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İş Hayatında Üretken Yapay Zekâ: Dönüşümün Eşiğinde Sistematik Bir İnceleme

Year 2024, Volume: 5 Issue: 2, 156 - 175, 20.12.2024
https://doi.org/10.58769/joinssr.1597110

Abstract

Bu sistematik inceleme, bilgi teknolojisi, eğitim, üretim, yaratıcı endüstriler, sağlık, ulaşım, yönetim, pazarlama, finans, enerji, hukuk, medya, tarım ve e-ticaret dahil olmak üzere çeşitli sektörlerde Generative Artificial Intelligence'ın (GenAI) dönüştürücü potansiyelini inceler. Çalışma, uygulamalarını analiz ederek GenAI'nın verimliliği nasıl artırdığını, inovasyonu nasıl teşvik ettiğini ve sektöre özgü zorlukları nasıl ele aldığını vurgular. Temel avantajlar arasında karmaşık süreçlerin otomasyonu, kaynak kullanımının optimizasyonu ve karar alma sürecinin hızlandırılması yer alır. Ancak, işgücü yer değiştirmesi ve etik ikilemler gibi gecikmiş benimseme riskleri de tartışılmaktadır. İncelemede veri gizliliği endişeleri, algoritmik önyargı ve düzenleyici zorluklar gibi kritik engeller de tanımlanmaktadır.
Başarılı GenAI entegrasyonu için pratik stratejiler araştırılmakta ve altyapı hazırlığı, işgücü becerilerinin geliştirilmesi ve etik yönetişim vurgulanmaktadır. Bu, Generative Adversarial Networks (GAN'lar), Transformer tabanlı modeller, Variational Autoencoders (VAE'ler) ve sektöre özgü taleplere uyum sağlamak için difüzyon modelleri gibi generatif modellerden yararlanmayı içerir. Ayrıca, çalışma, riskleri en aza indirmek ve toplumsal faydaları en üst düzeye çıkarmak için teknolojik ilerlemeleri sorumlu AI dağıtımıyla dengelemenin gerekliliğini vurgular.
Mevcut araştırmaları sentezleyerek, bu inceleme, GenAI'nin dönüştürücü yeteneklerinden sorumlu bir şekilde yararlanmayı amaçlayan paydaşlar için eyleme geçirilebilir içgörüler sağlar. Hızla gelişen pazarlarda rekabet gücünü ve sürdürülebilirliği korumak için GenAI teknolojilerini benimsemenin aciliyetini vurgular. Çalışmanın sonucuna göre, bu paradigmayı değiştiren teknolojinin ortaya koyduğu karmaşık zorlukları ele almak için sektörler arası iş birliğini savunur ve inovasyonu etik ilkeler ve toplumsal değerlerle uyumlu hale getirmek için uyarlanabilir politikalar çağrısında bulunur.

References

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  • [26] N. Rane, S. Choudhary, and J. Rane, “Integrating ChatGPT, Bard, and leading-edge generative artificial intelligence in building and construction industry: applications, framework, challenges, and future scope,” SSRN Electronic Journal, 2023, doi: 10.2139/ssrn.4645597.
  • [27] J. Rane, Ö. Kaya, S. K. Mallick, and N. L. Rane, “Artificial general intelligence in industry 4.0, 5.0, and society 5.0: Applications, opportunities, challenges, and future direction,” in Future Research Opportunities for Artificial Intelligence in Industry 4.0 and 5.0, Deep Science Publishing, 2024. doi: 10.70593/978-81-981271-0-5_6.
  • [28] I. Jackson, D. Ivanov, A. Dolgui, and J. Namdar, “Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation,” Int J Prod Res, vol. 62, no. 17, pp. 6120–6145, 2024, doi: 10.1080/00207543.2024.2309309.
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Generative Artificial Intelligence (GenAI) in Business: A Systematic Review on the Threshold of Transformation

Year 2024, Volume: 5 Issue: 2, 156 - 175, 20.12.2024
https://doi.org/10.58769/joinssr.1597110

