Methodology demonstration: How we apply our pilot-to-production approach to triage stuck pilots and move them toward production readiness in a modelled 12-week engagement
This is an illustrative Physical AI scenario applying our pilot-to-production approach to a familiar mid-market manufacturing pattern: multiple stuck pilots, no MLOps foundation, and board pressure for ROI. It is a theoretical deployment scenario, not a delivered client project; the metrics and timeline are modelled, not measured.
Size: Indicative engagement: 200–2,000 employee manufacturers
Six AI pilots running for 18 months with no path to production — a classic pilot purgatory scenario we encounter frequently in manufacturing.
Six AI pilots running for 18 months with no path to production — substantial budget invested with nothing to show
No MLOps infrastructure: models were trained locally with no reproducibility, versioning, or deployment pipeline
Severe data quality issues across factory floor sensors, ERP systems, and quality control databases
No production architecture — pilots were built as Jupyter notebooks and Flask demos, not production systems
Each pilot built by a different vendor with no integration plan, incompatible tech stacks, and siloed data
Leadership needed to demonstrate AI ROI to the board within one quarter or risk losing the entire AI budget
A representative engagement would audit and triage all six pilots, kill the two unviable ones, build a shared MLOps foundation, and take the four viable systems to production in a modelled 12 weeks.
The engagement would triage all 6 pilots into 'ship', 'pivot', and 'kill' categories, build a shared MLOps pipeline and data quality layer as the foundation, then take 4 systems to production in parallel using standardized deployment patterns.
Audit all 6 pilots against production-readiness criteria. Triage into 'ship' (4 pilots with viable models and clear ROI) and 'kill' (2 pilots with fundamental data or business case flaws). An honest assessment saves months of wasted effort.
2 weeksBuild the shared MLOps pipeline with CI/CD for models, a model registry, and automated retraining triggers. A data quality layer across factory sensors and ERP systems ensures clean, reliable inputs.
2 weeksTake the 4 viable systems to production in parallel: (1) predictive maintenance for CNC machines, (2) quality inspection with computer vision, (3) demand forecasting integrated with ERP, (4) energy optimization across the factory floor.
8 weeksIllustrative scenario: applying our pilot-to-production approach to triage stuck pilots and move a subset toward production, with a shared MLOps foundation as the reusable outcome.