Gartner Forecasts IT Spending Surge Driven by AI

Gartner revised its 2026 worldwide IT spending forecast upward, now projecting 13.5% growth to $6.31 trillion as cloud and AI infrastructure investments accelerate. John-David Lovelock, Distinguished VP Analyst at Gartner, attributed the increase to hyperscaler datacenter expansion and strong demand for AI-optimized compute and memory. Gartner also highlights a rapid rise in spending on AI-related systems and services, with data center systems forecasted to grow 55.8% in 2026.

Gartner breaks the growth into distinct pockets of demand and price dynamics. Key figures and drivers on first mention: data center systems (55.8% growth), software (15.1% growth), IT services (projected to surpass $1.87 trillion), and overall IT reaching $6.31 trillion. Practitioners should note these technical inflection points:

• •Hyperscalers are buying servers purpose-built for large model training and inference, increasing per-server spend by roughly three-to-four times versus legacy servers.
• •High-bandwidth memory (HBM) and other advanced memory types are facing supply constraints and price inflation, creating a cost and procurement risk for AI deployments.
• •Datacenter power consumption is expected to rise sharply, with Gartner forecasting a doubling in datacenter power demand within roughly four years driven by AI workloads.

This forecast signals a structural shift where AI infrastructure, not general-purpose IT, is the primary growth engine. Gartner and other analysts show AI-related spending moving from a project line item to a platform-level allocation, with AI expected to represent around 41.5% of IT spending in 2026 and trending toward ~50% by 2027. That reorientation creates a multi-speed market: hyperscalers and cloud providers drive infrastructure growth, while many enterprise buyers remain cautious and focus on incremental ROI improvements rather than moonshot projects. Geopolitical factors are compounding the shift: Gartner notes the surge in sovereign cloud spending as countries pursue data locality and tech independence, creating regional demand outside the major US/China cloud providers.

Budget and procurement teams must contend with higher unit costs for AI-capable hardware, longer lead times for HBM and accelerators, and rising operating costs linked to power and cooling. Software and services teams will see increased project flow for model operationalization, migrations to AI-ready IaaS, and integration of AI features into incumbent enterprise apps. Security, compliance, and sovereignty requirements will push some workloads to regional providers or to on-prem configurations designed for regulatory control.

Monitor HBM pricing and supplier roadmaps, hyperscaler capex signals, and the pace at which enterprises move from pilot AI projects to measurable ROI-driven deployments. Also watch sovereign-cloud rollouts and regional procurement policies, which will reshape where and how AI infrastructure capacity is provisioned.