Case Study: Lufthansa achieves scalable AI and improved operational efficiency with IBM Cloud Pak for Data

Combined talents help the airline raise efficiency

Lufthansa, Germany’s largest airline, faced the challenge of turning scattered data and early AI proofs of concept into scalable, production-ready services to improve timing, customer experience and operational efficiency. To overcome limits on test data, scalability, security and model management, Lufthansa partnered with IBM Cloud Pak for Data and IBM teams, leveraging IBM Garage methods and Watson products to accelerate their AI strategy.

IBM Cloud Pak for Data helped build a modern data science platform—using Watson Studio, Watson Machine Learning, Watson Assistant/Explorer and Watson OpenScale delivered as PaaS/SaaS on IBM Public Cloud or via Cloud Pak for Data—after a 10‑week engagement by IBM’s Data Science and AI Elite team. The solution unified disparate sources for natural‑language search, handles about 100,000 customer queries annually, enabled rapid prototyping of three use cases (avoiding delays, predicting boarding times, reducing check‑in queues), and delivered greater scalability, faster deployments and built‑in model monitoring so Lufthansa can more quickly roll out additional AI projects.

Lufthansa

Mirco Bharpalania

Head of Data Analytics