Michelin: From siloed data to a global AI fabric with Dataiku

Michelin's engineering teams controlled data/model access while business experts like process engineers had limited involvement, models weren't reusable across factories, and generating insights took months of manual work. Michelin adopted Dataiku in 2021 for a unified hybrid-cloud/on-prem AI platform for technical and non-technical users, integrating with Snowflake, Databricks, Azure, Kafka and Power BI; usage grew from 35 users in digital manufacturing to 1,500+ users across 50+ factories by mid-2025, 80% of them business experts. More than 600 process engineers/quality technicians use the Parameters Analyzer Solution to find root causes of defects, cutting analysis from '6 months before Dataiku' to about 1 hour, while sensor data from tires and vehicles enables real-time pressure-loss alerts and end-of-life predictions. Innovation manager Matthieu Leynet called Dataiku 'a key enabler of our Industry 4.0 transformation, enabling daily usage of AI at the heart of our factories.'

Dataiku is a low-code/no-code AI platform whose Prompt Studios and LLM Mesh let teams prototype and A/B-test candidate LLM approaches before committing to a production workflow.

This sits in the Design & Stress-Test stage of the AI use-case lifecycle: Named examples from the tools pharma teams use to scope, prototype and benchmark an AI use case before committing to build it — data-catalog, sandbox and evaluation platforms.