Cloud maturity is directly shaping how well organizations can adopt and scale AI. According to NTT Data’s research, only 14% of firms are at the highest level of cloud expertise. At the same time, 99% of organizations say AI is increasing demand for cloud investment, but 88% believe their current cloud spending is putting AI, cloud-native, and modernization initiatives at risk. This creates a gap between ambition and reality. While most businesses see cloud as essential for innovation, fewer than half are satisfied with the impact of their cloud programs or the progress of their modernization efforts. In practice, this means AI initiatives often hit limits because the underlying cloud foundations—architecture, governance, skills, and operating models—haven’t kept pace. NTT Data’s view is that cloud has moved beyond being just infrastructure; it is now the execution layer for AI. Organizations that are “cloud evolved” (those with advanced cloud adoption, measurable business impact, and solid performance) are significantly better positioned to capture AI value. They are more likely to: - Align cloud and AI strategies from the outset - Use AI in cloud migration and optimization projects - Treat cloud as a value creator tied to business outcomes, not just a technology project In short, without maturing cloud capabilities, AI investments risk being constrained, underutilized, or more costly than expected.

Developing cloud and AI strategies in tandem helps organizations avoid misaligned investments and stalled initiatives. NTT Data’s research shows that AI is driving cloud demand, but the alignment between the two is uneven. A few data points highlight this: - Chief AI Officers (CAIOs) are 22% more likely than CIOs and CTOs to say that AI increases cloud investment needs. - AI is cited as the top cloud skills gap. - Nearly half of cloud leaders used AI in their last cloud migration project, compared with about a third of other organizations. When cloud and AI are planned separately, teams often: - Underestimate the compute, data, and networking requirements of AI workloads - Struggle with fragmented architectures across public, private, hybrid, and sovereign cloud - Face higher costs and complexity due to duplicated efforts and inconsistent governance By contrast, organizations that align cloud and AI strategies: - Design cloud architectures with AI workloads, data gravity, and compliance in mind - Prioritize modernization of legacy applications and data platforms that block AI use cases - Shift cloud KPIs from purely technical metrics to business outcomes, with AI helping to optimize performance and cost The core idea is to treat cloud as the operating platform for AI. When both strategies are integrated, it becomes easier to scale AI responsibly, manage costs, and show clear business value.

Several cloud challenges are slowing AI adoption and modernization, even as demand grows. Key obstacles highlighted in the research include: 1. **Legacy applications and data platforms** Half of respondents say legacy systems are holding back cloud’s ability to drive innovation. These older platforms are harder to integrate with modern AI tools, limit data accessibility, and often require significant reengineering before they can support AI at scale. As a result, modernization has become the top priority for the next two years. 2. **Complex, mixed cloud environments** Organizations are increasingly using a mix of public, private, hybrid, and sovereign cloud models. Nearly all respondents expect private cloud usage and sovereign cloud adoption to grow by 50% within two years. While this mix can offer flexibility and compliance benefits, it also adds architectural and operational complexity that can slow AI projects if not well designed. 3. **Cloud cost management and operating model** More than half of organizations report challenges managing cloud costs, and they expect a threefold increase in fully managed cloud platforms. Without clear governance, financial controls, and shared responsibility models, AI workloads can quickly become expensive and unpredictable. 4. **Security confidence and governance** Security is the top cloud investment priority, but confidence is uneven. Among cloud leaders, 68% feel highly confident in their cloud security posture, compared with 36% of other organizations. Leaders are more likely to define clear roles and responsibilities and back them with regular audits, which supports safer AI deployment. To respond, organizations that are progressing fastest are: - Resetting cloud transformation KPIs to focus on business value, not just technical milestones - Using AI within cloud programs (e.g., for migration, optimization, and operations) - Treating cloud as a value creator and strategic platform for AI, rather than a standalone IT initiative This combination—modernizing legacy estates, simplifying architectures, tightening cost and security governance, and embedding AI into cloud operations—is helping them reimagine how they deliver and scale AI across the business.