AI value needs more than a token total.

Define the outcome before the unit price

Choose a unit that the business recognizes, such as a correctly classified document, an accepted draft, or a resolved case. Define who accepts the result and what happens when it fails. A “resolved” case should not quietly include one that reopens or still needs the same manual work.

FinOps Foundation guidance distinguishes resource metrics, such as cost per token, from business metrics, such as cost per case resolved. Both matter. Keep the scope and definition consistent over time so a change in the metric reflects a change in performance, rather than a different counting method.

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Build a baseline you can explain

Use a representative operating period and separate production from development or experiments. Join usage to a workload, owner, model or deployment, and the task outcome where practical. A total cloud invoice or a single shared API key may not provide that attribution by itself.

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Example: cheaper invoice processing creates more review work

Illustrative scenario: a multi-location distributor uses AI to read supplier invoices and extract purchase-order numbers, line items, freight, and totals. The application checks that data against purchase orders and receiving records. Invoices that pass those checks continue through the existing approval process. Mismatches go to an accounts-payable exception queue.

The team considers a less expensive model. Most invoices look fine in the pilot. The problems appear in familiar exceptions: a supplier spreads one order across several pages, a freight charge is grouped with a product line, or a purchase-order line number is missed. The matching checks flag the discrepancy. An AP specialist opens the invoice, compares it with the order and receipt, corrects the entry, and sometimes checks with the requester.

Assume the workload handles 4,000 invoices a month. The current path sends 5% to that queue; the candidate sends 7%. That modest change adds 80 reviews. At an assumed average of six minutes each, the finance team picks up eight more hours of work.

The figures below are hypothetical planning assumptions, not customer results, model prices, or industry benchmarks. Both paths are assumed to bring the same 4,000 invoices to a validated handoff for normal approval after exceptions are resolved. The comparison includes model processing, including retries, and exception-review effort. Hosting, document ingestion, standard approvals, and other overhead are assumed unchanged and excluded from both columns. Staff time is valued at an assumed loaded rate of $45 an hour.

Use the result to choose the next test

The team now has a useful decision: test the lower-cost path on predictable supplier formats, keep a more capable path for the difficult documents, or improve extraction and matching before changing models. Compare each option using the same invoice mix and approval rules, including the work required to handle exceptions.

Check errors that slip through validation as well as invoices correctly sent for review. A lower exception rate helps only when the data reaching approval is correct. Have AP validate representative results and assess the time spent resolving them. Adopt the change when the evidence supports the required accuracy, controls, processing time, and overall workload economics.

Choose the next optimization based on evidence

Start where the baseline shows avoidable consumption or a poor fit between resources and the task. The FinOps Foundation’s Usage Optimization guidance connects actual demand to choices about model size, caching, batching, routing, and other consumption controls. Treat each as a candidate change with a measurable benefit, implementation effort, and acceptance test.

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Confirm what your telemetry really measures

Aggregate token or request totals need a reliable connection to the workload and the work performed. Define the evidence you need before choosing a dashboard: workload and owner, release or configuration, consumption, failures and retries, and the accepted outcome. Validate the connection by tracing a sample task from its request to its cost and result.

The FinOps Foundation’s Allocation guidance supports using account structures, tags, labels, and other metadata to connect spending to accountable owners. Document how shared costs are assigned and which costs cannot yet be attributed. Keep model changes and pricing assumptions with the comparison so later results remain explainable.

Choose log fields, access, and retention deliberately. The NCSC’s secure-development guidance treats logs as sensitive assets. Useful operating evidence may be possible through identifiers, usage records, and limited review samples; retaining every raw prompt or response should be an explicit data-handling decision.

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Run a bounded comparison

Agree on the quality floor, required controls, latency or throughput, and acceptable operating cost before testing a candidate. Use the same representative cases, include difficult and failed requests, and repeat variable cases enough to avoid selecting an approach on one unusually good run.

Report the result as a tradeoff: measured improvement, quality and service results, implementation effort, and remaining uncertainty. Start with limited exposure, retain a known working configuration, and review live behavior before expanding. A controlled test samples a defined set of conditions; production monitoring should look for failures and changing usage outside that sample. NIST’s work on deployed AI monitoring explains why both forms of evidence matter.

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Turn efficiency into a defensible value case

Faster work, more throughput, a better customer experience, or reduced rework can be valuable even when they do not reduce payroll. State which benefit has been observed, who owns it, and how it changes the business. Treat estimated hours saved as an assumption until you verify that the time is actually reclaimed and used.

Bring one workload, its current operating costs, available usage records, representative outputs, and an outcome owner. A useful first engagement should produce an agreed baseline and one testable improvement. GlobalLinc can help frame that comparison and identify the relevant technical and commercial expertise.