Worldwide IT spending is set to hit $7.35 trillion in 2027, with software growth expected to add $215 billion, reaching $1.68 trillion. Gartner highlights AI models growing 117% and AI platforms 39% as key drivers alongside significant software price increases impacting the tech market.
- Software spending grows 14.6% to $1.68T worldwide in 2027.
- AI model spending surges 117%, AI platforms grow 39%.
- Price increases and AI add-ons account for significant growth share.
Market signal
Gartner’s latest forecast projects global IT spending at $7.35 trillion in 2027, fueled by a 15% annual increase and crossing the $7 trillion threshold for the first time. Within this, software spending is expected to rise 14.6% to $1.68 trillion. This rapid growth is highly uneven across categories, with AI-driven software segments dominating the increase.
Specifically, spending on generative AI models is anticipated to grow by 117% in 2026, with AI application development platforms growing 39%. These figures highlight where capital is flowing within software, distinguishing AI infrastructure and platform investments from conventional B2B software categories. Price increases on software licenses and AI-related features further amplify this growth.
Operator impact
Software vendors and SaaS operators should note that much of the headline 14.6% growth rate reflects outsized advances in AI model consumption and AI platform adoption. These areas are often concentrated among a few major providers receiving large portions of the $215 billion incremental spend. Consequently, companies focused on traditional B2B software may experience growth tied more heavily to price increases than volume expansion.
Moreover, enterprise buyers are becoming more vigilant in managing AI-related costs. The rise of AI FinOps practices aims to monitor real-time usage and prevent redundant calls that inflate costs, putting pressure on operators to provide transparent pricing and value. Rapid creation of temporary AI-powered applications boosts platform demand but may decrease reliance on established application vendors.
What to watch next
Operators and buyers should track how much software growth derives from AI models, AI platforms, and embedded AI features in incumbent products versus traditional software applications. Understanding this breakdown will be crucial for go-to-market, pricing strategies, and investment in AI capabilities.
Additionally, closely monitoring AI usage cost management trends, including enterprise adoption of AI FinOps, will become increasingly important. This shift signals a growing demand for usage transparency, flexible pricing models, and tools to optimize AI consumption. The landscape could further evolve as temporary, AI-generated applications proliferate, shifting software spend patterns towards platforms rather than classic app vendors.