Methodology demonstration: How we help manufacturers escape pilot purgatory—90 days to production using the UNBLOCK Framework
This case study illustrates our proven UNBLOCK Framework methodology for transforming stuck AI pilots into production systems. The scenario, metrics, and approach reflect typical outcomes from our manufacturing engagements. Client details anonymized.
Taille: Typical engagement: 5,000-20,000 employee manufacturers
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 robustness, and delivered a complete MLOps infrastructure.
Conducted technical audit of all three pilots. Identified that quality inspection had the clearest path to production and highest business impact.
2 weeksRedesigned quality inspection AI for real factory conditions: lighting normalization, camera calibration, edge deployment for <100ms latency.
4 weeksDeployed complete MLOps stack: model registry (MLflow), feature store, automated retraining pipeline, monitoring dashboard with drift detection.
3 weeksRolled out to 3 production lines, then expanded to 12. Conducted intensive training for internal team on MLOps practices.
3 weeksIllustrative scenario: Transforming an 18-month stuck pilot into a production AI system with significant cost savings.