A multinational enterprise reviews AI operating expenses across several standardized workflows.

In the CAIPM framework, understanding AI cost drivers is essential for measuring adoption efficiency and optimizing operational performance. One of the primary determinants of AI system cost—especially in large language model usage—is token consumption. Tokens represent the units of input and output processed by the model, and higher token usage directly translates to increased computational cost.

The scenario highlights that workflows with similar objectives and structures are producing different cost levels, suggesting that the variation is not due to business complexity but rather how AI interactions are structured. High token consumption per task is the most direct and quantifiable metric to assess this. It captures both prompt size and response length, providing a comprehensive view of how efficiently tasks are executed at the interaction level.

Option C, excessive prompt length, contributes to token usage but is only a partial indicator and does not account for output tokens. Option D, repeated clarification attempts, reflects interaction inefficiency across multiple attempts rather than per-task consumption. Option B focuses on user proficiency differences rather than prompt structure.

CAIPM emphasizes the importance of monitoring token usage as a key performance and cost optimization metric. By analyzing token consumption per task, organizations can identify inefficiencies in prompt design, standardize interactions, and reduce unnecessary cost variations across workflows.