Methodology demonstration: How we help manufacturers escape pilot purgatory — illustrative target: 90 days to production
This is an illustrative Physical AI scenario showing how the pilot-to-production methodology approaches a stuck manufacturing pilot. It is a theoretical deployment scenario, not a delivered client project; the figures are modelled, not measured.
规模: Indicative 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
A representative engagement would diagnose root causes, prioritise the production-viable pilots, and take working AI systems to production with full capability transfer.
In this modelled scenario, systematic diagnosis surfaces the same fundamental issue in all three pilots: demo-quality architecture. Lab conditions don't reflect production reality. The engagement would prioritise the quality inspection system (highest ROI), redesign it for production robustness, and stand up a complete MLOps infrastructure the internal team can maintain and extend.
Technical audit of all three pilots. In the modelled scenario, quality inspection shows the clearest path to production and the highest business impact.
2 weeksRedesign the quality inspection AI for real factory conditions: lighting normalization, camera calibration, edge deployment for <100ms latency.
4 weeksStand up the complete MLOps stack: model registry (MLflow), feature store, automated retraining pipeline, monitoring dashboard with drift detection.
3 weeksRoll out to 3 production lines, then expand to 12. Intensive training for the internal team on MLOps practices.
3 weeksIllustrative scenario: transforming a stuck pilot into a production AI system, with cost savings dependent on the client's baseline, scope and implementation conditions.