As of 13 August 2026, AI can classify incoming customer complaints.

What to actually do

1. Use a tool built for this

   The route this page recommends

2. Do it yourself

   Second choice

   A chat interface, chat-fluent skill, and roughly 30 minutes until you can act on the result.

   How to actually do it

   1. Open your complaints policy, escalation procedure and current support taxonomy, then combine the category definitions, routes and escalation rules in one document.
   2. Remove unnecessary names, addresses, phone numbers, account numbers and other personal data from a small batch of complaints, while keeping the words needed to understand each case.
   3. Paste the combined policy and taxonomy into a chatbot, followed by the copyable prompt and the prepared complaint batch.
   4. Check every returned category, urgency level and destination against the exact complaint text and your written rules, paying particular attention to low-confidence cases and any escalation flag.
   5. Ask a colleague who handles complaints to resolve the cases marked ambiguous, urgent or requiring human review, and record the approved label and reason.
   6. Send only the approved classifications to your ticketing or case-management system, keeping the original complaint and the human decision together for audit purposes.

   Prompt

   Classify each incoming customer complaint using only the taxonomy and escalation rules below.
   
   Business context:
   [Brief description of the business, products and support teams]
   
   Allowed categories and definitions:
   [Paste the complete category list and the definition of each category]
   
   Escalation rules:
   [Paste the rules for urgent, vulnerable-customer, safety, legal, regulatory, data-protection, refund, repeat-contact and senior-management cases]
   
   Examples of correctly classified complaints:
   [Paste representative examples with their approved labels and routes]
   
   Complaints to classify:
   [Paste the complaint text, with unnecessary personal data removed]
   
   For each complaint, return:
   1. complaint_id
   2. primary_category
   3. secondary_category, or null
   4. urgency: routine, priority or urgent
   5. escalation_required: yes or no
   6. destination_team
   7. confidence: high, medium or low
   8. evidence: quote only the relevant words from the complaint
   9. missing_information
   10. a short reason
   
   Do not invent facts, customer history, policy, deadlines or evidence. If the complaint could fit more than one category, show the competing category and explain the conflict. If any escalation rule might apply, mark escalation_required as yes and state which rule needs a human decision. Never decide that a safety, legal, regulatory, data-protection or vulnerable-customer issue is harmless. Return one structured result per complaint and finish with a list of all low-confidence or human-review cases.

   Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.

3. Hand it to a person

   The distant third

   A person who owns the outcome does this end to end, worth it when the failure is dear.