Generative AI takes centre stage: revolutionizing productivity and reshaping industries

• Autorzy: Sira Mariya

Identyfikatory

DOI

10.2478/czoto-2023-0007

• Języki publikacji: EN

Abstrakty

EN

The growing prominence of Generative AI in discussions on artificial intelligence has significant implications for productivity and industry dynamics. This article aims to examine the transformative role of Generative AI, specifically focusing on its revolutionary impact on productivity and its influence on various industries. The objectives of this article include conducting a detailed analysis of how systems have greatly enhanced efficiency for developers and knowledge workers. By examining both the positive and negative aspects of the Generative AI movement, this article aims to provide valuable insights into the innovations driven by Generative AI and the advancements that contribute to its evolution. Through this exploration, the goal is to offer a comprehensive understanding of the current landscape, highlighting the opportunities and challenges presented by the rise of Generative AI in the management sphere.

Słowa kluczowe

EN

AI; opportunities; challenge; managerial approach

PL

sztuczna inteligencja; podejście menedżerskie; rozwój sztucznej inteligencji; innowacyjne metody

• Wydawca: De Gruyter
• Czasopismo: System Safety : Human - Technical Facility - Environment
• Rocznik: 2023
• Tom: Vol. 5, iss. 1
• Strony: 57--65
• Opis fizyczny: Bibliogr. 28 poz., tab.

Twórcy

autor

Sira Mariya

• Department of Applied Social Sciences, Faculty of Organization and Management, Silesian University of Technology, Poland

