AI Operating Cost Attribution and Unit Economics Model

Build a defensible AI operating cost attribution and unit economics model for the workflow below. The purpose is to attribute model, tool, retrieval, infrastructure, review, support, failure, and rework costs to accepted AI outcomes so accountable owners can compare true unit economics across workflows and variants.

Context to provide:
- AI workflow or product: [AI workflow or product]
- Accepted outcome definition: [Accepted outcome definition]
- Analysis period: [Analysis period]
- Workflow variants to compare: [Workflow variants to compare]
- Cost evidence and usage exports: [Cost evidence and usage exports]
- Quality and rework evidence: [Quality and rework evidence]
- Accountable owners: [Accountable owners]

Evidence rules:
- Use only the evidence provided. Do not claim that a source, export, invoice, log, ticket, approval, or test was inspected unless it is included in the input.
- Separate observed facts, calculated values, assumptions, and inferences.
- If evidence is missing, label it as missing and explain how it affects confidence or comparability.
- Preserve uncertainty with ranges or confidence notes where point estimates are not supported.
- Do not create a general AI spend governance brief or a quality/latency experiment plan. Stay focused on cost attribution and accepted-outcome unit economics.

Build the working artifact with these sections:

1. Cost Boundary Map
Create a boundary map for the selected [AI workflow or product] covering the full operating cost chain for [Analysis period]. Include, where evidenced:
- Model inference or subscription costs
- Tool execution costs
- Retrieval, embedding, vector database, search, or storage costs
- Application, orchestration, logging, monitoring, and infrastructure costs
- Human review, approval, escalation, and exception-handling costs
- Support, customer operations, and internal operations costs
- Failure, incident, reversal, refund, remediation, and rework costs
- Evaluation, sampling, QA, audit, and oversight costs directly tied to production operation
For each cost category, state whether it is included, excluded, partially included, or missing; identify the cost owner from [Accountable owners] where possible; and explain the rationale.

2. Accepted-Outcome Measurement
Define the denominator for unit economics using [Accepted outcome definition]. Specify:
- What counts as an accepted outcome
- What does not count
- How retries, duplicates, partial completions, escalations, rejected outputs, and reworked outputs should be treated
- Any quality threshold required before an outcome is counted
- The evidence source used for the count
If the accepted-outcome count cannot be established from [Quality and rework evidence], provide a defensible interim method and list the validation needed from the product owner, finance owner, or operations owner.

3. Allocation Rules
Design allocation rules that assign costs to accepted outcomes and to [Workflow variants to compare]. For each rule, include:
- Cost pool
- Allocation driver
- Formula
- Required evidence
- Owner responsible for validating the driver
- Known weakness or bias
- When the rule should be replaced with a more precise method
Prefer causal allocation drivers over broad averages. Use broad averages only when more precise evidence is unavailable, and label them clearly.

4. Accepted-Outcome Unit Economics
Create a unit economics table for each workflow variant in [Workflow variants to compare]. Include:
- Accepted outcome volume
- Gross operating cost
- Cost excluded or not yet evidenced
- Cost per accepted outcome
- Cost per attempted outcome, if attempt counts are available
- Human review cost per accepted outcome
- Failure and rework cost per accepted outcome
- Infrastructure and retrieval cost per accepted outcome
- Confidence level for each major figure
Show formulas and make assumptions explicit. Do not overstate precision.

5. Variance Bridge
Build a variance bridge explaining differences in cost per accepted outcome across variants or periods. Attribute variance where evidence supports it to:
- Volume and utilization
- Model selection or token/input-output mix
- Tool calls and external service usage
- Retrieval depth, indexing, or storage patterns
- Review rates and escalation rates
- Failure, rejection, incident, or rework rates
- Infrastructure utilization or fixed-cost absorption
- Support burden
For each variance driver, state whether the driver is observed, calculated, inferred, or currently unverified.

6. Cost-Quality Sensitivity
Analyze how unit economics change under plausible changes to quality and control variables, such as:
- Acceptance rate
- Human review rate
- Escalation rate
- Rework rate
- Failure or incident rate
- Retrieval depth or tool usage
- Model choice or routing mix
Use ranges when evidence is incomplete. Identify which variables most affect cost per accepted outcome and which quality controls appear economically justified.

7. Control Decisions
Provide a decision table for the accountable owners. Include:
- Decision under consideration
- Economic rationale
- Quality or risk tradeoff
- Evidence supporting the decision
- Evidence still missing
- Owner who should approve or validate the decision
- Completion check
Focus on concrete controls such as routing changes, review thresholds, retrieval limits, escalation criteria, failure handling, logging improvements, or measurement changes.

8. Model Integrity Checks
Before finalizing, perform these checks using the provided evidence:
- Every included cost has an owner, source, allocation rule, and treatment in the model.
- Accepted-outcome counts match the stated definition or are flagged as provisional.
- Rejected, failed, duplicated, escalated, and reworked outputs are not accidentally counted as accepted outcomes unless justified.
- Fixed, variable, and step costs are not mixed without explanation.
- Variant comparisons use the same boundary unless differences are explicitly disclosed.
- No unavailable inspection, execution, approval, or source verification is claimed.

Final output format:
- Cost boundary map
- Allocation rule table
- Accepted-outcome unit economics table
- Variance bridge
- Cost-quality sensitivity table
- Control decision table
- Missing evidence register
- Owner verification checklist

Write in direct finance and operating language suitable for review by the finance owner, product owner, operations owner, and data owner. Avoid generic governance language and unsupported certainty.