Generative Artificial Intelligence in Manufacturing: Exploring Applications and Implications

Inteligencia artificial generativa en la industria manufacturera: explorando aplicaciones e implicaciones

Ane Arregi¹, Juan Ignacio Igartua², Jabier Retegi³, Dorleta Ibarra⁴

Received: 19/11/2025 | Accepted: 15/04/2026

Abstract

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.

Keywords: generative AI, industry 4.0, industry 5.0, manufacturing, literature review.

Resumen

Este estudio analiza el papel transformador de la inteligencia artificial generativa (IAG) en el marco de la Industria 4.0 y 5.0. Mediante una revisión sistemática de 22 artículos (2023-2025), se identifican 19 temas de segundo orden y 11 dimensiones agregadas que estructuran los impactos de esta tecnología. El análisis detalla 13 áreas de aplicación y seis áreas de implicación, destacando desafíos y oportunidades de adopción. Finalmente, se propone un marco integrador que ofrece orientaciones prácticas para una transformación sostenible y responsable en el sector manufacturero.

Palabras clave: inteligencia artificial generativa, Industria 4.0, Industria 5.0, industria manufacturera, revisión de la literatura.

1. Introduction

The trajectory of industrial evolution has been characterised by successive technological waves, each redefining the relationship among technology, production, and labour. In this context, Industry 4.0 (Lasi et al., 2014) has driven a profound transformation across manufacturing sectors, with a successful focus on process automation and data-driven decision-making, and an essentially technocentric approach that often neglected the human element and broader societal goals. Thus, Industry 5.0 emerged as a necessary paradigm shift, representing a significant philosophical correction that transcends Industry 4.0 by integrating innovative technologies to maximise production, augment human creativity, and address systemic societal challenges towards a human-centric, sustainable, and resilient industry (Lasi et al., 2014). This shift requires a technological capability that can bridge the gap between automation and augmentation, supporting genuine human creativity or creating highly intuitive, conversational human-machine interfaces. Generative artificial intelligence (GenAI) is strategically positioned as an essential technological bridge to accomplish this comprehensive vision (Jourabchi Amirkhizi et al., 2025).

GenAI represents a significant advancement in the field of artificial intelligence (AI), building on and extending the capabilities of previous AI approaches (Mariani and Dwivedi, 2024). Particularly in the form of large language models (LLMs) and foundation models, GenAI has emerged as a transformative technology with distinctive capabilities that distinguish it from traditional AI models. These systems can generate novel content (such as text, images, or other data types) based on patterns learned from extensive training data, thereby opening new avenues for automation, creativity, and decision-making in complex environments (Kim et al., 2022; Nti et al., 2022; Plathottam et al., 2023).

Unlike conventional AI systems, which are typically designed for narrowly defined, task-specific applications, GenAI demonstrates human-like cognitive abilities across various domains. This broader applicability enables GenAI to understand, reason, and learn in ways that closely mimic human intelligence, thereby facilitating the transfer of knowledge and adaptation to new contexts with minimal retraining (Kim et al., 2022). Consequently, GenAI is not only capable of analysing existing data but also of creating original solutions and content, thus playing a pivotal role in the advancement of multimodal AI, which integrates information from multiple data sources (e. g. text, images, and audio) (Kim et al., 2022; Waltersmann et al., 2021).

GenAI offers opportunities to optimise processes, personalise products and services, enhance decision-making, and accelerate innovation. However, it presents significant challenges related to technological integration, change management, ethics, and data governance (Badghish and Soomro, 2024; Chowdhury et al., 2024; Jorzik et al., 2024). Although there is broad consensus regarding the potential of AI to transform manufacturing, the literature reveals a fragmented understanding of how, where, and with what impact GenAI is being implemented across key industrial functions (Nti et al., 2022; Plathottam et al., 2023). Furthermore, most prior studies focus on classical AI, with limited systematic analyses of GenAI and its cross-cutting role in business model transformation (Jorzik et al., 2024; Nti et al., 2022).

It is crucial to recognise that GenAI does not affect all sectors equally. Certain industries are leading the way in GenAI adoption, already demonstrating significant gains in productivity, sustainability, and resilience, whereas others are progressing more slowly (Kim et al., 2022; Nti et al., 2022). In manufacturing, the integration of GenAI may not follow the patterns observed in sectors such as banking, retail, and tourism, highlighting the need for sector-specific research to comprehensively examine the opportunities, challenges, and implications of GenAI in industrial contexts (Badghish and Soomro, 2024; Nti et al., 2022).

