Reusable AI capability
Calculate AI Cost per Accepted Outcome
Apply a repeatable unit-economics method that allocates AI operating costs to quality-adjusted accepted outcomes rather than raw calls, tokens, tasks, or generated outputs.
This Skill packages a reusable way to use the linked Prompt or Workflow; Amo.ng does not run it for you.
# Calculate AI Cost per Accepted Outcome Skill ID: AMO-S-000014 Skill URL: https://amo.ng/skills/calculate-ai-cost-per-accepted-outcome Purpose: Help finance, product, process, and operations owners compare AI economics across periods, providers, models, workflows, or operating designs while exposing denominator, allocation, quality, and rework risk. Required inputs: - Workflow boundary, decision question, comparison period, and accountable owners - Accepted-outcome definition, quality gates, counts, failures, retries, rework, escalations, and rejected work - Provider, infrastructure, integration, monitoring, support, review, labor, control, and incident costs - Allocation policy, workload drivers, shared-cost assumptions, currency, and time basis - Baseline or comparison scenario and known missing evidence How to use: When to use: - AI cost must be connected to usable business or operational outcomes. - Teams need comparable economics across periods, models, providers, or workflow designs. When not to use: - Estimating generic ROI without accepted-outcome and cost evidence. - Using tokens, calls, generated items, or user seats as the outcome denominator when acceptance matters. Reusable method: 1. Fix the workflow, time, currency, environment, and decision boundary. 2. Define an accepted outcome with observable quality and completion conditions. 3. Reconcile attempted, completed, accepted, rejected, reworked, escalated, duplicate, and failed outcomes. 4. Inventory direct and shared provider, infrastructure, integration, labor, review, support, control, and incident costs. 5. State allocation rules and preserve unallocated or unknown amounts. 6. Calculate cost per accepted outcome and separate volume, mix, rate, quality, rework, and control variance. 7. Test sensitivity to denominator quality, allocation assumptions, and missing costs. 8. Record control decisions and the next evidence refresh. Expected output: A cost-boundary map, accepted-outcome reconciliation, allocation ledger, unit-economics table, variance bridge, sensitivity analysis, confidence limits, and owner decisions. Boundaries: Do not invent cost, volume, acceptance, savings, or attribution data. Finance approves accounting treatment and allocations; process and product owners approve outcome quality; investment decisions remain with the accountable sponsor. Source grounding: AMO-P-000280. Applicable Workflow: AMO-W-000015. Powered by Prompt: AI Operating Cost Attribution and Unit Economics Model Source ID: AMO-P-000280 https://amo.ng/prompts/ai-operating-cost-attribution-and-unit-economics-model Completion criteria: Complete when the cost and outcome boundaries reconcile; all material cost pools have supported allocation or an explicit unknown; the accepted denominator includes quality, rework, and failure treatment; sensitivities and confidence are visible; and finance plus process owners approve use of the result. Use this Amo.ng Skill with your preferred AI tool. Supply the required inputs and follow the usage instructions. # Calculate AI Cost per Accepted Outcome Skill ID: AMO-S-000014 Skill URL: https://amo.ng/skills/calculate-ai-cost-per-accepted-outcome Purpose: Help finance, product, process, and operations owners compare AI economics across periods, providers, models, workflows, or operating designs while exposing denominator, allocation, quality, and rework risk. Required inputs: - Workflow boundary, decision question, comparison period, and accountable owners - Accepted-outcome definition, quality gates, counts, failures, retries, rework, escalations, and rejected work - Provider, infrastructure, integration, monitoring, support, review, labor, control, and incident costs - Allocation policy, workload drivers, shared-cost assumptions, currency, and time basis - Baseline or comparison scenario and known missing evidence How to use: When to use: - AI cost must be connected to usable business or operational outcomes. - Teams need comparable economics across periods, models, providers, or workflow designs. When not to use: - Estimating generic ROI without accepted-outcome and cost evidence. - Using tokens, calls, generated items, or user seats as the outcome denominator when acceptance matters. Reusable method: 1. Fix the workflow, time, currency, environment, and decision boundary. 2. Define an accepted outcome with observable quality and completion conditions. 3. Reconcile attempted, completed, accepted, rejected, reworked, escalated, duplicate, and failed outcomes. 4. Inventory direct and shared provider, infrastructure, integration, labor, review, support, control, and incident costs. 5. State allocation rules and preserve unallocated or unknown amounts. 6. Calculate cost per accepted outcome and separate volume, mix, rate, quality, rework, and control variance. 7. Test sensitivity to denominator quality, allocation assumptions, and missing costs. 8. Record control decisions and the next evidence refresh. Expected output: A cost-boundary map, accepted-outcome reconciliation, allocation ledger, unit-economics table, variance bridge, sensitivity analysis, confidence limits, and owner decisions. Boundaries: Do not invent cost, volume, acceptance, savings, or attribution data. Finance approves accounting treatment and allocations; process and product owners approve outcome quality; investment decisions remain with the accountable sponsor. Source grounding: AMO-P-000280. Applicable Workflow: AMO-W-000015. Powered by Prompt: AI Operating Cost Attribution and Unit Economics Model Source ID: AMO-P-000280 https://amo.ng/prompts/ai-operating-cost-attribution-and-unit-economics-model Completion criteria: Complete when the cost and outcome boundaries reconcile; all material cost pools have supported allocation or an explicit unknown; the accepted denominator includes quality, rework, and failure treatment; sensitivities and confidence are visible; and finance plus process owners approve use of the result.Copy skill copies the Skill details. Use with AI adds a short instruction for your preferred AI tool; neither action runs the Skill.
