iFIT uses Forethought AI to deflect 33% of chats and save 436 agent hours
iFIT's customer support organization relied on a chatbot that could not deflect chats, knowledge spread across hundreds of documents in multiple systems agents could not easily access, and a manual topic-discovery process requiring a person to search through more than 10,000 lines of data before any knowledge article could be written.
The existing chatbot was not successful at deflecting chats and required every workflow or script change to go through a technical administrator, making it difficult to maintain and ultimately unscalable.
iFIT achieved 33% chat deflection via the Solve widget, 82% prediction accuracy from Triage, 3,689 deflections and 436 agent hours saved through Discover, plus over 20,000 instant chat resolutions and over 39,000 emails deflected.
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Frequently asked questions
What did this team achieve with this AI workflow?
iFIT achieved 33% chat deflection via the Solve widget, 82% prediction accuracy from Triage, 3,689 deflections and 436 agent hours saved through Discover, plus over 20,000 instant chat resolutions and over 39,000 emai…
What tools did this team use?
Forethought, Solve, Triage, Assist, Discover, Workflow Builder, Natural Language Understanding, Salesforce.
What results were reported?
Chat deflection rate: 33%; Triage prediction accuracy: 82%; deflections via Discover: 3,689; Agent hours saved: 436 agent hours (source-reported, not independently verified).
What failed first in this deployment?
The existing chatbot was not successful at deflecting chats and required every workflow or script change to go through a technical administrator, making it difficult to maintain and ultimately unscalable.
How is this customer support AI workflow structured?
Member contacts help center → NLU-powered chat resolution → Workflow Builder recommends automations → Triage classifies incoming tickets → Route ticket to right agent → Assist surfaces info in Salesforce → Discover uncovers insight gaps.