Case Study: H&M achieves 70% reduction in operational costs and AI-driven supply chain forecasting with Databricks

Revolutionizing Fashion with AI

H&M Group, a global fashion retailer with over 5,000 stores in 70+ markets and millions of customer interactions daily, faced a scalability crisis: their on-prem Hadoop architecture, fixed-size clusters and siloed data made ingestion, analytics and model deployment slow, costly and insecure. DevOps bottlenecks meant it could take up to a year to move ideas into production, limiting their ability to improve supply chain and demand forecasting at scale.

H&M moved to the Databricks Lakehouse Platform on Azure, adopting automated cluster management, a unified multi-language notebook environment and integrations for elastic model training. The change simplified operations, accelerated ML lifecycles and improved cross-team collaboration — cutting operational costs by 70%, speeding time-to-insight and enabling more granular, revenue-driving forecasting and decisioning.

H&M

Errol Koolmeister

Head of AI Technology and Architecture