Bridging the experience gap: strategic collaborations in the age of generative AI

• Autorzy: Malik-Kozłowska Beata , Kozłowski Rafał

Identyfikatory

DOI

10.29119/1641-3466.2025.217.18

• Języki publikacji: EN

Abstrakty

EN

Purpose: The primary aim of this study is to analyze the real-world adoption of generative AI within organizational contexts. This work aims to contribute to the broader understanding of how generative AI is reshaping business processes and driving innovation across industries. Design/Methodology/Approach: The study adopts a qualitative content analysis approach, grounded in a systematic review of peer-reviewed articles and grey literature published from 2022 to 2024. Articles were screened based on strict inclusion and exclusion criteria, emphasizing documented real-world applications of generative AI. Data extraction focused on identifying sector-specific use cases, challenges encountered during its integration, and the strategies employed by organizations to overcome these obstacles, as well as the organizational impacts of generative AI. Findings: The review identified six empirical studies documenting real-world implementations of generative AI across diverse sectors, including insurance, finance, creative industries, retail, and fact-checking. Generative AI is shown to enhance efficiency, streamline workflows, and support decision-making, while also fostering creative innovation. Challenges such as data reliability, legal ambiguities, and organizational readiness were commonly observed. Strategies to address these barriers include leveraging human-AI collaboration, ensuring regulatory compliance, and investing in cohesive organizational structures. Research Limitations/Implications: The research is constrained by the limited number of empirical studies available, reflecting the nascent stage of generative AI adoption. Additionally, the findings are subject to the rapid evolution of AI technologies, requiring ongoing updates through longitudinal research. Expanding future studies to include a broader geographic and sectoral scope is essential for generalizability. Practical Implications: The study provides actionable insights for organizations considering the adoption of generative AI. It highlights the importance of strategic planning, ethical data management, and fostering collaboration between human experts and AI systems. These findings are particularly relevant for small and medium-sized enterprises seeking to leverage AI to enhance efficiency and competitiveness. Social Implications: Generative AI democratizes access to advanced technology, enabling non-specialists to use AI tools. However, its rapid adoption demands robust regulation and stake-holder engagement from policymakers. Originality/Value: This study addresses a critical gap in the academic literature by focusing on the documented implementation of generative AI.

Słowa kluczowe

EN

generative AI; organizational challenges; AI adoption

PL

generatywna sztuczna inteligencja; wyzwania organizacyjne; przystosowanie sztucznej inteligencji

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
• Rocznik: 2025
• Tom: z. 217
• Strony: 301--318
• Opis fizyczny: Bibliogr. 34 poz.

