These days, cloud news often gets overshadowed by anything and everything related to AI. The truth is they go hand-in-hand since many enterprises use cloud computing to deliver AI and generative AI at scale.

“Hybrid cloud and AI are two sides of the same coin because it’s all about the data,” said Ric Lewis, IBM’s SVP of Infrastructure, at Think 2024.

To function well, generative AI systems need to access the data that feeds its models wherever it resides. Enter the hybrid cloud—which can access data on premises, at the edge or in a mixture of private and public cloud. So, businesses that adopt a hybrid cloud approach can better scale AI and generative AI projects and unlock the value of their data.

Varun Bijlani, Global Managing Partner for Hybrid Cloud Services at IBM, describes the symbiotic relationship between AI and cloud slightly differently.

“AI is supremely accelerating the execution of hybrid cloud and at the same time you can’t go from pilot to production to scale without a robust architecture,” says Bijlani.

Cloud spending swells

Cloud competition is likely only going to heat up. Worldwide spending on public cloud services is forecast to reach $805 billion by the end of 2024 and that amount will double by 2028, according to new research from the International Data Corporation.

“Cloud now dominates tech spending across infrastructure, platforms and applications,” says Eileen Smith, Group Vice President of Data and Analytics at IDC.

To that end, Microsoft recently announced it’s spending $19 billion on cloud and AI. “Cloud and AI-related spend represents nearly all of total capital expenditures,” said Microsoft CFO Amy Hood on a recent earnings call.

Many companies are expanding their cloud investments. In Texas alone, Google announced on August 15 that it will spend a billion dollars this year supporting cloud and data center infrastructure, which represents a 200% increase on the amount it invested last year for the same purposes in the Lone Star state.

Perhaps unsurprisingly, companies’ cloud units are also coming under closer scrutiny as a result. Amazon, for instance, recently announced it plans to close its CodeCommit service to new users. CodeCommit is a version control service that allows users to privately store and manage assets in the cloud. While AWS did not explain why it’s closing this service, it did provide guidelines for migrating one’s CodeCommit repository to another Git provider.

Uniting AI and cloud

Some cloud-AI investments are already starting to pay off. For example, Westfield Insurance, a US-based property and casualty insurance company, deployed a cloud-AI pilot to improve developer productivity and speed up the onboarding of new developers.

Specifically, over a period of eight weeks, a team of 16 Westfield developers used IBM’s generative AI tools deployed on the cloud to simplify the explanation and documentation of new code and applications. So, with the click of a button compared to weeks of manual research and reporting, developers could automatically perform analysis, generate reports and identify the impact of code changes. As a result, it took developers 80% less time to understand new applications and 30% less time to explain it and document it for new developers.

The ways in which AI and cloud complement one another will certainly continue to evolve. Already the combination of the two offers businesses greater flexibility.

“If this is a busy time of year for a business, then it can use a hyperscaler, such as Google Cloud or IBM Cloud, for more computing and storage capabilities,” says Mark Wass, a Strategic Sales Director at software company CloudBlue.

“AI gives the business insight into when the time is right to start using more resources, or when things slow down and you need to decrease the consumption of resources,” says Wass.