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The state of enterprise AI
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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.

2025 report

Four key findings stand out

—Ronnie Chatterji, Chief Economist OpenAI

Introduction

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.

Enterprise AI usage is accelerating and deepening

19x

20%

2x

50%

75%

36%

Original source

This story was published by OpenAI News. SyncAI.news shows a preview; the complete article is on the publisher's site.

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