Enterprise AI spending surges 75% as companies shift from pilots to production

Enterprise artificial intelligence spending has reached an inflection point, transitioning from experimental budgets to core business operations at breakneck speed. Recent survey data from Andreessen Horowitz (a16z), a prominent Silicon Valley venture capital firm, reveals that enterprise AI spending is growing 75% year-over-year as companies move beyond pilot programs into full-scale deployment.

The survey of over 100 chief information officers across 15+ industries provides a comprehensive view of how enterprises are approaching AI investments in 2025. The findings reveal fundamental shifts in procurement, deployment strategies, and vendor relationships that signal AI’s maturation from innovation project to business-critical infrastructure.

For business leaders navigating this landscape, understanding these trends is essential for making informed technology investments and competitive positioning decisions.

5 key trends shaping enterprise AI spending in 2025

1. AI budgets are exploding and moving to core IT spend

Enterprise AI spending is growing at an unprecedented 75% year-over-year rate, with a dramatic shift in funding sources. Innovation budget allocation for AI projects has plummeted from 25% to just 7% of total AI spend, indicating that AI has graduated from experimental projects to permanent budget line items within core IT and business units.

The scale of this transition is staggering. One CIO reported that “what I spent in 2023 I now spend in a week,” illustrating how quickly AI investments have accelerated. This shift represents more than just increased spending—it signals that enterprises now view AI as essential infrastructure rather than optional innovation.

For vendors, this transition fundamentally changes the sales landscape. Companies are no longer selling to innovation teams with modest budgets and flexible requirements. Instead, they’re competing for the same enterprise IT dollars as traditional software providers, which means navigating longer sales cycles, rigorous security requirements, and formal procurement processes that mirror established enterprise software purchases.

2. Multi-model deployment is crushing single-vendor strategies

The enterprise AI landscape is rapidly diversifying, with 37% of enterprises now using five or more AI models in production—up from 29% just one year ago. This multi-model approach reflects a sophisticated understanding that different AI tasks require different capabilities and cost structures.

OpenAI maintains overall market leadership, but the deployment patterns reveal interesting strategic choices. Among OpenAI users, 67% deploy non-frontier models (less advanced but more cost-effective versions) compared to only 41% for Google and 27% for Anthropic, Claude’s maker. This suggests enterprises are making nuanced decisions about when to use premium capabilities versus more economical options.

Cost considerations are driving much of this diversification. Google’s Gemini 2.5 Flash costs $0.26 per million tokens compared to GPT-4.1 mini at $0.70 per million tokens—a 63% cost advantage that’s particularly compelling for high-volume applications. A token represents roughly three-quarters of a word, so these pricing differences can translate to significant savings for enterprises processing large amounts of text.

The practical implication is that enterprises increasingly expect AI vendors to intelligently route between different models based on specific use cases and cost requirements. Success in this market isn’t about offering the single best model—it’s about orchestrating multiple models to optimize both performance and cost across diverse business applications.

3. “Buy vs. build” has completely flipped in 12 months

A dramatic reversal has occurred in enterprise AI strategy over the past year. More than 90% of enterprises are now testing third-party AI applications for customer support rather than building solutions internally—a complete flip from the previous preference for in-house development.

This shift reflects hard-learned lessons about the complexity of AI implementation. One public fintech company abandoned their internal AI development project mid-stream to purchase a third-party solution instead, recognizing they couldn’t match the pace of specialized vendors’ improvements.

However, this “buy” preference comes with new challenges. As AI workflows become more sophisticated and automated (what technologists call “agentic”), switching costs are rising dramatically. One technology leader noted that “all the prompts have been tuned for OpenAI…changing models is now a task that can take a lot of engineering time.” Prompts are the instructions given to AI systems, and optimizing them for specific models requires significant technical expertise.

For AI vendors, this creates both opportunity and responsibility. Enterprises recognize they can’t keep up with the rapid pace of AI optimization internally, creating demand for deep vertical solutions where continuous model optimization and prompt engineering create defensible competitive advantages.

4. AI-native companies are crushing incumbents by 2-3x speed

Companies built from the ground up with AI at their core are dramatically outperforming traditional software companies retrofitting AI capabilities. Leading AI-native companies are reaching $100 million in annual recurring revenue (ARR) significantly faster than previous software generations, driven by prosumer adoption that creates enterprise demand.

The term “prosumer” refers to professional consumers—individual users who adopt tools personally and then advocate for enterprise adoption at their companies. This bottom-up adoption pattern has accelerated enterprise sales cycles for AI-native companies.

The quality gap between AI-native and retrofitted solutions is becoming apparent to users. Developers who have adopted AI-native coding tools like Cursor, an AI-powered code editor, show “notably lower satisfaction” with previous-generation tools like GitHub Copilot, Microsoft’s AI coding assistant. This indicates a fundamental step-function improvement in capabilities rather than incremental enhancement.

This represents the classic platform shift moment in technology—a period when new architectural approaches enable capabilities that incumbents struggle to match through incremental improvements. For AI-native companies, the key is demonstrating measurably superior outcomes rather than just feature parity with established players.

5. Enterprise AI procurement now mirrors traditional software buying

AI purchasing decisions have matured to resemble traditional enterprise software procurement, complete with formal evaluation processes and standardized criteria. External benchmarks like LM Arena (Chatbot Arena), an independent platform that ranks AI models through user voting, have become the equivalent of Gartner’s Magic Quadrant for initial model filtering.

The evaluation criteria have also evolved beyond pure accuracy. Security and cost considerations have gained significant ground as key purchasing factors, reflecting enterprises’ need for AI solutions that integrate with existing compliance and budget frameworks. Despite this maturation, usage-based pricing still dominates, with CIOs remaining uncomfortable with outcome-based pricing models that tie costs directly to business results.

A notable shift in deployment preferences has emerged: more enterprises are now hosting AI services directly with model providers like OpenAI and Anthropic rather than going through cloud providers like Amazon Web Services or Microsoft Azure. This represents a complete reversal from last year when enterprises preferred cloud provider intermediaries for trust and compliance reasons.

While internal benchmarks using companies’ own data (often called “golden datasets”) remain critical for final decisions, enterprises increasingly reference external evaluations as their first filter, similar to how they use analyst reports for traditional software purchases.

The implications for AI vendors are clear: the “move fast and break things” approach that worked in AI’s experimental phase is over. Enterprises now expect rigorous evaluation frameworks, comprehensive security documentation, benchmark performance data, and transparent usage-based pricing models—all delivered with the speed and innovation that makes AI compelling.

Strategic implications for business leaders

These trends reveal that enterprise AI has crossed the chasm from early adoption to mainstream deployment. The rapid shift from innovation budgets to core IT spending, combined with sophisticated multi-model strategies and mature procurement processes, indicates that AI is no longer a nice-to-have technology but a competitive necessity.

For enterprises evaluating AI investments, the data suggests focusing on vendors that can demonstrate clear performance advantages through external benchmarks while providing the security and cost transparency required for core IT procurement. The rise of AI-native solutions also suggests that incremental AI features from traditional vendors may not be sufficient—transformational capabilities often require purpose-built platforms.

The acceleration in enterprise AI spending shows no signs of slowing, but the rules of engagement have fundamentally changed. Success in this mature market requires combining cutting-edge AI capabilities with enterprise-grade reliability, security, and cost management—a combination that separates serious AI vendors from those still treating enterprise sales as an experimental afterthought.