Cloud and AI costs are usage-based, so they can grow quietly. FinOps puts structure around that: tagging resources by team or product, setting budgets and alerts, reviewing spend regularly and removing idle capacity. For AI workloads, it also means tracking model usage for each workflow.
For example, a monthly review might reveal that a test environment has run at full size for months, or that one AI feature accounts for most model spend. The misconception is that FinOps is purely cost cutting. The goal is spending that matches value, which sometimes means spending more on a workload that clearly pays for itself.