Time-to-First-Token (Voice AI) measures the delay between the end of a caller’s utterance and the moment the voice AI generates the first token of its response (often the first audible sound once text-to-speech begins). It captures the combined impact of speech recognition finalization, model processing, and response generation.
Operationally, this metric matters because callers judge the system by how quickly it reacts. Longer time-to-first-token increases awkward silence, talk-over, and repeat requests, which can raise handle time, reduce containment, and increase transfers to agents.
Tracking time-to-first-token alongside barge-in rate, reprompts, and abandonment helps pinpoint whether delays are coming from recognition latency, model inference, or downstream systems (like knowledge lookup or CRM calls). It’s especially important for high-volume intents where small delays compound into noticeable queue and staffing impacts.
