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Moving workloads to the cloud is a milestone, not proof that an organization is ready to run AI at scale. Readiness also depends on modern applications and data, architecture matched to business and regulatory needs, disciplined operations, cost visibility, and clear security and governance ownership. NTT DATA’s September 2025 survey found that 14% of respondents rated their organizations at the highest cloud-maturity level; that is a self-assessment from a vendor-sponsored survey, not an independent measure of enterprise AI success.
Cloud adoption and cloud maturity are different
Cloud adoption describes where workloads run. Cloud maturity describes whether the organization can use that environment effectively: adapting applications and data, choosing suitable platforms, managing cost and performance, and applying security and governance consistently.
A migration can move a legacy system without changing its design or the processes around it. That may deliver some infrastructure benefits, but it does not automatically resolve data silos, operational friction, or governance gaps. Those limitations become more consequential when teams try to move AI beyond experiments and into production workflows.
In its 2026 report, NTT DATA reported that 14% of surveyed organizations rated themselves at its highest cloud-maturity level. The survey gathered responses from 2,335 C-suite and other senior leaders across 33 markets and 13 industries in September 2025. The figure reflects respondents’ assessments; it is not a census, an independent audit, or evidence that a particular maturity level causes AI success.
Why AI raises the stakes
AI initiatives can increase demand for cloud investment, but more spending alone does not make a cloud estate ready. AI workloads need access to useful data and often must connect to existing applications and business processes. They also introduce questions about where data is processed, who can access it, how outputs are monitored, and who is accountable for their use.
#1 Best Overall
NTT DATA’s survey found that 99% of respondents said AI was increasing their need for cloud investment. At the same time, 88% said current cloud investment levels put AI, cloud-native, and modernization initiatives at risk. These are reported views about investment pressure and risk—not proof that underinvestment caused failed AI programs.
The report also found that half of respondents said the need to modernize applications and data platforms was holding back cloud-related innovation. That points to a practical constraint: AI capability depends not only on compute, but also on whether the systems and information around it can support reliable, governed use.
Rank #2
What organizations need to mature
Modernize applications and data
Identify which applications need to be redesigned, refactored, or integrated differently before they can support AI-enabled workflows. Assess whether data is accessible, fit for its intended use, and governed across the systems that produce and consume it. A migration that leaves these constraints untouched may relocate them rather than remove them.
Prioritize modernization by business value and dependency. For example, an AI use case that relies on customer or operational data may require resolving data ownership and integration before expanding cloud capacity. The survey identifies modernization as a reported blocker, but it does not prescribe a tested sequence or prove which intervention produces the best result.
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Choose architecture for the workload and its constraints
Public, private, hybrid, multicloud, and sovereign environments are options to evaluate—not maturity levels or universal answers. Match placement to workload requirements and the organization’s ability to operate the resulting environment.
- Data sovereignty and regulation: Determine where information must reside or be processed, and what control obligations apply.
- Security and governance: Establish consistent access, policy, and accountability across environments.
- Resilience: Decide what availability and recovery the workload requires and how dependencies affect them.
- Workload fit: Consider application design, data movement, performance, and integration requirements.
- Cost and operational capability: Include the organization’s ability to measure spending and manage the architecture over time.
NTT DATA’s 2026 report says sovereign-cloud adoption is projected to grow 50% in two years. This is a survey-based projection, not an observed increase already completed, and it does not establish that sovereign cloud is the right choice for every workload.
Make operations and cost visible
Cloud maturity requires more than provisioning infrastructure. Teams need visibility into usage, performance, reliability, and spending, along with defined processes for responding when workloads change. NTT DATA reported that 57% of survey respondents cited cloud cost management as an ongoing challenge.
That figure describes a challenge respondents reported; it does not show how much any organization spends or which cost-management method works best. In practice, leaders should make ownership clear: who reviews usage, who can adjust resources, how costs are associated with products or teams, and how trade-offs between cost and service requirements are decided.
Best Value
Build security and accountability into the operating model
As AI connects cloud-hosted data, models, applications, and users, security and governance cannot be treated as a final deployment check. Define who approves access, who monitors systems and outputs, how incidents are handled, and which teams own decisions across infrastructure, data, AI, and business operations.
The exact controls depend on the workload, jurisdiction, and organizational risk. The central operational requirement is that accountability follows the system across its components and deployment locations rather than stopping at the cloud boundary.
A practical executive checklist
Use these questions to turn a cloud strategy into an AI-readiness plan. They are decision prompts, not interventions validated by the survey.
- Connect the plans: Do cloud, data, application-modernization, and AI roadmaps share priorities and dependencies?
- Define outcomes: What business result should each AI workload deliver, and what measures will show whether it is working?
- Prioritize modernization: Which application or data constraints block that outcome, and what needs to change before scaling?
- Choose placement deliberately: Where should each workload run given its data, regulatory, security, resilience, cost, and operational requirements?
- Assign owners: Who is accountable for platform operations, spending visibility, security, data governance, and AI oversight?
- Review as workloads evolve: How will the organization reassess architecture and controls as usage, costs, and business needs change?
How to interpret the survey evidence
NTT DATA’s report provides a snapshot of senior leaders’ views collected in September 2025 and published in 2026. Its percentages help describe perceived maturity, investment pressure, and operational challenges among its respondents. They should not be read as independently verified performance data, causal findings, or forecasts of what every enterprise will experience.
TechRadar Pro’s September 30, 2026 article presents the broader argument that AI magnifies the strengths or weaknesses of the cloud foundation beneath it. That is the article’s analysis, not a measured survey result or independent validation of NTT DATA’s findings. The useful conclusion for decision-makers is narrower: cloud adoption alone does not establish readiness, so assess the applications, data, architecture, operations, and accountability needed for each AI workload.
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