Abstract

This systematic review examines the transformative potential of Generative Artificial Intelligence (GenAI) across diverse sectors, including information technology, education, manufacturing, creative industries, healthcare, transportation, management, marketing, finance, energy, law, media, agriculture, and e-commerce. By analyzing its applications, the study highlights how GenAI enhances efficiency, fosters innovation, and addresses sector-specific challenges. Key benefits include the automation of complex processes, optimization of resource use, and acceleration of decision-making. However, delayed adoption risks such as workforce displacement and ethical dilemmas are also discussed. The review identifies critical barriers like data privacy concerns, algorithmic bias, and regulatory challenges.
Practical strategies for successful GenAI integration are explored, emphasizing infrastructure readiness, workforce upskilling, and ethical governance. This includes leveraging generative models such as Generative Adversarial Networks (GANs), Transformer-based models, Variational Autoencoders (VAEs), and diffusion models to adapt to industry-specific demands. Furthermore, the study underscores the necessity of balancing technological advancements with responsible AI deployment to minimize risks and maximize societal benefits.
By synthesizing existing research, this review provides actionable insights for stakeholders aiming to leverage GenAI's transformative capabilities responsibly. It emphasizes the urgency of adopting GenAI technologies to maintain competitiveness and sustainability in rapidly evolving markets. As the study concludes, it advocates for cross-sectoral collaboration to address the complex challenges posed by this paradigm-shifting technology and calls for adaptive policies to align innovation with ethical principles and societal values.

