Cloud maturity is now directly tied to how effectively you 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. This means most companies want more AI, but their cloud foundations are not ready to support it.

Key implications for AI initiatives:

• Cloud is the execution layer for AI – AI workloads depend on scalable, flexible cloud infrastructure, not just basic hosting.
• Spending gaps create risk – 88% of organizations say current cloud spending levels are putting AI, cloud-native, and modernization initiatives at risk.
• Experience matters – “Cloud leaders” (those that are most advanced in adoption and impact) are significantly better positioned to capitalize on AI. Nearly half of these leaders used AI in their last cloud migration project, compared with about a third of other organizations.

In practice, if your cloud environment is still focused on infrastructure rather than value creation, you’ll likely see:

• Slower AI deployment cycles
• Higher integration and operating costs
• Difficulty moving from pilots to production at scale

Organizations that treat cloud as a business value creator, not just a technology initiative, are the ones that can reimagine processes and get measurable returns from AI.

Cloud and AI strategies are increasingly inseparable. NTT Data’s report highlights that AI demand is rising, but alignment is uneven.

Some key data points:

• 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, showing that AI and cloud capabilities are tightly linked in practice.

If cloud and AI are planned separately, organizations often run into:

• Underpowered infrastructure for AI models and data pipelines
• Fragmented architectures across public, private, hybrid, and sovereign clouds
• Unclear ownership of AI-related cloud costs, security, and compliance

By contrast, a joint strategy allows you to:

• Design cloud architectures that are purpose-built for AI workloads
• Prioritize modernization of the legacy applications and data platforms that are currently holding innovation back (a concern for about half of organizations)
• Set shared KPIs that move from technical metrics to business outcomes for both cloud and AI

In short, aligning cloud and AI strategy helps you reimagine how you deliver value, rather than treating AI as a bolt-on to an already stretched cloud environment.

The research points to a few clear priority areas if you want to get more value from cloud and AI in the near term:

1. Modernize legacy applications and data platforms
   Half of organizations say legacy systems are holding back cloud-driven innovation. Modernization is identified as the top priority for the next two years. This includes refactoring applications, cleaning up data platforms, and preparing them for AI-driven use cases.
2. Make deliberate cloud architecture choices
   Organizations are increasingly using a mix of public, private, hybrid, and sovereign cloud. Nearly all respondents expect private cloud growth and sovereign cloud adoption to grow by 50% in two years. Planning this mix intentionally—rather than letting it grow “by accident”—is critical for performance, compliance, and AI scalability.
3. Tighten cost and operations management
   More than half of organizations report cloud cost management challenges, and they expect a threefold increase in fully managed cloud platforms. Building cost visibility, governance, and FinOps practices into your AI and cloud roadmap will help you avoid stalled investments.
4. Strengthen security and accountability
   Security is the top cloud investment priority, but confidence varies: 68% of cloud leaders feel highly confident, versus 36% of others. Leaders are more likely to define clear roles and responsibilities and back them with regular audits—an approach that becomes even more important as AI expands your attack surface.
5. Reset KPIs around business value
   NTT Data recommends resetting cloud transformation KPIs. AI can help shift from purely technical metrics (uptime, capacity) to business metrics (time-to-market, revenue impact, risk reduction). Cloud leaders are already using AI in their cloud projects to support this shift.

Focusing on these areas helps you not just adopt AI, but reimagine how cloud and AI together support your broader business strategy.