Despite the increasing volume of research on the impact of AI in business, the literature on GenAI in manufacturing remains limited in several aspects. The current academic literature on GenAI focuses primarily on its implications for higher education, (Batista et al., 2024; Law, 2024; Nikolic et al., 2024; Agbo et al., 2025; Wang et al., 2025), general work productivity (Al Naqbi et al., 2024), or consumer behaviour (Panda et al., 2026). Even where management is discussed, the focus remains on broad knowledge management or strategic implications (Pimentel and Véliz Palomino, 2024; Santos Gabriel, 2024; López-Solís et al., 2025) rather than the technical and operational dimensions unique to the manufacturing sector. This paper addresses this gap by providing a targeted analysis of GenAI dimensions and specific application areas, offering a granular perspective that existing cross-disciplinary reviews lack. Specifically, while many existing works focus on traditional AI without offering a consolidated view of the role of GenAI across diverse manufacturing functions (Jorzik et al., 2024; Nti et al., 2022). There is a fragmented understanding of the applications, benefits, and risks of GenAI in manufacturing, which hinders the development of integrative frameworks (Nti et al., 2022). Moreover, few studies offer a comprehensive perspective on the impact of GenAI in areas such as operations management, supply chain, product innovation, sustainability, and talent management (Kim et al., 2022). Consequently, it is essential to advance the understanding of the opportunities and implications of GenAI adoption in manufacturing companies to support managers and decision-makers in anticipating the potential impact of these technologies on their activities, strategies, and business models (Badghish and Soomro, 2024; Chowdhury et al., 2024; Jorzik et al., 2024).

This article attempts to answer the following questions through a systematic review and critical analysis of recent literature: (1) What are the fundamental GenAI dimensions for manufacturing companies? (2) What are the key areas of application? (3) What are the associated challenges and implications? and (4) How does GenAI support human-centric, sustainable, and resilient approaches associated with Industry 5.0?

The remainder of this paper is organised as follows. Section 2 contextualises GenAI technology within the broader field of AI. Section 3 describes the research method, literature review, and systematic analysis design. Section 4 presents the results obtained. Section 5 focuses on the conclusions, highlights its contributions and managerial implications, and outlines the limitations of the study and future lines of research.

2. GenAI

2.1. AI in manufacturing

AI refers to the development of computer systems capable of performing tasks that typically require human intelligence, such as reasoning, learning, perception, and decision-making. AI encompasses a broad range of techniques, including machine learning (ML), deep learning, natural language processing, computer vision, and generative models. In the context of manufacturing, AI is increasingly recognised as a key enabler of the digital transformation associated with Industry 4.0 and the emerging paradigm of Industry 5.0, in which the integration of advanced digital technologies, automation, and sustainability is a central priority for management (Kim et al., 2022; Nti et al., 2022).

AI systems in manufacturing can be categorised as either ‘narrow AI’, designed for specific tasks such as predictive maintenance or quality inspection, or as more general systems capable of learning and adapting across multiple domains (Schwaeke et al., 2025). The evolution from rule-based expert systems to data-driven ML and deep learning models has significantly expanded the potential applications of AI in industrial environments (Plathottam et al., 2023).

AI adoption in manufacturing offers a wide range of opportunities. For instance, AI enables the real-time monitoring and optimisation of production processes, leading to increased efficiency, reduced downtime, and improved resource utilisation. Predictive maintenance powered by ML algorithms can proactively anticipate equipment failures and schedule maintenance, thus reducing costs and unplanned outages (Kim et al., 2022; Plathottam et al., 2023; Waltersmann et al., 2021). Additionally, computer vision and deep learning models are increasingly being used for automated inspection and quality assurance, achieving higher accuracy and consistency than manual methods (Kim et al., 2022; Plathottam et al., 2023). Regarding supply chain and inventory management, AI-driven analytics support demand forecasting, inventory optimisation, and supply chain resilience, enabling manufacturers to respond more effectively to market fluctuations and disruptions (Nti et al., 2022; Plathottam et al., 2023). AI also contributes to sustainability and resource efficiency, supporting applications that contribute to energy savings, waste reduction, and optimisation of material and water usage (Goel et al., 2024; Waltersmann et al., 2021); it also facilitates the integration of circular economy principles (Jobstreibizer et al., 2025; Jorzik et al., 2024).

