Customer Service Automation · Enterprise Knowledge Search
Iron Mountain's support agents had to swivel between apps and manually search the knowledge base to answer cases, which increased backlog, slowed order handling, and limited the value of large amounts of customer data.
Reported impact
Iron Mountain improved reply quality and speed, reduced repeat calls and chat abandonment, and gave agents faster access to grounded knowledge without having to manually build or search documentation for every case.
Iron Mountain uses Einstein AI inside Service Cloud and the Einstein 1 platform to turn case handling from a manual search-and-response workflow into an AI-assisted service operation. Instead of asking Live Chat agents to gather history and draft every response themselves, Einstein generates case replies from prior cases and the knowledge base, then lets the agent review or lightly edit the response before sending it. The same AI layer automatically creates new knowledge articles from resolved cases, generates work summaries from conversation history, and recommends relevant articles to agents so support improvements compound over time instead of depending on manual documentation. Behind the scenes, MuleSoft helps provide a unified customer view so AI-generated content stays grounded in the right account context, making it possible to reduce repeat calls, lower abandonment, and keep support quality high as case volume grows.
Customer chats and case details are worked in Service Cloud, Einstein uses prior cases and the knowledge base to draft replies and summaries, new knowledge is generated from completed cases, and MuleSoft-connected context helps ground the response in the correct customer and order history.
76% of AI-generated responses don't require any editing at all.
Due to automation routing, chat abandonment rates decreased 70% from 5% to 1.5%.
Einstein automatically generates personalized case replies, based on past cases and the knowledge base, that lead to a 80% close rate.
With every resolved case, Einstein automatically creates new knowledge articles.
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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