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Overview

AI ambition meets cloud reality

Our new report, based on a global survey of more than 2,300 senior decision-makers across 33 countries, reveals a gap between AI ambition and cloud reality. As cloud takes on a critical role as the execution layer of the AI operating model, just 14% of organizations rate themselves at the highest level of cloud maturity, despite nearly two decades of cloud adoption. Discover the new rules for driving value with cloud at a time of unprecedented opportunity.

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* Research data is sourced from 2,335 C-suite and other senior decision-makers across 33 markets and 13 industries during September 2025.

Highlights

FAQs

What does this research cover?

This large-scale global study of over 2,300 senior IT and business leaders in 33 markets and 13 industries examines cloud maturity in the era of AI, the practices that separate cloud leaders from the rest and the new rules for creating value.
Who are the cloud leaders?

In this report, cloud leaders are defined as "cloud-evolved" organizations — the most advanced when it comes to cloud adoption and impact — that achieved operating profit growth of more than 10% in the past year, alongside an increase in their annual revenue.

These leaders are mature, well-structured and translating cloud maturity into measurable business performance. They are nearly 2.5 times more likely than other organizations to deliver revenue growth exceeding 10%, and more than twice as likely to operate at margins of 15% or higher.
Who should read this report?
CIOs, CTOs, CAIOs and other business leaders responsible for cloud strategy, AI transformation, modernization, security and digital operating models.

The new rules

  • Cloud and AI strategies need to be developed in tandem. Interlock AI and cloud strategies and ensure strong alignment between your CAIO and CIO or CTO so ambitions translate into scalable, enterprise-wide value.
  • Cloud architecture choices will make or break your success. Architect for scale at the outset. Cloud deployment decisions should be driven by the business outcomes you want to achieve.
  • Reimagine how you drive business value with modern applications. Modernize with the end in mind. Build an end-to-end cloud modernization program designed to operationalize AI across the organization, moving beyond surface-level proofs of concept to sustained business impact.
  • A platform-led approach is no longer optional. Develop a strategy to standardize your cloud automation and productivity efforts, and consolidate these into a single, platform-based cloud management approach driving gains that can be reinvested into the business.
  • Reset your cloud transformation KPIs. Shift from technical to business metrics when measuring cloud transformation. Use AI to elevate cloud initiatives and drive measurable business outcomes.
  • Make cloud secure with a focus on the basics. As cloud and security ecosystems become more complex, take a top-down, enterprise-wide approach to security with clear roles and responsibilities backed by regular audits.
Impact

The state of cloud maturity

Only 14%

of organizations say they’re cloud-evolved — the most advanced when it comes to cloud adoption and impact.

99%

say AI is increasing their need for cloud investment.

88%

say their current cloud investment puts AI, cloud-native and modernization initiatives at risk.

Just 49%

are fully satisfied with the role of cloud in innovation.

AI is accelerating faster than enterprise cloud maturity. Cloud has moved well beyond infrastructure and is now the execution layer for AI. Organizations that fail to evolve their cloud foundations risk constraining the growth and value of their AI investments. Our clients who are succeeding are treating cloud as a value creator, not a technology initiative.”

Charlie Li
President, Global Head of Cloud and Security, NTT DATA, Inc.
Insights

Research insights

  1. Cloud and AI are increasingly interconnected as the execution layer of the AI operating model
    AI demand is rising, yet alignment is uneven. AI skills are the top cloud-related skills gap, and CAIOs are 22% more likely than CIOs and CTOs to say that AI increases the need for greater cloud investment.

  2. Cloud architecture choices are in the spotlight
    Cloud deployment choices are now directly influencing cloud outcomes, and organizations are increasingly adopting a mix of public, private, hybrid and sovereign cloud models. With data security, privacy and compliance driving AI workload decisions, nearly all organizations (99%) expect private cloud adoption to rise. Sovereign cloud adoption is projected to grow 50%.

  3. Modernization is critical for success
    Despite widespread agreement that cloud should drive innovation, 50% of organizations say the need to modernize applications and data platforms is holding them back from cloud-related innovation. Cloud modernization is cited as the top cloud priority in the next two years.

  4. Platform-led cloud management can turn investment into impact
    With 57% of organizations citing cloud cost management as an ongoing challenge and 59% of cloud leaders saying visibility across clouds, networks, data centers, applications and digital engagements is essential, organizations expect a threefold increase in fully managed cloud platforms.

  5. Cloud transformation metrics need to change to reflect AI influence
    While AI is critical for helping organizations shift from technical to business metrics for cloud initiatives, adoption is uneven: 47% of cloud leaders say they used AI in their last cloud migration project, compared with only 35% of all other organizations, and 62% of all organizations expect the ROI of agentic AI for cloud-native modernization and infrastructure to fall short of expectations despite the significant potential of AI in cloud transformation.

  6. The fundamentals of security are essential
    Although security is the top cloud investment area, confidence is uneven: 68% of cloud leaders are very confident in their cloud security posture, compared with only 36% of all other organizations. Leaders are also much more likely to define clear roles and responsibilities backed by regular audits, reinforcing the importance of the fundamentals as technology ecosystems grow more complex.
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