Returns are a good automation candidate for the same reason they are a support burden: the process is rule-driven, high-volume and repetitive, right up until the point it is not.
Key takeaways
Start with what your policy actually is, including the informal parts. Most businesses have a stated window and an unstated flexibility for good customers, damaged goods or a genuine mistake at your end.
Automation forces that to become explicit, which is uncomfortable and useful. Decide which flexibility is a rule the system can apply and which is a judgement that goes to a person.
An in-policy return of an eligible item with the order located and the reason recorded can run end to end: eligibility confirmed against Shopify or your order system, the return authorised, the label issued, the customer told what happens next and when.
That is the majority of return volume in most businesses, and clearing it is what gives your team the time to handle the rest properly.
Out-of-window requests, damaged goods, missing orders and anything with a complaint attached go to a person — but they arrive with the order located, the history attached and the request summarised.
The customer should never be asked for their order number twice. Collecting information is the part that automates; deciding what to do about it is not.
Consumer protection legislation in Canada is provincial, so the rules that apply to a return can differ between provinces, and terms drafted for one market may not fit another.
Keep the province on the record and treat it as an input to the rules rather than an afterthought. Where your obligations are genuinely unclear, that is a question for your own legal advisor, not a default the system should invent.
Review the automated refusals more closely than the approvals. An approval that should have been a refusal costs you a margin; a refusal that should have been an approval costs you a customer and sometimes a public review.
Set the threshold so borderline cases go to a person, and revisit it once you have real volume through the workflow.
A 30-minute call is enough to tell you whether AI pays for itself here.
