OpenAI Still Leads in Enterprise AI Arms Race

The people have spoken! Well, OK… some people have spoken about AI.

Results from Andreessen Horowitz’s (a16z) third annual CIO Survey suggest that rather than fragmenting, the market is consolidating around a small group of dominant model providers, while incumbents continue to wield outsized influence at the application layer.

The survey, conducted among 100 Global 2000 companies, shows that OpenAI still leads the enterprise, for now, while Anthropic and Google are gaining fast.

It also paints a picture of accelerating spend, shifting market share, and a widening gap between technical progress and realized business value. While no single vendor can yet claim permanent leadership, the data shows clear winners, fast followers, and several trends that challenge prevailing narratives in online AI discourse.

A market that refuses to fragment

Industry observers have long predicted that open source alternatives and slowing model improvements would level the competitive playing field. Instead, the opposite appears to be happening. The enterprise AI market is increasingly dominated by a small group of frontier labs whose scale, pace of innovation, and enterprise trust are reinforcing their lead.

According to the survey, OpenAI remains the most widely deployed model provider in large enterprises. Seventy-eight percent of CIOs reported using OpenAI models in production, either directly or through cloud service providers. That incumbency advantage, however, is beginning to erode as competitors close the gap.

Anthropic and Google have both posted meaningful gains over the past year, with Anthropic’s rise standing out. Since May 2025, Anthropic increased its enterprise penetration by 25 percentage points, reaching 44 percent in production deployments and more than 63 percent when testing environments are included. Wallet share data mirrors this trend, with OpenAI still holding a majority at roughly 56 percent, but steadily losing ground to Anthropic and Google’s Gemini models.

Third-party data from Yipit tracking approximately 1,000 mid-market and enterprise companies shows adoption patterns that closely align with the survey, reinforcing the view that these shifts are structural rather than anecdotal.

Leadership depends on the workload

One of the clearest findings is that enterprise AI is not a single market. Leadership varies significantly by use case, and most large organizations are responding by diversifying their model portfolios.

OpenAI continues to dominate early horizontal use cases such as general-purpose chat, enterprise knowledge management, and customer support. These were among the first workloads enterprises deployed, and switching costs have grown over time.

Anthropic, by contrast, has emerged as a leader in software development and data analysis. CIOs consistently pointed to rapid capability gains since the second half of 2024 as a key driver of adoption. Google’s Gemini models showed strong performance across a broad set of tasks, though their enterprise penetration in coding workloads lagged competitors.

As a result, multi-model strategies are becoming the norm. Eighty-one percent of enterprises now use three or more model families in testing or production, up from 68 percent less than a year ago. This shift reflects both risk management and a growing recognition that different models excel at different tasks.

R&D as customer acquisition

The survey highlights how advanced model releases are increasingly functioning as customer acquisition tools. Anthropic’s growth, for example, has been driven primarily by its most capable models. Seventy-five percent of Anthropic customers reported having Sonnet 4.5 or Opus 4.5 in production, while adoption of older, cheaper models lagged.

OpenAI, meanwhile, still sees substantial usage of earlier model generations that were adopted early and continue to “work well enough” for many workloads. Forty-six percent of OpenAI customers reported running GPT 5.2 or 5.2 Pro in production, underscoring both the benefits and inertia of early enterprise deployments.

Reasoning models, heavily hyped in early 2025, appear to be delivering tangible value. Fifty-four percent of respondents said these models accelerated adoption by reducing prompt engineering, improving accuracy, and enabling more complex agentic workflows, including multi-step automation and AI-driven site reliability engineering.

Microsoft’s quiet dominance at the application layer

Perhaps the most striking divergence from online narratives is the continued dominance of Microsoft in enterprise AI applications. While debates rage over OpenAI versus Anthropic and the merits of competing coding assistants, much of Global 2000 AI adoption still runs through Microsoft’s ecosystem.

Microsoft 365 Copilot leads enterprise chat deployments, though ChatGPT has narrowed the gap. GitHub Copilot remains the leading coding assistant in large enterprises. Sixty-five percent of respondents said they prefer incumbent solutions when available, citing trust, integration with existing systems, and procurement simplicity.

Conclusion

The survey’s conclusion is clear: the enterprise AI market is massive, still expanding faster than expected, and far from settled.

While leaders are emerging, the ultimate winners will likely be determined not just by model quality, but by how effectively organizations adapt their people, processes, and workflows to a rapidly evolving technology.

Nearly 90% of workers are now using AI tools on the job, often doing so without clear guidance, training, or formal support from their employers.

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