Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)

DOI: http://dx.doi.org/10.15439/2025F1129

Citation: Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 43, pages 705–713 (2025)

Full text

Abstract. Artificial Intelligence (AI) adoption poses complex, multidimensional challenges for organizations that extend beyond technical implementation. While strategic interest in AI is rising, many initiatives struggle to scale sustainably. This paper addresses the fragmented state of AI adoption research by offering a theory-driven synthesis of the key management challenges organizations face. Based on a systematic literature review and grounded in the socio-technical systems (STS) perspective, the study identifies challenges across technological, organizational, and social domains---such as data governance, organizational inertia, skill shortages, and ethical ambiguity---as mutually reinforcing. The resulting framework highlights the systemic entanglement of these barriers, underscoring the limits of isolated interventions. This study contributes to Information Systems (IS) research by conceptualizing AI adoption as a socio-technical transformation and providing an integrative typology of adoption challenges. The findings offer a foundation for future empirical research and guide strategic decision-makers in navigating the organizational complexity of AI integration.

References

1. Accenture, “AI: Built to Scale – Achieving High Performance with AI at Scale,” Accenture, 2019.
2. A. Singla, A. Sukharevsky, L. Yee, M. Chui, and B. Hall, “The State of AI: How Organizations are Rewiring to Capture Value,” McKinsey & Company, 2025.
3. N. Maslej, “The AI Index 2025 Annual Report,” Stanford Institute for Human-Centered Artificial Intelligence (HAI), Stanford, CA, Apr. 2025.
4. World Economic Forum and Deloitte, “AI in Action: Beyond Experimentation to Transform Industry,” World Economic Forum, Genf, Jan. 2025.
5. I. M. Enholm, E. Papagiannidis, P. Mikalef, and J. Krogstie, “Artificial Intelligence and Business Value: a Literature Review,” Inf Syst Front, vol. 24, no. 5, pp. 1709–1734, Oct. 2022, https://dx.doi.org/10.1007/s10796-021-10186-w.
6. A. F. S. Borges, F. J. B. Laurindo, M. M. Spínola, R. F. Gonçalves, and C. A. Mattos, “The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions,” Int J Inf Manage, vol. 57, 2021, https://dx.doi.org/10.1016/j.ijinfomgt.2020.102225.
7. H. F. Hansen, E. Lillesund, P. Mikalef, and Ν. Altwaijry, “Understanding Artificial Intelligence Diffusion through an AI Capability Maturity Model,” Inf. Syst. Front., 2024, https://dx.doi.org/10.1007/s10796-024-10528-4.
8. Y. K. Dwivedi et al., “Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy,” Int J Inf Manage, vol. 57, 2021, https://dx.doi.org/10.1016/j.ijinfomgt.2019.08.002.
9. S. Alsheibani, C. Messom, Y. Cheung, and M. Alhosni, “Artificial Intelligence Beyond the Hype: Exploring the Organisation Adoption Factors,” Australasian Conference on Information Systems, 2020.
10. M. Weber, M. Engert, N. Schaffer, J. Weking, and H. Krcmar, “Organizational Capabilities for AI Implementation—Coping with Inscrutability and Data Dependency in AI,” Inf. Syst. Front., vol. 25, no. 4, pp. 1549–1569, 2023, https://dx.doi.org/10.1007/s10796-022-10297-y.
11. X. Zhang, F. T. S. Chan, C. Yan, and I. Bose, “Towards risk-aware artificial intelligence and machine learning systems: An overview,” Decis Support Syst, vol. 159, 2022, https://dx.doi.org/10.1016/j.dss.2022.113800.
12. A. Asatiani et al., “Sociotechnical Envelopment of Artificial Intelligence: An Approach to Organizational Deployment of Inscrutable Artificial Intelligence Systems,” JAIS, vol. 22, no. 2, pp. 325–352, 2021, https://dx.doi.org/10.17705/1jais.00664.
13. S. AlSheibani, Y. Cheung, Y. Cheung, and C. Messom, “Re-thinking the Competitive Landscape of Artificial Intelligence,” HICSS, 2020.
14. H. Benbya, T. H. Davenport, and S. Pachidi, “Artificial Intelligence in Organizations: Current State and Future Opportunities,” SSRN Journal, 2020, https://dx.doi.org/10.2139/ssrn.3741983.
15. Y. Duan, J. S. Edwards, and Y. K. Dwivedi, “Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda,” International Journal of Information Management, vol. 48, pp. 63–71, Oct. 2019, https://dx.doi.org/10.1016/j.ijinfomgt.2019.01.021.
