Travel platforms are increasingly using AI for customer support, treating it as a $billion bet to scale service while reducing human intervention. Airbnb, Booking.com, and Expedia have each pursued different strategies—Airbnb aims for autonomous resolution, Booking focuses on smooth handoffs, and Expedia invests in multilingual deflection.

The core support pipeline starts with intent detection, often a layered model that first classifies the request domain and then drills into specifics like cancellation or refund details. State tracking carries conversation context, and an action layer can issue refunds or modify bookings. Each prediction carries a confidence score; a confidence threshold decides whether the system acts autonomously or routes the case to a human agent. Tuning this threshold balances automation volume against error risk.

Routine lookups—policy questions, date changes—are handled well by retrieval, but cases that require adjudication, such as disputed refunds or damage claims, resist automation regardless of model quality. When a case escalates, a strong handoff payload—including a summary, extracted intent, state, and suggested action—prevents frustration. Airbnb reports over 40% of guest issues resolved without agents, while Expedia notes more than 30% of its self‑service contacts are AI‑powered, reflecting different measurement bases.