Generative AI in Manufacturing and Industrial Contexts: A Systematic Review of Applications, Challenges, and Future Directions

Abstract

Generative artificial intelligence (GAI) is expanding from model-centered research into engineering and manufacturing activities, but its scope and maturity remain uneven. This PRISMA-guided bibliometric and abstract-level thematic review maps peer-reviewed industrial GAI research published from 2022 to 4 June 2026. Searches of Scopus, Web of Science, and the ACM Digital Library identified 492 records; 119 duplicates and 121 ineligible records were removed, leaving 252 studies. Keyword normalization, co-occurrence analysis, dominant and secondary thematic coding, and an abstract-reported evidence characterization were applied. The corpus shows two connected trajectories: engineering generation based on generative models for design, topology, materials, and electronics, and knowledge-intensive industrial intelligence based on large language models, retrieval-augmented generation, knowledge graphs, agents, and human–AI collaboration. Most studies report empirical or computational evaluation (72.2%), but 84.5% remain research-stage; only 0.8% indicate operational industrial evidence in their abstracts. The findings, therefore, distinguish publication activity from deployment maturity. Priority requirements for adoption include domain-grounded data, verification, manufacturability checks, traceability, cybersecurity, intellectual property protection, system integration, workforce preparation, and human accountability. This review contributes a reproducible cross-domain map, an overlap-aware synthesis, and stakeholder-specific guidance for trustworthy industrial GAI.