What key token metrics should CFOs monitor to manage AI expenditure?
CFOs managing enterprise AI expenditure should focus on three forward-looking and value-oriented metrics: cost-per-outcome (such as cost per merged pull request or deployed feature), spend run-rate forecasts based on rolling consumption trajectories, and value leak rate to identify spend that never connects to committed output.
Cost-Per-Outcome
Traditional token dashboards only show spend volume, not business return. Measuring cost-per-outcome tracks the fully-loaded expense against delivered units of value, such as deployed features or merged pull requests, revealing whether high consumption actually translates into higher productivity.
Source: 3 Token Cost Metrics Every CFO Should Be Watching
Spend Run-Rate Forecast
Month-to-date figures show spend retrospectively after budgets are already depleted. A forward-looking run-rate projection uses rolling trailing averages to alert finance teams when month-end consumption trajectories threaten to cross planned budget thresholds.
Source: 3 Token Cost Metrics Every CFO Should Be Watching
Value Leak Rate
Value leak rate measures the share of AI spend that doesn't connect to a committed output: a merged PR, a deployed commit, a shipped feature. The signal is a pattern, not a single session: sessions with consistently high spend and no output, compared against a team-median baseline and tracked over time.
Source: 3 Token Cost Metrics Every CFO Should Be Watching
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