The state of enterprise AI

Foreword

At OpenAI, our mission is to ensure that artificial intelligence benefits all of humanity, and helping enterprises solve problems is central to this mission.
The majority of economically valuable activity takes place inside organizations, where innovation translates directly into improved outcomes for workers, customers, and other stakeholders. Enterprise problems also present the hardest technical challenges for frontier intelligence, requiring reliability, safety, and security at scale. The revenue generated from solving these problems can help fund broad, free access to powerful AI for hundreds of millions of people worldwide.

For much of the past three years, the visible impact of AI has been most apparent among consumers. However, the history of general purpose technologies—from steam engines to semiconductors—shows that significant economic value is created after firms translate underlying capabilities into scaled use cases. Enterprise AI now appears to be entering this phase, as many of the world’s largest and most complex organizations are starting to use AI as core infrastructure.

More than 1 million business customers now use OpenAI’s tools. This report brings together evidence from de-identified and aggregated enterprise usage data and a variety of other sources to provide a grounded view of how AI is being deployed inside organizations today.

Four key findings stand out

• Enterprise usage is scaling, with deeper workflow integration. ChatGPT message volume grew 8x and API reasoning token consumption per organization increased 320x year-over-year, demonstrating that more enterprises are using AI and their intensity of usage has increased.
• Enterprises that leverage AI are experiencing measurable productivity and business impact. Enterprise users report saving 40–60 minutes per day and being able to complete new technical tasks such as data analysis and coding. Case studies indicate AI is contributing to important outcomes such as revenue growth, improved customer experience, and shorter product-development cycles.
• Enterprise growth is global and rapidly accelerating across industries. Over the past six months, international adoption has surged as organizations worldwide deepen their use of AI, complementing continued strong momentum in the U.S. In the past 12 months, the median sector grew by more than 6x, with the technology sector leading the pack at 11x.
• A widening gap is emerging between leaders and laggards. Frontier workers are sending 6x more messages and frontier firms are sending 2x as many messages per seat than the median enterprise. There’s a substantive gap in the likelihood to utilize the most capable AI tools today, despite broad availability of these tools. Models are capable of far more than most organizations have embedded into workflows, and this presents an opportunity for firms.

“Looking ahead, the next phase of enterprise AI will be shaped by stronger performance on economically valuable tasks, better understanding of organizational context, and a shift from asking models for outputs to delegating complex, multi-step workflows. As these capabilities mature, we expect organizations to not only improve efficiency, but discover new ways to serve customers and deliver value.

The findings in this report represent early signs of how AI is beginning to reshape the modern enterprise. As enterprise AI evolves, OpenAI will continue to share real-world evidence on how AI is influencing firms, workers, and the broader economy.”

Introduction

Over the past three years, enterprises have integrated AI systems across a wide range of use cases and operational workflows.

These deployments provide insights on how AI is shaping work, particularly in environments where accuracy standards are high, workflows are complex, and improvements in productivity or decision quality have direct economic outcomes. Because much of the world’s economically valuable activity occurs inside firms, enterprise adoption patterns provide a clear signal of where AI is delivering value today and where it will likely do so in the future.

The scale and diversity of OpenAI’s more than 1 million business customers provides a distinctive view into this shift. This report summarizes key findings from across OpenAI’s enterprise customer base, and what those patterns suggest about the current state and trajectory of enterprise AI. By examining how adoption varies across industries and functions, the analysis also highlights where AI is becoming deeply embedded in firms, and where gaps are emerging.

Findings are based on two primary data sources

1. Real-world usage data from enterprise customers of OpenAI.
2. An OpenAI survey of 9,000 workers across almost 100 enterprises documenting patterns of AI adoption.

All analyses in this report are based on de-identified, aggregated enterprise usage data. Message content was classified using automated systems, and no OpenAI employee reviewed individual enterprise, business, or API customer data as part of this analysis.

Enterprise AI usage is accelerating and deepening

Over the past year, enterprise AI adoption has increased substantially as organizations incorporate AI into repeatable, multi-step workflows across functions and business units. OpenAI now serves more than 7 million ChatGPT workplace seats, and ChatGPT Enterprise seats have increased approximately 9x year-over-year.

Since November 2024, weekly Enterprise messages have grown approximately 8x in aggregate, with the average worker sending 30% more messages. This growth reflects both more frequent use of ChatGPT and a deepening in the intensity of use.

Two shifts underscore the deepening integration of AI into core enterprise workflows.

