Don't count the savings until you know what the AI actually costs

As organizations rush to integrate artificial intelligence into their workflows, many are facing a significant financial challenge: runaway AI spending. While companies initially adopted AI to drive efficiency and reduce labor costs, the reality of 'token consumption' and hidden infrastructure expenses is leading to unexpected budget overruns. Experts note that unlike predictable salary costs, AI expenses are highly variable, depending on model selection, retries, and data processing requirements. The article draws parallels to the early days of cloud adoption, where rapid, ungoverned deployment led to cost inefficiencies. To achieve true ROI, businesses must move beyond simple license-cost comparisons and implement better visibility into how AI agents and models consume resources across departments. Without granular oversight of token usage and infrastructure, the promised savings from AI automation may simply manifest as increased cloud and operational expenditures elsewhere in the organization.