cloud leaders” are more likely to achieve higher revenue growth, better operating margins, faster innovation, and stronger confidence in AI’s impact. Their success comes from treating the cloud as a business platform rather than a cost‑saving tool.

AI workloads are data‑intensive, compute‑heavy, and highly dynamic. As AI becomes embedded in daily operations, organizations depend more heavily on the cloud for scalability, reliability, governance, and security. However, many companies have not increased cloud investment at the same pace as their AI ambitions, which creates execution risks.

Closing the AI–cloud gap with strategy, investment, and modern operating models

Organizations should start by closing the gap between AI ambition and cloud readiness. This means treating the cloud not as a background IT utility but as a strategic foundation for AI execution. Leaders should align cloud and AI strategies, ensure that architectural decisions are made with long‑term scalability, governance, and cost control in mind, and increase investment in areas that directly support AI workloads, such as cloud‑native platforms, data modernization, and automation.

At the same time, organizations need to modernize how they operate and measure success. Moving to a platform‑led cloud operating model can reduce complexity, improve visibility across environments, and enable better cost and security management as AI scales. It’s also equally important to redefine cloud transformation metrics to focus on business outcomes (such as speed, resilience, and value creation) rather than legacy measures like migrations completed.