Using GenAI in IT project management: case studies, insights and challenges

• Autorzy: Ostrowski Sławomir

Identyfikatory

DOI

10.29119/1641-3466.2025.215.26

• Języki publikacji: EN

Abstrakty

EN

Purpose: This study examines the potential of integrating Generative Artificial Intelligence (GenAI) into IT project management, with a view to identifying how it could transform project management processes. Design/methodology/approach: A case study approach was employed in order to analyse IT companies across a variety of industries. In order to gain a comprehensive understanding of GenAI's applications and impact, the research combined qualitative interviews with project managers and technical leads with quantitative analysis of project performance metrics. Findings: The results demonstrate GenAI's capacity for markedly enhancing project management, encompassing enhanced project efficiency, more effective risk management, and more efficacious stakeholder communication. Key applications include predictive analytics for risk identification, resource optimisation algorithms to mitigate bottlenecks, and automated quality assurance tools for defect detection. However, challenges such as data quality, algorithmic bias, organisational resistance, and the necessity for transparent AI frameworks were also identified. Research limitations/implications: The findings, based on IT companies, may have limited generalizability to other industries. The study primarily addresses short-term impacts, with long-term implications yet to be explored. Future research should examine GenAI's applicability in different sectors, its ethical considerations, scalability, and integration with traditional project management frameworks. Practical implications: Organisations can use GenAI to overcome long-standing project management challenges. The case study examples presented in the article demonstrate GenAI's ability to manage the complex dynamics of IT projects, making it an invaluable tool for IT professionals seeking to optimise project outcomes. Originality/value: This study contributes to the limited research on GenAI in IT project management by presenting empirical evidence from case studies. It offers actionable insights for practitioners and proposes directions for future research, including exploring long-term impacts, ethical implications, and hybrid methodologies integrating GenAI with traditional frameworks.

Słowa kluczowe

EN

Generative Artificial Intelligence; GenAI; Project Management; IT Project Management; GenAI in PM

PL

generatywna sztuczna inteligencja; GenAI; zarządzanie projektami; zarządzanie projektami informatycznymi; GenAI w PM

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
• Rocznik: 2025
• Tom: z. 215
• Strony: 431--447
• Opis fizyczny: Bibliogr. 19 poz.

Twórcy

autor

Ostrowski Sławomir

• Gdańsk University of Technology, Faculty of Management and Economics

• https://orcid.org/0000-0002-1969-0844

Bibliografia

• 1. Bahai, P., Nguyen, T., Langer, F. (2024). Challenges of AI Integration in IT Project Management: Empirical Evidence from Case Studies. Global Journal of Business and Information Technology, 5(11), 100-122.
• 2. Bodea, C.-N., Mitea, C., Stanciu, O. (2020). Artificial Intelligence Adoption in Project Management: Main Drivers, Barriers, and Estimated Impact. In: Proceedings of the 3rd International Conference on Economics and Social Sciences (pp. 758-767). Bucharest: Bucharest University of Economic Studies. https://doi.org/10.2478/9788395815072-075.
• 3. Curcirito, M., Sekuterski, D. (2023). The Impact Artificial Intelligence Has on Productivity and Efficiency. Boston: Wolf & Company. Retrieved from: https://www.wolfandco.com/ resources/blog/impact-artificial-intelligence-productivity-efficiency/, May 26, 2024.
• 4. Gałuszka, J., Ćwiąkała, M., Antczak-Jarząbska, R. (2024). Intellectual property leasing as an instrument of tax advantage. Silesian University of Technology Scientific Papers, Organization and Management Series, 191, 149-158.
• 5. Gartner (2023). Gartner Poll Finds 55% of Organizations are in Piloting or Production Mode with Generative AI. Retrieved from: https://www.gartner.com/en/newsroom/pressreleases/2023-10-03-gartner-poll-finds-55-percent-of-organizations-are-in-piloting-orproduction-mode-with-generative-ai/, May 15, 2024.
• 6. Górniak, J. (1998). Analiza czynnikowa i analiza głównych składowych. Research and Methods, 7, 83-102.
• 7. Grandview Research (2024). Artificial Intelligence Market Size, Share & Trends Analysis Report By Solution (Hardware, Software, Services), By Technology (Deep Learning, Machine Learning, NLP), By Function, By End-use, By Region, And Segment Forecasts, 2023-2030. Retrieved from: https://www.grandviewresearch.com/industry-analysis/ artificial-intelligence-ai
• 8. Hashfi, M.I., Raharjo, T. (2023). Exploring the Challenges and Impacts of Artificial Intelligence Implementation in Project Management: A Systematic Literature Review. International Journal of Advanced Computer Science and Applications, 14(9), 366-374. https://doi.org/10.14569/IJACSA.2023.0140948.
• 9. Hess, T., Kunz, J. (2024). Innovative Models to Revive the Global Economy: Artificial Intelligence Adoption in Project Management. Journal of Project Management Innovations, 17(12), 32-48.
• 10. Kerzner, H. (2017). Project Management: A Systems Approach to Planning, Scheduling, and Controlling. Wiley.
• 11. Kowalska, M., Misztal, A., Gniadkowska-Szymańska, A. (2024). The impact of environmental taxes on the financial security of the logistics sector. Silesian University of Technology Scientific Papers, Organization and Management Series, 191, 291-304.
• 12. Maphosa, V., Maphosa, M. (2022). Artificial Intelligence in Project Management Research: A Bibliometric Analysis. Journal of Theoretical and Applied Information Technology, 100(16), 5000-5012. https://doi.org/10.5281/zenodo.7134073.
• 13. McKinsey&Co. (2022). The State of AI in 2022 and a Half Decade in Review. Retrieved from: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-aiin-2022-and-a-half-decade-in-review/, May 15, 2024.
• 14. Nycz-Wróbel, J. (2024). Activity of Polish organisations participating in the EMAS scheme to reduce the emission of air contaminants. Silesian University of Technology Scientific Papers, Organization and Management Series, 191, 387-404.
• 15. Šmite, D., Moe, N.B., Klotins, E., Gonzalez-Huerta, J. (2023). From Forced WorkingFrom-Home to Voluntary Working-From-Anywhere: Two Revolutions in Telework. Journal of Systems and Software, 195, 111509.
• 16. Stronczek, A. (2024). Implementation status of Lean Management in Polish manufacturing enterprises. Silesian University of Technology Scientific Papers, Organization and Management Series, 191, 519-540.
• 17. Taboada, A., Daneshpajouh, N., Toledo, T. de Vass. (2023). Artificial Intelligence Enabled Project Management: A Systematic Literature Review. Applied Sciences (Switzerland), 13(8). https://doi.org/10.3390/app13085014.
• 18. Tomala, M. (2024). Political and economic rationale for the development of renewable energy in European Union countries. Silesian University of Technology Scientific Papers, Organization and Management Series, 191, 551-570.
• 19. Tominc, P., Oreški, D., Rožman, M. (2023). Artificial Intelligence and Agility-Based Model for Successful Project Implementation and Company Competitiveness. Information, 14(6), 337. https://doi.org/10.3390/info14060337.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).