16. Z. Jan et al., “Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities,” Expert Sys Appl, vol. 216, 2023, https://dx.doi.org/10.1016/j.eswa.2022.119456.
17. S. Raisch and S. Krakowski, “Artificial Intelligence and Management: The Automation–Augmentation Paradox,” AMR, vol. 46, no. 1, pp. 192–210, Jan. 2021, https://dx.doi.org/10.5465/amr.2018.0072.
18. J. Holmström, “From AI to digital transformation: The AI readiness framework,” Business Horizons, vol. 65, no. 3, pp. 329–339, May 2022, https://dx.doi.org/10.1016/j.bushor.2021.03.006.
19. M. C. M. Lee, H. Scheepers, A. K. H. Lui, and E. W. T. Ngai, “The implementation of artificial intelligence in organizations: A systematic literature review,” Inf Manage, vol. 60, no. 5, 2023, https://dx.doi.org/10.1016/j.im.2023.103816.
20. M. H. Jarrahi, “Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making,” Business Horizons, vol. 61, no. 4, pp. 577–586, Jul. 2018, https://dx.doi.org/10.1016/j.bushor.2018.03.007.
21. P. Mikalef and M. Gupta, “Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance,” Information & Management, vol. 58, no. 3, p. 103434, Apr. 2021, https://dx.doi.org/10.1016/j.im.2021.103434.
22. J. Jöhnk, M. Weißert, and K. Wyrtki, “Ready or Not, AI Comes— An Interview Study of Organizational AI Readiness Factors,” Bus Inf Syst Eng, vol. 63, no. 1, pp. 5–20, Feb. 2021, https://dx.doi.org/10.1007/s12599-020-00676-7.
23. H. M. Cooper, “Scientific Guidelines for Conducting Integrative Research Reviews,” Review of Educational Research, vol. vol. 52, pp. 291–302, 1982, https://dx.doi.org/10.3102/00346543052002291.
24. J. Webster and R. T. Watson, “Analyzing the Past to Prepare for the Future: Writing a Literature Review,” MIS Quarterly, vol. 26, no. 2, pp. xiii–xxiii, 2002, https://dx.doi.org/10.2307/4132319.
25. S. Boell and B. Wang, “www.litbaskets.io, an IT Artifact Supporting Exploratory Literature Searches for Information Systems Research,” in Proceedings of the 30th Australasian Conference on Information Systems (ACIS 2019), Perth, Australien: Australasian Association for Information Systems, 2019, pp. 1–12. Accessed: Feb. 20, 2025. [Online]. Available: https://aisel.aisnet.org/acis2019/71/
26. S. Kass, S. Strahringer, and M. Westner, “Drivers and Inhibitors of Low Code Development Platform Adoption,” in 2022 IEEE 24th Conference on Business Informatics (CBI), Amsterdam, Netherlands: IEEE, Jun. 2022, pp. 196–205. https://dx.doi.org/10.1109/cbi54897.2022.00028.
27. V. Braun and V. Clarke, “Using thematic analysis in psychology,” Qualitative Research in Psychology, vol. 3, no. 2, pp. 77–101, Jan. 2006, https://dx.doi.org/10.1191/1478088706qp063oa.
28. R. P. Bostrom and J. S. Heinen, “MIS Problems and Failures: A Socio-Technical Perspective, Part II: The Application of Socio-Technical Theory,” MIS Quarterly, vol. 1, no. 4, p. 11, Dec. 1977, https://dx.doi.org/10.2307/249019.
29. R. P. Bostrom and J. S. Heinen, “MIS Problems and Failures: A Socio-Technical Perspective. Part I: The Causes,” MIS Quarterly, vol. 1, no. 3, p. 17, Sep. 1977, https://dx.doi.org/10.2307/248710.
30. E. Kučević and J. J. Brandes, “Guidance for Formulating an AI Strategy: A Taxonomy for Organizational Strategic Perspectives Towards AI,” presented at the Hawaii International Conference on System Sciences, 2025. https://dx.doi.org/10.24251/HICSS.2025.724.
31. N. Prinz, M. Huber, C. Riedinger, and C. Rentrop, “Two Perspectives of Low-Code Development Platform Challenges – An Exploratory Study,” 2022.
32. W. Wang and K. Siau, “Artificial intelligence, machine learning, automation, robotics, future of work and future of humanity: A review and research agenda,” J. Database Manage., vol. 30, no. 1, pp. 61–79, 2019, https://dx.doi.org/10.4018/JDM.2019010104.
33. J. G. March, Ed., Handbook of organizations, 1 vols. Abingdon, Oxon: Routledge, 2013.
34. C. Riedinger, M. Netscher, and S. Zimmermann, “Organizational Capabilities for Business-IT Integration in Digital Enterprises,” presented at the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), Belgrade, Serbia, Oct. 2024, pp. 493–500. https://dx.doi.org/10.15439/2024F9800.