As AI moves from chat interactions to long-running agentic workflows, traditional cost metrics like token price no longer tell the full story. OpenAI notes that while GPT-5.6 delivers better performance with 54% fewer output tokens and 57% less time per task, token price alone does not capture value creation. Enterprise leaders face a growing tension: rising AI spend is hard to interpret without visibility into who is using what, for which tasks, and whether that usage represents waste, experimentation, or emerging business-critical processes. The article argues that the real question is not cost per token but useful work per dollar—tasks completed, time saved, and decisions improved.

To invest with confidence, the article lays out five concrete strategies. First, sharpen visibility into usage and spend using ChatGPT Work admin console analytics, which break down adoption by workspace, team, user, product, and model. Second, evaluate model efficiency by outcome ROI—track cost per accepted outcome (e.g., resolved support case, tested code change) rather than raw token price, and match model capability to task complexity. Third, govern advanced workflows before they scale: define what context, tools, and actions AI can use, with centralized controls for access, approvals, and spend limits. Fourth, manage AI investments as a portfolio—fund exploration, validation, and production separately, with shared infrastructure like identity, connectors, and evaluations funded centrally. Fifth, match product capacity to proven demand, using ChatGPT Work as a foundation and extending with proprietary data only where it creates differentiated value.

The serious takeaway is that scaling AI in the enterprise requires shifting from cost tracking to value-based governance. The article provides a practical framework: use outcome-based evals to define “good enough,” apply spend controls that support high-value workflows without broad limit increases, and treat governance as the operating layer that determines what can scale. Leaders who invest this way can move from interpreting a growing bill as a risk to seeing it as a portfolio of compoundable work—and they can leverage OpenAI‘s Deployment Engineers and enterprise privacy controls to do so in high-trust environments. The core insight is that useful work per dollar, not token price, is the metric that aligns AI investments with business outcomes.