Cloud is no longer infrastructure — it’s the execution layer of AI

If you’re treating cloud as just infrastructure, you’re already behind. In the era of AI, it’s no longer merely the place where systems run. It’s the execution layer of the AI operating model.

Across industries, cloud enables AI at scale, with data-driven automation and intelligent decision-making powering everything from real-time customer recommendations and faster insurance claims processing to dramatically improved IT operations. It’s embedded in underwriting decisions, credit risk models, manufacturing optimization and customer engagement.

This shift has rewritten the rules.

NTT DATA’s latest global report, Cloud-led innovation in the era of AI: The new rules for driving value with cloud, draws on insights from more than 2,300 senior decision-makers across 33 countries and 13 industries. It points to a cloud maturity gap: Despite more than a decade of cloud adoption, only 14% of organizations have reached the highest level of cloud maturity.

Nearly every organization recognizes cloud as critical to innovation, yet fewer than half are fully satisfied with the value it delivers. Only 44% are satisfied with their overall IT modernization progress, and just 49% are fully satisfied with cloud’s role in innovation.

The gap between ambition and execution is real, and AI is making it more urgent.

The investment gap

Expectations for cloud are increasing, but cloud investment is not.

Our research shows that 84% of organizations report flat cloud spending over the past year, even as 99% say the rise of AI has increased their need for cloud investment. At the same time, 88% acknowledge that their current cloud investment levels put cloud-native, AI and modernization initiatives at risk.

In other words, many are underinvesting in the critical foundation of AI — and AI initiatives will therefore struggle to progress beyond experimentation without the appropriate investment.

Cloud leaders operate differently

Cloud leaders — organizations that are “cloud-evolved,” the most advanced status when it comes to cloud adoption and impact, and that demonstrate strong revenue growth and operating profit — are clearly better positioned than others to capitalize on AI.

According to our report, 55% of cloud leaders describe their cloud funding as “more than sufficient” to reach their business goals, compared with 34% of all other organizations, and 75% of cloud leaders plan to significantly increase cloud investment, while only 43% of all other organizations will do the same.

But what separates these leaders from everyone else isn’t just investment. It’s also their mindset.

Cloud leaders treat cloud as a strategic foundation for innovation. They are far more focused on AI readiness over the coming year and much more aligned with the opportunities presented by AI.

For example:

• 48% of cloud leaders want their cloud capabilities to enable greater AI readiness over the next 12 to 18 months, compared with 34% of all other organizations.
• 53% of cloud leaders view agentic AI as a complete game changer for cloud, while just 30% of all other organizations share this view.

These are not small differences. Achieving a “leader” level of cloud maturity in the AI era requires more than the acceleration of existing programs. It also demands a reframing of the role of cloud — from where systems are hosted to the execution layer of AI that’s critical for delivering business value.

• ALSO READ → AI leadership: The ultimate competitive advantage in 2026 and beyond

6 rules redefining cloud value

Against this backdrop, organizations must revisit their approach to cloud. Our report outlines six rules for unlocking greater value with cloud at a time when expectations are higher than ever.

1. Cloud and AI strategies need to be developed in tandem

AI depends on cloud infrastructure, data services, governance and orchestration to function and scale. Our data shows that cloud and AI are now inseparable. CAIOs are 22% more likely than CIOs and CTOs to say AI increases the need for greater cloud investment, highlighting the link. Additionally, AI skills are cited as the top cloud-related talent gap.

To ensure AI ambitions become reality, interlock AI and cloud roadmap from the outset.

2. Cloud architecture choices will make or break your success

Decisions about public, private, hybrid, multicloud and sovereign cloud environments directly affect how organizations control spending, manage governance and security, and drive value as they expand their AI use cases.

Nearly all organizations (99%) expect private cloud adoption to rise, and sovereign cloud adoption is projected to grow 50% — from 28% of organizations to 42% — within two years. Cloud choices now directly influence AI outcomes, and there is no longer such a thing as a cloud-neutral strategy. Make cloud architectural choices based on the desired business outcomes you want to achieve.

3. Reimagine how you drive business value with modern applications

In an AI-focused world, modernization is no longer about moving to the cloud for cost or efficiency gains but about delivering concrete business outcomes. Although modernization is the top cloud priority for the next two years, underscoring the need for cloud-native applications to achieve business outcomes with AI, 50% of organizations say the need to modernize is holding them back from innovation. This signals that a key AI requirement could also be a major AI constraint.

To scale with AI, rather than pursuing surface-level proofs of concept, modernize your business processes end to end and build from there.

4. A platform-led approach is no longer optional

As AI increases the complexity of cloud management and investment stalls, most organizations (57%) say managing cost is an ongoing challenge. In this context, they expect their use of fully managed and automated cloud platforms to triple over the next 12 to 18 months — and 34% are already building centralized, cloud-native platforms to manage AI demand.

Among cloud leaders, 59% say visibility across clouds, networks, data centers, applications and digital engagements is essential.

In the era of AI, follow a platform-based cloud management approach, not only to enable greater efficiency but also to drive gains that can be reinvested in innovation and business growth.

5. Reset your cloud transformation KPIs

Cloud transformation metrics need to reflect the significant impact AI can have on the speed and effectiveness of cloud initiatives. Traditional metrics — such as the number of applications migrated — measure activity, not value. But although AI-enabled migration tools are widely available, only 35% of organizations, excluding cloud leaders, used AI in their most recent cloud migration projects. Among leaders, 47% used AI for this purpose.

Additionally, skepticism around AI execution persists, with 62% of organizations expecting the ROI of AI in cloud-native modernization and cloud infrastructure to fall short of expectations. This signals that organizations may be missing quick wins for AI gains. Also, their cloud transformation key performance indicators (KPIs) are likely to remain transactional rather than strategic at a time when business outcomes are more measurable — and achievable — than ever.

In your organization, use AI to replace transactional cloud transformation metrics with business-aligned objectives.

6. Make cloud secure with a focus on the basics

Security is now the number-one cloud investment priority. As cloud adoption speeds up and AI becomes embedded in core processes, traditional security models aren’t keeping up with the speed, scale and complexity of evolving cloud environments. But there’s a clear confidence gap: 68% of cloud leaders say they’re very confident in their cloud security posture, compared with just 36% of all other organizations.

As cloud, AI and security ecosystems become more distributed and increasingly reliant on third parties, security can no longer be managed in isolation. However, while most cloud leaders (54%) have established clearly defined security roles supported by regular audits and strong governance processes, only 34% of all other organizations have done the same.

Rather than limiting innovation or overengineering controls, taking cloud security seriously means getting the fundamentals right: Defining clear security roles, embedding governance into platforms and taking ownership of risk.

From infrastructure to competitive advantage

AI has introduced a new urgency: Cloud is no longer just infrastructure. It is where intelligence runs, where decisions are executed and where value is created at scale.

The six rules outlined in our report are not minor adjustments. They reflect a broader shift in how organizations define, deliver and govern cloud value in an AI-driven enterprise, and loud leaders are already moving in this direction. They’re putting in place the foundations for intelligence, flexibility and long-term growth rather than short-term efficiency.

The issue is no longer whether to invest in cloud, but how intentionally that investment is pursued. Reexamining assumptions, modernizing with purpose and embedding operational discipline are essential to realizing cloud’s full potential in the era of AI.

• ALSO READ → From cloud-ready to AI-ready: The new modernization mandate