Company perspectives of generative artificial intelligence in industrial work
The use of artificial intelligence (AI) technologies in the manufacturing industry is rapidly increasing. During this transformation, it can be difficult to understand how AI will change the way work is done. This study explores how generative AI could change manufacturing work. Data collection was...
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| Veröffentlicht in: | Procedia computer science Jg. 253; S. 217 - 226 |
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2025
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| Abstract | The use of artificial intelligence (AI) technologies in the manufacturing industry is rapidly increasing. During this transformation, it can be difficult to understand how AI will change the way work is done. This study explores how generative AI could change manufacturing work. Data collection was conducted using interviews and a questionnaire with seven representatives from three industrial companies. They identified several application areas for GenAI in the industrial work context, such as design, planning, training, problem solving, coding and data management. They also expressed positive attitudes but raised concerns about trust, safety, acceptability and interoperability. Changes in work were identified as being more related to cognitive aspects such as changing the way of thinking and altering the interaction with people and machines. Therefore, human-AI design efforts should focus especially on cognitive ergonomics. Findings from this study can be used in the manufacturing industry when adopting AI, as well as in identifying research topics in the human-AI research community. |
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| AbstractList | The use of artificial intelligence (AI) technologies in the manufacturing industry is rapidly increasing. During this transformation, it can be difficult to understand how AI will change the way work is done. This study explores how generative AI could change manufacturing work. Data collection was conducted using interviews and a questionnaire with seven representatives from three industrial companies. They identified several application areas for GenAI in the industrial work context, such as design, planning, training, problem solving, coding and data management. They also expressed positive attitudes but raised concerns about trust, safety, acceptability and interoperability. Changes in work were identified as being more related to cognitive aspects such as changing the way of thinking and altering the interaction with people and machines. Therefore, human-AI design efforts should focus especially on cognitive ergonomics. Findings from this study can be used in the manufacturing industry when adopting AI, as well as in identifying research topics in the human-AI research community. |
| Author | Aromaa, Susanna Jurvansuu, Marko Heikkilä, Päivi Väärä, Teijo Jurmu, Marko Pehlivan, Selen |
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| Keywords | manufacturing company perspective industry work Generative artificial intelligence human factors ergonomics |
