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<p>Generative artificial intelligence (GenAI) can be defined as a technology that creates original content from learned patterns, based on advanced architectures such as GANs, VAEs, and large-scale transformers. Initially employed in creative domains, its utilisation is progressively extending to industry to enhance processes and design products. This research systematically reviews the scattered evidence on the adoption of GenAI in industrial sectors, employing a rigorous review process by the PRISMA 2020 protocol. This approach ensures transparency and reproducibility. Applications are identified that include design generation and failure prediction to improve efficiency and customisation. Concurrently, the technical, regulatory, and organisational challenges hindering its integration are recognised. Consequently, the necessity for adaptive approaches that integrate technological innovation, change management, ethics, and skills development to enable responsible and effective adoption is emphasised. The study proffers a comprehensive and well-founded vision that will guide future research and industrial strategies.</p>

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Industrial applications of generative artificial intelligence: transformations in processes, design, and production

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月14日

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  • Alejandro Valencia-Arias,
  • Paula Andrea Rodríguez-Correa,
  • Jesus Alberto Jimenez-Garcia,
  • Jackeline Valencia,
  • Ada Gallegos,
  • Sebastián Cardona-Acevedo,
  • Martha Luz Benjumea-Arias

摘要

Generative artificial intelligence (GenAI) can be defined as a technology that creates original content from learned patterns, based on advanced architectures such as GANs, VAEs, and large-scale transformers. Initially employed in creative domains, its utilisation is progressively extending to industry to enhance processes and design products. This research systematically reviews the scattered evidence on the adoption of GenAI in industrial sectors, employing a rigorous review process by the PRISMA 2020 protocol. This approach ensures transparency and reproducibility. Applications are identified that include design generation and failure prediction to improve efficiency and customisation. Concurrently, the technical, regulatory, and organisational challenges hindering its integration are recognised. Consequently, the necessity for adaptive approaches that integrate technological innovation, change management, ethics, and skills development to enable responsible and effective adoption is emphasised. The study proffers a comprehensive and well-founded vision that will guide future research and industrial strategies.