Despite its potential, AI implementation in manufacturing faces several challenges. The effectiveness of AI models depends on the availability of large volumes of high-quality, structured, labelled data. In several manufacturing settings, data may be fragmented, proprietary, or subject to privacy and security constraints (Plathottam et al., 2023; Waltersmann et al., 2021). Additionally, integrating AI solutions with existing manufacturing infrastructure and legacy IT systems can be complex and costly (Badghish and Soomro, 2024; Kim et al., 2022). Moreover, many AI models, particularly deep learning systems, are perceived as ‘black boxes’, making it challenging for operators and managers to understand and trust their recommendations (Chowdhury et al., 2024; Plathottam et al., 2023). Other organisational challenges are also relevant, as AI usage raises questions about data privacy, intellectual property, cybersecurity, and the ethical implications of automation and workforce displacement (Chowdhury et al., 2024; Goel et al., 2024), along with the need for skilled personnel and resistance to change within organisations (Badghish and Soomro, 2024; Jorzik et al., 2024). Nevertheless, recent literature reveals that AI is central to the digital transformation of manufacturing, enabling smart factories, autonomous systems, and data-driven decision-making (Kim et al., 2022; Nti et al., 2022).

2.2. GenAI in manufacturing

GenAI is a rapidly evolving subfield within AI that focuses on new content creation (such as text, images, designs, and even code) by learning patterns from large datasets (Mariani and Dwivedi, 2024). Unlike traditional AI, typically designed for classification, prediction, or optimisation tasks, GenAI models can produce original outputs that are not explicitly present in their training data. This is achieved through advanced ML techniques, particularly deep learning and LLMs, which enable systems to generate, adapt, and innovate across a wide range of applications (Kim et al., 2022; Plathottam et al., 2023). In the context of manufacturing, GenAI is increasingly recognised as a transformative technology that can revolutionise design, process planning, quality control, and human-machine collaboration (Ghobakhloo et al., 2024; Jorzik et al., 2024).

Industry 4.0, also known as the Fourth Industrial Revolution, has marked a significant shift towards interconnected, smart, and autonomous industrial systems (Yang et al., 2024). AI plays a crucial role in Industry 4.0, enabling data-driven decision-making, predictive maintenance, and process optimisation (Kusiak, 2025). Industry 5.0 aims to complement Industry 4.0 by emphasising human-centricity, sustainability, and resilience. It seeks to reintegrate human workers into the manufacturing process by focusing on human-machine collaboration (Zhang et al., 2025).

In this context, the advent of GenAI has opened new possibilities for Industry 5.0. GenAI technologies have demonstrated potential in various aspects of manufacturing, including design and optimisation, process planning, human-machine interaction, quality control, and decision support (Ghobakhloo et al., 2024). GenAI can automate and accelerate the design process, enabling the rapid generation of new product concepts, optimised geometries, and customised solutions. This supports mass customisation and shortens time-to-market (Kim et al., 2022; Ghobakhloo et al., 2024). By generating optimised process parameters and simulating production scenarios, GenAI can enhance process efficiency, reduce waste, and improve resource utilisation (Plathottam et al., 2023; Waltersmann et al., 2021). GenAI models can also generate synthetic data for training inspection systems, improve defect detection, and support predictive maintenance strategies by simulating failure modes and maintenance scenarios (Kim et al., 2022; Plathottam et al., 2023).

GenAI enables more intuitive interfaces and adaptive systems, facilitating collaboration between human operators and intelligent machines and supporting the human-centric vision of Industry 5.0 (Chowdhury et al., 2024; Goel et al., 2024). By generating insights, recommendations, and documentation, GenAI can support decision-making at all levels of manufacturing organisations (Ghobakhloo et al., 2024; Jorzik et al., 2024). As this technology continues to develop, it is expected to play an increasingly important role in shaping the future of industrial manufacturing. Moreover, GenAI is driving new business models in manufacturing, including data-driven services, digital platforms, and the integration of generative capabilities into value propositions (Jobstreibizer et al., 2025; Jorzik et al., 2024).

However, although GenAI offers significant potential, its implementation in manufacturing can present similar challenges, in some cases more or less pronounced, such as the generic application of AI in manufacturing. These challenges relate to data scarcity; data subject to exclusive ownership or privacy and security restrictions; risk of bias in the results generated; technically complex and costly integrations; difficulty for engineers and operators to understand, validate, and trust results; concerns about intellectual property, cybersecurity, and ethical implications; adapting the organisation and people to change; and leadership commitment (Nti et al., 2022).

