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Moving workloads to cloud is an adoption milestone, not proof that an organization is ready to scale enterprise AI. In NTT DATA’s survey of 2,335 senior leaders across 33 markets and 13 industries, fielded in September 2025, 14% of respondents said their organizations had reached the highest cloud-maturity level. That is a measure of respondents’ self-assessments—not an independent audit of enterprise readiness or a forecast of AI success. NTT DATA’s 2026 report makes the distinction important: the work after migration—modernization, architecture choices, operational discipline and governance—determines whether cloud can support AI ambitions.
Why cloud adoption and cloud maturity are different
An organization can move a legacy workload into a cloud environment without changing how the application is designed, how its data is managed, or how teams operate and govern it. The infrastructure has moved, but the underlying constraints may remain. Cloud maturity means building the technical and organizational capabilities to use that environment effectively: modernized applications and data, architecture suited to each workload, visible costs, reliable operations, and security and accountability built into day-to-day decisions.
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That distinction matters as companies try to take AI beyond experiments. In its September 2025 survey, NTT DATA found that 14% of respondents rated their organizations at the highest cloud-maturity level. The finding is a reported perception from a vendor-sponsored survey; it does not establish how every enterprise performs, or prove that maturity alone causes successful AI deployment.
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Why AI puts more pressure on the cloud foundation
AI initiatives can increase demand for computing capacity and access to usable data, while requiring connections to applications and business workflows. Putting a model into production also raises questions about security, privacy, compliance and who is accountable for its operation. A cloud environment that is difficult to manage or poorly integrated with enterprise data and applications can make those demands harder to meet.
NTT DATA reports that 99% of surveyed leaders say AI is increasing their need for cloud investment. At the same time, 88% say current investment levels put AI, cloud-native and modernization initiatives at risk. These figures capture respondents’ assessments of investment pressure and risk; they do not demonstrate that a specific spending level will produce an AI outcome. The report describes the survey and its results.
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What organizations need to change after migration
Modernize applications and data platforms
Moving an application as-is may preserve dependencies and limitations that constrain performance, integration or change. Modernization can mean redesigning an application, updating its components, or improving the data platform and practices it relies on. The appropriate scope depends on the workload; not every system needs the same treatment or an immediate rebuild.
Half of the respondents in NTT DATA’s survey said the need to modernize applications and data platforms was holding back cloud-related innovation, according to the company’s March 26, 2026 release. That is a reported obstacle, not a measured estimate of how much modernization would improve innovation.
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Choose where workloads run based on requirements
Public, private, hybrid, multicloud and sovereign environments involve different operational and governance considerations. A workload’s data-sovereignty obligations, privacy and compliance requirements, security needs, resilience objectives, cost profile and technical dependencies should shape its placement. The organization also needs the skills and processes to operate that choice consistently.
No single deployment model is established as the best fit for every enterprise. NTT DATA’s report projects sovereign-cloud adoption will grow 50% in two years; this is a survey-based projection, not a completed or independently observed increase. A projected trend is a reason to evaluate sovereignty requirements, not a substitute for assessing a particular workload.
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Make operations and costs visible
Cloud maturity requires more than provisioning infrastructure. Teams need a platform-led way to manage environments, understand resource use, monitor services and assign operational responsibility. Cost visibility helps teams connect spending to workloads and decisions, then identify where usage or architecture should change.
In NTT DATA’s survey, 57% of respondents cited cloud cost management as an ongoing challenge. This is a reported difficulty, not a claim that every cloud program has the same cost problem.
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Build security and accountability into the operating model
Security and governance should be part of how cloud and AI systems are designed, deployed and operated—not a final review after technical choices are made. Teams need clear responsibility for access, data handling, policy compliance and ongoing oversight. The controls and owners should reflect the organization’s regulatory obligations and the sensitivity and use of the data involved.
A practical executive sequence for moving toward maturity
- Connect cloud and AI plans. Identify which AI initiatives depend on cloud capacity, data access, application integration or particular governance controls. Treat cloud investment as part of a defined business and technology plan, not as a proxy for AI readiness.
- Define measurable outcomes. Set objectives for the relevant initiative—such as service reliability, delivery speed, cost visibility or a business process result—and establish how owners will assess progress. The survey figures describe perceptions; they do not supply targets for an individual organization.
- Prioritize modernization by constraint. Identify the application and data dependencies that block the intended workload. Modernize where a concrete constraint or risk justifies it rather than assuming every migrated system needs the same redesign.
- Place each workload deliberately. Assess sovereignty, privacy, compliance, security, resilience, cost and workload fit, alongside the organization’s ability to operate the environment. Document the trade-offs and accountable owners.
- Assign operating and governance ownership. Make clear who manages the platform, tracks costs, maintains security controls and oversees AI-related responsibilities through production use.
How to interpret the survey in an enterprise decision
NTT DATA’s findings offer a signal about how senior leaders view cloud investment, maturity and obstacles, not a universal scorecard. The survey covered 2,335 C-suite and other senior leaders across 33 markets and 13 industries, with fieldwork in September 2025; it is vendor-sponsored and based on self-reported responses. It can frame questions for a leadership discussion, but it cannot diagnose an individual company or establish which intervention will deliver a particular result.
The useful next step is to compare the organization’s AI goals with the capabilities its cloud environment actually provides: modern applications and data, fit-for-purpose placement, operational and cost visibility, and accountable security and governance. Migration establishes a foundation; maturity is the work of making that foundation dependable for the workloads the business intends to run.
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