OpenAI's guidance for enterprises in the agentic era centers on a simple but pointed metric: useful work per dollar. Rather than treating AI investment as a lump sum to be allocated, the company argues that organizations should evaluate how much actual, valuable output each dollar of AI spend produces. This reframing pushes the conversation from raw cost toward productivity and outcome efficiency.
The approach also emphasizes continuous efficiency gains. As agentic systems become more capable, the cost of completing a given task can fall, so tracking the ratio of work to cost over time helps enterprises decide where to expand. OpenAI recommends scaling workflows that already show high value per dollar, rather than spreading investment thinly across many unproven use cases.
In practice, this means building measurement into AI deployments from the start and using those numbers to guide budget decisions. The source does not offer specific formulas or benchmarks, but the strategic direction is clear: treat AI like any capital investment, where the return is measured in work accomplished, not in the volume of compute consumed.