Use cases · Customer support
Return and refund requests
Check each return or refund request against your policy and order records, prepare the response and the system updates, and send anything outside policy to a person.
Where it starts
The trigger.
A customer asks to return an item, cancel an order or get a refund.
How it runs today
- Agents look up the order, the policy and the payment in separate systems.
- Similar requests get different answers depending on who handles them.
- Simple, in-policy requests wait in the same queue as difficult ones.
The workflow
Four bounded steps.
Each step has an owner and a stop rule.
- 01 / Gather
Pull the order, delivery and payment details for the request from the systems that hold them.
- 02 / Check
Compare the request with the written return and refund policy, and note the reason for the result.
- 03 / Prepare
Draft the reply and the refund, return label or order update for an agent to approve.
- 04 / Escalate
Requests outside policy, high-value orders and unhappy customers go straight to a person with a summary.
Where people stay in control
Approval points.
- An agent approves every refund and customer reply until the record supports more automation.
- Exceptions to policy are always decided by a person.
How we would measure it
Against a baseline.
Measured before and after, on the same work.
Time from request to resolution, before and after.
Share of in-policy requests prepared without edits.
Consistency of outcomes for similar requests.
Related
Go deeper.
More use cases
Other places to start.
Support triage and reply drafts
Classify incoming tickets, find the relevant answer, and draft a reply for an agent to check.
See the workflow Customer supportAt-risk customer alerts
Spot customers who may be about to leave from support tickets, usage and account notes, and give the account owner a short summary to act on.
See the workflow Customer supportNew customer onboarding
Guide each new customer through the first weeks with timely, relevant steps based on what they bought and what they have done so far, and alert a person when someone gets stuck.
See the workflowAn illustrative workflow pattern, not a result from a specific customer.
Bring the messy version
One workflow.
A better way to run it.
Show us the tools, the handoffs, and the part that keeps slowing your team down.
---
title: "Return and refund requests: AI Use Case"
description: "Check each return or refund request against your policy and order records, prepare the response and the system updates, and send anything outside policy to a person."
url: https://lidvizion.ai/use-cases/return-and-refund-requests/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---
[Use cases](https://lidvizion.ai/use-cases/) · Customer support
# Return and refund requests
Check each return or refund request against your policy and order records, prepare the response and the system updates, and send anything outside policy to a person.
[Talk through this workflow](https://lidvizion.ai/contact/?need=ai-workflow&from=%2Fuse-cases%2Freturn-and-refund-requests%2F#write) · [Automate the repeatable](https://lidvizion.ai/services/automation/)
Where it starts
## The trigger.
A customer asks to return an item, cancel an order or get a refund.
How it runs today
- Agents look up the order, the policy and the payment in separate systems.
- Similar requests get different answers depending on who handles them.
- Simple, in-policy requests wait in the same queue as difficult ones.
The workflow
## Four bounded steps.
Each step has an owner and a stop rule.
1. 01 / Gather
Pull the order, delivery and payment details for the request from the systems that hold them.
2. 02 / Check
Compare the request with the written return and refund policy, and note the reason for the result.
3. 03 / Prepare
Draft the reply and the refund, return label or order update for an agent to approve.
4. 04 / Escalate
Requests outside policy, high-value orders and unhappy customers go straight to a person with a summary.
Where people stay in control
## Approval points.
- An agent approves every refund and customer reply until the record supports more automation.
- Exceptions to policy are always decided by a person.
How we would measure it
## Against a baseline.
Measured before and after, on the same work.
### Time from request to resolution, before and after.
### Share of in-policy requests prepared without edits.
### Consistency of outcomes for similar requests.
Related
## Go deeper.
- [Automate the repeatable](https://lidvizion.ai/services/automation/): Give recurring work an owner, a trigger, and a stop rule.
- [Client vs. server: where code runs](https://lidvizion.ai/resources/basics/backend-client-vs-server/): What belongs in the browser or app, and what must stay on a server you control.
- [Handle returns and refunds consistently](https://lidvizion.ai/guides/handle-returns-and-refunds-consistently/): A four-step playbook to write your return and refund policy as clear rules, check each request against order records, prepare the response for an agent to approve and route exceptions to a person.
- [How to handle return and refund requests with AI](https://lidvizion.ai/answers/how-to-handle-returns-and-refunds-with-ai/): How can an online store use AI to handle return and refund requests faster while staying fair?
More use cases
## Other places to start.
[All use cases](https://lidvizion.ai/use-cases/)
- [Support triage and reply drafts](https://lidvizion.ai/use-cases/support-triage/): Customer support Classify incoming tickets, find the relevant answer, and draft a reply for an agent to check.
- [At-risk customer alerts](https://lidvizion.ai/use-cases/at-risk-customer-alerts/): Customer support Spot customers who may be about to leave from support tickets, usage and account notes, and give the account owner a short summary to act on.
- [New customer onboarding](https://lidvizion.ai/use-cases/new-customer-onboarding/): Customer support Guide each new customer through the first weeks with timely, relevant steps based on what they bought and what they have done so far, and alert a person when someone gets stuck.
An illustrative workflow pattern, not a result from a specific customer.
Bring the messy version
## One workflow. A better way to run it.
Show us the tools, the handoffs, and the part that keeps slowing your team down.
[Let’s map your workflow](https://lidvizion.ai/contact/) · [Read our approach](https://lidvizion.ai/approach/)