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| References | Jobin, Anna, Marcello Ienca and Effy Vayena. (2019) “The global landscape of AI ethics guidelines.” Kagermann, Henning, Wolfgang Wahlster and Johannes Helbig. (2013) Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0: securing the future of German manufacturing industry. 82. Institute, Future of Life. (2023). “Policymaking in the pause - What can policymakers do now to combat risks from advanced AI systems?” Retrieved from 30-40. Larsson, Stefan and Fredrik Heintz. (2020) “Transparency in artificial intelligence.” 277–304. Murphy, Kevin P. (2022) Probabilistic machine learning: an introduction. 389–399. 117–135. Joskowicz, Jose and Daniel Slomovitz. (2024) “Engineers’ Perspectives on the Use of Generative Artificial Intelligence Tools in the Workplace.” Bender, Emily M, Timnit Gebru, Angelina McMillan-Major and Shmargaret Shmitchell. (2021) “On the dangers of stochastic parrots: Can language models be too big?” Zhou, Ji, Yanhong Zhou, Baicun Wang and Jiyuan Zang. (2019) “Human–cyber–physical systems (HCPSs) in the context of new-generation intelligent manufacturing.” 336–378. Chignell, Mark, Lu Wang, Atefeh Zare and Jamy Li. (2023) “The evolution of HCI and human factors: Integrating human and artificial intelligence.” Heikkilä, Päivi, Susanna Aromaa, Hanna Lammi and Timo Kuula. (2023) “Framework of Future Industrial Worker Characteristics.” ) Lim, Weng Marc, Asanka Gunasekara, Jessica Leigh Pallant, Jason Ian Pallant and Ekaterina Pechenkina. (2023) “Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators.” 1–32. 624-636. 52138–52160. 220121–220139. 334-354. Karvonen, Hannu, Eetu Heikkilä and Mikael Wahlström. (2019) “Artificial intelligence awareness in work environments.” Peres, Ricardo Silva, Xiaodong Jia, Jay Lee, Keyi Sun, Armando Walter Colombo and Jose Barata. (2020) “Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook.” Salmon, Paul M, Chris Baber, Catherine Burns, Tony Carden, Nancy Cooke, Missy Cummings, Peter Hancock, Scott McLean, Gemma JM Read and Neville A Stanton. (2023) “Managing the risks of artificial general intelligence: A human factors and ergonomics perspective.” 47–53. European Commission, EU. (2021) Proposal for a regulation of the European parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. 258–267. 102642. Siau, Keng and Weiyu Wang. (2018) “Building trust in artificial intelligence, machine learning, and robotics.” Malik, Nishtha, Shalini Nath Tripathi, Arpan Kumar Kar and Shivam Gupta. (2021) “Impact of artificial intelligence on employees working in industry 4.0 led organizations.” Huang, Sihan, Baicun Wang, Xingyu Li, Pai Zheng, Dimitris Mourtzis and Lihui Wang. (2022) “Industry 5.0 and Society 5.0—Comparison, complementation and co-evolution.” Espoo, Finland: 175–185. Fui-Hoon Nah, Fiona, Ruilin Zheng, Jingyuan Cai, Keng Siau and Langtao Chen. (2023) “Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration.” 627–660. Elo, Satu and Helvi Kyngäs. (2008) “The qualitative content analysis process.” 659–689. Banh, Leonardo and Gero Strobel. (2023) “Generative artificial intelligence.” 1–7. European Commission, EU. (2019) Ethics Guidelines for Trustworthy AI. Dwivedi, Yogesh K, Nir Kshetri, Laurie Hughes, Emma Louise Slade, Anand Jeyaraj, Arpan Kumar Kar, Abdullah M Baabdullah, Alex Koohang, Vishnupriya Raghavan and Manju Ahuja. (2023) ““So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy.” 3333–3361. 107–115. 1–30. 424-428. 100790. Chien, Andrew A, Liuzixuan Lin, Hai Nguyen, Varsha Rao, Tristan Sharma and Rajini Wijayawardana. (2023) “Reducing the Carbon Impact of Generative AI Inference (today and in 2035).” Felzmann, Heike, Eduard Fosch-Villaronga, Christoph Lutz and Aurelia Tamò-Larrieux. (2020) “Towards transparency by design for artificial intelligence.” 1-17. 160–185. Gladysz, Bartlomiej, Tuan-anh Tran, David Romero, Tim van Erp, János Abonyi and Tamás Ruppert. (2023) “Current development on the Operator 4.0 and transition towards the Operator 5.0: A systematic literature review in light of Industry 5.0.” Einola, Katja and Violetta Khoreva. (2023) “Best friend or broken tool? Exploring the co‐existence of humans and artificial intelligence in the workplace ecosystem.” Gozalo-Brizuela, Roberto and Eduardo C Garrido-Merchan. (2023) “ChatGPT is not all you need. A State of the Art Review of large Generative AI models.” Glikson, Ella and Anita Williams Woolley. (2020) “Human trust in artificial intelligence: Review of empirical research.” 