The AI bill arrived. It included the work nobody counted.

The first AI bill is easy to see. The second may be sitting in somebody else's calendar: checking an answer, correcting a mistake, handling an escalation or explaining a result to a customer. If that work disappears from the budget, an apparently efficient system can quietly move effort rather than remove it. Capgemini's new snapshot surveyed 500 senior executives at organizations with more than $1 billion in annual revenue, across ten countries and thirteen industries, in September. Fifty-nine percent said AI spending had been significantly above expectations or budget. Among affected organizations, the reported average overrun was 24%. Those are respondents' assessments, not independently audited accounts, and the sample does not represent every small business or user. The release also says more than three in five executives expect a multi-model strategy within twelve months. Choosing a smaller or cheaper model for a bounded task can be sensible. But adding models can also add routing decisions, evaluations and operational complexity. A planned strategy is not a demonstrated saving. The hopeful part is that these are tractable management questions. Define an acceptable completed task, measure how often it succeeds without correction, and include the cost of people and controls needed to deliver it. Compare that result with a credible alternative, not merely with last month's token bill. We should resist two easy conclusions: that AI pays for itself because usage is growing, or that budget overruns prove AI has failed. Spending can rise because valuable work expands, because forecasts were poor, or because a workflow is inefficient. Distinguishing those explanations is the work the headline number cannot do for us.