Custom GPTs and Projects are enabling deeper workflow integration

GPTs and Projects are configurable interfaces built on ChatGPT that can be tailored with instructions, knowledge, and custom actions, enabling workers to execute repeatable, multi-step tasks.

• Weekly users of Custom GPTs and Projects have increased by approximately 19x year-to-date. In recent months, approximately 20% of all Enterprise messages were processed via a Custom GPT or Project. The most widely deployed GPTs either codify institutional knowledge into reusable assistants or automate workflows through integrations with internal systems. Some organizations have built a culture of developing and sharing Custom GPTs at scale. For example, BBVA regularly uses more than 4,000 GPTs, indicating that AI-driven workflows are increasingly implemented as persistent tools embedded in daily operations.*

Developer and API workflows are rapidly scaling

Companies build on the API to integrate models directly into their products and systems with a high degree of control and customization. As firms transition from experimentation to production deployments, API consumption has rapidly increased. More than 9,000 organizations have now processed over 10 billion tokens, and nearly 200 have exceeded 1 trillion tokens.

Average reasoning token consumption per organization has increased by approximately 320x in the past 12 months, suggesting that more intelligent models are being systematically integrated into expanding products and services. Codex, while still early in its enterprise lifecycle, is gaining rapid traction as teams adopt it for end-to-end software tasks: code generation, refactoring, testing, and debugging.

In the past six weeks, Codex engagement indicates growing penetration of AI-assisted development inside enterprises.

Workers report measurable value from using AI

In most settings, AI enables workers to produce higher quality work faster. However, productivity alone does not fully reflect how AI is reshaping work. Survey data from almost 100 enterprises highlights key operational gains across functions, and shifts in who performs specialized and technical work.

Enterprise workers report time saved and improved outcomes across functions

• Seventy-five percent of surveyed workers report that using AI at work has improved either the speed or quality of their output. On average, ChatGPT Enterprise users attribute 40–60 minutes of time saved per active day to their use of AI, with data science, engineering, and communications workers saving more than average (60–80 minutes per day). Time saved per message varies by function: accounting and finance users report the largest benefits followed by analytics, communications, and engineering.*

These gains translate into broad operational improvements across functions

• 87% of IT workers report faster IT issue resolution
• 85% of marketing and product users report faster campaign execution
• 75% of HR professionals report improved employee engagement
• 73% of engineers report faster code delivery

These results indicate that productivity benefits are already materializing across core enterprise functions, not only in early-adopting technical roles.

Technical work expands beyond traditional role boundaries

AI is not only accelerating existing work; it is also expanding the tasks and skills workers can perform. Several studies find that AI has an equalizing effect, disproportionately aiding lower performing workers. Consistent with these findings, 75% of workers report being able to complete tasks they previously could not perform, including programming support and code review, spreadsheet analysis and automation, technical tool development and troubleshooting, and custom GPT or agent design.

The broadening of individual capabilities is particularly apparent in technical settings, where non-technical teams are increasingly engaging in coding and data-analysis work that was previously confined to specialized roles. Among ChatGPT Enterprise users, coding-related messages have increased across all functions, and outside of engineering, IT, and research, coding-related messages have grown by an average of 36% over the past six months.

Among ChatGPT Enterprise users, coding-related messages have increased across all functions, and outside of engineering, IT, and research, coding-related messages have grown by an average of 36% over the past six months.

Workers report greater productivity from more intensive AI use

At the individual worker level, impact increases as workers deepen their use of AI. Across a large sample of workers, time saved is correlated with the use of more advanced ChatGPT features, including Deep Research, GPT‑5 Thinking, and Image Generation. Workers consuming the most intelligence (as measured by credits used) report higher time savings. Workers who save more than 10 hours per week are not just using more intelligence, they are also using multiple models, engaging with more tools, and using AI across a wider range of tasks.

Productivity gains increase with intensity of AI use

Pace of acceleration varies based on industry and geography

Over the last year we’ve seen overall rapid adoption as companies move from AI pilots to full deployments, and there are notable differences based on industry and geography.

Growth is rapid across most industries

OpenAI customer growth is broad-based across industries, with the median sector expanding more than 6x year-over-year and even the slowest-growing sector exceeding 2x.

AI adoption by industry: enterprise scale vs. year-over-year growth

Fastest-growing sectors	Year-over-year customer growth
Technology	11x
Healthcare	8x
Manufacturing	7x

In absolute terms, ChatGPT Enterprise customers are most concentrated today in professional services, finance, and technology, sectors that were early adopters and continue to lead in their scale of AI usage. Healthcare and manufacturing started from a much smaller base but are now among the fastest-growing sectors, rapidly closing the gap.
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