Expedia's AI Principles: Building Systems That Last

There's an important distinction between AI that just works today, and AI that lasts at scale. Many companies optimize hard for the first one without ever asking whether they're building the second. Velocity without discipline and strategic direction is a liability, not an asset.

The hardest part of building AI at scale isn't getting a model to work once. It's building systems that continue to work, scale beyond individual teams and use cases, and improve consistently over time. Today's AI systems do more than just predict and optimize.

They converse, reason, and increasingly take action. An autonomous system making decisions on a traveler's behalf creates a very different set of expectations around reliability, governance, and accountability. As AI takes on more of those roles, the principles behind how these systems operate matter more than ever.

We have spent years applying AI and machine learning (ML) across the traveler journey — from personalization, ranking, and recommendations, to fraud prevention, customer support, and, more recently, generative and agentic AI experiences. That depth of experience is what led us to develop a set of ML and AI principles to guide how we build, deploy, and evolve AI systems across our company. The goal is simple: Make sure the systems we build create real business value, scale, and operate safely.

These principles define how we measure, design, govern, and operate our systems. From principles to practice Publishing principles is the easy part. The harder and more important work is turning them into operating mechanisms: Recommendations, requirements, tooling, and release processes that teams actually use.

We have begun using 'Agentic Release' tollgates: A set of recommended and, in some cases, required checks before launching agentic AI features. These tollgates translate principles like clear ownership, risk-based governance, evaluation, safe rollout, and monitoring into concrete expectations for teams. Some of these recommendations and requirements are already being automated and integrated into the software development lifecycle (SDLC).

Over time, the goal is for these expectations to become embedded in how we design, evaluate, approve, launch, and monitor AI systems from the start. Outcomes: Measuring what actually matters The first test for any model is whether it improves a business outcome and, ultimately, the traveler experience — not whether it just improves a technical metric. Align models to metrics with business impact: Every ML effort must tie directly to a key business outcome or traveler experience metric.