As teams adopt more AI, admins and business leaders need more than raw usage and spend data: they need to know what AI is being used for and what outcomes it supports. The ChatGPT Admin Console brings together usage and cost data, task insights, and outcome metrics across ChatGPT Work and Codex to help answer those questions.
What the analytics provide
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Usage data: The Usage view shows active users, credits and token usage across ChatGPT Work and Codex. Filters such as group or user help reveal areas with low adoption, giving admins a reason to review onboarding workflows and training needs with the team owner.
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Tasks and use cases: The task classifier in Insights groups a sample of messages into use cases and tasks (for example, software engineering covers feature development and code maintenance; sales & revenue covers account research and planning). The Overview tab gives a quick picture of the work mix and the Use cases table provides a detailed task breakdown. Admins can filter by group to see how teams use AI and then collaborate with business owners to choose which workflows and outcomes to evaluate.
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Task-level settings: In task details, Models, Reasoning and Speed breakdowns show each setting’s share of credits for a task. This helps assess whether the chosen setup fits the work—for instance, whether a routine brief might be tested with a faster or lower-cost configuration and then compared for quality and review time.
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Plugins and Skills: The Plugin leaderboard and Skills view identify which tools support each task. Low usage of a relevant plugin may indicate an access or training need; frequent use of a skill may call for a clear owner and ongoing maintenance.
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Outcomes for Codex: The Outcomes view tracks Codex contributions to merged commits and lines of code alongside code-review activity. Group, user, and repository filters let admins and engineering leaders understand adoption and decide where to expand access or support teams. If Codex accounts for an increasing share of merged code, leaders can compare that trend with review time, defect rates, and rework to assess whether it improves software delivery.
Reporting and automation
The Admin plugin in ChatGPT Work allows admins to compare adoption, spend, and tasks and turn findings into reports for budgeting and rollout decisions. The plugin can also generate finished deliverables, such as a leadership deck with charts, key findings and recommended next steps. With the Admin API, teams can automate reports into their own dashboards and combine analytics with business-system data (for example, showing credit use alongside ticket resolution time in a support dashboard).
Evaluating workflow value in practice
Usage and task data are a starting point, but business owners provide the context to judge value: what changed in the workflow, did results improve, and what is that improvement worth? For example, if the task classifier shows account research is common in a sales group, an admin might check model choices, provide training where a CRM plugin is underused, and share a skill for consistent account plans. The sales owner would then compare preparation time and plan quality against baseline. If time is saved, they can track how much of that time goes into customer interactions, which conversations turn into qualified opportunities, and which opportunities become sales. Revenue and contribution margin on that business can show whether the extra capacity produces a financial benefit.
A hypothetical ROI example:
- 20 sellers × 2 briefs per week × 3 hours saved × 46 weeks = 5,520 hours
- If 50% of that time is put into productive work at a fully loaded cost of $75/hour, that yields a capacity value.
- Assume $60,000 for AI, setup, training and ongoing support.
- Illustrative ROI: (207,000 − 60,000) ÷ 60,000 = 245%.
All figures in this example are hypothetical; the ROI reflects estimated capacity value and excludes potential benefits from higher win rates, larger deal sizes, or other sales outcomes.
Customer examples
- 1Password uses Codex to build, review and test software; they estimate 553% ROI and $0.8M in annual engineering capacity value by using Codex to accelerate building production-ready features and internal tools while maintaining security policies.
- ATV Big Air Tour uses ChatGPT Work to check event listings, plan inventory and improve website visibility. Listing reviews decreased from eight hours to one hour per week, and inventory work fell from two–three days to two–three hours.
- Playco uses GPT‑6 Astra via OpenAI’s API to build and test playable game prototypes. They produced three themed prototypes from one foundation and reported 50% fewer manual fixes than with the prior model.
How to get started
Open Insights in the Admin Console, choose a common task that supports a business priority, review it with a business owner, agree on a baseline and the outcome to measure, and set a date to review progress. Use the results to decide whether to expand the workflow, improve how teams use it, or test a different approach.
Data privacy
By default, OpenAI does not train its models on your organization’s business data.
Overall, the Admin Console analytics give admins and business owners tools to link spend and usage to concrete workflows and outcomes, enabling targeted training, access decisions and investment choices based on measurable signals.
