Manufacturing Pilot-to-Production: How the UNBLOCK Framework Escapes AI Purgatory

About the Client

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. Illustrative scenario, not a specific client engagement.

Size: Typical engagement: 5,000–20,000 employee manufacturers

The Challenge

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 optimisation 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

My Solution

Applied the UNBLOCK Framework to diagnose root causes, prioritise 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. I prioritised the quality inspection system (highest ROI), redesigned for production reliability, and delivered a complete MLOps infrastructure that the internal team could maintain and extend.

Implementation Phases

• 2 weeksDiagnosis & PrioritisationConducted technical audit of all three pilots. Identified that quality inspection had the clearest path to production and highest business impact. Defined clear graduation criteria for 'production-ready'.
• 4 weeksProduction Architecture RedesignRedesigned quality inspection AI for real factory conditions: lighting normalisation, camera calibration, edge deployment for <100ms latency. Replaced lab-trained model with production-representative dataset.
• 3 weeksMLOps InfrastructureDeployed 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 weeksProduction Deployment & Capability TransferRolled 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.

PyTorch · ONNX Runtime · MLflow · Kubernetes · NVIDIA Jetson (Edge) · Apache Kafka · PostgreSQL · Grafana · Prometheus · SAP Integration · Azure ML

Results & Impact

Illustrative scenario: transforming a stuck pilot into a production AI system, with cost savings dependent on the client's baseline, scope and implementation conditions.

Services Delivered

AI Strategy Sprint · Pilot-to-Production Sprint · MLOps Infrastructure · AI Development Training · Capability Transfer

Want Results Like These?

Every engagement starts with a 30-minute diagnosis. Describe your situation, and I will tell you — honestly — whether I can help and how fast.