How 100 Enterprise CIOs Are Building and Buying Gen AI in 2025

Source: Andreessen Horowitz (a16z) · 2025-06

Just over a year ago, we highlighted 16 changes to the way enterprises approached building and buying gen AI.

Even in a field where the only constant is change, the gen AI market structure has evolved significantly beyond our expectations since we ran our last survey over a year ago.

Enterprise AI budgets grew beyond already high forecasts and graduated from pilot programs and innovation funds to recurring line-items in core IT and business unit budgets.

Organizations are much more sophisticated at mixing and matching multiple models to optimize across both performance and cost. OpenAI, Google, and Anthropic took dominant overall market share in our survey while Meta and Mistral were popular among open source options.

Procurement now mirrors traditional software buying—with more rigorous evaluations, hosting considerations, and benchmark scrutiny—while increasingly complex AI workflows are driving higher switching costs.

Meanwhile, the AI app landscape has matured: off-the-shelf solutions are eclipsing custom builds and rewarding AI-native third party applications.

LLM budgets have grown ahead of enterprises’ (already high) expectations from a year ago, and there are no signs of this slowing down. Enterprise leaders expect an average of ~75% growth over the next year. As one CIO noted, “what I spent in 2023 I now spend in a week.”

Last year, innovation budgets still made up a quarter of LLM spending; this has now dropped to just 7%. Enterprises are increasingly paying for AI models and apps via centralized IT and business unit budgets, reflecting the growing sentiment that gen AI is no longer experimental but essential to business operations. One CTO noted that, “more of our products are adding AI enablement, so our spending growth will rise across all of these products”—suggesting this shift toward core budgets will only accelerate.

While enterprises continued to use different models across both experimental and production use cases as explored above, a few players took the lead on overall adoption: OpenAI maintained overall market share leadership, while Google and Anthropic made considerable strides over the last year. Market share differed somewhat by scale of the enterprise, with more open source adoption occurring at the larger end of enterprises where on-prem is still a major consideration.

Enterprises use a broad suite of OpenAI’s models. GPT-4o is the model most deployed to production, while OpenAI o3 has generated significant interest as reasoning models are more integrated into production use cases. OpenAI’s non-frontier models are adopted at much higher rates than other vendors’, so it’s Google and Anthropic’s frontier models—Gemini 2.5 and Claude Sonnet 3.5 onward—that have gotten them in the door. Specifically, 67% of OpenAI users have deployed non-frontier models in production, compared to just 41% for Google and 27% for Anthropic.

Google’s rise has been more pronounced within large enterprises, as they frequently have existing relationships with GCP and can tap into the brand trust of a mega-cap company. Gemini models have long touted best-in-class context windows, but the overall performance of Gemini 2.5 vaulted them into true frontier model status. What has also become apparent—and may give Google staying power—is Google’s performance-to-cost ratio. In an example of models with comparable intelligence, Gemini 2.5 flash costs 26 cents / million tokens while GPT-4.1 mini costs 70 cents.

In contrast, Anthropic has seen the highest adoption in the most tech-forward companies, specifically software companies and startups. Their models have excelled in certain use cases, most notably code, and they power the fastest growing AI coding applications as a result. This use case-level leadership has not gone unnoticed in the enterprise, with tech-forward leaders actively evaluating performance by use case and typically selecting Anthropic for engineering and coding. This has been a boon to Anthropic’s revenue and given them the scale and reputation valued by more traditional large enterprises as well.

Adoption of open source models like Llama and Mistral tended to be higher at larger enterprises relative to their adoption at smaller companies. This has typically been driven by the preference for on-prem solutions, given data security and compliance considerations, as well as the ability to fine-tune for specific enterprise use cases.

Companies now approach model selection with disciplined evaluation frameworks, and factors such as security—which was heavily emphasized in our interviews—and cost have gained ground on overall accuracy and reliability. This shift underscores the increased trust enterprises have in model performance and the confidence that LLMs will be deployed at scale. As one leader succinctly summarized, “for most tasks, all the models perform well enough now—so pricing has become a much more important factor.”

Early in the AI product cycle, enterprises largely opted to work directly with AI models and build their own applications. However, we’ve seen a marked shift towards buying third party applications over the last twelve months as the ecosystem of AI apps has started to mature. This is particularly true as the dynamic performance and cost differentiation across models has resulted in incremental ROI gains from constant evaluation and optimization by use case, often best tackled by a dedicated AI application team instead of an internal team.

Moreover: in a space as dynamic as AI, companies are finding that internally developed tools are difficult to maintain and frequently don’t give them a business advantage—which further cements their interest in buying instead of building apps.