Business and Architecture

AI Cost vs ROI Simulator: When Token Burn Becomes Business Value

Does this AI workflow create enough value to justify total operating cost and risk?

AI ROI is not proven by claiming that AI is cheaper than humans. It is proven when the use case produces measurable business value after accounting for implementation cost, inference cost, monitoring, failure risk, governance, adoption, and the opportunity cost of not acting.

Run the Simulator See Formulas Sources

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Core thesis

The executive question is not How much does the model cost per token? The better question is: Does this AI workflow create enough revenue, cost reduction, risk reduction, speed, or quality improvement to justify total operating cost and risk?

ROI must be modeled at workflow level, not model level.
A cheap model can still have negative ROI if adoption fails.
An expensive model can be justified if it protects revenue or reduces material risk.
Not every problem needs AI — deterministic software may win.

Interactive simulator

Run the ROI simulation

Choose a domain, use case, objective, and routing strategy. Adjust any assumption. See cost, value, payback, ROI, and a worth-vs-not-worth recommendation.

Domain

Use Case

Priority View

Calculating…

Net value

$—

ROI

—%

Payback

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Selected use case context

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The research basis and use-case facts here are fixed external context. The cost, value, payback, and recommendation numbers elsewhere on this page are model outputs — they update as you change routing, automation, geography, or any assumption.

Value Type

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Main Risk

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Default Outcome

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Select a use case with a workflow diagram to view it.

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Use-case narration

Routing strategy meaning

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Automation level meaning

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Monthly Task Volume

Human Minutes per Task

AI-Assisted Minutes per Task

Human Review %

Adoption %

% Workflow Automated

Loaded Human Hourly Cost ($)

Reviewer Hourly Cost ($)

Input Tokens per Task

Output Tokens per Task

Model Input $/M tokens

Model Output $/M tokens

Retrieval Cost per Task ($)

Tool/API Cost per Task ($)

Implementation One-Time Cost ($)

CapEx Amortization (months)

Monthly Maintenance ($)

Monthly Governance ($)

Cloud Runtime Monthly ($)

Revenue Uplift / Month ($)

Cost Avoided / Month ($)

Risk Loss Avoided / Month ($)

Time-to-Market Value / Month ($)

Quality Value / Month ($)

Prob. of Successful Adoption (0-1)

Prob. of Quality Failure (0-1)

Estimated Cost per Failure ($)

Compliance/Security Risk Reserve ($)

Data Readiness Score (0-1)

Governance Maturity Score (0-1)

Regulatory Exposure Score (0-1)

Geography Complexity Score (0-1)

Human Adoption Score (0-1)

Deterministic Tool Build Cost ($)

Deterministic Tool Monthly Maint. ($)

Change Request Cost ($)

Scenario sensitivity controls

Use these sliders to stress-test adoption, token price, review load, and implementation uncertainty without losing the researched scenario defaults.

Recommendation

Calculating…

Adjust inputs to see the recommendation.

Scenario: —

Default outcome: —

Financial Outputs

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Monthly AI OpEx

$—

Gross Monthly Value

$—

Net Monthly Value

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Payback Period

—%

Annual ROI

$—

Human-Led Monthly

$—

Deterministic Monthly

$—

First-Year Total Cost

Priority View

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Priority Score

$—

Margin Impact

$—

Cost per Successful Task

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Break-Even Volume

Monthly Cost Breakdown

Cost Mix Donut

Value-to-Net Waterfall

Monthly Value Breakdown

Automation Level Comparison

Option	Monthly Cost	Cost per Task	Net Value	Payback	Recommendation

Readiness & Risk Dimensions

Formula transparency

How the simulator calculates

Every formula is visible and explainable. The simulator does not use opaque scoring — all math can be traced from inputs to outputs.

Token cost per task

input_cost = input_tokens / 1,000,000 * input_price_per_million output_cost = output_tokens / 1,000,000 * output_price_per_million token_cost_per_task = input_cost + output_cost + retrieval_cost

Human-led monthly cost

human_led_monthly = volume * human_min / 60 * loaded_hourly_cost

Monthly AI OpEx

inference = token_cost_per_task * automated_tasks review = reviewed_tasks * ai_min / 60 * reviewer_cost monthly_ai = inference + tools + review + maintenance + governance + cloud

Gross monthly value

gross_value = labor_avoided + revenue + cost_avoided + risk_avoided + ttm_value + quality_value

Risk-adjusted value

expected_failure = prob_failure * cost_per_failure * automated_tasks risk_adj_value = gross_value * prob_adoption - expected_failure - compliance_reserve

Net value & payback

net_monthly = risk_adj_value - monthly_ai_cost payback_months = impl_cost / net_monthly roi_percent = (annual_net - impl_cost) / impl_cost * 100

Readiness factor

readiness = avg(data_readiness, governance, human_adoption, (1-reg_exposure), (1-geo_complexity)) readiness_adj_value = risk_adj_value * readiness

Deterministic tool monthly

det_monthly = det_maintenance + (det_build / amortization_months) + change_request_allowance

Scenario-grounded sources

Research sources for each use case

Every use case is grounded in a specific set of empirical research sources. Switch use cases above and the linked citations below update to show exactly which studies, benchmarks, and analyst reports back that scenario's defaults, research-basis narrative, and economic assumptions. Sources not cited by the current scenario are listed separately for transparency. All defaults remain editable.

Research-first rule

Every scenario is designed after collecting reference economics. The page does not invent defaults first and cite them later.

Worth vs not-worth

The simulator intentionally includes scenarios that produce strong ROI, pilot-only, fix-prerequisites, and not-worth-pursuing outcomes.

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AI Cost vs ROI Simulator: When Token Burn Becomes Business Value

Core thesis

Run the ROI simulation

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Scenario sensitivity controls

Recommendation

Financial Outputs

Priority View

Monthly Cost Breakdown

Cost Mix Donut

Value-to-Net Waterfall

Monthly Value Breakdown

Automation Level Comparison

Readiness & Risk Dimensions

Full view

How the simulator calculates

Token cost per task

Human-led monthly cost

Monthly AI OpEx

Gross monthly value

Risk-adjusted value

Net value & payback

Readiness factor

Deterministic tool monthly

Research sources for each use case

Research-first rule

Worth vs not-worth

Source categories represented