Linking AI utilization with measurable business outcomes

Understanding Adoption and Spend

As AI tools such as ChatGPT Work and Codex spread across enterprises, administrators are asked to demonstrate the financial return of those investments.

OpenAI’s newly added analytics in the ChatGPT Admin Console combine usage statistics, cost data, task classifications and outcome metrics to make that connection visible.

The first tab, called Usage, aggregates active user counts, credit consumption and token volume across both ChatGPT Work and Codex.

By filtering the view by department, team or individual, admins can instantly spot where adoption is lagging and where spend is concentrated.

Low‑adoption pockets give administrators a reason to engage team owners about onboarding gaps or workflow redesign.

Conversely, high‑spend clusters highlight areas that may merit additional licensing or capacity planning.

The Usage overview also plots credit trends over time, allowing finance partners to monitor budget alignment month over month.

Seeing the Work Behind the Credits

A second tab, Insights, introduces a task classifier that groups a sample of messages into business‑relevant use cases.

For example, the classifier may label software‑engineering interactions as “feature development” or “code maintenance,” while sales conversations appear as “account research” or “planning.”

The Overview sub‑tab presents a high‑level pie chart of credit distribution across these categories, giving a quick visual of AI’s role in daily work.

The Use Cases sub‑tab drills down into a table that lists each task, the number of messages, credits spent and active users involved.

Administrators can apply the same group filters used in the Usage view to see how specific teams allocate AI resources.

This granular view enables business owners to decide which workflows merit deeper outcome measurement.

Within each task, the Models, Reasoning and Speed breakdown shows how much credit each model setting consumes.

That data helps admins assess whether a faster, more expensive model is truly needed for a routine brief versus a lower‑cost alternative.

The Plugin leaderboard and Skills view surface which third‑party tools or internal skills are attached to each task.

Sparse plugin usage may signal an access restriction or a training opportunity, while heavily used skills might require a dedicated owner for maintenance.

“OpenAI’s analytics help us understand how teams use AI, giving us a foundation for future guidance and policies,” said Bharadwaj Tanikella, AI Product Manager at Datadog.

“We’re already using OpenAI’s task categories in Agent Console, our product for monitoring AI agents, to show customers the kinds of work their agents are doing.”

“Getting that data directly from OpenAI gives us a more reliable way to deliver those insights as our customers’ use of AI grows.”

Measuring Engineering Impact

A third tab, Outcomes, focuses on Codex’s impact on software‑engineering deliverables.

The view reports the share of merged commits and lines of code that contain Codex‑generated contributions.

Trend lines can be filtered by group, user or repository, letting engineering leaders trace adoption over time.

When Codex’s share of merged code rises, leaders can compare that metric with review cycle time, defect rates and rework volume.

Those correlations provide evidence on whether AI assistance is accelerating ship‑rate or compromising quality.

The Outcomes view also surfaces code‑review activity linked to Codex, offering a fuller picture of the development workflow.

Together, the three tabs give administrators a unified dashboard that ties raw AI consumption to concrete business outcomes.

By surfacing both cost and productivity signals, the console equips decision‑makers to allocate licenses, plan training and justify future AI spend.

Why This Matters: OpenAI’s admin analytics give businesses a concrete way to link AI usage to cost and outcome metrics.