610–623. Ooi, Keng-Boon, Garry Wei-Han Tan, Mostafa Al-Emran, Mohammed A Al-Sharafi, Alexandru Capatina, Amrita Chakraborty, Yogesh K Dwivedi, Tzu-Ling Huang, Arpan Kumar Kar and Voon-Hsien Lee. (2023) “The potential of generative artificial intelligence across disciplines: perspectives and future directions.” Chui, Michael, Vishnu Kamalnath and Brian McCarthy. (2020). “An Executive’s Guide to AI.” Retrieved from Adadi, Amina and Mohammed Berrada. (2018) “Peeking inside the black-box: a survey on explainable artificial intelligence (XAI).” Kar, Arpan Kumar, PS Varsha and Shivakami Rajan. (2023) “Unravelling the impact of generative artificial intelligence (GAI) in industrial applications: A review of scientific and grey literature.” Breque, Maija, Lars De Nul and Athanasios Petridis. (2021) Industry 5.0: Towards a sustainable, human-centric and resilient European industry. 10.1016/j.procs.2025.01.085_bib11 10.1016/j.procs.2025.01.085_bib33 10.1016/j.procs.2025.01.085_bib12 10.1016/j.procs.2025.01.085_bib34 10.1016/j.procs.2025.01.085_bib13 10.1016/j.procs.2025.01.085_bib14 10.1016/j.procs.2025.01.085_bib15 10.1016/j.procs.2025.01.085_bib16 10.1016/j.procs.2025.01.085_bib17 10.1016/j.procs.2025.01.085_bib18 10.1016/j.procs.2025.01.085_bib4 10.1016/j.procs.2025.01.085_bib5 10.1016/j.procs.2025.01.085_bib6 10.1016/j.procs.2025.01.085_bib7 10.1016/j.procs.2025.01.085_bib1 10.1016/j.procs.2025.01.085_bib30 10.1016/j.procs.2025.01.085_bib2 10.1016/j.procs.2025.01.085_bib31 10.1016/j.procs.2025.01.085_bib3 10.1016/j.procs.2025.01.085_bib10 10.1016/j.procs.2025.01.085_bib32 10.1016/j.procs.2025.01.085_bib22 10.1016/j.procs.2025.01.085_bib23 10.1016/j.procs.2025.01.085_bib24 10.1016/j.procs.2025.01.085_bib25 10.1016/j.procs.2025.01.085_bib26 10.1016/j.procs.2025.01.085_bib27 10.1016/j.procs.2025.01.085_bib28 10.1016/j.procs.2025.01.085_bib29 10.1016/j.procs.2025.01.085_bib20 10.1016/j.procs.2025.01.085_bib21 10.1016/j.procs.2025.01.085_bib8 10.1016/j.procs.2025.01.085_bib9 10.1016/j.procs.2025.01.085_bib19 |
| References_xml | – reference: ): 277–304. – reference: Ooi, Keng-Boon, Garry Wei-Han Tan, Mostafa Al-Emran, Mohammed A Al-Sharafi, Alexandru Capatina, Amrita Chakraborty, Yogesh K Dwivedi, Tzu-Ling Huang, Arpan Kumar Kar and Voon-Hsien Lee. (2023) “The potential of generative artificial intelligence across disciplines: perspectives and future directions.” – reference: Gladysz, Bartlomiej, Tuan-anh Tran, David Romero, Tim van Erp, János Abonyi and Tamás Ruppert. (2023) “Current development on the Operator 4.0 and transition towards the Operator 5.0: A systematic literature review in light of Industry 5.0.” – reference: ): 334-354. – reference: ): 107–115. – reference: Adadi, Amina and Mohammed Berrada. (2018) “Peeking inside the black-box: a survey on explainable artificial intelligence (XAI).” – reference: Chui, Michael, Vishnu Kamalnath and Brian McCarthy. (2020). “An Executive’s Guide to AI.” Retrieved from – reference: ): 100790. – reference: ): 627–660. – reference: ): 117–135. – reference: Bender, Emily M, Timnit Gebru, Angelina McMillan-Major and Shmargaret Shmitchell. (2021) “On the dangers of stochastic parrots: Can language models be too big?” – reference: ): 47–53. – reference: 220121–220139. – reference: 102642. – reference: Felzmann, Heike, Eduard Fosch-Villaronga, Christoph Lutz and Aurelia Tamò-Larrieux. (2020) “Towards