AI Economics: Cost, Usage, and Business Value

Understand the full cost of AI across cloud infrastructure, models, software, people, and vendors. Yarken AI Economics brings together AI spending, consumption, and ownership, helping you see where investments are going, who is using AI, and how efficiently it operates. Connect these insights to measurable business outcomes to evaluate value, improve financial accountability, and make informed investment decisions.

Overview

AI costs reach beyond cloud bills and model APIs. They also include software licenses, labor, vendors, and the services AI changes.

Those costs sit across different systems and owners. Without a shared view, you cannot reliably explain total cost or measure business value.

Yarken AI Economics connects AI spend, usage, ownership, and business outcomes in one governed financial model.

AI Economics builds on Yarken's Technology Business Management (TBM) and FinOps foundation. It helps you classify, allocate, plan, and evaluate AI investment.

What AI Economics helps you understand

Area	Question
AI Cost	What are we spending across compute, models, software, people, and vendors?
AI Usage	How much AI are we consuming, and which teams, models, or workloads use it?
AI Value	Which measurable business outcomes does the investment support?

Together, these inputs support AI unit economics such as cost per 1,000 tokens, request, active seat, or verified business outcome. A meaningful metric needs comparable cost and usage data and a clearly defined outcome.

What you can do with AI Economics

With AI Economics, you can:

Combine AI and related service costs across cloud, models, licenses, labor, and vendors.
Track tokens, requests, GPU consumption, active seats, and other usage measures.
Normalize billing and usage data, using technology domains and FOCUS-formatted cloud records where supported.
Classify AI expenditure using the financial, technology, and business dimensions already available in Yarken.
Assign spend to applications, services, teams, cost centers, and business owners.
Baseline full service costs, including non-AI and non-IT expenses when the source data is available.
Compare AI spending, unit costs, budgets, forecasts, and verified business outcomes.
Find unusual consumption, financial variances, and opportunities for review.
Prepare traceable cost evidence for AI capitalization reviews.
Use Ask Yarken and AI Studio to investigate costs and support governed financial workflows.

Core capabilities

See the full cost of AI and the services it changes

A token bill tells only part of the story. AI products also use infrastructure, licenses, data, labor, and vendors.

Start with the full cost of the service. That baseline shows what changes after AI enters the workflow.

Source records can include cloud and GPU charges, model tokens, AI software and licenses, labor, vendor contracts, and data services.

Classify those records using the Yarken structures below. These are model dimensions, not a new list of generic cost categories.

Yarken structure or dimension	How it organizes AI expenditure
Cost Pools and Sub Cost Pools	Classify AI-related financial costs using the Cost Pools and Sub Cost Pools available in the TBM model.
Towers and Sub Towers	Classify AI compute, platforms, and operations by technology function.
Technology Domain and Sub Domain	Group consumption records using consistent technology labels.
Solutions	Connect AI costs to applications, products, and services using the existing Solution structure.
Solution Offerings	Report service offerings within the existing TBM model.
Consumers	Link allocated costs to business units or groups that use the service.
Owners, entities, and cost centers	Add financial accountability using the available source data and model dimensions.

Bring source records into Yarken through supported integrations, cloud usage data, financial uploads, and consumption files.

Use financial and ownership details to find AI spend buried in software, cloud, or operating expenses, including purchases outside procurement.

Normalize and classify AI cost data

AI providers use different service names, billing periods, currencies, prices, and usage measures. Align the source data before you compare them.

Yarken supports field mapping and normalization. Technology Domain and Technology Sub Domain help classify AI consumption; Cost Pool, Tower, Solution, and Consumer fields connect it to financial reporting.

For supported cloud sources, Yarken accepts FinOps Open Cost and Usage Specification (FOCUS) data. FOCUS standardizes technology billing fields so you can compare costs, prices, and consumption across providers.

FOCUS helps standardize cloud billing fields. Labor, SaaS, vendor, and other AI expenses still need suitable source records and financial mappings.

Yarken supports FOCUS 1.0 and 1.2.

Connect AI cost to actual consumption

Spend shows what you pay. Consumption shows how people use it.

Bring AI consumption records into Yarken through file uploads or supported Advanced Integrations.

Documented sources include OpenAI API Platform, Anthropic, GitHub Copilot, Cursor, and LiteLLM. Available fields vary by source.

Depending on the provider, records can include:

Input, output, and cached tokens
Model requests and API usage
Provider, model, project, and workspace details
User, team, or application attribution
Usage costs and pricing categories
AI credits and supported license-related measures
Cloud and GPU consumption

Review loaded consumption data in Analytics > AI Consumption. Use consistent periods, currencies, and usage units when comparing sources.

