Extract the promise before the ticket escalates
Contract review preparation is a strong first AI use case for customer service because the source record already exists. The signed agreement defines support hours, escalation path, service levels, and commercial commitments. Salesforce State of Service 2025 is relevant because service teams are being asked to absorb more complex work as AI changes front-line responsibilities; the practical opportunity is giving agents better context before they respond.
The first workflow should not interpret legal risk. It should extract specific obligations, attach clause evidence, and route exceptions to a human owner. NIST AI Risk Management Framework gives the right operating frame: map the context, measure the risk, manage the control, and govern the deployed system.
Keep permissions and legal authority explicit
Contract data is sensitive. Microsoft 365 Copilot data protection architecture is relevant because enterprise AI inherits identity, access, sensitivity labels, and audit controls from the underlying content estate. If contract folders are over-permissioned, an AI workflow can expose problems that were already present in the operating model.
PwC Responsible AI survey is useful here because responsible AI needs practical controls, not policy language alone. For this article, that means a clause library, source citations, escalation thresholds, and a legal owner for ambiguous terms.
Measure preparation quality before automation breadth
The scorecard is narrow: extraction accuracy, clause evidence coverage, legal correction rate, escalation frequency, and ticket cycle time. The goal is a better-prepared support response, not automated contract interpretation.
Use AI governance and training to set the review boundary, then run a QuickStart AI Audit before contract data enters a broader workflow.
