Cloud modernization: AI is exposing the limits of legacy systems

Organizations are reassessing their cloud priorities as they look for greater business value from AI. In the process, a new reality is becoming clear: There is no realistic path to AI-driven value without cloud modernization.

For many years, modernization was associated with migration, efficiency and cost optimization. Now, that definition has changed. Cloud is no longer simply a place to host applications. It has become the operating environment for AI-led decision-making and execution.

Supporting this shift requires applications and environments that are built to operate differently.

Why modernization has become the top cloud priority

An NTT DATA report, Cloud-led innovation in the era of AI: The new rules for driving value with cloud, shows that cloud modernization — using cloud-native capabilities to modernize applications and workflows — remains the top cloud priority for the next two years, ahead of migration, optimization and managed services.

At the same time, organizations recognize that they are not where they need to be. Fifty percent say the need to modernize applications and data platforms is holding them back from cloud-related innovation.

This highlights that, although modernization is central to delivering outcomes with AI, many organizations continue to face barriers in building the environments needed to support it.

Modernization is not simply a technology exercise. It has become a business requirement.

There are no shortcuts to modernization

AI has fundamentally changed what modernization means. Organizations increasingly need truly cloud-native applications that can scale on demand, connect directly to data and AI services and continue operating effectively as conditions change.

However, many remain constrained by legacy environments built around applications that were not designed for the cloud. These environments can limit scale, slow change and create operational drag.

Surface-level automation may improve productivity at the margins, but it doesn’t resolve the structural constraints of legacy architectures. There are no shortcuts here. Meaningful business impact requires modernizing the applications themselves.

This shift is already visible among leading organizations. NTT DATA’s global AI research shows that AI leaders are more likely to rebuild applications with AI embedded at the core rather than relying on bolt-on tools or superficial automation.

Organizations that layer AI onto existing application environments may struggle to achieve the same level of impact and scalability.

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Data readiness has become impossible to ignore

Modernization is also no longer limited to applications. Data readiness has become equally important. Our research shows that 36% of organizations say agentic AI has intensified the need for clean, well-governed and AI-first data models and architectures.

Data readiness and analytics challenges are the number-one reason organizations report dissatisfaction with their efforts to build cloud-native AI applications.

Moving data into the cloud alone is not enough. Without a data-readiness strategy that links data quality, governance, integration and real-time access directly to business goals, cloud and AI initiatives fail to deliver value.

Industry cloud is becoming a pathway to value

Organizations are also increasingly looking toward industry cloud solutions as a way of accelerating modernization efforts.

Nearly two-thirds of organizations say industry cloud solutions will be extremely important to their cloud strategy. These solutions combine modern application architectures with preintegrated industry data, workflows and governance frameworks that help make AI execution practical and scalable.

Cloud can provide access to models and scale, but AI creates meaningful outcomes only when it operates within real business and industry context. Built on cloud-native services, industry cloud solutions provide a faster route from experimentation to enterprise impact.

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The third rule for cloud in the AI era

In response to these changes, our research identifies a clear principle: Organizations must reimagine how they drive business value with modern applications.

Modern applications are not optional, and AI agents are not a substitute for extensive modernization. Organizations that modernize with this understanding will be far better positioned to translate cloud investment into sustained business value.