Assessment · Governance · Assess and know

3Use-case value and priorities

Where does AI pay off, and where should we invest next?

Which AI workflows are worth funding, scaling or stopping, ranked by value and risk.

Stakeholder interviewsAutomatedUses your existing assessments 2 controls 4 engines

How we collect

Stakeholder interviewsBusiness owners: volumes, work time versus waiting time, outcomes, targetsRuns today
AutomatedCopilot and app usage from Graph: licensed, active, repeat usersRuns today
AutomatedROI engines price each candidateRuns today
Uses your existing assessmentsBusiness cases and pilot results you already havePlanned

Status labels show what runs today, what needs an export or setup from you, and what is planned.

What you get

Use-case register ranked by value, cost and risk
Process check: waiting and handoffs are not AI problems
Adoption funnel from licensed to proven value

Questions we ask

What does success look like in numbers?
What happens today without AI?

We can start from

Business cases, pilot reports, PMO portfolios

Data sources

Source	What we read	Status
AI gateway telemetry	LiteLLM, Cloudflare AI Gateway or AICost CostWall logs: cost per team, workload, agent and outcome.	Partly automated
Invoices, card and AP exports	AI vendors found on statements and invoices, including ones never approved. Spend is exact because it is on the statement.	Partly automated
Questionnaire	The short interview for what data cannot show: purpose, owners, decisions AI informs, regulations in scope.	Runs today

Built on governance engine modules

P3 PlannedUse Cases, ROI & Business Value

Which AI use cases are worth doing, and what are they worth?

Value & Optimization

P16 PlannedAdoption, Training & Change

Are people actually using it, safely, and measurably better?

Value & Optimization

AICost governance controls in this module

AICost-GovControl504Runs today

Value and ROI per AI use

What it is

Cost per outcome, total cost of ownership and payback for each AI use, measured against the result it was approved for.

Why it is needed

AI that costs more than the work it replaces should be changed or stopped, and that needs numbers.

Why enterprises need it

Boards ask what AI returns. Product owners need proof to keep funding.

What you get:Keep, change or stop decisions on evidenceCost per outcome trackedBusiness cases checked after launch

Run by: AI Value and Workflow engineTotal Cost and ROI engine Agent TCO and ROI engine Support Deflection ROI engine AI Margin engine
Data: AI gateway telemetry, Invoices, card and AP exports, Questionnaire
Evidence produced: Value table: system, outcome measure, cost per outcome, baseline, payback, decision.
Maps to: NIST AI RMF: Measure; FinOps for AI

AICost-GovControl102Partly automated

Purpose and intake approval

What it is

Each new AI use states the business purpose, the users, the data it needs and the expected cost before it is approved.

Why it is needed

Without a stated purpose there is nothing to judge access, data use or spend against, so every request looks reasonable.

Why enterprises need it

Teams want AI quickly. A light intake with clear rules lets low-risk uses through in a day and slows only the ones that need it.

What you get:Faster approvals for low-risk usesCost estimate attached before money is committedA record of why each system exists

Run by: Governance Assessment wizardAI Workflow Integration engineTotal Cost and ROI engine Token Estimator engine
Data: Questionnaire
Evidence produced: Intake record: purpose, users, data classes, expected monthly cost, risk tier, approver, decision date.
Maps to: NIST AI RMF: Govern, Map; ISO/IEC 42001: AI system impact assessment

Frameworks


FinOps Framework and FOCUS	Cost visibility, allocation, budgets, forecasting and unit economics for AI

Stakeholders

CEOCFOBusiness unit leads

← 2. Workflow map and owners All modules 4. Risk tiering and risk register →

Vendor / Model	Field	Why it’s inferred
Anthropic — Claude Sonnet 4.6	`cachedInput`	Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.
Anthropic — Claude Sonnet 4.5	`cachedInput`	Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.
Anthropic — Claude Sonnet 4.5	`batchInput`	Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Sonnet 4.5	`batchOutput`	Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Haiku 4.5	`cachedInput`	Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.
OpenAI — GPT-5.4 Mini	`cachedInput`	Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.
OpenAI — GPT-5.4 Nano	`cachedInput`	Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Nano	`batchInput`	Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Nano	`batchOutput`	Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro	`cachedInput`	Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Pro	`batchInput`	Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro	`batchOutput`	Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.2	`cachedInput`	Derived at 10% of input; no residency uplift.
OpenAI — GPT-5.2	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5.2	`batchOutput`	Derived at 50% of output.
OpenAI — GPT-5	`cachedInput`	Derived at 10% of input.
OpenAI — GPT-5	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5	`batchOutput`	Derived at 50% of output.
OpenAI — GPT-5.5 Pro	`cachedInput`	Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.
OpenAI — GPT-5.5 Pro	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5.5 Pro	`batchOutput`	Derived at 50% of output.
OpenAI — GPT-5.2 Pro	`cachedInput`	Derived at 10% of input — pro-tier convention.
OpenAI — GPT-5.2 Pro	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5.2 Pro	`batchOutput`	Derived at 50% of output.
OpenAI — GPT-5.1	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5.1	`batchOutput`	Derived at 50% of output.
OpenAI — GPT-5 Pro	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5 Pro	`batchOutput`	Derived at 50% of output.
OpenAI — GPT-5 Nano	`cachedInput`	Derived at 10% of input.
OpenAI — GPT-5 Nano	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5 Nano	`batchOutput`	Derived at 50% of output.
Google — Gemini 3 Flash	`cachedInput`	Derived at 10% of input — Google caching discount convention ~90%.
Google — Gemini 3.1 Flash-Lite	`cachedInput`	Derived at 10% of input — Google caching convention.
Google — Gemini 3.1 Flash-Lite	`batchInput`	Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 3.1 Flash-Lite	`batchOutput`	Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Pro	`cachedInput`	Derived at 10% of input.
Google — Gemini 2.5 Flash	`cachedInput`	Derived at 10% of input.
Google — Gemini 2.5 Flash-Lite	`cachedInput`	Derived at 10% of input — Google caching convention.
Google — Gemini 2.5 Flash-Lite	`batchInput`	Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Flash-Lite	`batchOutput`	Derived at 50% of output — Google Batch API uniform 50% discount.
xAI — Grok 4 (legacy)	`cachedInput`	Extrapolated at 25% of base.

