---
title: "Booking change requests: AI Use Case"
description: "Read incoming requests to change dates, rooms, guests or services, check them against availability and your change policy, and draft a reply for an agent, with exceptions routed to the right person."
url: https://lidvizion.ai/use-cases/booking-change-requests/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---

[Use cases](https://lidvizion.ai/use-cases/) · Customer support

# Booking change requests

Read incoming requests to change dates, rooms, guests or services, check them against availability and your change policy, and draft a reply for an agent, with exceptions routed to the right person.

[Talk through this workflow](https://lidvizion.ai/contact/?need=ai-workflow&from=%2Fuse-cases%2Fbooking-change-requests%2F#write) · [Automate the repeatable](https://lidvizion.ai/services/automation/)

Where it starts

## The trigger.

A guest or travel partner asks to change or cancel a booking by email, form or chat.

How it runs today

- Agents look up each booking, policy and availability by hand.
- Simple changes wait in the same queue as complicated ones.
- Replies quote fees and policies inconsistently.

The workflow

## Four bounded steps.

Each step has an owner and a stop rule.

1. 01 / Read
   Identify the booking, the change requested and the guest’s contact details.
2. 02 / Check
   Look up availability, rates and the change or cancellation policy that applies.
3. 03 / Draft
   Draft a reply with the options and any fees, quoting the policy wording.
4. 04 / Route
   An agent approves routine replies; refunds, complaints and policy exceptions go to a supervisor.

Where people stay in control

## Approval points.

- An agent approves every reply before it is sent.
- Refunds, complaints and policy exceptions are decided by a person.

How we would measure it

## Against a baseline.

Measured before and after, on the same work.

### Time from request to reply.

### Share of drafts sent with light edits.

### Replies corrected for wrong fees or policy.

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 booking changes without the backlog](https://lidvizion.ai/guides/handle-booking-changes-without-the-backlog/): A four-step playbook to write down your change rules, connect bookings and availability, draft replies that quote the policy and route exceptions to a supervisor.
- [How to handle booking change requests with AI](https://lidvizion.ai/answers/how-to-handle-booking-change-requests-with-ai/): How can a hotel, travel or tour operator use AI to answer requests to change or cancel bookings faster and quote policies consistently?

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.
- [Return and refund requests](https://lidvizion.ai/use-cases/return-and-refund-requests/): Customer support 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.

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/)