The cloud is no longer treated as a place to experiment. For many enterprises, it has become the default environment for running AI systems that support daily work. That shift, more than any headline figure, explains why cloud spending continues to climb.
Instead of short trials or isolated pilots, AI workloads are now tied to core functions such as forecasting, planning, and customer operations. Once these systems move into regular use, they demand steady access to compute power, storage, and networking. That need has kept demand for cloud infrastructure strong, even as companies apply more discipline to technology spending.
Market data supports this trend. Research from Synergy Research Group shows global cloud infrastructure services spending passed the $100 billion mark per quarter in late 2025, with year-on-year growth driven largely by AI-related demand. The biggest providers continue to hold most of the market, reflecting how scale matters when workloads grow unevenly and quickly.
What has changed is not just how much enterprises spend, but how they think about what the cloud is for. Earlier waves of adoption focused on moving existing systems out of data centres. Today, cloud infrastructure is often chosen because it can support workloads that are hard to run elsewhere. Training models, running inference, and storing large datasets all place demands on systems that on-premise setups may struggle to meet without frequent upgrades.
This helps explain why cloud use has held up even as budgets come under pressure. AI workloads do not behave like traditional enterprise software. They scale up and down, consume resources in bursts, and are often shared across teams. Cloud environments make it easier to absorb that variation, even if the cost is harder to predict.
Rather than asking whether to use the cloud, many IT teams are now focused on how to run it well.
Running AI as part of daily operations
The questions enterprise leaders raise today sound different from those of a few years ago. Migration timelines matter less than stability, performance, and cost control. AI systems that support live services cannot tolerate the same level of downtime that testing environments once did.
Forecasts from Gartner reflect this shift, with the firm expecting worldwide spending on public cloud services to exceed $700 billion in 2026, with growth spread across infrastructure, platforms, and AI-related services. That growth suggests cloud use is no longer driven by one-off moves, but by ongoing operational needs.
AI also changes how capacity planning works. Training a model can push usage sharply higher for short periods, while inference workloads may run constantly in the background. This mix makes it harder to plan for average demand. As a result, some enterprises are separating AI workloads from other applications so they can track usage more closely and avoid surprises.
These choices are often less about optimisation and more about control. When AI systems deal with sensitive data or influence decisions, teams want clearer boundaries around who can access what and how resources are used.
Skills and uneven progress
Spending patterns also reflect gaps inside organisations. Running AI systems in production requires skills that many teams are still building. Engineers, security staff, and application owners need to work together more closely than before. When that coordination is missing, cloud services can fill in some of the gaps, even if they raise costs.
Progress varies widely by industry. Regulated sectors such as finance and healthcare tend to move slowly, balancing cloud use with legal and data location rules. Manufacturing and retail firms, on the other hand, often move faster, using cloud-based AI to improve planning and supply chains.
Data growth adds another layer of pressure. AI systems depend on large and growing datasets, and many enterprises keep data longer than they once did. Managing that volume on-premise can be costly and rigid.
Cloud storage offers a way to expand without constant hardware changes, though it brings its own cost trade-offs.
When reliability and cost take priority
As AI becomes part of daily work, tolerance for failure drops. Outages that once affected test systems can now disrupt operations. This raises expectations around reliability and puts pressure on both cloud providers and customers to design systems that can cope with disruption.
Cost control remains an open issue. AI workloads can drive spending higher faster than expected, and pricing models are not always easy to forecast. Some enterprises respond by setting stricter limits or shifting stable workloads back in-house. Others rely on hybrid setups, using the cloud for peaks while keeping steady demand elsewhere.
Together, these patterns point to a cloud market that has grown up. Spending continues to rise, but the reasons are more practical than before. The cloud is no longer a destination. It is part of how work gets done.
As AI becomes harder to separate from everyday operations, cloud infrastructure is likely to stay central to enterprise IT plans. The next challenge is not whether to invest, but how to make sure that investment holds up over time.
(Photo by Dylan Gillis)
See also: A pivotal 2026 for cloud strategy
Want to learn more about Cloud Computing from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.
CloudTech News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