• https://orcid.org/0000-0002-9970-1549

Bibliografia

• 1. Åström, J., 2020. Value creation and value capture in AI offerings A process framework on business model development, https://www.diva-portal.org/smash/get/ diva2:1441414/FULLTEXT01.pdf
• 2. Bilan, S., Šuleř, P., Skrynnyk, O., Krajňáková, E., Vasilyeva, T., 2022. Systematic Bibliometric Review of Artificial Intelligence Technology in Organizational Management, Development, Change and Culture. Business: Theory and Practice, 2022, 23(1), pp. 1-13, DOI: 10.3846/btp.2022.13204
• 3. Cognite, 2023. The Definitive Guide to Generative AI for Industry, www.cognite.com. https://www.cognite.com/en/resources/guides/the-definitive-guide-to-generative-ai-for-industry
• 4. Fobel, P., Kuzior, A., 2019. The future (Industry 4.0) is closer than we think. Will it also be ethical? AIP Conference Proceedings, 2019, 2186, 080003, DOI: 10.1063/1.5137987
• 5. Ingaldi, M., Klimecka-Tatar, D., 2022. Digitization of the service provision process - Requirements and readiness of the small and medium-sized enterprise sector, Procedia Computer Science, 200, 237-246, DOI: 10.1016/j.procs.2022.01.222.
• 6. Klimecka-Tatar, D., Ingaldi, M., 2022. Digitization of processes in manufacturing SMEs - Value stream mapping and OEE analysis, Procedia Computer Science, 200, 660-668, DOI: 10.1016/j.procs.2022.01.264
• 7. Ikechukwu, S., 2023. Harnessing the Power of Generative AI to Advance Industry 4.0 Solutions, StevenSquare. https://stephenikechukwu.com/2023/08/07/harnessing-the-power-of-generative-ai-to-advance-industry-4-0-solutions/
• 8. Kemendi, Á., Michelberger, P., Mesjasz-Lech, A. 2022. Industry 4.0 and 5.0–organizational and competency challenges of enterprises, Polish Journal of Management Studies, 26(2), 209-232. DOI: 10.17512/pjms.2022.26.2.13
• 9. Kuzior, A., Kettler, K., Rąb, Ł., 2021. Digitalization of Work and Human Resources Processes as a Way to Create a Sustainable and Ethical Organization, Energies, 15(1), 172, DOI: 10.3390/en15010172
• 10. Kuzior, A., Kwilinski, A., 2022. Cognitive Technologies and Artificial Intelligence in Social Perception, Management Systems in Production Engineering, 30(2), 109-115, DOI: 10.2478/mspe-2022-0014
• 11. Kuzior, A., Kwilinski, A., Tkachenko, V., 2019. Sustainable development of organizations based on the combinatorial model of artificial intelligence, Entrepreneurship and Sustainability Issues, 7(2), 1353-1376, DOI: 10.9770/jesi.2019.7.2(39)
• 12. Kwilinski, A., Tkachenko, V., Kuzior, A., 2019. Transparent cognitive technologies to ensure sustainable society development, Journal of Security and Sustainability Issues, 9(2), 561-570, DOI: 10.9770/jssi.2019.9.2(15)
• 13. LABS, x]cube, 2023, June 21. Harnessing Generative AI in Agriculture, [X]Cube LABS. https://www.xcubelabs.com/blog/harnessing-generative-ai-in-agriculture-a-game-changer-for-agri-tech-growth/
• 14. Mittal, N., Perricos, C., Sterrett, L., Dutt, D., 2023. The Generative AI Dossier A selection of high-impact use cases across six major industries. https://www2.deloitte.com/ content/dam/Deloitte/us/Documents/consulting/us-ai-institute-gen-ai-use-cases.pdf
• 15. Orchard, T. J., Tasiemski, L., 2023. The rise of generative AI and possible effects on the economy, The Poznań University of Economics Review, 9(2), DOI: 10.18559/ebr.2023.2.732
• 16. Pattam, A., 2023. Generative AI Applications: Episode #10: In Agriculture. Arunapattam. https://medium.com/arunapattam/generative-ai-applications-episode-10-in-agriculture-4ac24b6da8ea
• 17. Pietraszek, J., Skrzypczak-Pietraszek, E., 2015. The uncertainty and robustness of the principal component analysis as a tool for the dimensionality reduction, Solid State Phenomena 235, 1-8. DOI: 10.4028/www.scientific.net/SSP.235.1
• 18. Pietraszek, J., Szczotok, A., Kołomycki, M., Radek, N., Kozień, E. 2017a. Non-parametric assessment of the uncertainty in the analysis of the airfoil blade traces, In METAL 2017 - 26th International Conference on Metallurgy and Materials, Conference Proceedings, Ostrava, Tanger, 1412-1418.
• 19. Pietraszek, J., Szczotok, J., Radek, N., 2017b. The fixed-effects analysis of the relation between SDAS and carbides for the airfoil blade traces, Archives of Metallurgy and Materials 62(1), 235-239. DOI: 10.1515/amm-2017-0035
• 20. Radek, N., Pietraszek, J., Antoszewski, B., 2014. The average friction coefficient of laser textured surfaces of silicon carbide identified by RSM methodology, Advanced Material Research 874, 29-34. DOI: 10.4028/www.scientific.net/AMR.874.29
• 21. Radek, N., Tokar, D., Kalinowski, A., Pietraszek, J., 2021. Influence of laser texturing on tribological properties of DLC coatings, Production Engineering Archives 27(2), 119-123. DOI: 10.30657/pea.2021.27.15
• 22. Rizzoli, A., 2021. AI in Agriculture: 8 Practical Applications [2023 Update], www.v7labs.com. https://www.v7labs.com/blog/ai-in-agriculture#h3
• 23. Skrzypczak-Pietraszek, E., Pietraszek, J., 2014. Seasonal changes of flavonoid content in Melittis melissophyllum L. (Lamiaceae), Chemistry and Biodiversity 11(4), 562-570. DOI: 10.1002/cbdv.201300148
• 24. SoftServe., 2023. Agriculture innovation with Generative AI, https://info.softserveinc.com/ hubfs/files/generative-ai/softserve-gen-ai-agriculture.pdf
• 25. Sujová, E., Bambura, R., Vysloužilová, D., Koleda, P., 2023. Use of the digital twin concept to optimize the production process of engine blocks manufacturing, Production Engineering Archives, 29(2), 168-174, DOI: 10.30657/pea.2023.29.20
• 26. Stareček, A., Babeľová, Z.G., Vraňaková, N., Jurík, L. 2023; The impact of Industry 4.0 implementation on required general competencies of employees in the automotive sector, Production Engineering Archives, 29(3), 254-262
• 27. Ulewicz, R., Krstić, B., Ingaldi, M. 2022. Mining Industry 4.0 – Opportunities and Barriers, Acta Montanistica Slovaca, 27(2), 291-305
• 28. Usmani, H., 2023. Unleashing the Potential of Generative AI in Agriculture: Applications and Benefits, Medium. https://medium.com/@hishamusmani/unleashing-the-potential-of-generative-ai-in-agriculture-applications-and-benefits-43fe9a2a1f69#2-how-does-generative-ai-enhance-precision-crop-management