Global AI Spending Set to Surge 49.5% to $2.7 Trillion in 2026, Gartner Forecasts

AI Infrastructure Drives Unprecedented Spending Growth

Global AI spending is forecast to reach $2.7 trillion in 2026, representing a 49.5% year-over-year increase, according to a Gartner forecast1

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. This surge positions AI infrastructure as the dominant force behind enterprise technology investment. John-David Lovelock, Distinguished VP Analyst at Gartner, describes the buildout of AI data center capacity as "the largest infrastructure project humanity has ever undertaken."1

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Demand for AI infrastructure—including AI-optimized IaaS, AI-optimized servers, network fabric, semiconductors, and devices—remains strong despite memory-related pricing pressures. Hyperscalers and service providers continue purchasing AI-optimized servers at scale, making this the largest single spending category.

Software Vendors Rapidly Embed Agentic AI

The software market is experiencing a transformation as vendors race to embed agentic AI within existing products to maintain market relevance and defend against emerging cross-functional agents1

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. With GenAI entering the Trough of Disillusionment in 2026, enterprises are turning to simpler embedded AI features from incumbent software providers. These capabilities focus on growing operational efficiency, automating workflows, improving customer engagement, and enhancing decision-making processes. AI software spending is projected to reach $461.6 billion in 2026, up from $288.2 billion in 20252

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. AI agents and assistants will grow from $16.5 billion to $29.2 billion, while generative AI models will see spending increase from $13 billion to $28.3 billion.

AI Services Market Shifts Toward Indirect Projects

Enterprises are changing how they engage service providers, moving away from large-scale business transformation projects toward smaller indirect initiatives that exploit AI features in incumbent software systems1

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. AI services spending will reach $576.5 billion in 2026, climbing from $434 billion in 2025. Lovelock notes that risks associated with vendor lock-in, data sovereignty, and runaway costs are not deterring buyers from adopting proprietary capabilities. The combination of transformation and indirect projects is forecast to drive a $1.2 trillion opportunity in AI services by 20301

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Domain-Specific Models Gain Traction

The pressure to deliver more cost-efficient models aligned with enterprise use cases is opening opportunities for domain-specific language models (DSLMs)1

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. The 2026 growth rate for generative AI models has increased from 110% in the previous Gartner forecast to 117% in the current projection. AI application development platforms are also seeing accelerated adoption, with growth increasing from 28% to 39% in 2026 as enterprises, software providers, and services firms develop custom AI applications tailored to specific needs2

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. Organizations are demanding their providers embed usage tracking into workflows to evaluate success and manage costs effectively.

Infrastructure Investment Remains Inelastic

Despite economic pressures and rising costs for components like memory, AI infrastructure spending shows remarkable resilience. AI infrastructure will reach $1.48 trillion in 2026, up from $981.9 billion in 2025, and is projected to hit $1.98 trillion by 20272

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. This reflects the sustained commitment from hyperscalers to build out capacity for anticipated future workloads. Watch for continued consolidation among semiconductor suppliers and potential supply chain constraints as demand for AI processing capabilities outpaces manufacturing capacity. The trajectory suggests total AI spending will reach $3.64 trillion by 2027, underscoring how deeply AI is becoming embedded across enterprise technology stacks2

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