The cloud is not considered a place for experimentation. For many enterprises, it has become the default environment for running artificial intelligence systems that support daily work. This shift, more than any headline figure, explains why cloud spending continues to grow.
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 are in regular use, they require constant access to computing power, storage and networks. This need has kept demand for cloud infrastructure strong even as companies exercise greater discipline in technology spending.
Market data supports this trend. Research by Synergy Research Group shows that spending on global cloud infrastructure services will exceed $100 billion per quarter by the end of 2025, with year-over-year growth driven primarily by artificial intelligence-related demand. The largest providers continue to hold the majority of the market, reflecting how scale matters when workloads grow unevenly and rapidly.
Not only has it changed how much businesses spend, but also how they think about what the cloud is for. Earlier waves of adoption focused on moving existing systems out of data centers. Today, cloud infrastructure is often chosen because it can support workloads that are difficult to run elsewhere. Training models, running inferences, and storing large datasets all place demands on systems that an on-premises setup may struggle to meet without frequent upgrades.
This helps explain why cloud adoption has held back even as budgets are under pressure. AI workloads don’t behave like traditional enterprise software. They scale up and down, consume resources in batches, and are often shared across teams. Cloud environments make it easier to absorb these changes, although the costs are more difficult to predict.
Rather than asking whether to use the cloud, many IT teams are now focusing on how to operate it well.
Running AI as part of daily operations
The questions that business leaders are asking themselves today sound different than they did just a few years ago. Migration timelines are more important than stability, performance and cost control. AI systems that support live services cannot tolerate the same level of downtime that test environments once did.
Gartner’s forecasts reflect this shift, with the company expecting global spending on public cloud services to exceed $700 billion in 2026, with growth spread across AI-related infrastructure, platforms and services. This growth suggests that cloud usage is not driven by one-off moves, but by ongoing operational needs.
AI is also changing the way capacity planning works. Model training can spike usage in the short term, while workloads can run continuously. This combination makes it more difficult to plan for average demand, and as a result, some businesses are separating AI workloads from other applications to better track usage and avoid surprises.
Choices are often less about optimization and more about control. When AI systems deal with sensitive data or influence decisions, teams want to clarify the boundaries of who can access what and how resources are used.
Skills and uneven progress
Spending patterns also reflect gaps within organizations. Running AI systems in production requires skills that many teams are just building. Engineers, security professionals, and application owners need to work more closely together, and when that coordination is lacking, cloud services can fill some of the gaps, even if they increase costs.
Progress varies 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 and use cloud-based artificial intelligence to improve planning and supply chains.
Data growth adds another layer of pressure. AI systems depend on large and growing data sets, and many businesses are keeping data longer than before. Managing this volume on site can be costly and inflexible.
Cloud storage offers a way to expand without constant hardware changes, though it comes with its own cost trade-offs.
When reliability and price come first
As AI becomes part of everyday work, the tolerance for failure decreases. Outages that once affected test systems can now disrupt operations. This increases reliability expectations and puts pressure on cloud providers and customers to design systems that can handle a breach.
Cost control remains an open issue. AI workloads can increase spending faster than expected, and pricing models aren’t always easy to predict. Some businesses are responding by setting tighter limits or moving stable workloads back within the business. Others rely on hybrid setups, using the cloud for peaks while keeping demand steady elsewhere.
Together, these patterns point to a cloud market that has grown. Expenses are still rising, but the reasons are more practical than before. The cloud is not a destination, but part of how work is done.
As it becomes increasingly difficult to separate artificial intelligence from day-to-day operations, cloud infrastructure is likely to remain central to corporate IT plans. Another challenge is not whether to invest, but how to ensure that the investment lasts over time.
(Photo by Dylan Gillis)
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