Moreover, research on GenAI in manufacturing suggests future research directions, such as exploring how companies can apply GenAI in data- and resource-constrained environments (Badghish and Soomro, 2024; Plathottam et al., 2023); identifying frameworks that can facilitate the integration of GenAI in manufacturing companies (Kim et al., 2022; Nti et al., 2022); and understanding how GenAI accelerates the transition to sustainable and circular manufacturing models, along with the associated risks and trade-offs (Goel et al., 2024; Waltersmann et al., 2021).

3. Research method

3.1. Methodological framework

To produce valuable insights and make relevant contributions to the literature, this study adopted a systematic literature review methodology combined with structured guidelines to ensure transparency and replicability. The review was complemented by an inductive concept development approach following (Gioia et al., 2013). The integration of these frameworks ensures methodological rigour and supports the development of robust evidence-based conclusions.

3.2. Review protocol and preferred reporting items for systematic reviews and meta-analyses (PRISMA) flowchart

The review protocol for this study was structured according to the multi-stage methodology proposed by Tranfield et al., (2003), which emphasises a clear sequence for planning, conducting, and reporting reviews. To operationalise and document each step of the review, the protocol was aligned with the PRISMA guidelines. The PRISMA framework (Moher et al., 2009) provides a standardised checklist and flowchart that support the systematic review process by making each phase (identification, screening, eligibility, and inclusion) explicit.

The following eligibility criteria were applied:

• Timeframe: Articles published (or available online) from January 2017 to May 2025.

• Databases: Web of Science and Scopus.

• Language: Only articles and reviews published in English were considered.

• Document Type: Peer-reviewed articles and reviews. Editorial and non-research articles were excluded.

• Subject Focus: Articles addressing GenAI, LLMs, and their intersection with Industry 4.0/5.0, digitalisation, automation, intelligent/smart manufacturing, operations, and industrial contexts.

The review was performed in June 2025 and included all available issues up to the search date. The databases were systematically searched using the following search string, which was developed based on the research focus on GenAI, LLMs, and their application in manufacturing and industrial contexts: (TITLE(“generat* AI” OR “generat* Artificial Intelligence” OR “generat* model*” OR “generat* algorithms” OR “Large Language Model*” OR “LLM*”) AND TITLE-ABS-KEY(“Industry 4.0” OR “Industry 5.0” OR “digitalisation” OR “automatisation OR “intelligent manufacturing” OR “smart manufacturing”) AND TITLE(“manufact*” OR “operation*” OR “industr*”)) AND (PUBYEAR > 2017 AND PUBYEAR < 2025 OR PUBDATETXT(“January 2025” OR “February 2025” OR “March 2025” OR “April 2025” OR “May 2025”)) AND SRCTYPE(j).

A total of 67 articles were identified (Scopus: 36; Web of Science: 31). After excluding 20 duplicate records, 47 unique articles remained for screening. During the screening phase, 7 articles could not be retrieved, and 2 articles were excluded for being in languages other than English, resulting in 38 articles assessed for eligibility through abstract reading. Of these, 16 articles were excluded for the following reasons: lack of alignment with the research objective (n=4), theoretical or framework-only focus (n=5), editorial nature (n=2), and focus on algorithms and software development without an application context (n=5). Ultimately, 22 articles were included in the qualitative synthesis.

The entire review process is summarised in the PRISMA flowchart (see Figure 1), which documents each stage of identification, screening, eligibility assessment, and inclusion.

3.3. Data analysis and synthesis

Following the systematic literature review process described above, data analysis and synthesis were conducted in several structured stages to ensure methodological rigour and obtain robust conceptual insights. First, all articles included after the PRISMA-based selection process (Figure 1) were imported into ATLAS.ti for qualitative coding. The analysis followed an inductive approach, inspired by the Gioia methodology (Gioia et al., 2013), to allow new concepts and patterns to emerge directly from the data. Each article was carefully read and coded, resulting in an initial set of 287 codes that captured relevant concepts, findings, and contextual information related to the application of GenAI in the manufacturing and industrial contexts.

To enhance the reliability and transparency of the coding process, codes were iteratively reviewed, compared, and discussed among the research team. This collaborative approach helped ensure consistency in interpretation and minimised individual bias. Subsequently, 287 first-order terms were exported to Microsoft Excel for further analysis and synthesis. Through iterative comparison and clustering, the initial first-order terms were then consolidated into second-order terms representing recurring ideas and phenomena identified in the literature. The next step involved aggregating second-order terms, resulting in aggregate dimensions that reflected the main conceptual categories relevant to GenAI applications in manufacturing.

