Why your AI program’s biggest financial risk isn’t what you think — and what to do about it.
Enterprise AI programs routinely exceed budget — not because they chose the wrong tools or hired the wrong people, but because no one modeled the economics of AI token consumption before the program launched. The Gartner 2025 AI Infrastructure Survey found that 67% of enterprise AI programs exceeded first-year AI infrastructure budget — a finding consistent with a decade of research on AI implementation challenges.
A token is the basic unit of work that AI models charge for. Every question asked, every document analyzed, every line of code generated consumes tokens. In isolation, tokens cost fractions of a cent. At enterprise scale — hundreds of engineers, dozens of automated workflows, millions of interactions per month — token costs compound in ways that defeat conventional IT budgeting entirely.
This paper gives executives a plain-language framework for understanding AI token economics: what drives costs, how to forecast them, how to govern them, and what the ROI of doing so looks like. No equations. No vendor jargon. Just the strategic picture you need to make informed decisions.
Read the full report AI TOKENOMICS
