How to Set Up Shopify Returns and Refund Tickets with AI: Rules, Approvals and What to Never Automate

Returns and refunds are usually the second biggest source of support tickets after "where is my order?". They repeat, they follow your policy, and they need an order lookup. That makes them a good fit for automation. They also involve money, so the setup matters more than the tool.

What an AI return and refund workflow actually does

A good workflow does four things for each ticket:

• Reads the request. It works out whether the customer wants a return, an exchange, a refund, or is just asking about the policy.
• Finds the order. It matches the customer to the Shopify order, items, date and delivery status.
• Applies your rules. It checks the order against your return policy.
• Acts or hands off. It either takes the action it has permission for, or passes the ticket to you with the context already gathered.

Step 3 is where most setups fail. The AI cannot apply a rule you never wrote down.

Step 1: Write your return rules as plain yes/no conditions

Take your public return policy and turn it into conditions the AI can check. Vague policy language ("items in good condition") produces vague decisions. Write each rule as a condition and an outcome:

• Return window: if the order was delivered within your window, the return is eligible. If not, it is declined or sent to a human.
• Item type: which products are final sale, personalized, or excluded for hygiene reasons.
• Reason for return: changed mind, wrong size, damaged, wrong item sent, never arrived. Each can lead to a different outcome.
• Who pays for return shipping: the customer, or you, depending on the reason.
• Refund method: original payment method, store credit, or exchange first.
• Proof needed: for damaged or wrong items, whether you require a photo.

Keep the whole list on one page. If you cannot decide a rule, that is a case for a human, not for the AI.

Step 2: Decide what the AI may do alone, and what needs you

This is the permissions layer. Relay's AI can act on an order (refund, promo code, cancellation, address change) only within the permissions you give it, so you decide how much freedom it has. A practical way to set it up is to sort requests into three groups.

• Safe to automate: policy questions, return eligibility checks, in-window return requests for standard items, cancellations before shipping. The AI decides, within your rules.
• Automate with limits: refunds on clear-cut cases such as a confirmed return, a wrong item, or a lost parcel. The AI decides only up to a refund amount you choose.
• Keep with a human: fraud, chargebacks, high-value orders, legal or safety issues, and policy exceptions. You or your team decide.

Start narrow. Give the AI the first group, watch the tickets for a week or two, then widen its permissions once you trust the answers. It is easier to grant more later than to undo a mistake.

Step 3: Set a refund limit

Pick a maximum order value the AI can refund on its own. Anything above it goes to you with the order details and the AI's suggested outcome already attached. Choose the limit based on what you could absorb if a decision turned out to be wrong. A small shop selling low-priced items can set a higher limit than a brand selling expensive products. You know your margins, so you set the number.

What to never automate

Some tickets should always reach a person. Keep these out of the AI's permissions:

• Suspected fraud or abuse. Repeat refund requests from the same customer, mismatched addresses, or "item not received" claims on tracked, delivered parcels.
• Chargebacks and payment disputes. These have deadlines and need evidence from you.
• High-value orders. Anything above your refund limit.
• Legal or safety complaints. Allergic reactions, injuries, damaged goods that could be hazardous, or any mention of a lawyer or consumer authority.
• Exceptions to your policy. A customer asking for a refund outside the window is a business decision. You may say yes to keep a good customer, and the AI should not decide that for you.
• Angry or distressed customers. If the tone shows a real problem, a human reply protects the relationship.
• Wholesale, custom or pre-order items with their own terms.
• Anything the AI is unsure about. Uncertain should mean "hand off", never "guess".

When a ticket is handed off, the useful part is that you receive it with the order found, the policy checked and the issue summarised. You make the call in seconds instead of searching for the details.

Step 4: Write the replies customers will see

Automation fails publicly when replies sound cold or confusing. Give the AI clear guidance for each outcome:

• Approved: say what happens next, the refund method, and when to expect it.
• Declined: give the specific reason from your policy, offer an alternative if one exists (exchange, store credit), and stay polite.
• Handed off: tell the customer a person is reviewing the request and what information they might still need to provide.

Use your knowledge base for the policy wording so the AI answers with your words, not generic ones. Keep one source of truth: if you change your policy, update it in one place.

Step 5: Test before you go live

Run a batch of real past tickets through your rules and read the outcomes. Cover:

• A standard return inside the window
• A return one day outside the window
• A final-sale item
• A damaged item with and without a photo
• A refund above your limit
• An angry message about a refund

Fix the rules, not just the replies. If one outcome surprises you, a rule is probably missing.

Step 6: Review the results and adjust

After launch, check three things each week at the start: handed-off tickets (are there patterns you could turn into a new rule?), customer follow-ups (if people write back confused, the reply needs rewriting), and time saved. Relay's statistics show resolution rate, time saved and payroll cost saved, so you can see whether the setup is paying off.

Relay's ambition is to automate the large majority of routine support, but your own number will depend on your products, your policy and your customers. Measure your own shop rather than assuming.

Example: a simple setup for a small apparel store

A store selling clothing might set up the following:

• Allowed alone: answer sizing and policy questions. Approve returns requested inside the window for non-final-sale items. Cancel orders that have not shipped.
• Allowed with a limit: refund wrong-item and confirmed-return cases up to the chosen order value.
• Always a human: out-of-window requests, orders above the limit, repeat refund requests, and any complaint about skin reactions or safety.

This is an illustration, not a benchmark. Adjust it to your catalogue and your risk tolerance. The safest way to automate returns is gradual: write the rules, grant limited permissions, keep the risky cases human, test on past tickets, and expand once the results are good.

FAQ

Can AI process Shopify refunds automatically? Yes, within the permissions you set. Relay can issue a refund only when you allow it, and you can cap it at an order value. Requests above that value or outside your rules go to a human.

Which return tickets should I never automate? Fraud suspicions, chargebacks, high-value orders, safety or legal complaints, policy exceptions and upset customers. Anything the AI is unsure about should be handed off.

Do I need to rewrite my return policy before using AI? Not entirely, but you need to turn it into clear conditions: return window, excluded items, reasons, shipping costs and refund method. Vague wording leads to vague decisions.

How do I know the AI is making good refund decisions? Test it on past tickets before going live, then review handed-off tickets and customer follow-ups each week. The statistics in Relay show resolution rate and time saved so you can track results.