AI in travel is already doing more than answering passenger questions: the documented deployments below analyze service calls, help staff query data, guide bookings, personalize travel offers and process expenses. The systems serve different users and workflows, so their reported results are not a like-for-like comparison.
Seven deployments, seven different travel workflows
These examples show where named travel and hospitality companies have put AI into use, who interacts with it, and what the companies or their technology vendors report. Each linked source is a vendor-published case study or customer story; the figures should be read as attributed claims, not independent evaluations.
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| Company | Workflow and users | What the system does | Reported evidence |
|---|---|---|---|
| Jet2 | Pre-travel service analysis; service and data teams | Analyzes call transcripts for customer intent and sentiment. | Snowflake says it was put into production in three months and analyzes about 2,000 calls a day, or 25% of the pre-travel service line’s calls. The system informs service improvements; the case does not say it resolves calls. |
| TUI | Internal analytics; data users | TACO lets users ask questions of data in natural language; Cortex Search makes metadata searchable. | Snowflake reports more than 500 complex natural-language queries answered during early user-acceptance testing. |
| Ryanair | Passenger support | A customer-service assistant answers questions about baggage, refunds, bookings and disruptions. | AWS reports latency falling from 18 seconds to 2.9 seconds and a 25% accuracy improvement in an evaluation of five foundation models against 12,000 production questions. The case also reports 10 million answers generated, 120,000 daily answers, 94% accuracy and a 70% reduction in service contacts per passenger carried. |
| Vernost | Travel discovery, booking and post-booking; customers and staff | Chatbots and agents recommend hotels, destinations and flights; guide reservations and loyalty-reward use; and assist with ticket downloads, changes and cancellations. | Vernost’s chief business officer says customers save “at least 60 percent” of the time needed to find and book the right holiday package. |
| Air India | Passenger support and contact-center operations | AI.g answers questions on bookings, flight status, baggage, check-in, loyalty awards and lounge access, with escalation to human support. Related uses include document scanning for web check-in and voice analysis for contact-center quality and training. | Microsoft’s 2024 customer story reports an average of 30,000 questions a day across 1,300 topic areas, nearly four million queries handled, and 97% full automation. |
| Sabre and Virgin Australia | Travel commerce and trip planning; travelers and travel sellers | SabreMosaic IQ products support personalized offers; Concierge IQ provides conversational trip planning and was first adopted by Virgin Australia. | Google Cloud’s case study presents product claims of up to 10% higher ancillary revenue for Ancillary IQ and up to 20% higher upgrade revenue for Upgrade IQ. |
| Yatra | Corporate travel and expenses; business travelers and finance teams | Google Cloud AI supports receipt processing and expense logs, alongside DIYA, a travel assistant. | Google Cloud reports receipt-scanning latency falling from 20 seconds to 3–4 seconds and expense-log automation for more than 6,000 firms. |
How airlines are using AI for customer service
Ryanair: direct answers at high volume
Ryanair’s assistant is a traveler-facing service for routine questions and disruption-related topics. AWS says the airline compared five foundation models using 12,000 production customer questions. In that evaluation, the selected model reduced response latency from 18 seconds to 2.9 seconds and improved accuracy by 25%. AWS’s case study also says the service launched in October 2024, had generated 10 million answers, and was handling 120,000 answers daily at publication. The same case reports 94% accuracy and 70% fewer customer-service contacts per passenger carried. Those last figures are AWS’s reported outcomes, not independent measurements.
The architecture described in the AWS case was updated in February 2026. Because model and system configurations can change, the comparison figures should be understood in their case-study context rather than assumed to describe every later version.
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Air India: automation with a human route available
AI.g has a broader stated topic range, spanning bookings, flight status, baggage, check-in, loyalty awards and lounge access. Microsoft’s 2024 story says the assistant averages 30,000 questions per day across 1,300 topic areas and had handled nearly four million queries, 97% with full automation. Air India also describes connections to operational systems: document scanning supports web check-in, while voice analysis is used for contact-center quality and training. The assistant can escalate to human support when needed, an important distinction from a system intended to automate every interaction.
