How to manage AI investments in the agentic era

Managing AI investments effectively requires understanding how AI systems are being used and measuring their actual value to the organization. Leaders should track usage patterns and spending across teams and workflows, evaluate different AI models based on the actual business outcomes they produce rather than just their cost per token, and establish governance controls before new AI capabilities are widely deployed. As organizations move from simple chat applications to longer-running automated workflows, matching investment levels to proven demand and funding workflows that can deliver repeating value becomes increasingly important for controlling costs and maximizing returns.