The race to win a traveller’s attention is moving from the search results page to the AI answer. Answer engine optimization for travel is the practice of making sure that when a traveller asks an AI assistant where to fly, which airline to trust or what a fare includes, your brand is the one being recommended. Raven’s recent research across 101 airlines frames the stakes in one striking line: if AI shapes where travellers spend one dollar in every ten online, it could influence $120 billion in annual bookings. The sharper insight is that the company earning the recommendation may not be the one that makes the sale.
This analysis unpacks what that claim means, what we can and cannot verify from the headline figures, and what airlines and travel brands can do now. It is deliberately cautious about numbers. Where the available evidence is a headline and a framing, we say so and reason from it rather than inventing detail.
What Is Answer Engine Optimization?
If you are asking what is answer engine optimization, start with the problem it solves. Traditional search engine optimization aims to rank a page in a list of blue links. Answer engine optimization (AEO) aims to have your brand, facts and offers cited, summarised or recommended inside a generated response. The user may never see a list at all.
The engines doing the answering are large language models inside products such as ChatGPT and Perplexity, and AI summaries layered onto Google Search. Many of these systems use retrieval-augmented generation, pulling live web content and blending it with what the model already learned. That makes the sources a model trusts a new competitive battleground. Related terms such as generative engine optimization describe overlapping practices, and the vocabulary is still settling.
Decoding the $120 Billion Figure
The headline number deserves a careful read. It rests on a conditional: if AI shapes where travellers spend one dollar in ten online, the influenced value reaches $120 billion a year. Simple arithmetic shows the implied base is roughly $1.2 trillion in annual online travel spending. That is our derivation from the framing, not a separately sourced market statistic.
Influence Is Not Revenue
The word that matters is influence. The $120 billion is not new revenue and not a guaranteed prize for any single airline. It is the value of bookings in which an AI recommendation plays a role in the decision. Capturing it depends on whether a brand is mentioned, how it is described, and whether the traveller then books directly or through an intermediary.
A Scenario, Not a Forecast
Treat the 10 percent share as a scenario that helps leaders size the issue. Adoption of AI assistants for trip planning is moving quickly, but any precise share for a given year is a prediction rather than established fact. The useful question is not whether the number is exactly right. It is how exposed your revenue would be if the share were even half that size.
Why the Recommendation and the Sale Can Belong to Different Companies
The most provocative part of Raven’s framing is the split between the party that earns the recommendation and the party that completes the transaction. Travel distribution has always had this tension. Online travel agencies such as Booking.com and Expedia Group, and metasearch engines like Skyscanner and Google Flights, have long sat between airlines and customers. Review platforms such as Tripadvisor shape opinion without selling a seat.
AI assistants add a new layer. An assistant may name an airline as the best option on a route, then hand the traveller to a third-party site to buy. The airline did the work of earning trust, and a middleman captures the margin and the customer relationship. Alternatively, an airline with thin or inconsistent web content may never be named, and a competitor wins by default.
In an answer-driven journey, being visible, being trusted and being bookable are three different jobs, and they may be won by three different companies.
AEO for Airlines: What Determines Who Gets Recommended
Raven’s study of 101 airlines points to a wide field of brands whose AI visibility is uneven. I will not claim specific findings beyond what the headline states. But the mechanics of how answer engines work suggest the factors that matter most for AEO for airlines.
Clear, Consistent, Machine-Readable Facts
Answer engines favour sources that state facts plainly and consistently: baggage allowances, fare families, cabin features, route networks, loyalty benefits and disruption policies. Contradictions between an airline’s own pages, its app, and third-party listings invite models to pick the wrong version or to skip the brand. Structured data helps. The Schema.org Airline type and Google’s structured data documentation explain how to label content so machines can parse it. Building on Schema.org markup also feeds the entity understanding behind a knowledge graph.
Third-Party Corroboration
Models do not rely on a brand’s self-description alone. Reviews, press coverage, forum discussions, comparison sites and reference pages all contribute to how a brand is characterised. An airline that is excellent but poorly documented outside its own site may be described vaguely or inaccurately.
Answerable Content
Travellers ask conversational questions: which airline has the best legroom on a long-haul route, whether a basic fare includes a carry-on, what happens after a cancellation. Content that answers these directly, in plain language and under question-style headings, is easier to quote than marketing copy. This is the same logic that earns a featured snippet, extended to a conversational interface.
How AI Recommendations Influence Airline Bookings
Understanding how AI recommendations influence airline bookings means looking at the whole journey rather than the click.
- Inspiration: a traveller asks for destination ideas, and the assistant’s shortlist frames the consideration set.
