An advanced cloud strategy is essential for most organizations to fully benefit from AI. Unfortunately for the majority of CIOs, their organizations still lack the cloud proficiency and investment to drive advanced deployments. Decisions to invest significantly in AI at the expense of cloud operations may only be making things worse.
According to a report from NTT DATA, just 14% of organizations are “cloud evolved,” the optimal level for AI success, with cloud-led innovation accelerating business transformation and cloud-native services embedded in core strategies.
Another 34% of senior IT decision-makers surveyed for NTT DATA consider their cloud approach “mature,” the next level down from evolved and defined as having broad and strategic cloud use across business units, with strong governance, best practices, and scalable workloads.
That leaves more than half of organizations behind the cloud curve for AI effectiveness, with more than a quarter simply “cloud enabled” and nearly a quarter being cloud novices.
Regardless of where CIOs find themselves on the cloud maturity curve, forgoing cloud investments to fund AI projects can be dicey.
Nearly nine in 10 IT leaders (88%) are worried that a lack of cloud investment at their organizations will put their AI, cloud-native, and modernization initiatives at risk. Despite AI driving more cloud use, 84% of survey respondents say their cloud spending has been flat over the past year.
Robbing Peter to pay Paul
The survey suggests that as organizations reallocate money for AI pilots, they’re neglecting the cloud, an essential piece of the AI puzzle, says Charlie Li, president and global head of cloud and security at NTT DATA.
“The cloud side is not getting the money,” he says. “The frustration is, ‘In order to do AI, I’ve got to spend money here, and I have no money here, but I’ve been throwing in a lot of money for AI. So I end up wasting a lot of money doing a bunch of pilots.’”
Some customers of NTT DATA have the budget to run dozens of AI pilots, but CIOs have not gotten any new money to spend on cloud services, Li adds.
“The CIO is sitting there going, ‘Wait a minute, I’ve got to do these things in the cloud side in order to be able to do AI, but I have no money to do it,’” he says.
NTT DATA sees cloud services as essential for AI development because of the computing power it requires, Li says.
“You need humongous scales of data and processing power,” he explains. “Those two things are what’s really led to the advent of the gen AI trend that we actually see today. You cannot do this with 100 servers sitting in your own data center.”
An evolved cloud strategy is also needed to run successful AI projects because of the data maturity that it enables, Li adds. “If you don’t have a mature cloud strategy or implementation, your data is still all over the place,” he says. “If you’ve got junk data, if you have poor data governance, none of your trained models are going to be accurate.”
No AI on messy clouds
Other cloud and AI experts agree that the cloud often plays a huge role in successful AI deployments. The link is quite direct, says Adnan Masood, chief AI architect at digital transformation solutions provider UST.
“I’ve yet to see an AI program scale cleanly on top of a messy cloud estate,” he says. “Teams can get a demo running that way, sure. Production is where weak data governance, brittle integrations, poor observability, and runaway compute costs show up.”
The NTT DATA survey, with respondents concerned about a lack of cloud spending, is what Masood sees in the market.
While on-premises AI projects can work in limited circumstances, particularly for highly regulated companies, most organizations benefit from a cloud approach, he adds.
“Enterprises can get some AI footing without a strong cloud strategy — usually a contained assistant, internal search layer, or a narrow automation flow — but the odds of scaling it cleanly are low,” Masood says. “In practice, on-prem only works when the management layer is mature — AI-ready data, orchestration, model serving, observability, security, cyber-recovery, and governance — and that is where most enterprises are still behind.”
Full cloud maturity can be the difference between deploying AI as a pilot and AI as an operational system, adds Quais Taraki, CTO at AI and data platform provider EnterpriseDB.
“Cloud maturity alone is not fully sufficient, but companies that are further along in the process of cloud maturity tend to have more modern data architecture, better governance, stronger interoperability across environments, and infrastructure that can actually support production-scale workloads without falling apart under real concurrency and data volume demands,” he says.
Cloud spending doesn’t necessarily lead to cloud maturity, however. Some companies that have invested heavily in the cloud also struggle with moving AI pilots into production, because they don’t have the data architecture to support the real-time, multi-environment workloads that production AI requires, Taraki says.
“Moving workloads to the cloud does not simplify an architecture that was fragmented before you moved it,” he adds. “What we see consistently is that cloud investment helps when it creates a more unified, flexible, and governed foundation where data and AI can operate together without silos.”
The wrong cloud investments
Cloud investment can hinder AI, however, when it creates increased operational complexity, when it includes pricing models that make advanced analytics and AI unpredictable to scale, and when vendor dependencies constrain how they respond to new requirements, Taraki adds.
“When the data and system governance architecture of the cloud environments are fragmented, teams spend too much time moving data across systems, absorbing unpredictable cost, and managing operational friction that compounds the moment AI becomes agentic and starts acting on data rather than just querying it,” he says.