Vendor / Model

Field

Why it’s inferred

Anthropic — Claude Sonnet 4.6

cachedInput

Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.

Anthropic — Claude Sonnet 4.5

cachedInput

Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.

Anthropic — Claude Sonnet 4.5

batchInput

Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.

Anthropic — Claude Sonnet 4.5

batchOutput

Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.

Anthropic — Claude Haiku 4.5

cachedInput

Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.

OpenAI — GPT-5.4 Mini

cachedInput

Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.

OpenAI — GPT-5.4 Nano

cachedInput

Derived at 10% of input — OpenAI 90% cache-hit convention.

OpenAI — GPT-5.4 Nano

batchInput

Derived at 50% of input — OpenAI Batch API uniform 50% discount.

OpenAI — GPT-5.4 Nano

batchOutput

Derived at 50% of output — OpenAI Batch API uniform 50% discount.

OpenAI — GPT-5.4 Pro

cachedInput

Derived at 10% of input — OpenAI 90% cache-hit convention.

OpenAI — GPT-5.4 Pro

batchInput

Derived at 50% of input — OpenAI Batch API uniform 50% discount.

OpenAI — GPT-5.4 Pro

batchOutput

Derived at 50% of output — OpenAI Batch API uniform 50% discount.

OpenAI — GPT-5.2

cachedInput

Derived at 10% of input; no residency uplift.

OpenAI — GPT-5.2

batchInput

Derived at 50% of input.

OpenAI — GPT-5.2

batchOutput

Derived at 50% of output.

OpenAI — GPT-5

cachedInput

Derived at 10% of input.

OpenAI — GPT-5

batchInput

Derived at 50% of input.

OpenAI — GPT-5

batchOutput

Derived at 50% of output.

OpenAI — GPT-5.5 Pro

cachedInput

Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.

OpenAI — GPT-5.5 Pro

batchInput

Derived at 50% of input.

OpenAI — GPT-5.5 Pro

batchOutput

Derived at 50% of output.

OpenAI — GPT-5.2 Pro

cachedInput

Derived at 10% of input — pro-tier convention.

OpenAI — GPT-5.2 Pro

batchInput

Derived at 50% of input.

OpenAI — GPT-5.2 Pro

batchOutput

Derived at 50% of output.

OpenAI — GPT-5.1

batchInput

Derived at 50% of input.

OpenAI — GPT-5.1

batchOutput

Derived at 50% of output.

OpenAI — GPT-5 Pro

batchInput

Derived at 50% of input.

OpenAI — GPT-5 Pro

batchOutput

Derived at 50% of output.

OpenAI — GPT-5 Nano

cachedInput

Derived at 10% of input.

OpenAI — GPT-5 Nano

batchInput

Derived at 50% of input.

OpenAI — GPT-5 Nano

batchOutput

Derived at 50% of output.

Google — Gemini 3 Flash

cachedInput

Derived at 10% of input — Google caching discount convention ~90%.

Google — Gemini 3.1 Flash-Lite

cachedInput

Derived at 10% of input — Google caching convention.

Google — Gemini 3.1 Flash-Lite

batchInput

Derived at 50% of input — Google Batch API uniform 50% discount.

Google — Gemini 3.1 Flash-Lite

batchOutput

Derived at 50% of output — Google Batch API uniform 50% discount.

Google — Gemini 2.5 Pro

cachedInput

Derived at 10% of input.

Google — Gemini 2.5 Flash

cachedInput

Derived at 10% of input.

Google — Gemini 2.5 Flash-Lite

cachedInput

Derived at 10% of input — Google caching convention.

Google — Gemini 2.5 Flash-Lite

batchInput

Derived at 50% of input — Google Batch API uniform 50% discount.

Google — Gemini 2.5 Flash-Lite

batchOutput

Derived at 50% of output — Google Batch API uniform 50% discount.

xAI — Grok 4 (legacy)

cachedInput

Extrapolated at 25% of base.

3Use-case value and priorities

How we collect

What you get

Questions we ask

We can start from

Data sources

Executable policy

Continuous checks

Engines that run it

Built on governance engine modules

AICost governance controls in this module

Frameworks

Stakeholders

Methodology

Primary sources

Inferred values (marked with * in calculator tables)

Immediate steps you can take