A stuck €2M AI initiative moved to production in 90 days — generating €4.2M annual savings
A France 2030 participant with multiple AI pilots stuck in development. The new VP of Digital brought in Hyperion to diagnose root causes and deliver production AI within the program's timeline.
Size: 5,000-20,000 employees
Transform three stuck AI pilots into production systems within the France 2030 timeline, while building internal AI capability.
Three AI pilots had been running for 18 months with no path to production—classic 'pilot purgatory'
Quality inspection AI achieved 94% accuracy in lab but failed in factory conditions with variable lighting
Predictive maintenance model generated too many false positives, causing maintenance team to ignore alerts
Supply chain optimization AI couldn't integrate with legacy SAP systems and ERP infrastructure
Internal team lacked production ML engineering experience—strong data scientists but no MLOps capability
France 2030 program required demonstrated production AI by Q4 2025 to maintain funding eligibility
Applied the UNBLOCK Framework™ to diagnose root causes, prioritize production-viable pilots, and deliver working AI systems with full capability transfer.
Systematic diagnosis revealed that all three pilots suffered from the same fundamental issue: demo-quality architecture. Lab conditions don't reflect production reality. We prioritized the quality inspection system (highest ROI), redesigned for production reliability, and delivered a complete MLOps infrastructure that the internal team could maintain and extend.
Conducted technical audit of all three pilots. Identified that quality inspection had the clearest path to production and highest business impact (€4.2M potential annual savings from defect reduction). Defined clear graduation criteria for 'production-ready'.
2 weeksRedesigned quality inspection AI for real factory conditions: lighting normalization, camera calibration, edge deployment for <100ms latency. Replaced lab-trained model with production-representative dataset.
4 weeksDeployed complete MLOps stack: model registry (MLflow), feature store, automated retraining pipeline, monitoring dashboard with drift detection, and A/B testing framework for model updates.
3 weeksRolled out to 3 production lines, then expanded to 12. Conducted intensive training for internal team on MLOps practices. Established governance framework for AI model lifecycle.
3 weeksTransformed an 18-month stuck pilot into a production AI system generating €4.2M annual savings. Internal team now independently manages the AI lifecycle and has launched two additional AI projects using the same infrastructure.
“The pattern is always the same: strong data science teams building demo-quality architectures. The model works in the lab but fails on the factory floor. Fixing this is not about better algorithms — it is about production engineering discipline.”