How to Manage AI Investment in the Age of Agents

As AI model performance improves, token prices are falling, but a lower price alone does not guarantee higher value. Enterprise leaders must now evaluate the efficiency of AI investment based on 'useful work per dollar.'

The 5 key strategies for effective AI investment are as follows.

• Securing visibility into usage and spending: Organizations must clearly identify who is using which model to perform which tasks. Tools like ChatGPT Work should be used to analyze demand and patterns by workflow, beyond simple credit consumption.
• Outcome-focused ROI evaluation: Beyond a model's per-token price, the total cost must be measured, including the number of attempts to complete a task, the need for revisions, and latency. It is important to track 'cost per accepted outcome,' such as case resolution in customer support or passing code review in engineering.
• Building workflow governance before scaling: Organizations must predefine the scope of AI agents' access to enterprise systems and tool use, approval processes, and authorization methods to establish governance as an operational layer.