Enterprise Adoption

Refund Management

“AI Proposes, Human Decides.”

80% auto-approved, 20% human review.

The system proposes, the human decides. This demo is about how AI actually gets adopted inside enterprises — by keeping humans in control of decisions that matter.

The Problem

80% of refunds are under $25 — small enough that human review destroys margin and satisfaction.
A 48-hour wait on a $15 refund is a churn event.
Auto-approving every refund creates fraud exposure.
One bad $500 chargeback wipes out the savings on thirty $15 refunds.
Pure manual is slow; pure auto is unsafe — neither is acceptable.

Why It Matters

Small / safe refunds resolve in seconds, not days.
Large / risky refunds get the human judgment they need.
Fraud patterns flagged automatically — a third refund in 30 days lands on a desk regardless of amount.
Adoption unlocked: humans control the decisions that matter; AI handles the rest.
Same primitive as Security Operations, applied to financial decisions — proof of platform, not point solution.

The Pattern

Request → Score → Route → Approve → Log. Same exception-handling primitive as Security Operations, different domain. One pattern, multiple domains, one platform.

How It Works

Refund request enters the system.
AI scores by amount, customer history, VIP status, and pattern signals.
Under $25: Auto-approve instantly.
$25–$100: Auto-approve and notify the manager.
Over $100, VIP, or pattern-flagged: Hold for human approval with AI recommendation.
Decision and rationale logged.

Stack: n8n + your refund system + structured audit log.

Request

AI Classify / Score

Decision

P1: Human Approve

P2: Auto Execute

P3: Auto Log

Audit Log

Same diagram as Security Operations and IT Ops, with the input label swapped. The repetition is the visual proof of platform-not-point-solution.

See how this looks for your organization.

This pattern is one slice of our Enterprise AI Operating Model. Read the full framework →