“Dynamic pricing has existed for years, but AI has changed both its speed and intent,” says Abhishek Daga, Co-Founder of Thrillophilia.
AI systems continuously adjust airfares, hotel rates, and activity prices based on real-time signals such as demand, search behaviour, inventory availability, seasonality, and currency fluctuations.
Unlike traditional methods, prices are no longer updated periodically. AI continuously recalculates them, prioritising revenue stability over the lowest possible price, Daga explains.
This changes how operators manage revenue and challenges the conventional idea of an optimal booking window for travellers.
How AI supports budgeting
AI can also help manage costs across entire multi-day trips. By analysing all components — flights, accommodation, activities, local taxes, and surcharges — AI identifies periods where total trip costs are stable or minimised.
“For longer journeys, identifying stable pricing windows matters more than chasing marginal discounts,” Daga says. AI can also simulate thousands of itinerary variations to ensure costs stay within a defined budget, and suggest adjustments when spikes occur.
How AI reduces risk
AI is used to mitigate financial and operational risk. By predicting when and where payments will occur, operators and travellers can limit exposure to foreign exchange fluctuations.
AI also analyses historical data to flag potential hidden charges, such as incidental hotel fees or overpriced local transfers.
However, Daga emphasises that AI is not infallible.
“It cannot predict sudden weather disruptions, strikes, or regulatory changes. Human oversight is required wherever decisions affect money, identity, or operational reliability,” he says.
Market implications
Analysts say AI’s main economic impact will be in complex, multi-day travel where cost and operational volatility are highest. While simple transactions like single-flight bookings may become automated, AI-assisted revenue and risk management provides operators with greater predictability and control.
“AI identifies patterns; humans ensure accountability and safeguard both revenue and operational outcomes,” Daga says.
