How Omio is shaping the future of conversational travel

What This Story Is About

If you're curious about how travel companies actually put AI to work—not just in marketing copy, but in real products—Omio's story is worth your time. Omio, the travel booking platform that lets people compare and book trains, buses, flights, and ferries across Europe and beyond, has been working with OpenAI to reshape both what it offers customers and how it builds those offerings internally. In this article, you'll learn how Omio uses OpenAI technology for conversational travel assistance, how it speeds up its own development process, and what it means for a company to reinvent itself as AI-native. This is for product managers, developers, founders, and anyone interested in practical enterprise AI adoption.

Making Travel Planning Feel Like a Conversation

Booking travel has traditionally been a rigid experience: fill in a form, pick from a list, click through checkout. Omio wanted something that felt more like talking to a knowledgeable friend. Using OpenAI's models, the company has developed conversational interfaces that let travelers describe what they need in plain language—something like "I need to get from Berlin to Prague on Friday afternoon, cheap as possible"—and receive relevant, bookable options.

This shift matters because travel planning is inherently messy. People have vague constraints, changing plans, and questions about routes and prices that don't fit neatly into search filters. A language model can interpret these open-ended requests, ask clarifying follow-ups, and translate the messy human intent into structured search queries that Omio's booking systems can actually fulfill.

The conversational approach also helps with the in-between moments: explaining fare rules, suggesting alternatives when a direct route is sold out, or helping users who aren't sure which station or airport makes the most sense for their trip. Instead of a static FAQ, customers get responsive, contextual answers.

Speeding Up How Omio Builds Products

The second big piece is internal. Omio hasn't just bolted a chatbot onto its website; it has woven OpenAI tools into its software development lifecycle. Like many engineering organizations, the company uses AI coding assistants to accelerate writing, reviewing, and refactoring code.

The reported benefits follow a familiar pattern: developers spend less time on boilerplate and routine tasks, new team members get up to speed faster by asking questions of AI tools instead of waiting on colleagues, and teams can prototype features more quickly. When your product is about helping people plan journeys, having your engineers move faster directly translates into shipping improvements for travelers sooner.

There's also the matter of internal knowledge. Large companies accumulate mountains of documentation, and finding the right answer inside them is often slow. AI-powered assistants can surface relevant internal information on demand, which reduces friction across departments, not just in engineering.

Becoming an AI-Native Company

Perhaps the most interesting part of Omio's story is the ambition behind it: becoming an AI-native company. That phrase gets thrown around a lot, but in practice it means restructuring how the organization thinks and operates so AI isn't an add-on—it's a foundation.

For Omio, this has involved executive-level commitment to the transformation, training employees to use AI tools in their daily workflows, and rethinking core product experiences around what AI makes possible. Rather than asking "where can we add AI to existing features?", the company is asking "what would this product look like if we built it today, with AI at the center?"

This kind of cultural shift is often harder than the technical one. Tools like ChatGPT and the OpenAI API are accessible to almost anyone, but getting an entire company to genuinely change its habits—how it writes, plans, codes, and serves customers—takes sustained effort from leadership and buy-in across teams.

Why This Approach Resonates

Omio's journey illustrates a few lessons that apply well beyond travel:

1. Start with real user pain. Conversational booking works because travel planning genuinely is conversational. The technology fits the problem rather than being forced onto it.
2. Use AI in two directions. Customer-facing features get the headlines, but internal productivity gains often deliver value faster and with less risk.
3. Leadership has to drive it. Becoming AI-native isn't a bottom-side experiment; it's a company-wide repositioning that requires top-down commitment.
4. Keep humans in the loop for high-stakes steps. AI can recommend and explain, but bookings, payments, and support escalations still benefit from careful oversight and clear paths to human help.

It's also a realistic example. Adopting AI at this scale comes with challenges—managing costs, ensuring accuracy of travel information, handling edge cases, and meeting privacy expectations. Companies like Omio treat these as ongoing engineering problems rather than reasons to hold back.

The Bigger Picture

Omio's partnership with OpenAI is part of a broader wave: travel is one of the industries where large language models fit naturally, since the domain is information-heavy, conversational, and full of personalization opportunities. Expect more booking platforms, airlines, and rail operators to follow similar paths—some building on OpenAI, others on competing models.

For anyone watching enterprise AI adoption, Omio offers a useful template: pick a genuine customer problem, use AI to improve both the product and the process, and commit to organizational change rather than surface-level experiments.