Quick Model 1.2.0

Compare AI cost against accepted business outcomes.

A fast, directional unit-economics check using five numbers and one consistent analysis period.

Start the one-minute estimateView framework case study

Choose the right depth

Start fast. Add detail only when it helps.

Both modes compare current and AI-assisted work against the same accepted-business-outcome standard.

Quick Estimate

Five inputs. One equivalent-output comparison.

Use a representative closed month, quarter, or year. Every value in the estimate must use that same period.

Private by design. Inputs stay in this browser and are not submitted.

Explore a realistic scenario, then adjust the numbers for your own workflow.

Analysis context

Use the same period for every volume and cost below.

01 Current process

Count only completed outcomes that passed your acceptance standard.

Where to get this number

Use approval, completion, or posting records from a representative closed period. Exclude drafts, attempts, corrected-but-not-approved work, rejected work, failed work, and incomplete work.

Direct cost of producing the accepted current-process outcomes above.

$
How to estimate this

Loaded labor hours × loaded hourly cost, plus agency, software, direct rework, and quality-control costs. Use the same closed period as the accepted count.

02 AI-assisted process

Count every attempt: accepted, corrected, rejected, failed, and unusable work.

Where to get this number

Use pilot logs, generation history, workflow records, projected task volume, or a representative sample. The number must include work that later needs correction or is rejected, failed, or unusable.

Share of attempts expected to pass the same acceptance standard as the current process.

%
Where to get this number

Prefer accepted pilot outcomes ÷ all pilot attempts. Without a pilot, use a conservative planning assumption and validate it later.

Calculate from a pilot sample

Direct technology, implementation, review, correction, governance, and operating cost.

$
What should be included

Use vendor invoices, platform budgets, implementation plans, labor estimates, and pilot expense for the same period. Include model usage, tools, allocated implementation, human review, QA, and integration support.

Need help building this total? Use Advanced Estimate.

Illustrative scenarios

Load a Quick example.

Use a realistic starting point to understand the model, then edit every field. These scenarios are illustrative—not benchmarks, client results, or industry-standard performance.

Ecommerce content

Ecommerce PDP production

A product-content team compares approved product detail pages before and after AI-assisted drafting and editorial review.

Period
Monthly
Unit of work
Approved PDP

Where values come fromPIM/CMS approvals, team timekeeping, agency invoices, QA records, and a representative editorial pilot.Why plausibleA scaled content team can handle thousands of monthly PDPs with a formal editorial gate.

Customer service

Customer-service response drafting

A support operation compares resolved cases with all AI-assisted response attempts, including work that is corrected, rejected, or escalated.

Period
Monthly
Unit of work
Resolved customer case

Where values come fromResolved-contact QA records, support ledgers, contact forecasts, pilot logs, reviewer time studies, and vendor budgets.Why plausibleHigh-volume support teams can draft more responses than become accepted resolutions because review and escalation remain.

Document operations

Invoice or document processing

An accounts-payable operation compares accepted invoice volume with every AI-assisted extraction and validation attempt.

Period
Monthly
Unit of work
Processed invoice

Where values come fromPosting records, processing labor and OCR cost, invoice forecasts, validation samples, and automation invoices.Why plausibleShared-services teams can process hundreds of thousands of monthly invoices while exceptions still affect acceptance.

Methodology and limits

A comparable-outcome model, not a promise of savings.

Quick uses five numerical inputs. It separates attempts from accepted outcomes, compares equivalent accepted-output volume, calculates the acceptance rate needed to break even, and annualizes the selected-period result.

Current accepted outcomes×AI cost per accepted outcome= Equivalent-output AI cost
Accepted outcomes versus attempts

Current-process accepted outcomes include only completed work that passed the stated acceptance standard; failed, rejected, incomplete, or merely attempted work is excluded. AI-assisted attempts include every accepted, corrected, rejected, failed, and unusable unit. Only the accepted share becomes accepted AI-assisted outcomes.

How the model works

Current and AI-assisted work use the same acceptance standard. AI cost is divided across every attempt; accepted outcomes determine unit economics. The selected period remains consistent, and monthly or quarterly savings or loss is annualized only after the selected-period comparison.

What the model does not assume

It does not automatically account for demand changes, nonlinear scale, vendor tiers, tax or financing treatment, working capital, discount rates, legal exposure, implementation delays, ramp time, unentered change-management cost, revenue effects, opportunity cost, inflation, or model-quality drift.

How to use the decision signal

Modeled savings or loss is directional—not guaranteed, realized, audited, validated, or committed. All loaded examples are illustrative. Validate inputs through a representative pilot and finance, legal, compliance, and operating review before acting.

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