See Exactly How ChatGPT Is Being Used: Admin Console Analytics Now Maps AI Spend to ROI
Hi everyone, I'm Shii-chan! Today I want to talk about a new analytics feature for businesses that I found on OpenAI News.
OpenAI News
openai.com
What was announced?
OpenAI News introduced the new Analytics feature in the ChatGPT Admin Console. It brings together usage and cost data, task insights, and outcome metrics in one place, so admins and business leaders can actually understand the value of their AI rollout. The post also covers how to use Codex analytics to track coding contributions.
The story so far
Even after rolling out AI, it hasn't been easy to get a full picture of how much it's actually being used, where the money is going, and which parts of the job it's genuinely helping with. Teams often had to piece together scattered data and make calls based on gut feeling.
What changes
Analytics in the Admin Console now has four main views:
- Usage: shows active users, credits, and token usage. Admins can filter by group or user to spot areas with low adoption or a need for training
- Insights: classifies sampled messages into use cases and jobs to be done, grouped by role, for example software engineering (feature development, code maintenance) or sales (account research, planning)
- Models / Reasoning / Speed breakdown: shows credit allocation across settings, plus a plugin leaderboard and skills view. Low usage here can be a sign of an access or training gap
- Outcomes: shows merged commits and lines of code Codex has contributed, which you can weigh against code review time and defect counts to gauge development efficiency
Dive Deep
The article also lays out a 5-step framework for connecting AI usage to actual business value:
- Pick the goal you want to improve (speed, quality, cost, etc.)
- Establish a baseline (how often the task happens, how long it takes, what the ideal outcome looks like)
- Measure the change after adopting AI (including review and revision time)
- Analyze the team-level effect (talk through how the freed-up time actually gets used)
- Verify the ROI (compare the benefit against the spend)
As an example, a 20-person sales team applied this process to account research. Before AI, each account research task took about 4 hours; after AI, it took about 1 hour including review and revisions. Assuming each rep handles 2 tasks a week over 46 weeks, that's 5,520 hours saved across the team per year. Reinvesting 50% of that saved time into productive work at $75/hour works out to roughly $207,000 in annual value, against a first-year cost of $60,000, for an estimated annual ROI of 245%.
The article also cites other companies: 1Password hit a 553% ROI and about $800,000 in annual value from its Codex rollout, ATV Big Air Tour cut list review from 8 hours a week down to 1, and Playco reduced manual fixes by 50% using GPT‑6 Astra.
For admins, there's also an Admin Plugin that compares adoption, spend, and task data to auto-generate leadership reports, plus an Admin API for pulling this data into custom dashboards.
Wrap-up
- Admin Console Analytics brings usage, cost, task trends, and Codex outcomes together in one place
- Four views (Usage, Insights, Models/Reasoning/Speed, Outcomes) give a well-rounded picture
- A 5-step framework helps connect AI adoption to business outcomes
- A modeled sales team example shows a 245% annual ROI
- The Admin Plugin and Admin API support automated reporting and custom dashboards
A great fit for IT admins, managers, and HR folks who need to explain their company's AI usage with real numbers.
