Start with classification and retrieval
Customer ticket triage is a strong first workflow for knowledge management teams when requests are repetitive, knowledge articles are scattered, and escalation rules are inconsistent. Salesforce State of Service and IBM Institute for Business Value AI capabilities research both point toward the need for better service context and trusted AI capabilities, not just faster responses.
The first implementation should classify the issue, retrieve likely source articles, summarize the request, propose a priority, and route the case to a human-owned queue. It should not send unsupervised customer answers until quality is measured.
Govern the route and the answer
PwC Responsible AI survey and NIST AI Risk Management Framework support a governed approach: define intended use, affected users, quality measures, and human accountability. For ticket triage, that means the AI output should show why a category, article, or escalation was suggested.
The workflow also needs a fallback path. If the request references a sensitive account, a service outage, a refund, legal language, or conflicting source data, the system should route to a reviewer instead of trying to resolve the case.
Measure support quality
McKinsey State of AI research reinforces that AI value shows up in redesigned work. For ticket triage, measure time to first owner, correct routing, reopened cases, knowledge article usefulness, escalation aging, and reviewer acceptance of AI suggestions.
Use AI for customer service and AI knowledge systems to build a support workflow that improves routing before automating customer responses.
