AI infrastructure spending soars across sectors, signaling widespread deployment maturity and capital commitment from enterprises and hyperscalers.

Enterprises are now pouring more resources into operating AI technology at scale rather than on training models, according to Gartner. As companies mature their AI deployment plans and lean more heavily on agentic systems, their need for AI infrastructure is growing. Enterprises are moving past building and training models to focus on deploying them and operating them throughout their organizations. Gartner forecasts that global spend on infrastructure as a service will continue to rise, reaching $66 billion in 2027.

The shift reflects a fundamental change in enterprise priorities. According to Gartner, "For the last few years, AI infrastructure demand was largely driven by model providers training the large foundation models. Now, enterprises are embedding AI into applications, business processes and customer experiences, which requires continuous inference rather than periodic training."

Forrester projected early this year that global technology spending would grow to reach $5.6 trillion in 2026, up from $5.2 trillion in 2025, amid rising demand for AI services. For CIOs and other tech decision-makers, AI infrastructure represents a strategic investment to operationalize AI across the business, rather than an experimental budget line item. Enterprises now see demand for AI-optimized compute, storage, networking and orchestration capabilities that traditional infrastructure was not designed to support efficiently.

Hyperscalers and frontier model developers continue to account for a large share of AI infrastructure investment. Top hyperscalers Google Cloud, Microsoft Azure and AWS plan to invest more than $500 billion in capital expenditures for AI infrastructure this year. Enterprise demand has accelerated rapidly, with enterprises more than doubling their spending on generative AI models and AI agents this year, according to May Gartner data. Vendor-driven AI infrastructure that supports AI work—including AI-optimized IaaS, AI-optimized servers, AI network fabric, AI processing semiconductors and devices—accounted for more than 45% of spending.

As AI becomes embedded in enterprise workflows, organizations require infrastructure capable of supporting higher performance, lower latency and greater scalability than before. CIOs focused on scaling AI from pilots to business-critical production need to address compute capacity, data gravity, governance, operational resilience, security and cost management. Many enterprises are reassessing their cloud approaches, seeking hybrid solutions that combine public cloud, private cloud, colocation, edge and sovereign environments. Ultimately, AI infrastructure is increasingly becoming a business capability that determines how quickly and effectively enterprises can scale AI across the organization.