Cloud in the age of AI: Six things to consider

Recently I spoke to a senior leader of a Europe-based global consumer goods business, and they said that they had always believed that fundamentally cloud would be more efficient than the alternatives. That in a globalized economy pooling resources on infrastructure would always work out to be cheaper in the end. And that they no longer believe that! 

In part that is due to plain old capitalism: cloud vendors make money. So once customers are dependent on them, costs increase. If cloud was always more efficient, Fin Ops wouldn’t be a thing.  

To be fair to cloud providers: the other issue is the increasing demands of their customers, focused on data, analytics, and AI.  

CIOs are building AI-ready organizations. Some feel they need greater control over tomorrow’s compute, connectivity, and storage costs and capabilities. Others are signing up to long-term contracts with vendor groups offering ‘next gen’ technology in perpetuity, including cloud computing. It is a time of churn, and repatriations are happening. (See: 5 reasons the enterprise data center will never die.) 

There is no perfect solution for all organizations. Most will want some combination of cloud, datacenter and – with the rise of AI PCs – edge computing.  

Below are six themes and talking points concerning cloud computing in the age of AI. We discussed these issues in the latest episode of the Global Tech Tales podcast, which you can view here or in the YouTube player below: 

Cloud spend: value vs cost 

Organizations are seeking to be more data-driven. To apply generative and agentic AI inside to drive efficiency and outside for new products and services.  

Under all of this is the need to have infrastructure that is effective and efficient now, and able to scale with the organization’s AI strategy. The old days of chucking everything in the cloud and managing costs is becoming outmoded. Cost is a driver, but value includes scalability and the ability to accelerate and grow with the organization. 

AI- native cloud services

Responding to market demand cloud providers are embedding generative- and agentic AI capabilities into platforms. This will suffice for some organizations, but others will wish to have more direct control over their AI technology stack.  

It is a time of big opportunity and threat for vendors. They can build long-term partnerships with organizations, locking them into future-proofed AI capabilities and technology that is evolving with their partnership.  

The threat to those vendors is that we are seeing increased cloud repatriation as organizations seek to architect their own future-proofed infrastructure and keep their options open rather than being locked in. (See: CIOs are rethinking how they use public cloud services. Here’s why.) 

Data sovereignty

This is an issue impacting many multinational organizations, driving the growth for regional- and even industry clouds. These offer specific tailored compliance, security, and performance options. 

As organizations try to architect infrastructure that supports their future states, with a blend of cloud and on-prem, data sovereignty is an increasingly large issue. I hear a lot from IT leaders about how they must consider local and regional regulations, which adds a consideration to the simple concept of migration to the cloud.  

In areas such as the UAE and the EU, geographical data sovereignty is a hugely important consideration that flows into this idea of architecting the future organization. For some it just makes on-prem more attractive, or others industry and region-specific clouds are a critical tool in building that infrastructure. 

Cloud security

Cloud vendors are also adding AI-enhanced threat detection to protect distributed workloads. 

I attended an event recently in which a CIO of a large enterprise was talking about how Confidential Computing had to become table stakes for their organization to remain in the cloud. 

Data security for them and their customers (as well as the trust that comes with it) is just the start. There is also regulatory compliance. That organization wants to work in collaboration with suppliers and partners on AI projects and needs to be able to process data in an encrypted and anonymous way. One solution could be narrow and specific private clouds incorporating only a limited group of organizations.  

Cloud sustainability

Sustainability was always the hidden cost of connected computing. Hosting data in the cloud consumes a lot of energy.  

Financial cost is most top of mind when IT leaders talk about driving efficiency through the cloud right now. It’s also at the root of a lot of talk about moving to the edge and using AI-infused end user devices.  

But expect sustainability to become an increasingly important factor in cloud: geo political instability, the cost of energy, and the increasing demands of AI will see to that. 

Edge computing and AI PCs

The AI PC pitch from hardware vendors is that organizations will be able to build small ‘clouds’ of end user devices. Specific functions and roles will work on AI PCs and do their computing at the edge. The argument is compelling: better security and efficient modular scalability. Not every user or function needs all capabilities and access to all data. 

But real-world use cases just aren’t there… yet. The future for many organizations will likely involve some combination of cloud, on-prem with AI devices at the edge. (See: Should you buy AI PCs for your workforce in 2025?)