At a DAX-listed company, 76% of eco.mio users choose the recommended option, showing how guided booking can help travel teams reduce costs without waiting for stricter policies
Berlin / London, 24 June 2026 – eco.mio today released new client data for its Recommendation Engine, showing average travel cost savings of 9-14% across online-booked spend categories, after eco.mio fees. At a DAX-listed company, 76% of eco.mio users currently choose the recommended option, demonstrating strong traveler adoption of AI-driven booking guidance.
The data is based on early client usage since the product launched in November 2025, covering companies of different sizes and industries. The strongest value creation is seen in global and complex travel programs with more than EUR 10 million in annual online travel spend.
Unlike simple “cheapest option” logic, eco.mio’s Recommendation Engine compares today’s booking behavior against a baseline modelled from historic booking choices on the same routes, including airline mix, service class mix and fare choices. This allows companies to see where actual behavior is changing and where savings can be linked to specific recommendations at point of sale.
“Changing travel policy can create savings, but in large organizations it is often a long and sensitive process involving many stakeholders,” said Kati Riederer, CEO and co-founder of eco.mio. “What we are showing with the Recommendation Engine is that companies can also unlock savings within the existing policy framework – by helping travelers make better choices at the moment of booking.”
Closing the gap between policy and actual booking behavior
Many travel programs already ask employees to book the lowest logical fare, use preferred suppliers or choose rail over air where it makes sense. Yet the gap between policy intent and actual booking behavior remains large.
Travelers often face dozens of options in their Online Booking Tool, from flight alternatives and fare types to hotel rooms and rental cars. This creates decision fatigue and makes it difficult for travelers to identify the choice that best balances cost, convenience, policy and company goals.
eco.mio’s Recommendation Engine addresses this by reducing complexity at the point of booking. This makes the recommendation feel like useful support rather than another layer of control. Instead of showing travelers an overwhelming list of options, the solution surfaces the three most relevant choices and highlights one curated recommendation, configured around the company’s travel goals and the traveler’s likely booking behavior.
“We do not simply recommend the cheapest option,” said Riederer. “We recommend the most relevant option – balancing what the company would prefer travelers to book with what travelers are realistically willing to choose. That is where AI creates value: Not as a generic layer, but as a scalable decision engine across routes, categories and traveler behavior.”
Four dimensions of travel choice
eco.mio guides traveler decisions across all major online-booked travel categories, including air, rail and hotel. Recommendations are configured around four main dimensions:
Supplier choice: On routes where comparable suppliers offer similar comfort and connectivity, eco.mio can guide travelers toward the option that creates better value for the company. For example, on the New York-Zurich route, eco.mio observed cases where Swiss flights were on average around 30% cheaper than United flights, while both options offered comparable comfort and alliance benefits. In such cases, changing supplier choice can create meaningful savings without reducing the traveler experience.
Ticket fare: eco.mio helps companies steer travelers toward the right level of flexibility. For example, outbound rail journeys are often fixed because travelers need to arrive for a specific meeting, while inbound journeys may require more flexibility if a meeting ends earlier than planned. The Recommendation Engine can reflect this difference and recommend less flexible fares where appropriate and, vice versa, more flexible fares where they add real value.
Service class: The solution can also identify opportunities to guide travelers from business class to premium economy where the savings potential is significant. Even low adoption can create meaningful impact. If only a small share of travelers accepts a premium economy recommendation they would not otherwise have considered, the savings can still be substantial.
Air-to-Rail: On routes where door-to-door travel time is comparable, eco.mio can recommend rail instead of air. In Germany, routes such as Berlin-Munich are a strong example: A four-hour train journey that arrives directly in the city center can be highly competitive once airport transfers and security checks are considered. eco.mio can also surface relevant rail content that is often not fully integrated into standard TMC or OBT setups, even if the final booking happens outside the OBT. Beyond simple air-to-rail substitution, the Recommendation Engine can support connected travel decisions, for example by identifying the best combination of train and flight when several airports are within similar reach. This door-to-door and multi-modal perspective is often missing in standard booking environments.
Built for existing travel systems
The Recommendation Engine works within existing travel systems and is designed to be TMC-agnostic. eco.mio supports major Online Booking Tools including Cytric, Concur, KDS Neo, Onesto, Egencia and others. Existing booking, payment and approval processes remain unchanged. Unlike static sorting rules or preferred options displayed at the top of a page, eco.mio’s recommendations adapt to the route, category, current price environment, company goals and historic traveler behavior.
This makes the solution especially relevant for travel teams that want to capture savings faster, while avoiding lengthy policy changes or large-scale internal communication campaigns. Instead of manually defining which options should appear first across hundreds of routes, categories and traveler scenarios, eco.mio helps companies scale decision guidance across the travel program by combining current prices, route-specific context, company goals and historic traveler behavior.
“More information does not automatically lead to better decisions,” said Riederer. “Travel booking is not the traveler’s main job. Pop-ups are clicked away, banners are overlooked and emails are ignored. The future is not more information. It is better guidance at the moment of decision.”
Visit eco.mio at Business Travel Show Europe
eco.mio is showcasing the Recommendation Engine at Business Travel Show Europe in London on 24-25 June 2026. Travel managers and industry partners can visit the eco.mio booth L71 to see how AI-driven recommendations work across air, hotel, rail and rental cars.
About eco.mio
eco.mio offers a next-generation portfolio of software solutions for better travel decisions. The company helps enterprises guide travel behavior through actionable, data-driven decision support within existing corporate travel systems.
The eco.mio Impact Suite helps companies engage travelers with incentives, CO2 visualizations, carbon price and budget features, and SAF fee integration. The eco.mio Recommendation Engine adds an AI-driven recommendation layer to existing booking systems, helping travelers make faster, more cost-efficient and more policy-aligned decisions.
Founded in Berlin, eco.mio works with global organizations to reduce travel costs, improve traveler experience and support sustainability goals without replacing existing travel infrastructure.
