AI-led · 70%
Routine intake and three-way matching
AI extracts, validates and matches approved records. The scenario retains an average of 4 human minutes per invoice for sampling, oversight and routed exceptions.
Worked example
Follow one bounded business question from current work through human–AI allocation, deterministic economics, implementation requirements and outcome observation.
All numbers are illustrative assumptions used to demonstrate the method. They are not customer results or performance claims.
Illustrative example — not a customer or performance claim
Decision in scope
How should invoice intake, matching and exception handling change while people retain supplier, exception and payment authority?
Annual volume
12,000 invoices
illustrative assumption
Handling time
18 min / invoice
illustrative assumption
Loaded labour cost
A$55 / hour
illustrative assumption
Calculated baseline
3,600 hours · A$198,000
volume × time × cost
AI-led · 70%
AI extracts, validates and matches approved records. The scenario retains an average of 4 human minutes per invoice for sampling, oversight and routed exceptions.
People work with AI · 20%
AI assembles evidence and suggests the relevant policy; an AP specialist resolves the case.
Human-accountable · 10%
Authorised people decide material exceptions and release payment. AI receives no approval authority.
Baseline: 12,000 × 18 min = 3,600 hours
Designed human touch: (8,400 × 4 human min) + (2,400 × 10 human min) + (1,200 × 24 human min) = 1,440 hours
Gross human-capacity difference: 3,600 − 1,440 = 2,160 hours
Illustrative gross annual human-capacity value: 2,160 × A$55 = A$118,800
This is not ROI or realised savings. AI runtime is not counted as human touch time; implementation cost, software and inference run cost, ramp-up, demand, risk and adoption must be added before an authorised investment decision.
Discuss your challenge
Then bring a real business decision and replace every illustrative input with authorised evidence from your organisation.