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easyJet Innovates with Databricks and Generative AI in Aviation

DatabricksTravel & Hospitality

Client Overview

easyJet, a leading European airline, operates over 300 aircraft across nearly 1000 routes, serving more than 150 airports in 36 countries. In 2022, easyJet carried over 69 million passengers, demonstrating its significant footprint in the aviation industry.

Challenges

easyJet faced challenges around customer experience and digitalization, with rapidly changing consumer preferences and high expectations for customer service. A robust data and AI strategy was essential to unlock opportunities in digital customer service, personalization, and operational process optimization.

Solution Overview

easyJet embarked on utilizing Databricks’ lakehouse architecture, which separates compute from storage, allowing easyJet’s teams to collaborate effectively. The project aimed to harness generative AI to empower non-technical users to extract insights from data using natural language queries.

Deep Dive into the Solution

The solution involved a web UI that allowed users to input queries via speech, which were then transcribed and processed to generate SQL queries using AI-driven text-to-SQL conversion. This process was powered by open-source large language models (LLMs) and Databricks’ MLflow and Model Serving technologies, which facilitated rapid deployment and model serving using GPU capabilities.

Business Outcomes

The implementation of this AI-driven query tool significantly enhanced the decision-making process at easyJet, enabling business users to interact with data seamlessly and make informed decisions. The project not only streamlined access to data but also positioned easyJet as a data-driven leader in the aviation industry.

Innovative Impact and Future Prospects

This initiative was one of the first under easyJet’s generative AI roadmap, quickly moving from concept to a minimum viable product (MVP) that was showcased to over 300 staff members. The success of this project has spurred further innovation, with potential new use cases like AI-driven personal assistants for travel recommendations and chatbots for operational processes. The enthusiastic reception of the MVP by easyJet’s board has led to a dedicated budget for exploring and implementing more generative AI applications to enhance customer and employee experiences.

Conclusion

Through its collaboration with Databricks, easyJet has taken a significant step forward in integrating advanced AI capabilities into its operations, setting a new standard for innovation in the aviation industry. This case study demonstrates how leveraging cutting-edge technology like generative AI can transform business operations and drive substantial value.

Learn more about this case study here: https://www.databricks.com/blog/easyjet-bets-on-databricks-lakehouse-for-gen-ai