OpenAI provides guidance on evaluating AI usage and controlling spend as teams transition from simple chat to longer-running, multi-step workflows. The article emphasizes that leaders must look beyond token prices to measure useful work per dollar, such as tasks completed or time saved.
- Updated usage analytics in the Admin Console allow admins to track adoption, credit usage, and spend by user, product, and model.
- Model selection should be based on real task performance, including edge cases, rather than just lowest token cost.
- Governance requires defining context, tool access, and approval paths for high-risk steps as teams adopt plugins and Computer Use.
- AI investments should be managed as a portfolio, funding shared capabilities centrally to make new workflows easier and safer to launch.
The article concludes that matching product capacity to usage patterns and using OpenAI’s deployment engineers can help enterprises scale proven work efficiently.