From AI Pilot Graveyard to Production: a Modelled 90-Day Triage-and-Ship Scenario
How a 12-week engagement can triage a portfolio of stalled pilots and move the production-viable systems toward operation
運用コンテキスト
A mid-market manufacturing context with multiple stalled pilots, no shared MLOps foundation and board pressure for a defensible ROI decision.
規模: Indicative engagement: 200–2,000 employee manufacturers
課題
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
ソリューション
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.
実装フェーズ
Audit & Triage
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 weeksMLOps Foundation & Data Quality
Build 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 weeksProduction Deployment — 4 AI Systems
Take 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 weeksテクノロジー＆アプローチ
想定する成果
The intended outcome is a clear ship, pivot or stop decision for every pilot, with production-viable systems advanced on a shared MLOps foundation.