How frontier enterprises are building an AI advantage

How frontier enterprises are building an AI advantage

B2B Signals shows how the AI advantage is beginning to compound for firms using AI more deeply, more broadly, and in more delegated workflows.

TLDR

• Frontier firms—those at the 95th percentile of usage—now use 3.5x as much intelligence per worker as typical firms, up from 2x a year ago.
• The gap is about depth, not just activity: Message volume explains only 36% of the frontier advantage; most of the gap comes from richer, more complex AI use.
• Agentic workflows are becoming a frontier marker: The largest advantage shows up in advanced tools, with frontier firms sending 16x as many Codex messages per worker as typical firms.
• Organizations can move toward the frontier: Leading firms measure depth, build governance for production use, invest in enablement, scale what works, and move from chat-based assistance to delegated work with agents.

For many enterprises, the first phase of AI adoption was about access: who had AI tools, how many seats had been deployed, and whether employees were experimenting. That still matters. But access is no longer the differentiator.

Our latest research suggests the AI advantage is beginning to compound. Frontier firms are pulling ahead because they use more intelligence per worker, adopt advanced tools more intensively, and embed AI more deeply into workflows.

Today, we’re introducing B2B Signals, a business extension of OpenAI Signals. It provides a recurring measure of how AI is diffusing across businesses, based on privacy-preserving, aggregated signals from enterprise use of OpenAI products, including:

• How deeply AI is being used inside firms
• Which tools and tasks are most associated with frontier adoption
• Where business use cases are broadening across industries, products, and functions

Note: 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.

The clearest signal is depth. Frontier firms now use 3.5x as much intelligence per worker as typical firms, up from 2x in April 2025. Message volume explains only 36% of that gap; the majority comes from deeper usage. Workers at the frontier are asking AI to take on more complex work, providing richer context, and generating more substantive outputs.

In this report, we use tokens generated as a proxy for intelligence demanded. Tokens are not a direct measure of business value, but they help measure how much work employees are asking AI to do, making them a useful proxy for the depth of AI use.

Put simply: Typical firms are using AI to answer questions; frontier firms are using it to help execute complex work. They are not just sending more messages; each interaction is doing more of the actual work.

Together, these signals suggest frontier firms are using AI for more complex and challenging work. For leaders, the question is shifting from how many people have access or how often they use AI to where AI is deepening workflows and changing how teams operate.

The frontier is also moving toward delegation.

The advantage is largest in advanced and agentic tools. Codex shows the largest gap, with frontier firms sending 16x as many messages per worker as typical firms. ChatGPT Agent, Apps in ChatGPT, Deep Research, and GPTs show similar directional patterns, suggesting frontier firms are better at adopting tools that help workers code, delegate multi-step tasks, apply company context, and conduct more complex research.

As AI systems become more capable of using tools, working across files and codebases, and completing longer horizon tasks, enterprises will need to adapt to delegating meaningful work to AI agents.

The firms moving first are building the operating muscle to use AI not just as a faster interface, but as a way to redesign work from the ground up.

Cisco uses Codex to speed up complex software work across a large enterprise engineering organization. In production workflows, Codex helped reduce build times by about 20%, save 1,500+ engineering hours per month, and increase defect-resolution throughput by 10-15x. As Cisco’s team put it, the biggest gains came when they treated Codex as “part of the team.”

AI is also moving into production workflows across the business.

Companies are deploying API use cases across in-app assistants, coding and developer tools, and customer support. These are places where AI can become part of products, services, and internal systems.

AI use is broadest in writing and communication, but function-specific usage is growing. IT and Security teams concentrate their queries heavily in how-to and procedural guidance, Software Development and Data Science teams show high coding usage, and Finance teams are using AI for analysis and calculation. The pattern suggests AI is moving beyond general productivity and into work more closely tied to each function’s core responsibilities.

There is no single AI adoption leaderboard. Some industries lead in broad ChatGPT adoption, others in Codex use, API intensity, or message intensity. That means organizations have multiple entry points: scale access, deepen usage, adopt agentic tools, or build AI directly into products and systems.

Travelers Insurance shows what this looks like in practice. Its AI Claim Assistant, built with OpenAI, guides customers through first notice of loss, answers policy questions, gathers the information needed to start a claim, and creates claims directly inside Travelers’ systems. Travelers expects the assistant to handle approximately 100,000 first notice of loss calls in its first year.

The gap between frontier firms and typical firms should not be read as a fixed divide. Many organizations are still early in the process of moving from broad access to deeper, more integrated AI use. The value of the frontier is that it shows which practices appear to help firms build momentum over time.

One of the clearest signals is education and learning, where the task-level frontier advantage is largest. That suggests leading firms use AI not only to complete work, but to help employees build the skills, habits, and confidence needed to use AI well.

Organizations can move toward the frontier by measuring depth of use, building governance that enables production use, treating enablement as core infrastructure, identifying frontier teams and scaling their impact, and moving beyond chat toward delegated work with agents.

Enterprise AI is evolving quickly, and leaders need clear data to understand what helps translate AI adoption into business value.

B2B Signals tracks the behaviors and patterns of leading firms, giving organizations a clearer view of how leading companies are translating intelligence into business value.

This first release focuses on depth of use, agentic workflows, and emerging patterns across industries and functions. Future updates will track progress on these measures and adapt the signals as enterprise AI evolves.