How to manage AI investments in the agentic era

OpenAI has outlined strategies for enterprise leaders managing AI investments as teams transition from chat to multi-step agentic workflows. According to the guidance, token price alone fails to show whether AI creates value, as cheaper models may require more retries or corrections. Instead, leaders should measure useful work per dollar, including completed tasks, time saved, and decisions improved. To interpret growing bills, administrators need visibility into demand, spend, and risk. Updated usage analytics and spend controls in the Admin Console for ChatGPT Work allow leaders to track adoption and credit usage by user, product, and model. Furthermore, evaluating models should involve testing real tasks against a defined quality bar and measuring the full cost of reaching that standard, including latency, attempts, and human review. OpenAI suggests that governance should act as the operating layer determining which AI work scales, managing access, context, tools, and actions. Enterprises are advised to manage AI investments as a portfolio consisting of everyday productivity, repeatable workflows, and strategic bets. Funding should follow maturity, progressing from exploration and validation to production funding for integrations and controls. Once a workflow proves its value, organizations can match commercial structures like Guaranteed Capacity or Scale Tier to their usage patterns to maintain cost efficiency.