Manufacturing embraced Industry 4.0 and AI with enthusiasm, expecting transformation in quality control, predictive maintenance, and operational efficiency. Instead, 76% of manufacturing AI projects fail to scale. The gap between laboratory success and factory floor reality proves insurmountable for most initiatives.
Frequently Asked Questions
Manufacturing AI struggles to scale due to heterogeneous equipment and data formats, legacy OT/IT infrastructure gaps, shopfloor resistance to change, insufficient data labelling for quality inspection, and the challenge of proving ROI across different production lines.
The biggest barrier is typically the OT/IT convergence gap — connecting operational technology (machines, sensors, PLCs) with information technology (cloud, analytics platforms). Without reliable data pipelines from the shopfloor, AI models cannot function in production.
Predictive maintenance, quality inspection automation, and demand forecasting typically deliver the fastest ROI because they address clear cost centres, have measurable before-and-after metrics, and can start with a single production line before scaling.