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.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Member contacts help center
iFIT uses Solve as its chat widget to handle incoming member support requests.
Tools used
ForethoughtSolveTriageAssistDiscoverWorkflow BuilderNatural Language Understanding
Outcome
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.
What failed first
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.