Researchers propose Two-Ledger Model for tracking AI costs in enterprise transactions
Researchers at an unnamed institution have introduced the Two-Ledger Model to address a critical gap in AI cost accounting for enterprises. Current systems track AI expenses only at settlement, aggregating costs by product or time period, which fails to answer basic questions like the true cost of a specific order or claim. The model combines a settlement ledger (aligned with the FinOps Open…
Key points
- Two-Ledger Model splits AI cost tracking into settlement and attribution ledgers for granular transaction-level billing
- Uses OpenTelemetry GenAI telemetry and probabilistic matching to link AI usage to business transactions
- Simulation shows 99%+ cost coverage, 61% peak cost reduction, and 0.24% residual error recovery
The proposed system uses a layered correlation spine—including trace context, agent-to-agent metadata, and probabilistic matching—to link AI invocations to business transactions. A versioned rate card prices usage in near real time, while reconciliation ensures alignment with invoices. Simulation results show the model achieves over 99% cost coverage even as trace propagation weakens, recovers unmetered spend within 0.24 percentage points, and reduces peak transaction costs by up to 61% with minimal false pauses. The authors advocate for standardization of the attribution ledger as an invocation-grain extension of FOCUS.
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