Expedia Group Innovation & Technology Culture

Expedia Group encourages employees to embrace emerging technologies and continuously evolve their skills as AI transforms the way work gets done. Employees describe learning how to combine automation with human judgment, using AI to streamline routine tasks while ensuring outputs remain accurate, reliable and valuable.

“Over the next few years, I think some of the most important skills will be having a clear vision for how to simplify processes and apply AI to automate bulky or time-consuming tasks. Attention to detail, flexibility, and a willingness to keep learning will also be essential as the role continues to evolve. I also think we will increasingly become QA experts for the agents and automated tools we use, guiding their work, checking outputs carefully, and making sure the results are reliable and useful.”

Expedia Group encourages employees to combine technology with human expertise to improve the traveler experience. Employees say innovation is embedded in their day-to-day work, using digital tools alongside personal problem-solving to resolve issues efficiently, maintain service quality and continuously strengthen the customer experience.

“My team is at the forefront of traveler experiences, serving as the first point of contact and gatekeeper for Expedia Group. We handle all types of inquiries, complaints, and issues related to our platform, whether positive or challenging, ensuring a seamless experience for our customers.

I’ve learned that every business needs a strong frontline to protect its reputation. By combining human effort with technology, we maintain consistent performance and deliver exceptional service in the travel industry.”

How do engineers influence innovation and product strategy?

The analytics platform team influences product strategy by looking across the organization and identifying where common problems are being solved repeatedly. Engineers often spot patterns that individual teams can’t see, then help turn those recurring challenges into shared capabilities that accelerate innovation across the business.

A good example is our investment in common metric definitions, semantic foundations and analytics ready datasets. Business meaning is often fragmented across queries, tools and the knowledge of a few experts. Engineers helped shape a strategy that makes trusted definitions reusable across analytics, experimentation and AI.

Engineers also explore emerging technologies, prototype capabilities and help decide which ideas should become part of the platform, including skills, plugins, model context protocol and AI-assisted analytics experiences. They shape capabilities that can be reused across teams and experiences. What I find most rewarding is solving a hard problem with product, analytics and business partners, then turning it into something others can build on.
 

How do you decide when to adopt new tech or tools?

Knowing when to adopt something new matters just as much as knowing when to retire something old. As the analytics platform team, we’re constantly evolving the platform while balancing innovation, operational excellence and long-term sustainability. We look beyond features and ask a few simple questions: Does it improve the experience? Does it make trusted data easier to access? Can it scale and be operated reliably? Does it simplify the platform rather than add complexity?

That thinking has guided some of our biggest decisions. We’ve moved our core analytics platform to the cloud, retired a legacy reporting platform and modernized analytics experiences while maintaining the reliability and continuity teams depend on every day. We also rigorously evaluated modern analytics engines against real workloads before selecting an approach that delivered the right balance of performance, flexibility, operational simplicity and scale.

The same principles continue to shape our investments today. We’re simplifying the analytics estate, strengthening our semantic foundations and creating supported paths for analytics and AI that are easier to adopt, operate and scale. The goal isn’t to adopt technology for its own sake. It’s to build a trusted foundation that helps teams move faster today while creating new possibilities for analytics and AI tomorrow.

What practices help engineers balance experimentation and reliability?

One of our core beliefs is that the safest path should also be the easiest path. We encourage engineers to experiment and challenge assumptions while creating an environment where promising ideas can evolve into reliable, reusable capabilities.

One of our model context protocol services is a good example. What started as an experimental, side-of-desk project quickly proved valuable. The team then focused on turning that prototype into a trusted, enterprise-ready capability that teams across the company could confidently build on.

That journey reflects our broader approach to innovation. We create space for experimentation, but we also invest in the engineering disciplines that allow successful ideas to scale safely. Strong observability, governance, automation and clear ownership help us move quickly without compromising reliability.

Just as importantly, we measure what we build. Adoption, performance, reliability, cost and user feedback are all signals that help us improve. The combination of experimentation and engineering discipline allows us to turn promising ideas into trusted capabilities at scale.