How to manage AI investments in the agentic era

OpenAI’s goal is to make AI more accessible, capable and affordable over time. From GPT‑4 to GPT‑5.4, the price per million tokens fell 97%. GPT‑5.6⁠ continues that progress, delivering better performance in the Artificial Analysis Coding Agent Index with 54% fewer output tokens and 57% less time per task.

But token price alone does not show whether AI is creating value. Leaders should look at useful work per dollar: tasks completed, time saved, decisions improved, and workflows ready to scale.

As teams move from chat to longer-running workflows, admins need clearer visibility into demand, spend, and risk. 

Here are five ways to invest with confidence.

1. Sharpen visibility into usage and spend

Enterprise leaders need a plain view of AI usage: who is using it, which products or models they are using, how much capacity they are consuming, and what kind of work that usage supports. Without that visibility, a growing bill is hard to interpret. It could reflect waste, productive experimentation, or a workflow that is starting to become business-critical.

ChatGPT Work⁠ supports longer, multi-step tasks, so usage can vary widely by workflow. Admins need to see the work behind that usage, not just the credits consumed. This is possible thanks to a shared view of demand across ChatGPT. Updated usage analytics and spend controls⁠ in the Admin Console⁠(opens in a new window) help admins see adoption, credit usage, and spend by user, product, and model; track trends over time; identify emerging patterns; and understand when usage reflects broad adoption, a power-user workflow, or a recurring business process that may deserve more investment.

Insights at different altitudes help guide investment and enablement decisions:

• Workspace: Are adoption and spend moving together?

• Team and user: Where is demand growing, and who may need more support?

• Product and model: Where is more expensive intelligence being used, and is that demand sustained?