Twórcy

autor

Malik-Kozłowska Beata

• University of Economics in Katowice, Poland

• https://orcid.org/0000-0002-8992-6074

autor

Kozłowski Rafał

• University of Economics in Katowice, Poland

• https://orcid.org/0000-0002-9258-8248

Bibliografia

• 1. Bevan, O., Chui, M., Kristensen, I., Presten, B., Yee, L. (2024). Implementing generative AI with speed and safety. McKinsey & Company.
• 2. Biloš, A., Budimir, B. (2024). Understanding the adoption dynamics of ChatGPT among Generation Z: Insights from a modified UTAUT2 model. Journal of Theoretical and Applied Electronic Commerce Research, 19(2), 863-879.
• 3. Buhler, K. (2024). How AI 50 companies are powering a new tech economy. Forbes. Retreived from: https://www.forbes.com/sites/konstantinebuhler/2024/04/11/how-ai-50-companies-are-powering-a-new-tech-economy/, 03.11.2024.
• 4. Bukhtueva, I. (2024). GenAI multiple: The influence of generative AI components in business models on company valuation. German International Journal of Modern Science, 90, 15-18. Retreived from: 10.5281/zenodo.13951893.
• 5. Bulau, C.M., Mustatea, A.O., Matei (Pana), M. (2024). Can Artificial Intelligence (AI) Become an Active Assistant to the Finance, Audit, and Accounting Functions? A Credit Control Data Analysis Approach. Acta Universitatis Danubius. Œonomica, 20(4), 109-126. Retrieved from http://dj.univdanubius.ro/index.php/AUDOE/article/view/2952,3.11.2024.
• 6. Cuartielles, R., Ramon-Vegas, X., Pont-Sorribes, C. (2023). Retraining fact-checkers: The emergence of ChatGPT in information verification. Profesional de la Información, 32(5). 1-15.
• 7. Dahle, J., Langdale, L., McMullen, T. (2024). The impact of AI and emerging technologies in rewards management. Journal of Total Rewards, 9(3), 45-67.
• 8. Davenport, T.H., Holweg, M., Jeavons, D. (2024). How AI is helping companies redesign processes. Harvard Business Review. Retrieved from: https://hbr.org/2023/03/how-ai-is-helping-companies-redesign-processes, 12.11.2024.
• 9. DeVon, C. (2024). From Amazon to Walmart: How companies plan to incorporate AI. Retireved from https://www.cnbc.com/2023/05/19/from-amazon-to-walmart-how-companies-plan-to-incorporate-ai.html, 12.11.2023.
• 10. Duong, C.D. (2024). ChatGPT adoption and digital entrepreneurial intentions: An empirical research based on the theory of planned behaviour. Entrepreneurial Business and Economics Review, 12(2), 129-142. Retreived from: http://doi.org/10.15678/EBER.2024.120208.
• 11. Erickson, K. (2024). AI and work in the creative industries: Digital continuity or discontinuity? Creative Industries Journal, 1- 21. Retreived from: https://doi.org/10.1080/17510694.2024.2421135.
• 12. Feuerriegel, S., Hartmann, J., Janiesch, C., Zschech, P. (2024). Generative AI: Opportunities and challenges for the BISE community. Business & Information Systems Engineering, 66(1), 111-126. Retreived from: http://doi.org/10.1007/s12599-023-00834-7.
• 13. Fonseca, L., Oliveira, E., Pereira, T., Sá, J.C. (2024). Leveraging ChatGPT for sustainability: A framework for SMEs to align with UN SDGs and tackle sustainable development challenges. Management & Marketing, 19(3), 471-497. Retreived from: https://doi.org/10.2478/mmcks-2024-0021.
• 14. Gulati, A., Saini, H., Singh, S., Kumar, V. (2024). Enhancing learning potential: Investigating marketing students' behavioral intentions to adopt ChatGPT. Marketing Education Review, 34(3), 201-234. Retreived from: http://doi.org/10.1080/10528008.2023.2300139.
• 15. Haan, K. (2024). How businesses are using artificial intelligence in 2024. Forbes Advisor.
• 16. Haleem, A., Javaid, M., Singh, R.P. (2024). Exploring the competence of ChatGPT for customer and patient service management. Intelligent Pharmacy, 2, 392-414. Retreived from: https://doi.org/10.1016/j.ipha.2024.03.002.
• 17. Jokar, M., Abdous, A., Rahmanian, V. (2024). AI chatbots in pet health care: Opportunities and challenges for owners. Veterinary Medicine and Science, 10(e1464), 1-3. Retreived from: https://doi.org/10.1002/vms3.1464.
• 18. Kalota, F. (2024). A primer on generative artificial intelligence. Education Sciences, 14(172), 1-15. Retreived from: https://doi.org/10.3390/educsci14020172.
• 19. Kelly, S., Kaye, S.A., Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, 1-33. Retreived from: https://doi.org/10.1016/j.tele.2022.101925.
• 20. Lamarre, E., Singla, A., Sukharevsky, A., Zemmel, R. (2024). A generative AI reset: Rewiring to turn potential into value in 2024. McKinsey & Company.
• 21. Lee, D.K.C., Guan, C., Yu, Y., Ding, Q. (2024). A comprehensive review of generative AI in finance. FinTech, 3(3), 60-478. Retreived from: https://doi.org/10.3390/fintech3030025.
• 22. Li, W., Zhang, X., Li, J., Yang, X., Liu, Y. (2024). An explanatory study of factors influencing engagement in AI education at the K-12 Level: An extension of the classic TAM model. Scientific Reports, 14, 1-17. Retreived from: http://doi.org/10.1038/s41598-024-64363-3.
• 23. Liu, L.X., Sun, Z., Xu, K., Chen, C. (2024). AI-Driven Financial Analysis: Exploring ChatGPT's Capabilities and Challenges. International Journal of Financial Studies, 12(3), 1-36. Retreived from: https://doi.org/10.3390/ijfs12030060.
• 24. McKinsey Global Institute. (2024). A microscope on small businesses: The productivity opportunity by country - Poland. McKinsey & Company.
• 25. McKnight, M. A., Gilstrap, C.M., Gilstrap, C.A., Bacic, D., Shemroske, K., Srivastava, S. (2024). Generative Artificial Intelligence in applied business contexts: A systematic review, lexical analysis, and research framework. Journal of Applied Business and Economics, 26(2), 119-131.
• 26. Salmon, P.M., Carden, T., Hancock, P.A. (2021). Putting the humanity into inhuman systems: How human factors and ergonomics can be used to manage the risks associated with artificial general intelligence. Human Factors and Ergonmics in Manufacturing and Service Industries, 31 (2), 223-236. Retreived from: https://doi.org/10.1002/hfm.20883.
• 27. Sarbay, İ., Berikol, G.B., Özturan, İ.U. (2023). Performance of emergency triage prediction of an open access natural language processing-based chatbot application (ChatGPT): A preliminary, scenario-based cross-sectional study. Turkish Journal of Emergency Medicine, 23(3), 56-161. Retreived from: https://doi.org/10.4103/tjem.tjem_79_23.
• 28. Sheikh, R.A., Jarvis, R., Whitehall, J., Jawad, F. (2024). Managing projects successfully through artificial intelligence (AI) and ChatGPT. PM World Journal, XIII(IX). 1-17.
• 29. Strickland, E. (2024). What is Generative AI. IEEE Spectrum. Retrieved from https://spectrum.ieee.org/what-is-generative-ai, 30.09.2024.
• 30. Urdan, A.T., Marson, C. (2024). Morality and Modeling of Intention to Use ChatGPT Technology. International Journal of Innovation, 12(1), 1-42. Retreived from: https://doi.org/10.5585/2024.26378.
• 31. Ventayen, R.J.M. (2024). OpenAI ChatGPT, Google Bard, and Microsoft Bing: Similarity Index and Analysis of Artificial Intelligence-Based Contents. International Journal of Multidisciplinary, Applied Business and Education Research, 5(3), 917-924. Retreived from: https://doi.org/10.11594/ijmaber.05.03.15.
• 32. Yue, Y., Ng, S.I., Basha, N.K. (2024). Consumption values, attitudes and continuance intention to adopt ChatGPT-driven e-commerce AI chatbot (LazzieChat). Pakistan Journal of Commerce and Social Sciences, 18(2), 249-284.
• 33. Zhecheva, D. (2024). Exploratory Data Analysis and the Rise of Large Language Models, TEM Journal, 13(1), 561-569. Retreived from: https://doi.org/10.18421/TEM131-59.
• 34. Zheng, X., Gildea, E., Chai, S., Zhang, T., Wang, S. (2024). Data Science in Finance, Challenges and Opportunities. AI, 5, 55-71. Retreived from: https://doi.org/10.3390/ai50-10004.