See which providers, models, applications, and teams drive consumption, then compare usage with the available cost data.

For detailed ingestion and reporting instructions, see AI Usage Data and AI Cost and Usage.

Measure Tokenomics and model efficiency

Tokenomics, in simple terms, connects the cost of producing and consuming AI with the value of its output. A token is a useful unit, but not a complete measure of an AI service. The goal is to move from cost per token to cost per outcome and, ultimately, cost per unit of value.

Start with input, output, and cached tokens, requests, model choice, and available rates. Compare them across a consistent period and scope. Look for cost changes linked to routing, caching, or usage patterns.

Then include the rest of the service cost: compute, licenses, engineering, operations, and shared services. Compare the result with verified work completed. Compute-only metrics can make a service look cheaper than it is, especially when people still review or fix the output.

A lower token rate does not always mean a lower cost per resolved case, document, or business outcome. That is why tokenomics connects to Yarken's unit economics and business case analysis.

Bring AI spend into the financial model

AI Economics uses Yarken's existing financial model. You can analyze AI investment alongside other technology and service costs.

Use dedicated AI Spend models where needed. Publish supported AI consumption and qualifying AI-related cloud spend into selected models.

Apply the configured taxonomy, technology domain fields, and ownership attributes to keep reporting consistent.

Use that model to examine:

Total AI expenditure across the organization
AI spending by provider, application, and technology category
AI costs associated with specific teams or business initiatives
Actual expenditure compared with financial plans
The relationship between AI investment and existing technology services

TBM and FinOps connect these AI records to the wider technology cost picture.

Establish ownership and financial accountability

Knowing the total matters. Knowing who owns the cost makes it actionable.

Use available financial and operational fields to assign costs to:

Vendors and providers
Applications, products, and services
Teams and cost centers
Business units and entities
Initiatives and projects
TBM Cost Pools and Towers
Solutions and business consumers

Assign direct costs to the right owner. Allocate shared costs using documented rules where the model supports them.

Separate centrally funded AI platforms from costs consumed by specific services or business teams.

Flag unassigned records for review rather than assuming every cost has an owner.

Use the results for showback, chargeback, and funding decisions.

See AI Economics Ownership and Governance.

Baseline services, then plan AI investment

Before you forecast an AI-enabled service, establish what the service costs today. Include the work AI is expected to change.

The baseline can include IT costs, software, vendors, and non-IT labor. Support staff often sit outside the IT budget, but AI still changes what they cost.

With the relevant financial and operational data in Yarken, you can:

Compare the current full service cost with the planned AI-enabled service.
Track AI and non-AI costs against the budget.
Explain variances from demand, pricing, adoption, and service volume.
Test the cost of scaling an AI workload.
Compare actual usage and service performance with forecast assumptions.
Assess investments through budgets, forecasts, and business cases.

An AI-enabled workflow can change labor, supplier, and service costs even when those costs sit outside the IT budget.

For customer support

For customer support, baseline the current team's labor, applications, vendors, and infrastructure. Then add model usage, AI licenses, integration work, and ongoing support.

Compare the total cost per resolved case, service quality, and completed work before and after AI. That is a stronger test of value than token cost alone. Then compare the value the business case promised with the value you actually realized. That result becomes the benchmark for the next funding decision.

See Investment Planning and Business Cases.

Measure AI unit economics and business value

AI Economics measures more than spending. It connects consumption and the full cost of a service to the work that service delivers.

Choose units that match the workload and use the same cost period and scope for each comparison.

Unit economics measure	What it helps explain
Cost per 1,000 tokens	Economics of model consumption
Cost per request	Cost efficiency of AI application usage
Cost per active seat	AI software expenditure relative to adoption
Cost per workload	Financial performance of an AI process or service
Cost per completed outcome	Cost of achieving a measurable business result
Value per dollar invested	Relationship between measured benefit and AI expenditure

Some metrics require custom calculations or additional outcome data. They are not guaranteed to appear automatically in every Yarken environment.

Match the measure to the decision.

For an AI coding assistant, compare license costs and active use with verified productivity measures, net of reverted or reworked code.

For AI customer support, compare the full service cost with cases that stay resolved, net of reopened tickets.

For a document agent, measure cost per completed and validated document.

The question is whether AI improves the economics of the work, not whether it uses fewer tokens.

Keep two kinds of value apart. Booked value is hard-dollar savings or revenue that Finance has verified in the budget or P&L. Reported value is estimated benefit, such as time returned to employees. Report them separately so estimated gains don't get counted as hard savings.

Reliable unit economics need complete cost data, comparable usage records, and agreed outcome measures.

See AI Unit Economics and Value.

Support AI capitalization and financial traceability

AI development can draw on labor, compute, model APIs, licenses, data work, and external vendors.

