OpenAI‘s ChatGPT Admin Console now gives admins a path from raw AI usage and spend to business value. The Usage view aggregates active users, credits, and token consumption across ChatGPT Work and Codex, with group and user filters that expose adoption gaps and cost concentration. The task classifier in Insights samples messages and groups them into use cases and tasks—software engineering, for example, breaks into feature development and code maintenance, while sales and revenue includes account research and planning. Admins can filter by group, see the credit mix in the Overview tab, and drill into a Use cases table with credits, messages, and active users per task.

Task details add Models, Reasoning, and Speed breakdowns showing each setting’s share of credits for a task, so admins can judge whether model selection fits the work and target training. The Plugin leaderboard and Skills view show which tools support a task, flagging low plugin use as a possible access or training issue and frequently used skills as candidates for an owner and updates. For Codex, the Outcomes view tracks contributions to merged commits and lines of code along with code-review activity, with group, user, and repository filters. Engineering leaders can compare Codex‘s rising share of merged code with review time, defects, and rework.

Two additional tools link analytics to action. The Admin plugin in ChatGPT Work lets admins compare adoption, spend, and tasks and turn findings into reports, including a leadership deck with charts and recommended next steps. The Admin API supports automated dashboards that combine analytics with business-system data, such as credit use alongside ticket resolution time.

OpenAI frames usage data as a starting point for conversations with business owners, who add context on workflow changes, quality, and the value of improvements. A suggested evaluation starts with choosing an outcome to improve, documenting the current process, comparing results over a defined period including review and correction time, identifying what the saved time enables, and deciding whether benefits justify AI, setup, and support costs.

A worked example walks through the calculation. A team of 20 sellers each prepares two account briefs per week; AI saves three hours per brief, for 5,520 hours saved annually. If 50% of that time goes to productive work valued at $75 per hour, the estimated annual capacity value is $207,000. With $60,000 in first-year costs, the illustrative ROI is 245%. OpenAI notes the figures are hypothetical, the ROI reflects estimated capacity value, and it excludes potential benefits from higher win rates, larger deal sizes, or other sales outcomes.

Customer examples illustrate the pattern. 1Password uses Codex to build, review, and test software and estimates 553% ROI with $0.8M in annual engineering capacity value. ATV Big Air Tour uses ChatGPT Work to check event listings, plan inventory, and improve website visibility, cutting listing reviews from eight hours to one hour per week and inventory work from two to three days to two to three hours. Playco uses GPT-6 Astra through OpenAI‘s API to build and test playable game prototypes, creating three themed prototypes from one foundation and reporting 50% fewer manual fixes than with the previous model. The article closes by advising admins to open Insights, pick a common task tied to a business priority, agree on a baseline and outcome with a business owner, and set a review date.