Finally, we theorised the logic and links between the aggregated dimensions and second-order themes. As we sought to understand how GenAI is applied in manufacturing and the implications and insights that should be considered, we configured the lines of relationship by evaluating opinions based on the articles analysed by the research team. This enabled us to further refine the data structure and generate a graphical outline.

4. Results

4.1. Descriptive analysis

The descriptive analysis provides an overview of the main characteristics of the articles included in this research. This section summarises the distribution of articles by publication year, journal, methodological approach, and research focus, offering a foundational understanding of the current landscape of GenAI research in manufacturing contexts.

The 22 articles were published between June 2023 and May 2025, reflecting the recent and rapidly growing interest in the intersection of GenAI and manufacturing. The temporal distribution of the publications further highlights the growing academic interest in this field. Of the 22 articles analysed, 2 were published in 2023, 15 in 2024, and 5 in 2025. This sharp increase in publications in 2024, followed by an equally sustained output in 2025, highlights the rapid evolution of GenAI in the manufacturing sector.

The articles were published in various high-impact journals, reflecting both the technical depth and multidisciplinary reach of GenAI research in manufacturing. Notably, Robotics and Computer-Integrated Manufacturing stands out with two highly cited articles, including the top-cited paper ‘Leveraging error-assisted fine-tuning large language models for manufacturing excellence’ (22 citations), and a recent survey on LLMs in intelligent manufacturing. Other prominent journals represented in the sample include IEEE Transactions on Systems, Man, and Cybernetics: Systems, Journal of Manufacturing Systems, Journal of Manufacturing Technology Management, IEEE Access, Technological Forecasting and Social Change, and Equilibrium. These articles have attracted substantial academic attention, with citation counts ranging from 1 to 22. The most cited works were published in 2024, indicating both the recent and rapid scholarly uptake of GenAI-related research in manufacturing. This diversity of publication venues, spanning robotics, manufacturing systems, engineering management, economics, and digital transformation, underscores the multidisciplinary nature of GenAI research and its broad appeal to both technical and managerial audiences.

This research demonstrates strong international engagement in terms of geographical distribution. China leads with 5 publications and 37 citations, followed by Lithuania (3 publications, 24 citations), Australia and Malaysia (2 publications each, with 15 citations each), and Germany (2 publications, 9 citations). The United States and the United Kingdom are also represented with 2 publications each, while other contributing countries include Czechia, France, India, Italy, Poland, Saudi Arabia, Singapore, South Africa, South Korea, Sweden, and Vietnam. This global authorship highlights the widespread interest in and the collaborative potential of advancing GenAI applications for manufacturing across continents.

Overall, the journal distribution, citation impact, and country participation highlight not only the scientific relevance but also the growing international influence of GenAI studies across the manufacturing, engineering, and management research communities.

Table 1 shows the key trends identified through qualitative analysis of the 22 articles on GenAI in manufacturing considered in the research. The table groups the articles according to key trends and contributions of GenAI and LLMs in Industry 4.0 and 5.0. Details of this analysis can be found in Table 5 (APPENDIX).

Our analysis reveals the following key trends.

• Human-centric systems rising: Several articles underline the importance of GenAI as a tool for reducing cognitive load and supporting systems where a balance between humans and machines is prioritised (Doanh et al., 2023; Colabianchi et al., 2024; Grybauskas and Cárdenas-Rubio, 2024; Zhang et al., 2025).

• Diversity of GenAI Applications: Beyond simple text generation, GenAI is being applied across a wide spectrum of manufacturing domains. This includes predictive maintenance, process optimisation, and the development of more intuitive human-machine interfaces and sustainability strategies (Gholami, 2024; Chen and Zhang, 2025; Mustapha, 2025; Zhang et al., 2025).

• Emerging Functions: A significant trend is the convergence of GenAI with the existing Industry 4.0 stack, such as IoT, edge computing, and blockchain. Key functions identified include fault detection, personalised production, and the use of LLMs for strategic decision-making and semantic search (Alsaif et al., 2024; Colabianchi et al., 2024; Shi et al., 2024; Xia et al., 2024).

