From assistance to execution: How enterprises put AI to

What happened

Organizations are expanding both where they use AI and what they ask it to do. Enterprise AI is moving from assistance to execution, yet not all firms are making that transition at the same pace. 3× as many output tokens per active user as typical firms.

Complementary investments in continuous employee learning, shared workflows, data infrastructure, and governance can support broader and deeper adoption. AI agents need access to the right context and tools to be effective. Plugins⁠(opens in a new window) bundle capabilities that help agents complete specific workflows. They can combine skills that provide reusable instructions with apps that connect to company data, tools, and actions.

For example, a sales Plugin can combine a team’s playbook with access to its CRM, allowing an agent to use current customer information and past proposals to prepare a tailored response for review. Frontier firms have a clear lead in advanced capabilities. Among weekly active users, 21% at frontier firms use Plugins and 19% use skills, compared with just 9% and 3% at typical firms.

However, frontier firm adoption represents only a fraction of what is possible. OpenAI’s internal usage highlights the potential for deeper usage of these capabilities, with weekly Plugin usage at 95% of active users. Our research on how agents are transforming work provides a closer look at how employees at OpenAI use advanced capabilities.

Software engineering was an early center of agentic adoption, but Codex use is now growing quickly across knowledge-work functions. Since February, weekly active enterprise Codex users grew 108× in legal, 41× in sales, 41× in recruiting, and 26× in marketing, compared with 5× in engineering. At Virgin Atlantic, that shift is visible across the business. Engineering teams use Codex to refactor legacy code in 30 minutes instead of two weeks.

Why it matters

The measure is a proxy for depth of use, and the widening gap appears alongside greater adoption of capabilities that connect agents to company context, tools, and repeatable workflows. Today we are publishing two complementary studies that examine this shift. Enterprise Signals leads with a practical view of agentic AI across OpenAI’s enterprise customer base, including what frontier firms are doing differently and where agentic work is spreading.

The companion working paper, How Organizations Use AI: Evidence from ChatGPT⁠(opens in a new window), examines how adoption grows across companies, roles, and levels of seniority. Together, the studies point to a practical enterprise agenda: connect agents to the context and tools needed to complete valuable work; establish clear permissions, review, and governance; and help employees turn effective individual workflows into shared ways of working.

Enterprise AI is moving from answering questions to carrying out work. Assistants help people think through work; agents help them complete it. Products like ChatGPT Work and Codex can use tools, create files, and produce work for review. Instead of asking AI how to prepare a presentation, for example, a worker can ask an agent to gather relevant information across sources and draft the presentation itself.

This shift is visible in enterprise usage. As of June, Codex generated 64% of combined Codex and ChatGPT output tokens among enterprise customers. Agentic workflows typically generate more output because they carry out longer, multi-step tasks, so the figure reflects both how often Codex is used and how much output those tasks produce. Each month, we rank enterprise customers by output tokens per active user.

Frontier firms are those in the top 10% that month, while typical firms fall between the 45th and 55th percentiles. 6× gap in January. The frontier gap appears across industries and company sizes, showing that intensive AI use is not limited to technology companies. Enterprise Signals examines how the gap varies across industries and functions. S.

public companies studied in How Organizations Use AI: Evidence from ChatGPT⁠(opens in a new window), enterprise adopters had stronger financial measures compared to non-adopters. They held more assets, employed more workers, and had higher levels of R&D investment. Read together, the reports suggest that access alone may not be enough to scale AI.

What to watch

Their product teams use ChatGPT Work to complete weeks of competitive research in hours, shaping the airline’s five-year digital strategy. Enterprise Signals explores how these capabilities are spreading across industries and functions, drawing on a sample of more than 10 million messages. Many surveys have reported higher levels of AI use among leaders and executives. However, administrative data from millions of conversations finds the opposite.

Six months after adoption, early-career employees sent 13 more messages per week than executives. For leaders, the result points to a practical opportunity to identify employees with the strongest AI habits and make their workflows visible, helping effective practices spread across all levels. Companies may have access to the same models, but frontier firms are putting them to work faster and more deeply across their organizations.