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Airlines and travel companies are using AI, but that does not mean travelers routinely rely on AI assistants to plan, book, or manage trips. The gap reflects fragmented customer data, security and accuracy demands, skills and infrastructure constraints, uncertain returns, and travelers’ reluctance to hand over consequential decisions. Digital self-service is familiar; autonomous AI is a different proposition.
“AI adoption” can mean very different things
Some airline AI works behind the scenes: predicting delays, supporting operational decisions, or helping staff manage disruptions. Customer-facing generative AI is narrower. It means a system that directly answers a traveler, recommends an action, or helps complete a task. A high figure for broad airline AI use therefore does not show that passengers are booking trips through AI assistants.
The surveys below describe different populations, years, questions, and kinds of AI use. They are useful context, not a single comparable adoption rate or a time series.
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| Finding | What it measures | Why the scope matters |
|---|---|---|
| 80% of surveyed companies used AI in some capacity; 35% had deployed it in guest-facing applications. In the same 2025 survey, 41% said their organizations used AI tools daily or weekly, and 12.5% felt confident they were prepared to scale AI. | Amperity’s announcement of a 2025 survey of 800 U.S. hotel and airline professionals, published October 21, 2025. | “Used AI” includes more than customer-facing applications. The results are reports from surveyed professionals, not a count of applications or a measure of their success. |
| 83% of airlines used AI for operational decisions and passenger services; 51% used it to predict delays and disruption. | SITA’s summary of its 2025 airline IT survey. | This broad measure includes operational uses. It does not isolate customer-facing generative AI. |
| Fewer than a quarter of surveyed travelers frequently used AI while exploring or booking trips; more than a third had never experienced AI in travel. | McKinsey’s survey of 7,000 travelers. | This measures traveler-reported experience and use, not airline deployment. The publication date was not confirmed on the reviewed page. |
| 71% preferred online or app-based booking, and 53% favored airline digital platforms. | IATA’s summary of its 2024 Global Passenger Survey. | These are preferences for digital channels, not for delegating decisions to AI. |
| 80% of American travelers checked in electronically in 2024. | Airlines for America’s 2025 survey report, conducted with Ipsos. | Electronic check-in shows digital self-service use, not acceptance of AI making decisions. |
Amperity and Expedia Group have commercial interests in travel technology, so their survey findings should be read as attributed reports, not independent proof of why adoption succeeds or stalls. The other studies also differ in scope and method. Taken together, they show a gap between AI activity and traveler-facing use, but they do not establish a universal adoption rate or prove that one barrier causes it.
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Why customer-facing AI is hard to deliver reliably
Customer information is scattered
Useful travel assistance depends on more than a well-written answer. To respond to an individual traveler, a system may need reliable access to identity, booking details, loyalty status, preferences, current policies, and live disruption information. If those records sit in disconnected systems or are incomplete, the assistant can give generic, inconsistent, or outdated help.
In Amperity’s 2025 survey, 58% of respondents said customer data remained fragmented or incomplete. The company also reported higher guest-facing AI deployment among organizations with a customer data platform than among those without one. That is an association in the survey, not proof that adopting a platform causes successful deployment. Amperity CEO Tony Owens summarized the underlying challenge this way: “The challenge isn’t vision—it’s connection. Without a unified view of the customer and reliable access to quality data, AI can’t consistently deliver the personalized experiences travelers expect.”
Security, privacy, and accuracy are operational requirements
Travel conversations can involve payment details, identity, itineraries, and time-sensitive changes. A plausible-sounding answer is not enough if it misstates a fare rule, overlooks a traveler’s actual booking, or fails to reflect a current disruption. Companies must decide which information a system can access, how it is protected, how answers are checked, and what happens when it is wrong.
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In a 2024 Amadeus study of 306 senior travel technology decision-makers across ten markets, 35% cited data security as a rollout barrier. The finding records respondents’ concerns; it does not show that a particular airline suffered a breach or that AI is inherently less secure than another channel.
Skills and service processes have to catch up
A customer-facing assistant needs maintained source information, clear escalation rules, monitoring, and an accountable team that can correct errors. It also needs a defined role in the service operation: when should it answer, when should it hand off, and what context must reach the human agent?
In the same Amadeus study, 34% cited a lack of expertise or training, while 33% cited data quality or inadequate infrastructure. Separately, 36% of respondents in Amperity’s 2025 survey said their company provided no formal AI training. These findings point to reported readiness gaps, not a claim that every carrier lacks the necessary controls.
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Benefits are difficult to judge before integration costs are known
A demonstration may show that an assistant can answer a question, but a production service also has to connect to airline systems, stay current, meet privacy and security requirements, and hand off difficult cases. Those costs make the business case harder to estimate. Amadeus found that 30% of surveyed decision-makers cited ROI or uncertainty about use cases as a barrier to rollout.
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Passenger trust falls as the stakes rise
Travelers may welcome help comparing options while still wanting to choose the flight, approve the payment, and control what happens to a booking. Expedia Group’s 2026 YouGov survey of more than 5,700 adults in the U.S., U.K., and India, conducted March 10–25, found that 68% preferred booking with a trusted travel brand over AI chatbots or agents, and 66% would not trust an AI assistant to book or buy on their behalf.
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Respondents’ concerns included losing control, payment or personal-data privacy, and misuse of personal data. More than a third were concerned about poor customer service if something went wrong after an AI-made purchase. These results describe attitudes among adults in the three surveyed countries; they should not be treated as a global estimate. Expedia Group Chief AI & Data Officer Xavi Amatriain put the distinction bluntly: “Travelers don’t have a technology problem with AI. They have a trust problem.”
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The more promising role is often bounded assistance: explain a policy in the context of a trip, surface an accurate disruption update, or guide a traveler through available change options. The traveler can then review and confirm a consequential action. This approach offers help without requiring passengers to give an assistant open-ended authority over money or bookings.
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McKinsey’s traveler survey summary reports interest in timely assistance and preflight customization, and emphasizes utility, self-service, and clear choices. Amadeus listed digital assistance during booking as a leading generative AI use case. Neither finding guarantees demand for any particular product; they suggest that usefulness and control matter more than novelty alone.
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When assessing a travel AI feature, ask:
- Journey stage: Does it help with inspiration, booking, check-in, disruption, or post-trip support?
- Authority: Does it explain or suggest, or can it spend money or change a booking?
- Information quality: Can it access the traveler’s current itinerary, relevant policy, and timely operational updates?
- Privacy and security: What passenger data does it use, and how is that data protected?
- Control and recovery: Can the traveler review, approve, undo, or challenge an action?
- Human escalation: Is there a clear route to support when the system cannot resolve a consequential problem?
- Operational fit: Does it work with the systems and service processes behind the interaction, and can its benefits be measured against its costs?
These questions synthesize reported concerns and priorities from the cited surveys; they are not a published industry standard.
What would close the gap
Customer-facing AI is more likely to become dependable when airlines connect accurate customer and trip information, define strict limits on what the system can do, keep its knowledge current, and make human support easy to reach. They also need to measure outcomes that matter to travelers and operators—such as accuracy, service quality, and efficiency—rather than counting pilots or chatbot interactions alone.
The evidence points to a transition problem, not an absence of AI: companies are applying it in operations and experimenting with traveler-facing tools, while the data, service controls, economics, and trust needed for routine use remain uneven. Digital check-in and app booking show that travelers use digital channels; they do not show that travelers want an AI agent to make travel decisions for them.
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