• Industry 4.0 vs. 5.0: Most articles explicitly situate their contributions within the shift from machine-centric automation (Industry 4.0) to human-centric, sustainable, and resilient ecosystems (Industry 5.0). This evolution highlights a growing priority for workforce upskilling and collaborative manufacturing models (Ghobakhloo et al., 2024; Kiangala and Wang, 2024; Kliestik et al., 2024; Lazaroiu et al., 2024).

• Challenges: Despite the potential, common barriers persist across the literature. These include data availability and quality, high computational costs, integration with legacy systems, and the urgent need for domain-specific adaptation to prevent “hallucinations” in safety-critical environments (Gholami, 2024; Chen and Zhang, 2025; Mustapha, 2025; Zhang et al., 2025).

4.2. Exploring the dimensions of GenAI in manufacturing

Based on the consolidated 19 second-order terms reflecting the main conceptual categories relevant to GenAI applications in manufacturing, we developed a deeper understanding of the landscape of AI applications and implications in manufacturing, identifying a set of 11 overarching aggregate dimensions that map the content and context of current research and practice (Table 2). This approach allowed us to focus on deeper meanings, interrelations, and emerging unexplained concepts. Second-order themes and aggregate dimensions were grouped into three interdependent categories: applications, dimensions, and implications (Figure 2). Each dimension represents a critical area in which AI is driving change, thereby fostering applications in manufacturing while generating some concerns and implications.

The interaction between these dimensions, applications, and implications in relation to GenAI in manufacturing underscores the complexity of implementing AI in manufacturing, as well as the need for holistic and multidisciplinary approaches in both practice and research. By relating, linking, and integrating these themes and dimensions into a coherent framework (Figure 2), we offer a unified view of the landscape of AI applications in manufacturing.

4.3. Applications

Based on the consolidated 19 second-order terms, we categorised 13 domain applications in which GenAI is being deployed within the manufacturing sector.

A1. Technical and Domain-Specific Aspects: Specialised GenAI applications address technical challenges, such as computer vision, object detection, and domain-specific requirements (Vrochidou et al., 2025; Xia et al., 2024a).

A2. Quality and Reliability: GenAI enhances quality control and reliability, supports trustworthy decision-making, and provides consistent product standards (Shi et al., 2024).

A3. Manufacturing Processes and Operations: GenAI enhances production efficiency and sustainability through adaptive and intelligent manufacturing, process optimisation, workflow planning, advanced assembly, and additive manufacturing techniques (Mustapha, 2025).

A4. AI and ML: In cases with insufficient data, GenAI can be used to create synthetic data that have the same statistical properties as a real-world dataset to train ML algorithms (Liu et al., 2025; Vrochidou et al., 2025).

A5. Data and Knowledge Management: GenAI augments advanced analytics and knowledge extraction, enabling improved decision-making, data interoperability, integration, and effective knowledge transfer across manufacturing systems (Shim et al., 2025).

A6. Predictive and Diagnostic Applications: GenAI supports anomaly detection, demand forecasting, fault diagnosis, predictive maintenance, real-time decision-making, and quality control (Zhu et al., 2024).

A7. Automation and Efficiency: GenAI helps automate repetitive tasks and increases accuracy, performance, and operational efficiency, while reducing troubleshooting time and resource consumption (Colabianchi et al., 2024; Kiangala and Wang, 2024).

A8. Information and Communication: GenAI improves information flow and semantic interoperability through advanced extraction, cooperation systems, and enhanced communication scenarios (Kliestik et al., 2024; Shim et al., 2025).

A9. Digital and Intelligent Systems: GenAI fosters integration with digital twins, intelligent assistants, and digital asset management, enabling more accurate simulations, optimisations, and sustainable manufacturing practices (Xia et al., 2024b; Mata et al., 2025).

A10. Optimisation and Performance: GenAI enables the conversion of problems into mathematical models (optimisation algorithms, system models, motion planning, or other performance-oriented optimisations) (Gholami, 2024; Mustapha, 2025).

A11. Human-Machine Interaction: GenAI-enhanced interfaces and virtual assistants foster more effective human-machine collaboration (Gamage et al., 2023; Colabianchi et al., 2024).

A12. Strategic and Marketing Applications: GenAI informs strategic planning, resilience, servitisation, supply chain management, and sustainability initiatives (Doanh et al., 2023).

A13. Innovation and Customisation: GenAI enables innovative and customised product designs, content generation, and the development of standardised models (Mustapha, 2025).

Table 3 analyses these applications to demonstrate their relationship with the previously identified dimensions.

