How ready is your travel company for agentic AI?

There’s no argument: A lot of functions in travel will be replaced by agentic artificial intelligence (AI), and it’s happening already. A new agentic AI-dominated ecosystem is on the horizon, prompting many business leaders to look at what the future holds and ask the question: How can my company excel in this new world?

As agentic AI embeds deeper into travel’s B2B and B2C value chain, the need for answers is intensifying. Recent Phocuswright research found that one in three travelers from Europe and the U.S. were using generative AI to plan or enhance their trip, creating the market for agentic AI to come in and do more.

At the same time, agentic AI continues to get better in areas where it is already amplifying human capabilities while bringing in new functionalities that drive new use cases, replacing more tasks but also creating room for innovation. 

Every business will have its own checklist, but there are some constants to consider. Agentic AI needs tools—anything the AI can use to perform a task—to fulfill its potential within travel, as many of the emerging use cases can only be delivered if the agentic AI has the right tools for the specific task it has been assigned.

Tools will become the differentiator in the age of AI. Every agent needs to invoke other agents or tools.

Foundational thinking

A good starting point is to think about how exposed you are to what large language models (LLMs) can directly replace and where your moats are.

As things stand, anything content-related—translations, new content, tones of voices—is well within the capabilities of agentic AI and its underlying LLMs. These models, which are based on neural networks, excel at applying pattern matching, reasoning, problem solving and derivative creation to a wide range of content-related tasks.

These LLMs can also be enhanced with tools, either embedded directly into the agentic AI (as appears to be the case with the recently-launched ChatGPT agent) or tools which can be  discoverable and invokable by the agentic AI. This combination of LLMs and tools can vastly enhance the field of application of agentic AI and create paradigm shifts in the value chain.

Agentic, APIs and data

In order to play an important role in this new world, businesses need a strong data foundation. The good news is that agentic AI systems can operate within the existing travel tech ecosystem, which is a great advantage for companies who have done the heavy lifting of getting (and keeping) their data in order.

If the data is high quality, an AI agent can relatively easily transform unstructured into structured. What’s more challenging is turning low-quality, incomplete and inconsistent data into something robust and clean. It’s essentially an entropy problem, trying to create order from disorder.

Ever since the emergence of APIs, the demand for better data has been part of the travel tech conversation. APIs transformed many touchpoints across the value chain but in an industry as vast as travel there remain many suppliers, often long tail, with weak APIs that are in danger of missing out on the benefits of agentic AI. Businesses should be asking: Does my business have the data structure and the necessary APIs that make it compatible with AI agents and tools?

APIs are the backbone of an agentic system. If you don’t have a robust API, AI agents and their tools will not find you. There are ways to rewrite and restructure existing APIs to be even more optimal for agentic AI, but the underlying principles behind APIs are a prerequisite.

The launch of Model Context Protocol (MCP) reinforces the need for strong APIs. MCP standardizes how LLMs and AI systems integrate and share data; in simple terms, it acts as a "universal translator." APIs are the primary interface, the doorway between agentic AI and the content they need to interact with. The API exposes what can be done, and MCP provides the standard for how to ask for it.

Travel choices and options

In travel, MCP will allow agentic AI to become transactional because it can connect to tools built around a supplier’s inventory, pricing and availability APIs. AI agents, based on LLMs, can know what the traveler wants and return a static itinerary, but tools are needed so that the traveler can book, manage and service that itinerary.

Tools are becoming more “supplier aware” in travel. Some are starting to use machine learning to identify which APIs are engineered in a way that allows access to live pricing and availability in real time. Access might be to a cache rather than the full database. There is a better end-user experience when these results are prioritized over those surfaced from less reliable sources.

So, the question every travel leader should be asking is whether or not their data and APIs are strong enough for this new reality. If the answer is yes, the follow-on is: How can I make the API/MCP interaction better, quicker and faster? If the answer is no, then it’s: What can I do to bridge the gap, and if that gap is too wide or deep, what are my options?

There’s another fundamental question the industry leaders are asking: To what extent do travelers actually want to use agentic AI? Is there demand, at scale, for a tool that can find, plan and book a trip automatically when there’s evidence that travelers delight in crafting their own trips?

The answer is that agentic AI should be able to support all possible scenarios. It can invoke tools to look for and return an itinerary that the AI agent believes is what the traveler wants based on their inputs but do so in a way that allows the traveler, or even a human travel agent using an AI agent as part of their toolkit, to play around with the itinerary and customize further with real-time pricing and availability. A user-centric agentic AI system has an “autonomy slider” that allows the user to influence how much they want to be involved in the process.

Final thoughts

The pace of change in the AI world shows no sign of slowing down. If anything, it is accelerating, which means that businesses need to double down on clarifying internally and externally what their position is within the new age of AI, if their legacy value proposition still stands.

There are many moving parts to this dialogue and, as we saw with MCP 10 months ago, ChatGPT agent and GPT-5 more recently, businesses also need to be flexible enough to adapt when the landscape changes. It is a challenge but also a chance for businesses to thrive once they understand the importance of their data, ensure the robustness of their APIs and make the right choice when it comes to which AI agent to use, which tools to invoke and which functions to provide.