AI has become one of the biggest drivers of cloud adoption in recent memory.
Research shows that firms are increasing their cloud spend by 18% on average to support AI ambitions. It’s a strong show of intent, but intent alone is not delivering results.
Despite this investment surge, only 20% of organisations say their data is structured, accessible and ready for AI. And fewer than one-third of AI projects are delivering measurable ROI. For many, this is proving a painful lesson: cloud adoption alone does not equal AI readiness.
AI and the cloud: connected but not interchangeable
It is no surprise that AI and the cloud are linked. Training AI models demands vast compute and storage resources. Running them at scale requires elastic infrastructure. Cloud platforms make it easier to experiment with AI services, manage fluctuating workloads and access specialised tools without heavy up-front investment.
But migrating to the cloud is not the same as preparing for AI. In my experience, this is where most strategies fall short. AI workloads place very specific demands on infrastructure: unified data pipelines, optimised performance for training and inference, and governance models that balance accessibility with compliance.
What gets missed in enterprise cloud strategies
There are three common gaps that stall AI progress, even after significant cloud spend.
First is data architecture. Many organisations lift and shift legacy systems into the cloud without rethinking how data will flow across teams and tools. They end up with the same fragmentation problems, just in a new environment.
Second is the skills gap. Research has found that 27% of organisations lack the internal expertise to harness AI’s potential. And it is not just data scientists. You need cloud architects who understand how to design environments specifically for AI workloads, not just generic compute.
Third is data quality and accessibility. AI models cannot perform well without clean, consistent input. But too often, data governance is an afterthought. Only 1 in 5 organisations feel confident that their data is truly AI-ready. That is a foundational issue, not a fine-tuning one.
Defining cloud readiness in the AI era
Being cloud-ready today means more than having workloads hosted in the cloud. It means having the right data, in the right place, in the right shape, and the internal maturity to make use of it.
Cloud-ready organisations architect their data so that AI tools can access it easily, across departments and geographies. They invest in data governance frameworks that balance agility with accountability. And they make deliberate choices about which platforms and services support their AI goals, rather than retrofitting legacy systems into unsuitable environments.
Why hybrid cloud is a smarter route to AI value
Public cloud platforms offer compelling tools for AI development. But they are not always the right home for every dataset or workload. Hybrid cloud allows firms to keep sensitive data closer to home, reduce latency for real-time processing and control costs more effectively.
It also enables flexibility. Teams can run training workloads in the cloud while keeping inference closer to end users. Or store structured data in public cloud services while maintaining unstructured data in on-premise systems with caching for local performance. This level of architectural choice is critical for organisations looking to balance experimentation with enterprise control.
Hybrid cloud also helps address the skills issue. Rather than betting everything on a wholesale cloud transformation, firms can evolve gradually, building internal capability over time while still accessing cutting-edge AI services.
Getting the foundations right
Before investing in another AI pilot or data science hire, organisations should take a step back. Is the data ready? Are the pipelines in place? Do internal teams have what they need to turn compute into insight?
This means prioritising data integration and governance before algorithms. It means investing in internal training and hiring with long-term capability in mind. And it means treating cloud and AI as part of the same strategy, not separate silos.
With only 27% of AI projects currently delivering measurable ROI, there is a clear opportunity for those who get this right. Aligning cloud infrastructure, data management and organisational skill sets will be the difference between firms that talk about AI value and those that actually realise it.
About the Author
Nick Burling is Chief Product Officer at Nasuni, where he leads the roadmap for its hybrid cloud storage offerings and ecosystem. An accomplished entrepreneur and IT product executive, he has built and led product teams at Microsoft, IBM, and multiple startups from launch to successful exit.