The diversity of application domains underscores the transformative potential of GenAI across both operational and strategic spheres, facilitating novel approaches to optimisation, personalisation, and decision-making. Furthermore, the analysis of interconnections revealed the significance of all aggregated dimensions in relation to the 13 identified application domains, particularly those related to processes, automation, optimisation, decision-making, and efficiency.

4.4. Implications

Implications refer to the anticipated or observed consequences, requirements, and considerations arising when applying GenAI within manufacturing contexts. These implications are discussed below.

I1. Sustainability and Supply Chain: Realising the benefits of GenAI-supported applications in manufacturing requires sustainable (economic, social, and environmental) and scalable solutions across the entire manufacturing supply chain, as well as across the GenAI value chain. Overcoming both types of barriers to sustainability is essential to achieving a lasting impact (Gholami, 2024).

I2. Quality and Reliability: While GenAI enhances decision support, ensuring trustworthy, transparent, quality-based, and reliable decision-making remains a priority (Zhu et al., 2024).

I3. Technical Challenges and Limitations: Issues, such as data quality, algorithmic accuracy, computational requirements, and cybersecurity, limit the effectiveness of GenAI. Complexities related to implementation, scalability, and adaptation to diverse workflows also present ongoing obstacles (Mustapha, 2025).

I4. Efficiency and Performance: GenAI promises significant gains in efficiency, automation, and productivity. However, challenges remain in scaling these technologies and integrating them seamlessly into existing systems. Achieving reliable accuracy, cost-effectiveness, and customisation is essential for realising full benefits (Mustapha, 2025).

I5. Human and Ethical Aspects: GenAI adoption raises ethical concerns about job displacement, privacy, and transparency in decision-making. Ensuring human oversight, supporting workforce adaptation, and safeguarding well-being are critical for responsible implementation (Doanh et al., 2023).

I6. Specialisation and Domain-Specific Aspects: It is necessary to develop GenAI applications and models based on specific training for each discipline, which requires a high degree of specialisation, moving away from generalist approaches to GenAI. The effective use of GenAI depends on specialised and domain-focused robust data (Vrochidou et al., 2025; Xia et al., 2024a).

Table 4 analyses these applications to demonstrate their relationship with the previously identified dimensions.

The implications of these dimensions are multifaceted. GenAI adoption in manufacturing is linked to substantial gains in efficiency and performance; however, it also introduces new, technical, organisational, and ethical challenges and limitations. Human and ethical considerations are becoming increasingly prominent, reflecting a shift towards more human-centric and sustainable manufacturing paradigms. Technical and domain-specific aspects such as reliability and quality remain critical for successful implementation. Furthermore, AI can drive sustainability and optimise supply chains, although it also creates new sustainability concerns regarding GenAI use, which must be managed.

Analysis of the interconnections between the aggregated dimensions and the identified implications demonstrates that all aggregated dimensions play a critical and complementary role in addressing the challenges and limitations of GenAI applications in manufacturing. The overall pattern (Figure 2) indicates that no single dimension can be considered in isolation. Effective GenAI adoption requires an integrated approach that simultaneously considers technical, organisational, and strategic dimensions to manage complexities and maximise impact.

5. Conclusions

This study systematically explored the transformative role of GenAI in manufacturing, guided by four central research questions: (1) What are the fundamental GenAI dimensions for manufacturing companies? (2) What are the key areas of application? (3) What are the associated challenges and implications? And (4) How does GenAI support human-centric, sustainable, and resilient approaches associated with Industry 5.0?

First, our analysis identifies 11 core dimensions (processes, data, knowledge, automation, people, strategy, optimisation, innovation, technology, decision, and efficiency) that collectively define the foundational landscape for GenAI adoption in manufacturing. These dimensions serve as conceptual pillars that structure how GenAI technologies are integrated and leveraged within manufacturing organisations.

Second, the research reveals 13 key application areas in which GenAI is already demonstrating a significant impact. These include manufacturing processes and operations, data and knowledge management, automation and efficiency, predictive and diagnostic applications, digital and intelligent systems, synthetic data generation for AI/ML, information and communication, human-machine interaction, strategic and marketing applications, optimisation and performance, innovation and customisation, technical and domain-specific aspects, and quality and reliability. This breadth of application underscores GenAI’s versatility and capacity to drive both operational excellence and strategic transformation.

