
Learn how ChatGPT Work and Codex analytics help teams understand AI usage and spend, identify training needs, and connect adoption to business outcomes. As more teams use AI, admins and business leaders need to understand where it creates value and where to invest next. Analytics in the ChatGPT Admin Console bring together usage and cost data, task insights, and outcome metrics across ChatGPT Work and Codex. Admins can filter by group to see how teams use AI, then work with business owners to decide which workflows and outcomes to evaluate. The Plugin leaderboard and Skills view show which tools support a task, helping admins focus training and decide which workflows to maintain or share.
As more teams use AI, admins and business leaders need to understand where it creates value and where to invest next. Usage and spend tell part of the story, but admins also need to see what people use AI for and what it helps them accomplish.
Analytics in the ChatGPT Admin Console bring together usage and cost data, task insights, and outcome metrics across ChatGPT Work and Codex.
Here’s how admins can use these tools to understand adoption, support teams, and assess business value.
The task classifier in Insights helps admins understand what work AI supports by grouping a sample of messages into use cases and tasks. Software engineering, for example, includes feature development and code maintenance, while sales & revenue include account research and planning. The Overview tab shows the mix of work at a glance; the Use cases tab provides a detailed table with task breakdowns. Admins can filter by group to see how teams use AI, then work with business owners to decide which workflows and outcomes to evaluate.
The Use cases table breaks down a team’s work by task, with credits, messages, and active users.
Usage and task data are a starting point for admins to investigate value with business owners. Business owners add the context needed to assess it: what changed in the workflow, whether results improved, and what that improvement is worth. Together, they can connect product activity to measures such as delivery time, quality, or profitability.
Usage analytics show where adoption is growing and spend is concentrated, helping admins focus support, review costs, and assess capacity requests. The Usage view brings together active users, credits, and token usage across ChatGPT Work and Codex. For example, filtering by group or user can reveal where adoption is low, giving admins a reason to review starting workflows and training needs with the team owner.
The Plugin leaderboard and Skills view show which tools support a task, helping admins focus training and decide which workflows to maintain or share. Low use of a relevant plugin may point to an access or training need; a frequently used skill may need a clear owner and regular updates.
The Outcomes view shows Codex contributions to merged commits and lines of code, alongside code-review activity. Trends and available group, user, or repository filters help admins and engineering leaders understand adoption and decide where to expand access or support teams.
The Usage overview shows active users and credit trends across ChatGPT Work and Codex. All screenshots use illustrative demo data.
The Insights overview shows how credits are distributed across tasks, from implementing features to account research.
For the sales team shown below, account research and planning is the largest use of credits—a starting point for discussing how AI changes account preparation.
Learn how ChatGPT Work and Codex analytics help teams understand AI usage and spend, identify training needs, and connect adoption to business outcomes Reported by OpenAI on September 16, 2026.