Finance needs those costs linked to projects, work stages, owners, and reporting periods before reviewing capitalization.

Yarken helps organize the financial evidence for that review.

This includes:

Identifying the full cost base associated with an AI initiative
Linking expenditure to projects, products, teams, and owners
Distinguishing development activities from operational usage where supporting evidence is available
Documenting shared cost allocation methods
Preserving financial context for review and reconciliation

Finance can use the resulting records to assess capitalization eligibility under its accounting policies.

Yarken supports traceability and review. Your accounting team decides the treatment.

See AI Capitalization and Cost Traceability.

Monitor AI spending and identify opportunities

AI costs can change quickly when teams add workloads, switch models, or expand adoption.

Use Analytics and Insights to examine those changes in your AI Consumption data.

AI Consumption Insight rules support dimensions such as provider, region, billing account, service category, and service name.

Use them to:

Investigate unexpected increases in AI expenditure
Identify unusual usage or spending patterns
Examine changes by provider or service
Review budget and forecast variances
Investigate underused or inactive AI licenses
Track optimization opportunities through Recommendations

AI License Register adds license assignments, status, last-use dates, and configured usage or spend caps.

Together, these views help you focus reviews where the cost or consumption has changed.

See Yarken Insights Overview, Savings Recommendations, and AI License Register.

Investigate AI Economics with Ask Yarken and AI Studio

Investigate AI spending without rebuilding reports for every question.

Ask Yarken lets you question your technology finance data in plain language and explore spending patterns and variances.

For example, you can ask:

What changed in our AI spending this month?
Which applications or teams are driving increased AI costs?
How does actual AI expenditure compare with our forecast?
Which providers or services account for the largest consumption increases?
Where is AI expenditure missing a clear business owner?
Which AI investments need further financial review?

AI Studio adds Specialists, Data Sources, Skills, Playbooks, and Automations for governed analysis and action.

Use these tools for repeatable investigations, notifications, approval steps, and follow-up actions where configured.

Keep financial analysis and governed AI workflows close to the data, with people in control.

See Ask Yarken and AI Studio.

How AI Economics fits into Yarken

Yarken connects three financial disciplines:

Capability	Primary focus
Technology Business Management	Understand technology costs, services, business consumption, and financial allocation
FinOps	Understand and manage cloud expenditure, usage, and financial accountability
AI Economics	Understand the full cost, consumption, ownership, and business value of AI investment

AI Economics uses the connected data, cost models, planning, Analytics, and governance that support TBM and FinOps.

You can analyze AI investment in the context of the complete technology and business service cost.

Use other Yarken capabilities

Analytics to examine cost, usage, and trends using available data.
FinOps for related cloud and usage economics.
Technology Business Management for cost, service, and consumer structures.

Getting started with AI Economics

Start with the full service baseline, then add AI cost, consumption, ownership, and outcome data.

Set the baseline. Define the service and its current costs, including non-IT labor where available.
Connect the data. Bring in financial, cloud, license, and AI consumption records through supported sources.
Normalize and classify. Align source fields, technology domains, cost categories, and ownership attributes.
Assign ownership. Connect costs to applications, services, business units, and consumers.
Measure impact. Compare budgets, forecasts, unit economics, and verified service outcomes.
Investigate and improve. Use Analytics, Insights, Ask Yarken, and governed workflows to act on changes.

You don't need request-level tracking to begin. Start with monthly, service-level attribution, track one to three priority services in detail, then extend coverage as more cost sources, usage measures, and outcome data become available.

Explore AI Economics in more detail

Use these guides to explore the capabilities and configuration details:

AI Cost and Usage: Cost sources, consumption measures, and attribution.
AI Unit Economics and Value: Financial performance and business outcome measures.
Classify AI Costs for Reporting: Apply existing reporting dimensions and verify AI cost attribution.
AI Capitalization and Cost Traceability: Financial evidence for capitalization review.
AI Economics Ownership and Governance: Ownership, attribution, and accountability.
AI Usage Data: Ingestion, publishing, and reporting.
AI License Register: License assignment, utilization, and reporting.
Advanced Integrations: Supported source connections and data automation.

Additional resources

Explore these external resources to learn more about the standards and practices behind AI cost and consumption management:

FOCUS specification: Understand how standardized billing data supports consistent cost and usage reporting across technology providers.
FinOps Foundation: AI Tokenomics: Explore how token consumption, model costs, and usage efficiency contribute to AI unit economics.

Where to begin

Start with one AI-enabled service or business process. Establish its full cost, connect the relevant AI consumption data, and measure the outcomes it delivers.

Use that baseline to assess financial performance, identify opportunities for improvement, and make informed decisions about future AI investments.