An AI reply workflow should escalate reviews that require facts it does not have, decisions it cannot make or sensitive handling. The safest boundary is based on the type of issue, not just the number of stars.
Identify high-consequence categories
Account access, disputed charges, safety concerns, threats, allegations of data exposure and requests involving personal information deserve human attention. A model should not invent a refund, confirm an investigation or promise a release date to make a response sound helpful.
Repeated reports of a new technical failure also merit escalation. Even if the wording is ordinary, the pattern may indicate an incident that requires a coordinated response.
Give automation a bounded job
Use approved facts and response guidance to draft acknowledgments, classify themes and suggest next steps. Keep the system from inserting private account information into public replies. Separate internal analysis from text intended for publication.
An illustrative safe draft might say that support can investigate through the official contact route. It should not say the team has checked the user’s account unless that action actually happened.
Evaluate before expanding publishing rights
Create a representative review set with difficult cases, multiple languages and ambiguous complaints. Have a human reviewer score accuracy, empathy, privacy and escalation behavior. Track serious failures separately from minor tone issues.
Start with draft-only operation. Broader automation should follow evidence and an explicitly agreed approval model, with an easy way to pause publication.
Keep an audit trail
Record the source review, generated draft, final response and decision owner. When approved guidance changes, update the knowledge base and retest relevant examples.
The goal is not to make every reply sound human. It is to make every published response accurate, useful and appropriate to the issue. A short, honest escalation is better than a fluent answer that oversteps what the business knows.