Yanolja - a 2025 Changemaker at The #Phocuswright Conference 2025
Summary
Jeff Kim, CEO of Yanolja Cloud and CSO of Yanolja Group, presents the company's vision and strategy at the Phocuswright Conference 2025 Changemakers session, followed by an interview with Robert Cole, Senior Research Analyst at Phocuswright. Yanolja—whose name translates to 'hey, let's play'—is a South Korean travel technology platform serving 1.3 million suppliers, 18,000 channel customers, and over 20 million end consumers in Korea alone, representing nearly half the Korean population. The company operates 70 offices across 30 countries and provides services in 200 countries, backed by investment from GIC (Singapore's sovereign wealth fund) and Booking Holdings, and is actively expanding into Africa, Latin America, and North America.
Kim frames Yanolja's business model around three interconnected stakeholder groups—suppliers, channel partners, and consumers—each of which the company serves with a distinct data-driven solution. For suppliers, Yanolja built a cloud hospitality management system (cloud PMS) to digitize hotel operations and eliminate data quality issues inherent in manual extranet-based data entry. For channel customers, the platform provides a connectivity and distribution service built on big data infrastructure, offering standardized data including room-to-rate mapping. For consumers, Yanolja aims to construct a data lake that decomposes and recombines data to create unique inventory tailored to individual customer profiles and preferences, enabling true hyperpersonalization at scale.
On data quality, Kim identifies three tiers: primary data from suppliers (static content such as images and descriptions, and dynamic data like availability and rates), behavioral and activity data from users (reviews, journey touchpoints), and AI-generated data. Yanolja has developed its own AI foundation model to create contextually correct content—for example, generating or selecting property images that match a traveler's specific trip context (e.g., winter images for a winter Boston booking rather than generic summer photos).
On AI strategy, Kim outlines a three-horizon model. In the short term, Yanolja uses generative AI to automate internal processes, including customer service, using its AI foundation model integrated with MCP (Model Context Protocol) for API-level communication between AI and infrastructure. In the medium term, Kim argues that generative AI alone is insufficient—what differentiates providers is unique inventory combined with an AI interface, since commoditized inventory forces margin compression through price competition. Long term, Kim envisions agentic AI where separate AI agents represent each stakeholder (traveler and property), interact autonomously with each other, and dynamically negotiate personalized experiences that no current system can deliver. He draws an analogy to human biology: generative AI is the brain, but agentic AI represents the neurons that connect the brain to organs, enabling real-world action and service delivery.
Key Takeaways
- •High-quality, structured data is the foundational prerequisite for AI-driven personalization—without clean supplier and behavioral data, personalization is impossible.
- •Cloud-based PMS adoption by suppliers is not just an efficiency tool; it is a data collection mechanism that enables downstream AI and personalization capabilities.
- •Generative AI alone is insufficient for competitive differentiation—unique inventory per customer profile is the true differentiator, since commoditized inventory leads only to margin-destructive price competition.
- •MCP (Model Context Protocol) enables AI systems to communicate with existing infrastructure APIs, allowing companies to automate internal processes like customer service without rebuilding backend systems.
- •The long-term competitive advantage in travel will belong to platforms that can deploy per-stakeholder AI agents (traveler agent + property agent) that interact autonomously to negotiate personalized experiences in real time.
- •Context-aware content generation (e.g., showing winter images for a winter trip) is an underexplored but impactful application of AI in travel—static image libraries do not serve personalized search well.
- •Travel platforms serving all three stakeholder groups (suppliers, channels, consumers) have a structural data advantage over single-sided platforms because they can combine supply-side operational data with demand-side behavioral data.
- •Hyperpersonalization at scale requires decomposing standardized inventory into modular components that can be recombined per individual user preference, rather than surfacing the same inventory to all consumers.
- •Agentic AI should be understood as the 'neural layer' between generative AI reasoning and real-world execution—analogous to neurons connecting the brain to physical organs.
Transcript
Hello guys, it's a pleasure to introduce Yanuja here with our vision. I'm Jeff Kim from Yanuja. Yanuja means hey let's play. So we have a vision to transform the future of travel. At the same time we'd like to make travel 10x easier. Hey let's play. is very important mission for us because we are not talking about the travel only. We'd like to make you enjoy your life through the experience, travel and entertainment as well. For that we believe that high quality data is necessary. Without the hi...