References

  • [1] P. Gupta, B. Ding, C. Guan, and D. Ding, “GenAI: A systematic review using topic modelling techniques,” Data Inf Manag, vol. 8, no. 2, p. 100066, Jun. 2024, doi: 10.1016/j.dim.2024.100066.
  • [2] S. S. Sengar, A. Bin Hasan, S. Kumar, and F. Carroll, “Generative artificial intelligence: a systematic review and applications,” Multimed Tools Appl, Aug. 2024, doi: 10.1007/s11042-024-20016-1.
  • [3] “Gan Nedir?,” https://aws.amazon.com/what-is/gan/.
  • [4] IBM, “What is a transformer model?,” https://www.ibm.com/topics/transformer-model.
  • [5] A. Asperti, D. Evangelista, and E. Loli Piccolomini, “A Survey on Variational Autoencoders from a Green AI Perspective,” SN Comput Sci, vol. 2, no. 4, p. 301, Jul. 2021, doi: 10.1007/s42979-021-00702-9.
  • [6] L. Yang et al., “Diffusion Models: A Comprehensive Survey of Methods and Applications,” ACM Comput Surv, vol. 56, no. 4, pp. 1–39, Apr. 2024, doi: 10.1145/3626235.
  • [7] F.-A. Croitoru, V. Hondru, R. T. Ionescu, and M. Shah, “Diffusion Models in Vision: A Survey,” IEEE Trans Pattern Anal Mach Intell, vol. 45, no. 9, pp. 10850–10869, Sep. 2023, doi: 10.1109/TPAMI.2023.3261988.
  • [8] L. Banh and G. Strobel, “Generative artificial intelligence,” Electronic Markets, vol. 33, no. 1, p. 63, Dec. 2023, doi: 10.1007/s12525-023-00680-1.
  • [9] R. S. Peres, X. Jia, J. Lee, K. Sun, A. W. Colombo, and J. Barata, “Industrial Artificial Intelligence in Industry 4.0 -Systematic Review, Challenges and Outlook,” IEEE Access, 2020, doi: 10.1109/ACCESS.2020.3042874.
  • [10] H. Al Naqbi, Z. Bahroun, and V. Ahmed, “Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review,” Feb. 01, 2024, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/su16031166.
  • [11] R. A. Abumalloh, M. Nilashi, K. B. Ooi, G. W. H. Tan, and H. K. Chan, “Impact of generative artificial intelligence models on the performance of citizen data scientists in retail firms,” Comput Ind, vol. 161, Oct. 2024, doi: 10.1016/j.compind.2024.104128.
  • [12] K. B. Ooi et al., “The Potential of Generative Artificial Intelligence Across Disciplines: Perspectives and Future Directions,” Journal of Computer Information Systems, 2023, doi: 10.1080/08874417.2023.2261010.
  • [13] A. D. Samala et al., “Unveiling the landscape of generative artificial intelligence in education: a comprehensive taxonomy of applications, challenges, and future prospects,” Educ Inf Technol (Dordr), 2024, doi: 10.1007/s10639-024-12936-0.
  • [14] C. Zhou, “Integration of modern technologies in higher education on the example of artificial intelligence use,” Educ Inf Technol (Dordr), vol. 28, no. 4, pp. 3893–3910, Apr. 2023, doi: 10.1007/s10639-022-11309-9.
  • [15] G. J. Hwang and N. S. Chen, “Editorial Position Paper: Exploring the Potential of Generative Artificial Intelligence in Education: Applications, Challenges, and Future Research Directions,” Educational Technology and Society, vol. 26, no. 2, 2023, doi: 10.30191/ETS.202304_26(2).0014.
  • [16] N. L. Rane, Ö. Kaya, and J. Rane, “Advancing industry 4.0, 5.0, and society 5.0 through generative artificial intelligence like ChatGPT,” in Artificial Intelligence, Machine Learning, and Deep Learning for Sustainable Industry 5.0, Deep Science Publishing, 2024. doi: 10.70593/978-81-981271-8-1_7.
  • [17] B. Ramdurai IEEE and A. Balagopal Ramdurai, “The Impact, Advancements and Applications of GenAI,” 2023. [Online]. Available: https://www.researchgate.net/publication/371314493
  • [18] S. S. Sengar, A. Bin Hasan, S. Kumar, and F. Carroll, “Generative artificial intelligence: a systematic review and applications,” Multimed Tools Appl, 2024, doi: 10.1007/s11042-024-20016-1.
  • [19] N. Rane, “Role of ChatGPT and Similar Generative Artificial Intelligence (AI) in Construction Industry,” SSRN Electronic Journal, 2023, doi: 10.2139/ssrn.4598258.
  • [20] D. Patil, N. Liladhar Rane, and J. Rane, “Applications of ChatGPT and generative artificial intelligence in transforming the future of various business sectors”, doi: 10.70593/978-81.
  • [21] D. G. Takale, P. N. Mahalle, and B. Sule, “Advancements and Applications of Generative Artificial Intelligence.”
  • [22] Dr. M. B. Oluwagbenro, “GenAI: Definition, Concepts, Applications, and Future Prospects,” Jun. 04, 2024. doi: 10.36227/techrxiv.171746875.59016695/v1.
  • [23] J. He, S. L. Baxter, J. Xu, J. Xu, X. Zhou, and K. Zhang, “The practical implementation of artificial intelligence technologies in medicine,” Jan. 01, 2019, Nature Publishing Group. doi: 10.1038/s41591-018-0307-0.
  • [24] N. L. Rane, “ChatGPT and similar generative artificial intelligence (AI) for smart industry: role, challenges, and opportunities for Industry 4.0, Industry 5.0, and Society 5.0,” Innovations in Business and Strategic Management, Jun. 2024, doi: 10.61577/ibsm.2024.100002.
  • [25] D. Patil, N. L. Rane, and J. Rane, “Enhancing resilience in various business sectors with ChatGPT and generative artificial intelligence,” in The Future Impact of ChatGPT on Several Business Sectors, Deep Science Publishing, 2024. doi: 10.70593/978-81-981367-8-7_4.
  • [26] N. Rane, S. Choudhary, and J. Rane, “Integrating ChatGPT, Bard, and leading-edge generative artificial intelligence in building and construction industry: applications, framework, challenges, and future scope,” SSRN Electronic Journal, 2023, doi: 10.2139/ssrn.4645597.
  • [27] J. Rane, Ö. Kaya, S. K. Mallick, and N. L. Rane, “Artificial general intelligence in industry 4.0, 5.0, and society 5.0: Applications, opportunities, challenges, and future direction,” in Future Research Opportunities for Artificial Intelligence in Industry 4.0 and 5.0, Deep Science Publishing, 2024. doi: 10.70593/978-81-981271-0-5_6.
  • [28] I. Jackson, D. Ivanov, A. Dolgui, and J. Namdar, “Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation,” Int J Prod Res, vol. 62, no. 17, pp. 6120–6145, 2024, doi: 10.1080/00207543.2024.2309309.
  • [29] N. Berente, B. Gu, J. Recker, and R. Santhanam, “SPECIAL ISSUE: MANAGING AI MANAGING ARTIFICIAL INTELLIGENCE 1”, doi: 10.25300/MISQ/2021/16274.
  • [30] A. Hemalatha, P. B. Kumari, N. Nawaz, and V. Gajenderan, “Impact of Artificial Intelligence on Recruitment and Selection of Information Technology Companies,” in Proceedings - International Conference on Artificial Intelligence and Smart Systems, ICAIS 2021, Institute of Electrical and Electronics Engineers Inc., Mar. 2021, pp. 60–66. doi: 10.1109/ICAIS50930.2021.9396036.
  • [31] D. Patil, N. Liladhar Rane, and J. Rane, “Emerging and future opportunities with ChatGPT and generative artificial intelligence in various business sectors”, doi: 10.70593/978-81-981367-8.
  • [32] R. Gupta, K. Nair, M. Mishra, B. Ibrahim, and S. Bhardwaj, “Adoption and impacts of generative artificial intelligence: Theoretical underpinnings and research agenda,” International Journal of Information Management Data Insights, vol. 4, no. 1, Apr. 2024, doi: 10.1016/j.jjimei.2024.100232.
  • [33] M. Jakšič and M. Marinč, “Relationship banking and information technology: the role of artificial intelligence and FinTech,” Risk Management, vol. 21, no. 1, pp. 1–18, Mar. 2019, doi: 10.1057/s41283-018-0039-y.
  • [34] Yafei Xiang, Penghao Liang, Yulu Gong, Jintong Song, and Yichao Wu, “GenAI in Industrial Revolution: A Comprehensive Research on Transformations, Challenges, and Future Directions,” Journal of Knowledge Learning and Science Technology, no. 2, pp. 11–20, Jun. 2024, doi: https://doi.org/10.60087/jklst.vol3.n2.p20.
  • [35] B. Martini, D. Bellisario, and P. Coletti, “Human-Centered and Sustainable Artificial Intelligence in Industry 5.0: Challenges and Perspectives,” Sustainability (Switzerland) , vol. 16, no. 13, Jul. 2024, doi: 10.3390/su16135448.
  • [36] P. G. R. de Almeida, C. D. dos Santos, and J. S. Farias, “Artificial Intelligence Regulation: a framework for governance,” Ethics Inf Technol, vol. 23, no. 3, pp. 505–525, Sep. 2021, doi: 10.1007/s10676-021-09593-z.
  • [37] D. Patil, N. L. Rane, and J. Rane, “Challenges in implementing ChatGPT and generative artificial intelligence in various business sectors,” in The Future Impact of ChatGPT on Several Business Sectors, Deep Science Publishing, 2024. doi: 10.70593/978-81-981367-8-7_3.
  • [38] F. Fui-Hoon Nah, R. Zheng, J. Cai, K. Siau, and L. Chen, “GenAI and ChatGPT: Applications, challenges, and AI-human collaboration,” 2023, Routledge. doi: 10.1080/15228053.2023.2233814.
  • [39] D. G. Takale, P. N. Mahalle, and B. Sule, “Cyber Security Challenges in GenAI Technology.” 2024, Journal of Network Security Computer Networks.
There are 39 citations in total.

