Pathways and Challenges of Artificial Intelligence-Enabled Manufacturing Transformation and Upgrading
Artificial intelligence can improve manufacturing decisions, yet model performance alone does not establish operational value. This conceptual review examines how manufacturers can convert AI applications into improvements in quality, flexibility and organizational capability. The proposed pathway connects problem definition, contextual data, model selection, workflow integration and evaluation. Industrial ontologies and bounded AI agents are considered as possible means of connecting information with action. Fragmented data, uncertain transfer between operating conditions, integration costs and unclear decision authority emerge as interdependent barriers. The paper proposes staged deployment with explicit baselines, operator participation and limits on automated action. The framework guides empirical investigation without estimating a universal productivity effect.
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