In 2026, AI strategies will fail if cloud strategies lag behind

As cloud becomes the execution layer for AI operating models, cloud and AI strategies need to evolve together.

This is one of the central findings from our latest research, Cloud-led innovation in the era of AI: The new rules for driving value with cloud, based on insights from more than 2,300 senior decision-makers in 33 countries. As organizations accelerate their adoption of AI, the relationship between cloud and AI strategies is becoming increasingly important. Yet, for many, the two are still planned and funded separately.

AI is now the primary consumer of cloud, while cloud is the principal enabler of AI. When these strategies are developed separately, organizations end up funding two architectures that can’t deliver the outcomes their business depends on.

That separation creates friction. AI initiatives advance quickly, while cloud environments evolve at a different pace. Over time, this disconnect makes it harder to scale AI beyond early experimentation and embed it into core business processes.

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Cloud has moved beyond infrastructure

Cloud is no longer simply where systems run. As AI becomes embedded in operations, analytics and decision-making, cloud environments increasingly determine how effectively AI capabilities can be deployed and scaled.

Our research shows that organizations are already recognizing this shift. Nearly all respondents say AI has increased their need for cloud investment. At the same time, many acknowledge that their cloud foundations are still evolving.

When cloud environments are not designed with AI in mind, organizations often encounter challenges integrating AI into enterprise workflows, managing data pipelines or scaling AI models across business units. What begins as a promising AI pilot project can quickly become difficult to operationalize.

This is why the relationship between cloud and AI strategies has become so critical.

The talent signal that leaders are watching

One of the clearest indicators of this shift is the growing convergence of cloud and AI capabilities.

Organizations now cite AI skills as the top cloud-related talent gap. Among cloud leaders, nearly half (49%) expect AI skills to remain a challenge over the next 12 months, compared with 33% of other organizations. This reflects the reality that developing AI capabilities increasingly requires expertise in cloud architecture, data platforms and modern application environments.

The connection is also evident at the executive level. Chief AI Officers are 22% more likely than CIOs and CTOs to say that the rise of AI increases the need for greater cloud investment. As AI initiatives expand, leaders responsible for AI are increasingly recognizing that cloud strategy directly influences how quickly those initiatives can scale.

Together, these signals point to the same conclusion: Cloud and AI capabilities are now deeply intertwined.

The first rule for cloud in the AI era

In response to these shifts, our research identifies a clear principle for organizations seeking to unlock value from AI: Cloud and AI strategies need to be developed in tandem.

As cloud becomes the execution layer for AI operating models, strategies must evolve together. When cloud roadmaps and AI programs are developed independently, organizations often create misalignment between AI ambitions and the cloud environments required to support them.

Organizations that successfully scale AI approach this differently. They align cloud architecture, skills development, platform investments and governance frameworks with their AI strategy from the outset. This alignment allows cloud environments to support AI workloads more effectively while reducing the operational complexity that can slow innovation.

Developing cloud and AI strategies together also helps organizations prioritize the capabilities required for AI-driven growth, from modern data platforms to integrated security and governance models.

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Strategy alignment is now a scaling requirement

As AI adoption accelerates, organizations can no longer treat cloud purely as an infrastructure initiative. Cloud decisions increasingly determine how quickly AI initiatives move from experimentation to enterprise-scale impact.

Aligning cloud and AI strategies ensures that the architecture, skills and governance required to support AI evolve alongside the ambitions driving it. Without that alignment, organizations risk building AI capabilities that their cloud environments cannot fully support.

Organizations that recognize this shift early will be better positioned to translate AI innovation into measurable business outcomes.