Case Study: Michelin accelerates AI adoption across factories with Dataiku

Michelin Democratizing AI for Improved Industrial Performance

Michelin, a global manufacturing leader with 121 production sites, needed a way to break down siloed data, make AI usable for engineers and technicians, and reuse successful models across factories. With Dataiku, Michelin sought a platform that could support both visual and code-based workflows across cloud, on-prem, and hybrid environments while integrating with tools like Snowflake, Databricks, Azure, Kafka, and Power BI.

Dataiku implemented a unified AI foundation that scaled from 35 users in digital manufacturing to 1,500+ users across 50+ factories, with 80% being business experts. The result: analyses that once took up to six months can now be completed in about an hour, improving quality management, predictive maintenance, energy optimization, and innovation while delivering tangible operational savings and measurable ROI across Michelin’s factories, R&D, and services.

Michelin

Matthieu Leynet

Scrum Product Owner