transparency by design for artificial intelligence.” – reference: ): 258–267. – reference: ): 659–689. – reference: Glikson, Ella and Anita Williams Woolley. (2020) “Human trust in artificial intelligence: Review of empirical research.” – reference: Fui-Hoon Nah, Fiona, Ruilin Zheng, Jingyuan Cai, Keng Siau and Langtao Chen. (2023) “Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration.” – reference: Chignell, Mark, Lu Wang, Atefeh Zare and Jamy Li. (2023) “The evolution of HCI and human factors: Integrating human and artificial intelligence.” – reference: Breque, Maija, Lars De Nul and Athanasios Petridis. (2021) Industry 5.0: Towards a sustainable, human-centric and resilient European industry. – reference: Institute, Future of Life. (2023). “Policymaking in the pause - What can policymakers do now to combat risks from advanced AI systems?” Retrieved from – reference: Dwivedi, Yogesh K, Nir Kshetri, Laurie Hughes, Emma Louise Slade, Anand Jeyaraj, Arpan Kumar Kar, Abdullah M Baabdullah, Alex Koohang, Vishnupriya Raghavan and Manju Ahuja. (2023) ““So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy.” – reference: Salmon, Paul M, Chris Baber, Catherine Burns, Tony Carden, Nancy Cooke, Missy Cummings, Peter Hancock, Scott McLean, Gemma JM Read and Neville A Stanton. (2023) “Managing the risks of artificial general intelligence: A human factors and ergonomics perspective.” – reference: Larsson, Stefan and Fredrik Heintz. (2020) “Transparency in artificial intelligence.” – reference: Kagermann, Henning, Wolfgang Wahlster and Johannes Helbig. (2013) Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0: securing the future of German manufacturing industry. 82. – reference: Karvonen, Hannu, Eetu Heikkilä and Mikael Wahlström. (2019) “Artificial intelligence awareness in work environments.” – reference: Einola, Katja and Violetta Khoreva. (2023) “Best friend or broken tool? Exploring the co‐existence of humans and artificial intelligence in the workplace ecosystem.” – reference: ): 389–399. – reference: Malik, Nishtha, Shalini Nath Tripathi, Arpan Kumar Kar and Shivam Gupta. (2021) “Impact of artificial intelligence on employees working in industry 4.0 led organizations.” – reference: 424-428. – reference: 1–7. – reference: ): 1-17. – reference: Siau, Keng and Weiyu Wang. (2018) “Building trust in artificial intelligence, machine learning, and robotics.” – reference: ): 1–30. – reference: 160–185. – reference: Joskowicz, Jose and Daniel Slomovitz. (2024) “Engineers’ Perspectives on the Use of Generative Artificial Intelligence Tools in the Workplace.” – reference: ): 3333–3361. – reference: Murphy, Kevin P. (2022) Probabilistic machine learning: an introduction. – reference: Espoo, Finland: 175–185. – reference: Zhou, Ji, Yanhong Zhou, Baicun Wang and Jiyuan Zang. (2019) “Human–cyber–physical systems (HCPSs) in the context of new-generation intelligent manufacturing.” – reference: 30-40. – reference: ( – reference: 52138–52160. – reference: Heikkilä, Päivi, Susanna Aromaa, Hanna Lammi and Timo Kuula. (2023) “Framework of Future Industrial Worker Characteristics.” – reference: Chien, Andrew A, Liuzixuan Lin, Hai Nguyen, Varsha Rao, Tristan Sharma and Rajini Wijayawardana. (2023) “Reducing the Carbon Impact of Generative AI Inference (today and in 2035).” – reference: Huang, Sihan, Baicun Wang, Xingyu Li, Pai Zheng, Dimitris Mourtzis and Lihui Wang. (2022) “Industry 5.0 and Society 5.0—Comparison, complementation and co-evolution.” – reference: Elo, Satu and Helvi Kyngäs. (2008) “The qualitative content analysis process.” – reference: ): – reference: European Commission, EU. (2019) Ethics Guidelines for Trustworthy AI. – reference: