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Yes—available 2025 survey findings indicate that multi-model use is growing among enterprises, though they do not establish a single adoption rate for the whole market. In Andreessen Horowitz’s survey of 100 CIOs across 15 industries, 37% said their organizations used five or more models in 2025, up from 29% in the prior-year survey. Separately, a Cloud Security Alliance report summary hosted by Google Cloud puts the average at 2.6 models per enterprise. The measures and samples differ, so they are signals of a shift, not directly comparable market-wide statistics.
Here, “multi-model” means using more than one AI model across an organization; it does not imply a specific number of models or a particular vendor mix.
What the enterprise AI adoption figures show
The a16z survey’s five-or-more measure suggests that a substantial share of its respondents are managing a broader model portfolio than before. It is a survey of 100 CIOs across 15 industries, not a census of enterprises, and should be read within that scope. Andreessen Horowitz’s 2025 enterprise CIO survey reports 37% using five or more models, compared with 29% in its prior-year survey.
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A different measure appears in the Cloud Security Alliance report summary hosted by Google Cloud: an average of 2.6 models per enterprise. The average is not equivalent to the a16z share using five or more, because the studies use different measures and samples. The Cloud Security Alliance says Google commissioned its report. Google Cloud’s summary of the Cloud Security Alliance report also reports that 52% cited sensitive data exposure as their primary AI security risk.
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Taken together, the findings support a multi-model direction in surveyed organizations, not a universal enterprise standard. The reports also come from organizations with commercial interests in the AI ecosystem, so attribution and sample context matter when using their figures.
Why are enterprises using multiple AI models?
Organizations may choose different models for different jobs rather than expecting one model to lead on every task. In its 2025 survey, a16z describes use-case differentiation and avoiding lock-in as important reasons for buying from multiple vendors. Respondents reported distinctions across work such as coding, architecture, writing, and complex question answering. These are reported selection patterns, not universal model rankings.
Earlier a16z research describes a broader set of trade-offs that remain useful when evaluating a portfolio: task performance, model size, cost, access to new capabilities, control over proprietary data, customization, and hosting. The particular provider capabilities described in a 2024 report may have changed, so those examples should not be treated as current product comparisons. Andreessen Horowitz’s 2024 analysis of enterprise generative AI adoption discusses these selection and deployment considerations.
Multiple models can therefore reflect a deliberate fit between workloads and capabilities, as well as a desire to preserve options as models evolve. But each additional model also creates work: it must be assessed, secured, governed, and monitored.
How should a company choose between AI models?
There is no universally best provider established by these reports. Compare candidate models and deployment approaches against the organization’s actual work and obligations, rather than relying on general rankings or provider claims.
- Task performance: Test representative internal tasks. A generic benchmark alone does not show how well a model will perform on a company’s work.
- Cost and model size: Estimate costs for the workload and decide what level of capability it needs. The most capable model may not be necessary for every task.
- Data control and customization: Check whether the deployment and adaptation options meet requirements for sensitive data and task-specific behavior.
- Hosting and access: Compare access directly from a provider, through a cloud service, or by self-hosting in light of infrastructure and procurement needs.
- Governance capacity: Account for the people and processes needed to inventory, oversee, evaluate, monitor, and review each model for security and compliance.
These dimensions are questions to answer for each intended use, not evidence that one model or hosting arrangement is right for every organization.
What changes operationally when a company adds models?
A larger model portfolio requires visibility and repeatable oversight. The Partnership on AI’s 2025 report, based on workshops held in December 2024 and February 2025 with more than 20 participating organizations, identifies responsible-adoption readiness, evaluation and monitoring, compliance, and trust across the AI value chain as key challenges. It recommends formal governance, understanding both official and informal AI use inside organizations, and employee education. These are recommendations, not a statement of statutory requirements. Partnership on AI’s report on responsibly navigating enterprise AI describes those findings.
Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitches- Build an inventory: Identify the models and AI tools employees use, including informal or unapproved use.
- Set oversight: Define who reviews and approves models and deployments, and how changes are handled.
- Evaluate intended behavior: Test models against the tasks for which they will be used, then monitor performance after deployment.
- Review security and compliance: Assess data exposure and applicable organizational obligations for each use and deployment.
- Educate staff: Help employees understand approved tools, expected practices, and how to raise concerns.
These steps matter alongside model selection: more options do not automatically produce better results if the organization cannot keep track of their use or manage the associated risks.
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How to interpret provider-specific enterprise AI reports
Not every report describing enterprise AI use is a market-wide survey. OpenAI says its 2025 analysis used aggregated customer usage data and a survey of 9,000 workers across almost 100 enterprises. That provides context about OpenAI’s users and customers, but it does not represent all enterprise AI providers or establish how many models enterprises use overall. OpenAI’s 2025 State of Enterprise AI report explains its methodology.
When comparing adoption claims, check who was surveyed, what was measured, the year, and whether the evidence reflects one provider’s customers or a broader respondent group. A percentage using five or more models, an average number of models, and a platform’s usage data answer different questions.
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