AI in travel is moving beyond pilots, with more use cases now embedded into day-to-day workflows and delivering measurable value.
Explore two examples, and data-driven results, of agentic AI already in production, SkyLink and Amadeus Max for Travel Seller, showing how these capabilities are being applied in real travel environments.
Making agentic AI work requires strong integration, rich context, and interoperability across systems.
The real challenge now is scaling AI reliably across the travel ecosystem while staying focused on business outcomes.
When I last wrote about AI at Amadeus, I focused on orchestration and how we bring together systems, data, and workflows to make AI work in the real complexity of travel. Since then, the conversation has moved forward – significantly!
At the Skift Data + AI Summit two weeks ago, one theme came across clearly: the question is no longer what AI can do. It’s whether we can make it work reliably, at scale, in real operational environments.
At Amadeus, we have been applying artificial intelligence and machine learning across our platforms for many years, supporting pricing, forecasting, operations, and decision-making at scale.
What is changing now is the nature of that AI. With the emergence of generative and, increasingly, agentic AI, systems are no longer just predicting or recommending. Agentic AI systems are starting to act, navigate workflows, and coordinate tasks end-to-end.
This shift from experimentation to execution is where the industry now stands and it’s where much of our work is focused today.
As adoption accelerates, the focus is shifting toward making AI work in real operational environments, across complex systems and across the travel ecosystem.
It is about identifying the right problems, applying AI where it creates value, and ensuring it is embedded into operations to deliver measurable results.
One of the most important changes over the past year with agentic AI in travel is no longer limited to pilots. We are increasingly seeing real use cases in production and embedded into workflows, used day-to-day, and delivering measurable outcomes.
Let me share two examples that illustrate how this is taking shape at Amadeus and with our customers, each addressing real friction for travelers and travel sellers by making journeys easier to manage and travel operations more efficient.
The solution: it brings conversational AI into travel booking and servicing, enabling travelers to interact in natural language and complete transactions in a more direct way. What matters here is not just the interface, but the system’s ability to execute end-to-end workflows, connecting content, availability, pricing, and fulfillment in a reliable and timely way.
How long did it take you to book your last business trip? Did you forget to book your hotel, or need help because you could not find what you were looking for? SkyLink is designed to reduce this kind of friction, making travel booking and servicing simpler, faster, and more intuitive, with results so far including:
Up to 35% increase in hotel attachment rate when travelers were proactively prompted.
Median 99–120 second end-to-end time-to-book (from initial completed traveler request through completed booking).
10%+ increase in online adoption rates.
The solution: AI assistants embedded directly into booking environments. These assistants help travel agents navigate complex information such as fare rules or booking histories, and automate repetitive tasks. This improves efficiency, reduces errors, and makes everyday workflows easier to manage.
The results seen so far:
76% reduction in handling time across key workflows (11.2 hours saved vs. 14.7 hours manual over 320 interactions), with the solution cutting task time from 14.7h to 3.5h in one month of usage.
71–83% faster execution on core agent tasks, including Fare Rules, PNR History, and Smart Flows.
20% fewer booking errors, combining instant AI answers with improved accuracy in servicing workflows.
These two examples are different in how they are experienced - one traveler-facing, the other agent-facing – but they point to the same underlying shift. AI is becoming part of how work gets done and how we’re transforming travel with AI.
As more use cases move into production, I’ve reminded myself to step back and reflect on what really matters to make these use cases work in practice.
The first point is integration. AI does not create value in isolation. It needs to be embedded into real systems and workflows. In travel, where systems are interconnected and operate at global scale, this means thinking carefully about how components interact end-to-end. Simply layering AI on top of existing interfaces without evolving the underlying systems can limit both performance and impact.
The second point is context. AI systems are only as effective as the information they can access and use. Ensuring continuity of context across systems, workflows, and interactions is critical. Without it, even advanced models can produce results that are technically correct but operationally insufficient, which ultimately affects adoption.
The third point is interoperability. This becomes particularly important with the rise of agentic AI. Unlike earlier approaches that focused on prediction or optimization, agentic systems can take action. This means navigating multiple systems, executing workflows, and coordinating tasks across environments, and raises the bar for how systems need to interact. To support this, interoperability becomes essential. Systems and agents need shared ways to exchange information, operate within constraints, and coordinate actions reliably. Without this, there is a risk of creating isolated capabilities that do not connect into the broader travel ecosystem.
A fourth point is trust, observability and evaluation. It is essential to monitor how agents perform in production, ensure guardrails are consistently applied, and test the full solution across a wide range of real-world scenarios so it remains resilient and reliable in practice.
Taken together, these points reinforce a simple idea: the challenge is not just building more advanced AI but designing systems that can support it effectively at scale.
As more use cases reach production, the challenge is no longer to demonstrate that AI can work, but to make it repeatable across a large and complex travel ecosystem.
This is particularly relevant in travel, where systems have long lifecycles, high reliability requirements, and must operate consistently across markets and partners. Scaling AI is therefore less about increasing the number of initiatives, and more about changing how those initiatives are delivered.
At Amadeus, this has led us to introduce the AI Delivery Hub. It’s a transversal team designed to accelerate how AI capabilities move from exploration to production, and from isolated use cases to embedded and reusable components across our portfolio. This is where we’ll also be incubating new agentic use cases. This combination of scaling what is already working while incubating what comes next is how we’ll deliver meaningful AI-enabled solutions and orchestrate travel across an AI-enabled travel ecosystem.
As this transition continues, it becomes even more important to stay grounded in what actually matters. Start with the right business problem, then apply the right technology. AI is not the story in itself. Outcomes are.
AI is not a separate layer added on top of products. It needs to be embedded into how those products are designed, built, and operated, supporting better decisions, more efficient workflows, and improved experiences.
In that context, our role at Amadeus remains consistent. We operate as a system of record at the core of the travel ecosystem, connecting airlines, hotels, travel sellers, and travelers through highly integrated platforms. As AI transforms the travel industry, our role becomes even more relevant.
After more than a decade of applying AI across specific domains, we are now in a phase where it becomes part of the end-to-end execution of making the experience of travel better. Moving from experimentation to production is a significant step. Making it scalable, interoperable, and dependable across the ecosystem is the next one.
With our foundation as a system of record, our deep integration across the industry, and our ability to run at production scale, supported by strong governance through our AI Office and strategic partnerships, we are helping the industry embrace AI with integrity and trust.
That is the role Amadeus is built to play: orchestrating AI for the benefit of the travel industry and making the experience of travel better.
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