Gozalo-Brizuela, Roberto and Eduardo C Garrido-Merchan. (2023) “ChatGPT is not all you need. A State of the Art Review of large Generative AI models.” – reference: Peres, Ricardo Silva, Xiaodong Jia, Jay Lee, Keyi Sun, Armando Walter Colombo and Jose Barata. (2020) “Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook.” – reference: Jobin, Anna, Marcello Ienca and Effy Vayena. (2019) “The global landscape of AI ethics guidelines.” – reference: 1–32. – reference: ): 624-636. – reference: Banh, Leonardo and Gero Strobel. (2023) “Generative artificial intelligence.” – reference: ): 336–378. – reference: Lim, Weng Marc, Asanka Gunasekara, Jessica Leigh Pallant, Jason Ian Pallant and Ekaterina Pechenkina. (2023) “Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators.” – reference: European Commission, EU. (2021) Proposal for a regulation of the European parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. – reference: 610–623. – reference: Kar, Arpan Kumar, PS Varsha and Shivakami Rajan. (2023) “Unravelling the impact of generative artificial intelligence (GAI) in industrial applications: A review of scientific and grey literature.” – ident: 10.1016/j.procs.2025.01.085_bib20 doi: 10.1007/s12525-023-00680-1 – ident: 10.1016/j.procs.2025.01.085_bib16 doi: 10.1038/s42256-019-0088-2 – ident: 10.1016/j.procs.2025.01.085_bib26 doi: 10.1016/j.ijinfomgt.2023.102642 – ident: 10.1016/j.procs.2025.01.085_bib7 doi: 10.1002/hfm.20996 – ident: 10.1016/j.procs.2025.01.085_bib21 doi: 10.1080/15228053.2023.2233814 – ident: 10.1016/j.procs.2025.01.085_bib2 – ident: 10.1016/j.procs.2025.01.085_bib25 doi: 10.1111/j.1365-2648.2007.04569.x – ident: 10.1016/j.procs.2025.01.085_bib4 – ident: 10.1016/j.procs.2025.01.085_bib12 doi: 10.1109/ACCESS.2018.2870052 – ident: 10.1016/j.procs.2025.01.085_bib29 doi: 10.54941/ahfe1002927 – ident: 10.1016/j.procs.2025.01.085_bib22 doi: 10.1108/IJM-03-2021-0173 – ident: 10.1016/j.procs.2025.01.085_bib5 doi: 10.1145/3442188.3445922 – ident: 10.1016/j.procs.2025.01.085_bib14 doi: 10.14763/2020.2.1469 – ident: 10.1016/j.procs.2025.01.085_bib23 doi: 10.1007/s40171-023-00356-x – ident: 10.1016/j.procs.2025.01.085_bib27 – ident: 10.1016/j.procs.2025.01.085_bib9 doi: 10.1007/978-3-030-05297-3_12 – ident: 10.1016/j.procs.2025.01.085_bib30 – ident: 10.1016/j.procs.2025.01.085_bib6 – ident: 10.1016/j.procs.2025.01.085_bib17 doi: 10.1080/08874417.2023.2261010 – ident: 10.1016/j.procs.2025.01.085_bib1 – ident: 10.1016/j.procs.2025.01.085_bib28 doi: 10.1016/j.eng.2019.07.015 – ident: 10.1016/j.procs.2025.01.085_bib31 doi: 10.1016/j.jmsy.2023.07.008 – ident: 10.1016/j.procs.2025.01.085_bib15 – ident: 10.1016/j.procs.2025.01.085_bib11 – ident: 10.1016/j.procs.2025.01.085_bib34 doi: 10.1145/3604930.3605705 – ident: 10.1016/j.procs.2025.01.085_bib19 doi: 10.1002/hrm.22147 – ident: 10.1016/j.procs.2025.01.085_bib24 – ident: 10.1016/j.procs.2025.01.085_bib13 doi: 10.1007/s11948-020-00276-4 – ident: 10.1016/j.procs.2025.01.085_bib3 doi: 10.1016/j.ijme.2023.100790 – ident: 10.1016/j.procs.2025.01.085_bib33 doi: 10.5465/annals.2018.0057 – ident: 10.1016/j.procs.2025.01.085_bib10 doi: 10.1109/EMR.2023.3333794 – ident: 10.1016/j.procs.2025.01.085_bib32 doi: 10.1016/j.jmsy.2022.07.010 – ident: 10.1016/j.procs.2025.01.085_bib8 doi: 10.1145/3557891 – ident: 10.1016/j.procs.2025.01.085_bib18 doi: 10.1109/ACCESS.2020.3042874 |
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| SubjectTerms | company perspective ergonomics Generative artificial intelligence human factors industry work manufacturing |
| Title | Company perspectives of generative artificial intelligence in industrial work |
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