Details

Primary Language English
Subjects Machine Learning (Other), Artificial Intelligence (Other)
Journal Section Reviews
Authors

Osman Şahin

Durmuş Karayel 0000-0002-1639-568X

Publication Date December 20, 2024
Submission Date December 6, 2024
Acceptance Date December 17, 2024
Published in Issue Year 2024 Volume: 5 Issue: 2

Cite

APA Şahin, O., & Karayel, D. (2024). Generative Artificial Intelligence (GenAI) in Business: A Systematic Review on the Threshold of Transformation. Journal of Smart Systems Research, 5(2), 156-175. https://doi.org/10.58769/joinssr.1597110
AMA Şahin O, Karayel D. Generative Artificial Intelligence (GenAI) in Business: A Systematic Review on the Threshold of Transformation. JoinSSR. December 2024;5(2):156-175. doi:10.58769/joinssr.1597110
Chicago Şahin, Osman, and Durmuş Karayel. “Generative Artificial Intelligence (GenAI) in Business: A Systematic Review on the Threshold of Transformation”. Journal of Smart Systems Research 5, no. 2 (December 2024): 156-75. https://doi.org/10.58769/joinssr.1597110.
EndNote Şahin O, Karayel D (December 1, 2024) Generative Artificial Intelligence (GenAI) in Business: A Systematic Review on the Threshold of Transformation. Journal of Smart Systems Research 5 2 156–175.
IEEE O. Şahin and D. Karayel, “Generative Artificial Intelligence (GenAI) in Business: A Systematic Review on the Threshold of Transformation”, JoinSSR, vol. 5, no. 2, pp. 156–175, 2024, doi: 10.58769/joinssr.1597110.
ISNAD Şahin, Osman - Karayel, Durmuş. “Generative Artificial Intelligence (GenAI) in Business: A Systematic Review on the Threshold of Transformation”. Journal of Smart Systems Research 5/2 (December 2024), 156-175. https://doi.org/10.58769/joinssr.1597110.
JAMA Şahin O, Karayel D. Generative Artificial Intelligence (GenAI) in Business: A Systematic Review on the Threshold of Transformation. JoinSSR. 2024;5:156–175.
MLA Şahin, Osman and Durmuş Karayel. “Generative Artificial Intelligence (GenAI) in Business: A Systematic Review on the Threshold of Transformation”. Journal of Smart Systems Research, vol. 5, no. 2, 2024, pp. 156-75, doi:10.58769/joinssr.1597110.
Vancouver Şahin O, Karayel D. Generative Artificial Intelligence (GenAI) in Business: A Systematic Review on the Threshold of Transformation. JoinSSR. 2024;5(2):156-75.