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The 7 AI Terms That Will Define CFO Decision-Making
CFOs need to understand inference costs, compute constraints, and return on compute to evaluate AI investments. These seven terms are the new vocabulary of capital allocation.
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Santander announced Monday it would extend AI tools to its entire 185,000-person workforce. That scale of deployment forces a question finance teams rarely had to ask: what does it actually cost to run an AI model at enterprise scale?
The answer is not the software license. It is inference, throughput, compute capacity, and latency. These are not engineering metrics anymore. They are financial metrics.
CFOs who treat AI as a one-time software purchase will miss the real cost structure. Every customer interaction, every automated workflow, every prediction carries a variable expense. The companies that get the most value from AI will not be the ones that spend the most. They will be the ones that allocate compute most efficiently.
Here are the seven terms that matter.
Total cost of ownership captures the full picture: infrastructure, software, talent, data, governance, energy, maintenance, and inference expenses. Many organizations discover that operating costs far exceed the initial investment as deployments scale.
Inference is the economic engine. Every time a model generates a response, a prediction, or a recommendation, it consumes compute. Understanding inference economics is becoming as important as understanding transaction economics in payments or customer acquisition costs in digital businesses.