Aug 2026· Electronics· Vol 15, pp. 3485· 0 citations· 43 references
TL;DR
This PRISMA-guided bibliometric and abstract-level thematic review maps peer-reviewed industrial GAI research published from 2022 to 4 June 2026 contributes a reproducible cross-domain map, an overlap-aware synthesis, and stakeholder-specific guidance for trustworthy industrial GAI.
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.
This study systematically investigates the transformative role of generative artificial intelligence (GenAI) in the manufacturing sector, focusing on its integration within the paradigms of Industry 4.0 and Industry 5.0. Through a systematic literature review and qualitative synthesis of 22 peer-reviewed articles published between 2023 and 2025, we identify and categorise 19 second-order themes that capture the breadth of GenAI applications and their implications in manufacturing. These themes are further consolidated into 11 aggregate dimensions, which serve as foundational categories for understanding the multifaceted impact of GenAI across operational, technical, organisational, and strategic domains. Our analysis reveals 13 key application areas, each mapped to the aggregate dimensions, to illustrate the depth and diversity of GenAI’s influence. Furthermore, we delineate six principal implication areas, highlighting both the opportunities and challenges associated with GenAI adoption. By clarifying the interconnections between applications, dimensions, and implications, this study provides an integrative framework that promotes theoretical understanding and offers practical guidance for managers and policymakers aiming to leverage GenAI for sustainable and responsible manufacturing transformation.
Ane Arregi, Juan Ignacio Igartua, J. Retegi et al.· Dirección y Organización· 0 citations
This review highlights the field's strong interdisciplinary character and reveals the current challenges that the sector faces, and outlines a systematic research program to guide further studies of the implementation, impact, and problems of GenAI in enterprises and communities.
Majdouline Attaoui, Wissal Attaoui, Anas Moukrim et al.· International journal of mul...· 0 citations
The evidence indicates that LLMs are becoming useful semantic and coordination layers in engineering workflows, but not dependable engineering substitutes in human-in-the-loop, evidence-grounded systems where retrieval, validation, tool use, and structured knowledge help keep outputs useful and bounded in safety-relevant tasks.
Artificial Intelligence (AI), particularly generative AI based on Large Language Models (LLMs), has rapidly transformed the execution of knowledge-intensive activities across multiple domains. AI-powered tools such as ChatGPT, GitHub Copilot, Microsoft Copilot, Google Gemini, Claude, and Notion AI have increasingly been adopted to automate repetitive tasks, support decision-making, accelerate software development, and improve content production. However, despite the rapid expansion of these technologies, scientific evidence regarding their effectiveness as productivity enhancers remains distributed across different research areas. This study presents a systematic literature review aimed at synthesizing current evidence on the role of AI tools in improving productivity in business, education, software engineering, and scientific research. A structured literature search was conducted across major academic databases, including Google Scholar, IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Scopus. Studies published between 2020 and 2026 were analyzed according to predefined inclusion and exclusion criteria. The reviewed literature indicates that AI tools can improve productivity by reducing task completion time, assisting knowledge creation, supporting programming activities, and enhancing information processing. Nevertheless, significant challenges remain, including inaccurate outputs, algorithmic bias, privacy concerns, ethical risks, and excessive reliance on automated systems. The findings suggest that AI achieves the greatest productivity benefits when used as a collaborative technology that augments human capabilities rather than replacing human expertise. Future research should investigate long-term productivity impacts, organizational adaptation strategies, responsible AI governance, and the integration of advanced multimodal AI systems into professional workflows.
This review examines recent progress in large AI models for intelligent manufacturing, covering model architectures, adaptation strategies, system integration, and applications across product development, production processes, equipment maintenance, and manufacturing services.
Baotong Chen, Lu Dai, Chuangjian Wang et al.· IEEE Access· 0 citations
An integrative, stage-based analysis of how contemporary AI systems—particularly large language models, retrieval-augmented systems, and tool-using agents—are reshaping the research lifecycle, from the earliest formulation of a research question through data analysis, manuscript preparation, peer review, and post-publication dissemination is offered.
Dr. Gagandeep Singh, Ravi Ranjan, Anirudh Gupta, Harinakshi Aravind Shetty· International Journal of Adv...· 0 citations
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