AI Customer Support: What to Automate and What to Escalate
AI support works when the system can retrieve trustworthy business information and knows its boundaries. The goal is not to produce a plausible answer to every question. The goal is to resolve routine requests correctly and move exceptions to the right person.
Ground answers in approved sources
Policies, product information, account data, order status and troubleshooting steps should come from approved sources. When the answer depends on information the system cannot retrieve reliably, it should say so and escalate rather than fill the gap with a guess.
Good first-line support jobs
Start with requests that have repeatable resolution paths.
- Order and shipment status
- Account or appointment status
- Documented policy questions
- Basic product and service information
- Request classification and routing
- Conversation summarization
- Lead capture from support conversations
Escalation is part of the product
Refund exceptions, safety issues, legal threats, sensitive personal information, payment disputes and emotionally complex conversations often require a person. A support agent should create a clean handoff package with the conversation, relevant account context and the reason for escalation.
Evaluate correctness, not fluency
A polished response can still be wrong. Useful support evaluations test answer grounding, correct tool use, policy compliance, escalation behavior and whether the workflow changed the right record.
Automate a workflow when the event is frequent, the inputs are available, success can be measured and exceptions can be routed safely. Do not add an agent merely because the technology exists.
Turn the framework into an operating workflow.
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