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AI costs can shift with usage, model choice and frequent price changes, argues Sudipto Dasgupta, Aon’s global head of data, AI and automation platforms, in a Forbes Councils article.
Why it mattersHis practical advice: track consumption across models, teams and applications, then measure the cost of a useful business outcome, such as a transaction or claim. He also recommends model-selection rules and checks for AI-generated code. A token bill, it turns out, is not the same as knowing what you got for it.
Discuss: Should companies first prioritise measuring the cost of AI outcomes, or controlling which models and tools staff can use?
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