OpenAI Releases Analytics Feature in ChatGPT Admin Console to Link AI Usage with Business Value

OpenAI has released an Analytics feature in the ChatGPT Admin Console that integrates usage, cost, and task insights for ChatGPT Work and Codex. This feature focuses on measuring and verifying whether AI adoption leads to actual business value, going beyond simple usage statistics.

Key Features and Data Visualization

• Usage View: Integrates active users, credits, and token usage to identify low adoption rates or training needs.
• Insights View: Provides a Task Classifier that categorizes message samples into task types. It analyzes the credit share of specific tasks, such as account research for sales teams, to inform model selection or workflow optimization.
• Outcomes View: Tracks merged commits, lines of code contributed, and review activities in Codex. It evaluates whether an increased share of code contributions leads to reduced review time or fewer defects.
• Admin Plugin & API: Supports dashboard automation and allows combining data from external business systems, such as ticket resolution times, with AI analytics data.

Business Value Connection Process

Admins provide usage and task data, while business owners add context regarding workflow changes and outcome improvements to link them with metrics such as delivery time, quality, and profitability. For example, a sales team might check the frequency of Account research, then reduce preparation time through CRM plugin training or skill sharing, and verify this through revenue contribution margins.

ROI Measurement and Customer Cases

Using hypothetical figures, it suggests that automating Account research for a sales team could save 5,520 hours annually, yielding an expected ROI of 245%. Actual customer cases include 1Password, which used Codex to accelerate feature releases while maintaining security policies, achieving an estimated ROI of 553%. ATV Big Air Tour reduced event listing verification time from 8 hours to 1 hour per week, and Playco reduced manual corrections during prototype builds by 50% using GPT-6 Astra.