Clean the case context first
Customer service teams should automate data cleanup before they automate more visible support interactions. Salesforce State of Service and Salesforce State of Sales both underscore that frontline teams depend on accurate customer context across service and revenue workflows. AI cannot route or draft reliably when case fields, tags, account history, and knowledge links are inconsistent.
The first workflow should identify missing fields, inconsistent tags, stale account context, duplicate requests, and cases without relevant knowledge links. AI can propose cleanup actions, but a service operations owner should approve changes until the rules are trusted.
Govern customer-impacting data
PwC Responsible AI survey and NIST AI Risk Management Framework support a controlled approach because service data can affect customer treatment, escalation, and reporting. The cleanup workflow should show source evidence, reviewer identity, change history, and rules for protected or sensitive accounts.
Start with internal data hygiene and routing accuracy. Once the data is cleaner, the same foundation can support safer ticket triage, knowledge retrieval, escalation routing, and draft responses.
Measure whether cleanup helps service
IBM Institute for Business Value AI capabilities research reinforces that AI capability has to connect to business outcomes. For customer service data cleanup, measure field completeness, tag consistency, routing accuracy, time to first owner, reopened cases, and fewer manual lookups by agents.
Use AI for customer service and workflow automation to move from cleanup into service workflows that customers actually feel.
