AI customer service starts with source governance
Identify which pages, policies, documentation, product records, and links may support customer answers. Assign owners to important facts and resolve contradictions before automation.
Separate answer, action, and human-decision paths
For each common question, define the source, expected visitor-facing action, and fallback. Private account work, sensitive requests, and uncertain exceptions should not be treated like public FAQ answers.
Test realistic language and failure cases
Use short questions, follow-ups, misspellings, mixed intents, missing facts, and requests for a person. Recheck affected scenarios after source or configuration changes.
Use operational evidence instead of generic ROI claims
Track usable-answer rate, unresolved intents, time to a next step, operator workload, and customer feedback where collected. Cost, conversion, and satisfaction changes depend on the implementation and should be measured against a baseline.