Jet2: analyzing calls rather than replacing them
Jet2’s system works behind the scenes. Using Snowflake Cortex AI and a fine-tuned Mistral model, it examines transcripts of pre-travel service calls for intent and sentiment. Snowflake says the three-month deployment processes about 2,000 calls daily, equal to 25% of calls on that service line. The stated purpose is to inform service improvements; this case does not describe a chatbot or claim that AI itself handles those calls. Jet2 delivery manager Niraj Kulkarni credited close work with Snowflake’s account team with helping improve performance and move the use case into production quickly.
Rank #2
How AI is used for travel booking and trip planning
Vernost: from recommendations to routine trip changes
Vernost’s customer and staff chatbots and agents cover multiple stages of a trip. The case describes personalized discovery of hotels, destinations and flights, reservation guidance, loyalty-reward application and post-booking tasks such as downloading tickets, making changes and cancelling. That connection between suggestions and booking-related tasks makes this a broader workflow than a search-only assistant. Vernost chief business officer Himanshoo Patil says customers save at least 60% of the time required to find and book a suitable holiday package; this is his estimate in AWS’s case study, not an independently measured result.
Sabre and Virgin Australia: conversational planning and offers
Sabre launched the SabreMosaic AI-powered travel platform in 2024. Google Cloud’s case study describes Sabre IQ as supporting personalized offers and Concierge IQ as a conversational trip-planning product, first adopted by Virgin Australia. Sabre’s Santiago Larrazabal called conventional search’s fixed inputs “the tyranny of the five boxes: origin, destination, departure, arrival, and number of travelers.” The product claims in the case study say Ancillary IQ can produce up to 10% higher ancillary revenue and Upgrade IQ up to 20% higher upgrade revenue. These are vendor-reported product claims, not guaranteed gains for other airlines or travel sellers.
How AI supports travel analytics and corporate expenses
TUI: asking data questions in natural language
TUI’s Technology Analytics Companion, or TACO, uses Cortex Analyst so users can query data in natural language; Cortex Search makes metadata searchable. Snowflake reports that more than 500 complex queries were answered in natural language during early user-acceptance testing. TUI’s wider data platform supports analytics such as flight and hotel occupancy optimization and personalized travel offers. The 500-plus figure is specifically from early testing, not a reported measure of ongoing production query volume.
Yatra: reducing friction in expense processing
Yatra uses Google Cloud AI for expense processing and the DIYA travel assistant. Google Cloud reports that receipt-scanning latency dropped from 20 seconds to 3–4 seconds and that expense logs are automated for more than 6,000 firms. Yatra chief architect and data scientist Dr. Shakti Goel says Gemini 3.0 improved the system’s interpretation of whether a traveler is exploring options or ready to book, enabling a more relevant response. The same customer story describes planned convergence of travel and expense assistants and future proactive disruption handling; those are plans, not capabilities established there as already deployed.
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What these cases show—and what they do not
- Different jobs call for different forms of AI. The examples range from transcript analysis for staff to direct passenger support, data access, booking assistance, offer optimization and expense processing.
- Integration is central. A useful travel assistant needs access to relevant booking, customer, travel or expense information. Air India and Yatra describe enterprise connections; the examples also show how systems can sit inside broader travel platforms rather than act as standalone chat interfaces.
- Autonomy varies. Jet2’s model analyzes conversations for service teams, while Ryanair and Air India’s assistants answer customers. Air India explicitly describes escalation to human support, and Vernost’s case includes both traveler-facing and staff-facing agents.
- Reported numbers have different bases. Some are model evaluations, some usage totals, some accuracy or contact-rate figures, and some product claims or executive estimates. They cannot be ranked as a common measure of AI quality.
- These are vendor-published accounts. Snowflake, AWS, Microsoft and Google Cloud provide the linked stories. The figures are useful descriptions of what the companies report, but the cases do not supply independent cross-company verification or a controlled head-to-head comparison.
Sources: Snowflake on Jet2; Snowflake on TUI; AWS on Ryanair; AWS on Vernost; Microsoft on Air India; Google Cloud on Sabre and Virgin Australia; Google Cloud on Yatra.
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