Abstract : The manufacturing sector stands at a critical inflection point as artificial intelligence (AI) technologies reshape how production systems operate, decisions are made, and workers perform their roles. A persistent and counterproductive narrative frames AI as a replacement for human labor in manufacturing—generating workforce resistance, impeding adoption, and obscuring the empirically demonstrated model of AI as a powerful complement to human capability. This article presents an integrated framework spanning four manufacturing domains—AI-driven decision support systems, human-robot collaboration through collaborative robots (cobots), computer vision-based quality control, and machine learning-powered predictive maintenance—demonstrating how AI amplifies human capability rather than displacing it. Drawing on recent peer-reviewed evidence, the framework shows that AI-augmented decision support systems reduce production scheduling cycle times by 15–20%, AI-assisted quality inspection achieves defect detection accuracy of 94–99% while enabling operators to transition from passive inspection to active exception management, and predictive maintenance deployments reduce unplanned downtime by 25–50% compared to reactive maintenance baselines. The article argues that manufacturing leaders must approach AI adoption as a people-first organizational transformation—pairing technology investment with workforce upskilling, human-centred system design, and structured change management—to fully realize the complementarity dividend that Industry 4.0 implementations can deliver