Third, the study systematically maps the challenges and implications associated with GenAI adoption. Six principal implication themes emerge: efficiency and performance; technical challenges and limitations; human and ethical aspects; specialisation and domain-specific requirements; quality and reliability; and sustainability and supply chain. These findings highlight that, while GenAI offers substantial opportunities for value creation, its successful implementation requires careful attention to integration complexity, data quality, workforce adaptation, ethical considerations, and the development of robust, domain-specific solutions.

Finally, the research concludes that GenAI acts as a fundamental bridge between the efficiency of Industry 4.0 and the value-based objectives of Industry 5.0. The human-centred approach of Industry 5.0 is made possible by GenAI´s ability to go beyond simple automation and achieve “augmentation”, in which GenAI supports DIAs by creating personalised interfaces that reduce cognitive load and empower the workforce. Business sustainability, another key element of Industry 5.0, is supported by GenAI not only through operational optimisation but also by leveraging GenAI´s capabilities to manage complex sustainability data and promote supply chain transparency and circularity. Furthermore, the resilience of Industry 5.0 is enhanced by the integration of GenAI with DTs and predictive diagnostics, enabling manufacturing systems to be reconfigurable and adapt to global disruptions. By aligning the 11 dimensions identified in the research with these three pillars of Industry 5.0, this paper reinforces the idea that GenAI is a key enabler for a more responsible and robust manufacturing future.

Overall, this research provides an integrative framework that clarifies the dimensions, applications, and implications of GenAI in manufacturing. By explicitly connecting these findings to the research questions, this study offers both theoretical insights and practical guidance for managers, policymakers, and researchers seeking to leverage responsible and sustainable GenAI in manufacturing.

5.1. Theoretical and practical implications

The findings have important implications for both managers and policymakers. For manufacturing firm managers, the results highlight strategic priorities associated with the implications and areas of application of GenAI in manufacturing, which are essential for GenAI-based competitiveness in dynamic manufacturing environments. The study also underscores that the adoption of advanced GenAI-driven strategies must be accompanied by broader changes in data, ethical concerns, knowledge management, decision-making and so on, which requires a level of management maturity that is critical for successful GenAI implementation.

This study identified clear priorities for policymakers and intermediary organisations to support manufacturing companies. The analysis reinforces the importance of helping manufacturing companies integrate GenAI into their processes and organisations to become more competitive in an exponentially evolving AI environment. It also reveals the need for governments to develop support policies segmented according to management maturity (dimensions) and strategic orientation of firms (application areas and implications) to ensure that interventions are both targeted and effective.

5.2. Limitations and guidelines for future research

Although this research provides valuable insights into the influence and application of GenAI in manufacturing, some limitations should be acknowledged. First, the study is based on a systematic literature review and qualitative analysis using ATLAS.ti and the Gioia methodology (Gioia et al., 2013), which inherently rely on the interpretation and synthesis of published research rather than on primary empirical data. Second, the analysis was limited to 22 articles, reflecting the relatively recent emergence and rapid evolution of GenAI in manufacturing. New research papers and innovative applications are rapidly emerging, which may outdate some of the conclusions drawn here. The dynamic nature of GenAI technologies and their applications in manufacturing necessitates the continuous monitoring and updating of research findings.

Future studies should focus on developing robust strategies and frameworks to understand the strategic role and scalability of GenAI and address emerging challenges related to the management of the portfolio of GenAI initiatives, their integration, and ethics and governance. Comparative research across different business sectors and geographical regions would provide a broader perspective on strategic priorities and best practices. Case studies could further illuminate the long-term impacts of GenAI adoption while exploring diverse industrial and economic contexts. Finally, future research should expand the scope of analysis, incorporate a larger and more diverse set of studies, and consider complementary empirical approaches to further validate and enrich the understanding of GenAI’s impact on manufacturing.

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APPENDIX

_______________________________

¹ Mechanical and Industrial Production Dept., Mondragon University, Mondragon, Spain. Email: aarreguil@mondragon.edu ORCID: 0009-0000-8565-5521

² Mechanical and Industrial Production Dept., Mondragon University, Mondragon, Spain. Email: jretegi@mondragon.edu ORCID: 0000-0002-7039-4629

³ Mechanical and Industrial Production Dept., Mondragon University, Mondragon, Spain. Email: jigartua@mondragon.edu ORCID: 0000-0001-5953-0274

⁴ Mechanical and Industrial Production Dept., Mondragon University, Mondragon, Spain. Email: dibarra@mondragon.edu ORCID: 0000-0001-7819-4093