Voice AI

What it is

A voice AI stack traditionally chains three parts: speech recognition to transcribe the caller, a language model to decide what to say, and text to speech to speak the reply. Newer speech to speech models handle audio end to end, which reduces delay and preserves tone. Interruption handling, turn taking and latency matter as much as raw accuracy.

Why it matters

Spoken questions are longer and more conversational than typed ones, and the assistant normally reads out a single answer, so second place is rarely heard. That makes accurate, machine readable facts about opening hours, location, availability and pricing commercially important. Voice agents also change service economics, because a bot can handle routine calls that previously needed staff.

How it works

Practitioners keep structured business data accurate across their site, listings and knowledge bases so assistants can retrieve it, and write answers in short spoken sentences. For outbound and inbound voice agents, teams script the opening, define escalation rules to a human, and review call transcripts to fix misunderstandings and mispronounced brand names.

When it applies

It applies to phone-led businesses such as clinics, garages and hospitality, to hands-free contexts like driving and cooking, and to any brand whose customers use voice mode in an AI assistant.

Examples

• A dental practice uses a voice agent to answer out of hours calls and book appointments into its diary
• A driver asks an in-car assistant for the nearest open tyre fitter and hears one recommendation
• A user switches ChatGPT to voice mode to talk through holiday options while walking

How it is measured

• Containment rate, the share of calls handled without human escalation
• Average handling time and response latency per turn
• Transcription accuracy for brand, product and place names
• Booking or conversion rate from voice sessions compared with web sessions