Resources

You budgeted AI like software. It behaves like electricity.

Seventy-three percent of companies overshoot. The forecasting model was wrong before the first invoice arrived.

Enterprise AI budgets fail because AI is priced like software and behaves like electricity. Seventy-three percent of companies overshoot their AI budget, some by a factor of 2.4, while finance teams that forecast cloud spend within 1 to 3 percent miss AI spend by 2 to 3 times. This collection covers real cost benchmarks, the forecasting model that works, and the controls that make variable spend governable.

That architecture is not exciting. It is ontology layers, governance scaffolding, MLR workflow design, audit trails, and human in the loop accountability. Boring infrastructure that does not photograph well in an annual report. Foundational work that makes the difference between an AI deployment that ships and one that quietly stalls in pilot for the third year running.

This collection covers what enterprise AI actually costs and why the budget was wrong before the first invoice. Real pricing benchmarks by vendor and deployment size, the six factors that make bills rise while token prices fall, the forecasting model that lands inside 15 percent, and the controls that make variable spend governable. Written for whoever has to defend the number.

Key Insights

What the writing is showing.

  • 73 percent of companies overshoot their AI budget. Some overshoot by a factor of 2.4.

  • 98 percent of FinOps practitioners now manage AI spend, up from 31 percent in 2024.

  • The price per token keeps falling while total bills keep rising. Six factors compound to produce that result.

Questions

What people ask about ai economics.

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