- Comparison: the assistant summarises differences in price, comfort, schedule and reliability, often compressing a dozen research tabs into a paragraph.
- Validation: the traveller checks the recommendation against reviews, the airline’s site or a metasearch result.
- Transaction: the booking happens on an airline site, an app, an agency or, increasingly, possibly inside the assistant itself.
Attribution is the hard part. A booking credited to direct traffic or to an agency may have been decided in an AI conversation that no analytics tool recorded. That is precisely why the recommendation and the sale can be separated, and why last-click reporting may understate AI’s role.
The Distribution Complication
Airline distribution is technically intricate. The global distribution systems that feed travel agencies are being supplemented by richer airline-controlled offers through the New Distribution Capability standard. Alliances such as those described under airline alliance add further complexity to how products are described and compared. For an AI system, this fragmentation means ancillary details, bundles and live fares may be hard to retrieve accurately. Airlines that publish clear, current and crawlable offer information reduce the risk that assistants fill gaps with guesses.
Risks and Limits of Optimising for AI Answers
Hallucination and Misinformation
Language models can state incorrect details with confidence, a failure mode known as hallucination. For airlines, a wrong claim about refund rights or baggage rules can cause customer disputes and compliance headaches. Monitoring what assistants say about your brand is a service-quality issue as well as a marketing one.
Opacity and Volatility
Answer engines rarely disclose exactly how they rank sources, and outputs vary between prompts, models and sessions. Anyone promising guaranteed placement is overselling. AEO is better treated as ongoing measurement and improvement than as a one-time fix.
Platform Dependence
Optimising heavily for a few AI platforms creates a new dependency, much as reliance on one search engine did. Diversified visibility across owned channels, partners and AI surfaces remains prudent.
A Practical AEO Playbook for Travel Brands
- Audit your AI presence. Run a fixed set of realistic traveller prompts across major assistants monthly and record whether your brand appears, how it is described and which sources are cited.
- Fix factual inconsistencies. Reconcile baggage, fare, loyalty and policy information across the website, app, help centre and partner listings.
- Implement structured data. Apply relevant markup and keep it current, following official documentation.
- Write for questions. Create route, cabin and policy pages that answer specific traveller queries concisely.
- Earn credible mentions. Invest in genuine reviews, press, partnerships and expert coverage, not manufactured volume.
- Close the booking gap. Make sure that when an assistant recommends you, the traveller can reach a fast, trustworthy direct booking path.
- Rethink measurement. Add post-booking surveys asking how customers discovered you, since AI influence may be invisible in click data.
Common Mistakes
The most frequent errors are treating AEO as a keyword exercise, neglecting policy and ancillary content that assistants love to summarise, and assuming a strong brand guarantees a strong AI mention. Another is ignoring intermediaries. Partners that sell your seats also shape the information models ingest.
What the Next Few Years May Bring
Several developments are reasonable to anticipate, though none is certain. AI assistants will likely move from advising to acting, handling search, comparison and possibly checkout. That would sharpen the recommendation-versus-sale divide. Platforms may introduce sponsored placements within answers, which would turn organic AI visibility into a mixed paid and earned channel. Regulators may scrutinise how recommendations are disclosed and how consumer rights information is presented. Airlines, meanwhile, may negotiate data-sharing arrangements to ensure accurate offers reach AI systems.
What is already clear is that AI travel search compresses the funnel. Fewer brands will be considered, and the ones named early gain disproportionate advantage.
Frequently Asked Questions
What is the AEO opportunity in travel?
It is the potential value of travel bookings influenced by AI-generated answers. Raven’s framing suggests that if AI shapes one in ten online travel dollars, roughly $120 billion in annual bookings could be influenced.
Is answer engine optimization different from SEO?
They overlap, but SEO targets ranking in a list of results, while AEO targets being cited or recommended within a generated answer. Strong SEO fundamentals still help AEO.
Does being recommended by AI guarantee a booking?
No. A recommendation may send the traveller to an agency or metasearch site, so the airline must also ensure a compelling direct booking path.
How can airlines track AI visibility?
Use repeatable prompt testing across assistants, review cited sources, and supplement analytics with customer surveys about how they found you.
The Question That Will Define Travel Marketing
The deepest lesson of Raven’s research is that AI turns reputation into infrastructure. The airline that earns the recommendation is building an asset that others may monetise unless it controls the path from answer to ticket. Watch three signals over the coming year: whether major assistants introduce transactional booking, whether paid placements enter AI answers, and whether airlines start reporting AI-influenced bookings as a distinct metric. The unanswered question is simple and uncomfortable. When a traveller asks an assistant where to fly, will your brand be the answer, or only the reason someone else makes the sale?