Purpose
Help finance, product, process, and operations owners compare AI economics across periods, providers, models, workflows, or operating designs while exposing denominator, allocation, quality, and rework risk.
Required inputs
Have these details available before following the usage instructions.
- Workflow boundary, decision question, comparison period, and accountable owners
- Accepted-outcome definition, quality gates, counts, failures, retries, rework, escalations, and rejected work
- Provider, infrastructure, integration, monitoring, support, review, labor, control, and incident costs
- Allocation policy, workload drivers, shared-cost assumptions, currency, and time basis
- Baseline or comparison scenario and known missing evidence
How to use this Skill
When to use:
- AI cost must be connected to usable business or operational outcomes.
- Teams need comparable economics across periods, models, providers, or workflow designs.
When not to use:
- Estimating generic ROI without accepted-outcome and cost evidence.
- Using tokens, calls, generated items, or user seats as the outcome denominator when acceptance matters.
Reusable method:
1. Fix the workflow, time, currency, environment, and decision boundary.
2. Define an accepted outcome with observable quality and completion conditions.
3. Reconcile attempted, completed, accepted, rejected, reworked, escalated, duplicate, and failed outcomes.
4. Inventory direct and shared provider, infrastructure, integration, labor, review, support, control, and incident costs.
5. State allocation rules and preserve unallocated or unknown amounts.
6. Calculate cost per accepted outcome and separate volume, mix, rate, quality, rework, and control variance.
7. Test sensitivity to denominator quality, allocation assumptions, and missing costs.
8. Record control decisions and the next evidence refresh.
Expected output:
A cost-boundary map, accepted-outcome reconciliation, allocation ledger, unit-economics table, variance bridge, sensitivity analysis, confidence limits, and owner decisions.
Boundaries:
Do not invent cost, volume, acceptance, savings, or attribution data. Finance approves accounting treatment and allocations; process and product owners approve outcome quality; investment decisions remain with the accountable sponsor. Source grounding: AMO-P-000280. Applicable Workflow: AMO-W-000015.
Powered by an Amo.ng Prompt
AI Operating Cost Attribution and Unit Economics Model
Open the linked prompt to use the instructions that power this Skill.
Completion criteria
Complete when the cost and outcome boundaries reconcile; all material cost pools have supported allocation or an explicit unknown; the accepted denominator includes quality, rework, and failure treatment; sensitivities and confidence are visible; and finance plus process owners approve use of the result.
Was this useful?
Explore related Workflows
Browse WorkflowsMake an AI Initiative Value and Scale Decision
Reconcile an approved AI value case to realized evidence, diagnose value leakage, calculate accepted-outcome unit economics, and issue a Scale, Hold, Redesign, or Stop decision.
Related Prompts
Browse PromptsDraft a Reply to a Message
Draft a clear reply using the message you received and what you want to say.
Improve an Email Before Sending
Make an email clearer and easier to read while preserving its facts, requests and intended tone.
Automation Displacement and Augmentation Evidence Review
Decide which work should be automated, augmented, redesigned, or retained using task evidence, quality effects, capacity, transition risk, and accountable ownership.
AI Portfolio Capital Allocation Brief
Allocate constrained investment across AI initiatives using realized evidence, remaining option value, dependencies, risk capacity, and explicit funding trade-offs.
AI Vendor Cost Concentration Risk Review
Quantify AI vendor spend and capability concentration, switching exposure, contract constraints, and mitigation economics before dependency becomes decision-limiting.
Model Routing Economics Decision Brief
Choose a model-routing policy by workload slice using accepted-outcome quality, latency, reliability, capacity, switching, and full-cost evidence.