Gartner forecasts AI spending to hit USD $64bn in 2026

Worldwide end-user spending on AI models and platforms will reach USD $64 billion in 2026, up 63.4% from USD $39 billion in 2025, according to Gartner.

The research group expects spending on generative AI models to rise 117% this year, while spending on AI platforms increases 36.9%. Tighter scrutiny of enterprise AI budgets is changing how buyers choose suppliers and where they commit money.

Organisations are placing greater weight on usage efficiency, cost control and measurable outcomes. That is shifting demand towards providers that can show value on cost, latency, performance and reliability.

Arunasree Cheparthi, Senior Principal Research Analyst at Gartner, said the market is becoming more disciplined as companies look beyond early experimentation.

"Enterprise AI budgets are coming under greater scrutiny, with increased focus on usage efficiency, cost control and measurable outcomes," Cheparthi said.

Buyers are also looking for tools that make AI spending easier to monitor and manage.

"Spending is shifting toward providers who can demonstrate clear value across cost, latency, performance and reliability. This is giving an edge to providers that embed evaluation, cost transparency and usage tracking into customer workflows, making it easier to manage and optimise AI use. However, as spending becomes more usage-driven, providers face increasing pressure to demonstrate real adoption, sustained use and durable margins," she said.

Segment growth

The fastest expansion is expected in domain-specific language models and specialised generative AI models. Gartner forecasts spending in that segment will climb 210% in 2026, rising from USD $1.583 billion in 2025 to USD $4.91 billion.

Foundation generative AI models are also expected to post strong growth. Spending in that category is forecast to increase from USD $11.438 billion to USD $23.356 billion, a rise of 104.2%.

Platform segments are projected to grow at a slower but still substantial pace. AI application development platforms are expected to rise from USD $6.885 billion to USD $9.541 billion, while AI platforms for data science and machine learning are forecast to increase from USD $19.405 billion to USD $26.444 billion.

The figures suggest enterprises are still increasing spending across the AI stack even as procurement standards tighten. Rather than pulling back, buyers appear to be redirecting budgets towards products that offer clearer oversight of use, spending and results.

Buyer pressure

The forecast points to a market where usage-based pricing is becoming harder for customers to predict. That creates an opening for vendors whose products help companies select models, monitor output, apply policy controls and keep spending in check.

Cheparthi said those factors will shape which suppliers emerge strongest over time.

"Over the long term, the biggest winners will be vendors that help enterprises manage where and how AI is used across the business," she said.

She added that a more crowded market will raise the importance of control layers around model use.

"As more models enter the market and usage-based pricing becomes harder to predict, buyers will turn to platforms that help them choose the right tools, monitor performance, enforce policy and keep costs under control," she said.

The numbers also show that the largest single spending pool remains AI platforms for data science and machine learning, even though model-related categories are growing faster. That balance indicates many organisations still need the infrastructure and software layers required to build, test and manage AI systems at scale.

At the same time, the sharp rise in spending on specialised models suggests companies are moving towards tools designed for narrower industry or business tasks. Those models may appeal to enterprises seeking a stronger fit with internal workflows and more predictable returns from AI investments.

Total projected spending of USD $64.252 billion in 2026 compares with USD $39.311 billion in 2025, according to Gartner's market forecast.