Modeled measurement case
Finance Operations AI: A Measurable Exception and Close-Readiness Case
A modeled measurement case for reducing manual exception work and improving close readiness while keeping policy, materiality, and payment authority with finance leaders.
FIELDRUNTIME / CASE / 02
TRANSPARENCY / NOT CLIENT RESULTS
This is an evidence-ready ROI model, not a claim about a completed client deployment. Every baseline and target is an explicit example assumption. During Field Discovery, each number is replaced with the organization's verified operating data before a business case is approved.
THE OPERATING PROBLEM
Measure the work before changing it.
Finance specialists spend material time gathering evidence, matching records, investigating exceptions, routing approvals, and preparing close status. Reliable software should perform deterministic checks; AI should assemble context and propose resolution; people should own policy and financial authority.
MODEL INPUTS
Every assumption is visible.
The starting model
- Team modeled
- 8 finance specialists
- Manual exception load
- 6 hours per specialist per week
- Working year
- 48 weeks
- Loaded capacity value
- $75 per hour
OUTCOME MODEL
Targets require evidence.
What the pilot must prove
| Metric | Example baseline | Modeled target | Evidence source |
|---|---|---|---|
| Manual exception capacity | 2,304 hours/year | Return 806–1,267 hours | Queue, task, and time-sampling data |
| Modeled capacity value | $172,800/year | $60,480–$95,040 returned | Finance-approved loaded-cost model |
| Close-readiness cycle | 10 business days | 7–8 business days | Close checklist timestamps |
| Transactions requiring rework | 12% | 7–9% | Exception and reopening codes |
| Evidence completeness | Measure first 100 cases | 95% before approval | Required-document and citation checks |
THE VALUE MODEL
A calculation the CFO can challenge.
8 specialists × 6 hours/week × 48 weeks × $75/hour = $172,800 annual capacity exposed to exception work.
At 35–55% capacity recovery, the modeled annual value is $60,480–$95,040 before counting fewer errors, faster close, improved cash visibility, or reduced compliance risk.
HUMAN AUTHORITY + CONTROLS
- People retain policy, materiality, payment, supplier, and exception authority.
- Deterministic matching, limits, and validation remain outside probabilistic model judgment.
- No payment or accounting entry proceeds without the required evidence and approval path.
MEASUREMENT PLAN
Turn the model into evidence.
- Baseline exception categories, handling time, rework, and waiting before deployment.
- Run the system in recommendation-only mode through a complete close cycle.
- Measure time-to-evidence, first-pass acceptance, rework, and approval latency by exception type.
- Promote only changes that pass regression evals against policy and historical edge cases.
Replace assumptions with your operating data.
Map the real workflow and build the evidence case.
Bring one repetitive, expensive, or consequential workflow. We will map what stays human, what becomes reliable software, where AI creates measurable value, and what evidence will make the system improve.