Kompetencje sztucznej inteligencji a sukces projektu – analiza trendów badawczych

Autor

DOI:

https://doi.org/10.15611/pn.2026.2.05

Słowa kluczowe:

sztuczna inteligencja, zarządzanie projektami, kompetencje kierowników projektów, kompetencje cyfrowe

Abstrakt

Cel: Celem artykułu jest systematyczna analiza literatury dotyczącej kompetencji kierowników projektów związanych ze sztuczną inteligencją (AI) oraz ocena, w jaki sposób kompetencje te są ujmowane w kontekście zarządzania projektami.

Metodyka: Badanie przeprowadzono zgodnie z metodyką PRISMA 2020, wykorzystując analizę bibliometryczną (VOSviewer) oraz jakościową analizę treści. Do analizy bibliometrycznej włączono 38 publikacji, a jakościowej – 26 prac, zakodowanych w czterech kategoriach kompetencji: technologicznych, poznawczych, decyzyjnych i etyczno-organizacyjnych.

Wyniki: Literatura koncentruje się głównie na technologicznym wymiarze AI, a kompetencje kierowników projektów są opisywane marginalnie i niespójnie. Dominują kompetencje technologiczne i decyzyjne, natomiast poznawcze i etyczno-organizacyjne pozostają słabo rozwinięte, a spójny model AI kierownika projektu nie istnieje.

Implikacje i rekomendacje: Modele kompetencji kierowników projektów wymagają rozszerzenia o elementy AI, w kompetencje rozumienia AI, pracę z danymi, decyzje wspierane algorytmicznie i etykę technologii. Ramy DigComp 2.2 mogą wspierać ich uporządkowanie.

Oryginalność/wartość: Artykuł prezentuje lukę między rosnącą rolą AI a obecnymi modelami kompetencji kierowników projektów oraz wyznacza kierunki dalszych badań.

Pobrania

Statystyki pobrań niedostępne.

Bibliografia

Almeida, P. M., Fernandes, G., & Santos, J. M. R. C. A. (2025). Artificial Intelligence Tools for Project Management: A Knowledge-Based Perspective. Project Leadership and Society, 6, 100196. https://doi.org/10.1016/j.plas.2025.100196

Association for Project Management [APM]. (2023). APM Competence Framework (3rd edition). https://www.apm.org.uk/media/1cflfk3t/apm-competence-framework-3rd-editionfinalrevised.pdf

Association for Project Management [APM]. (2025). AI Use in Project Management Nearly Doubles in Just Two Years: APM Survey Finds. Retrieved June 24, 2026, from https://www.apm.org.uk/news/ai-use-in-project-management-nearly-doubles-in-just-two-years-apm-survey-finds/

Bento, J., Pereira, L., Gonçalves, R., Dias, Á., & Lopes da Costa, R. (2022). Artificial Intelligence in Project Management: A Systematic Literature Review. International Journal of Technology Intelligence and Planning, 13(2), 143-163. https://doi.org/10.1504/ijtip.2022.126841

Bragadin, M. A., Pozzi, L., & Kähkönen, K. (2023). Multi-Objective Genetic Algorithm for the Time, Cost, and Quality Trade-Off Analysis in Construction Projects. In G. Lindahl, & S. C. Gottlieb (Eds.), SDGs in Construction Economics and Organization (pp. 193-207). Springer. https://doi.org/10.1007/978-3-031-25498-7_14

Bulkrock, O., BaniMustafa, A., & Qusef, A. (2025). Data-Driven Decision-Making in IT Projects: A Regression Application in Software Estimation. In 2025 International Conference on New Trends in Computing Sciences (ICTCS). https://doi.org/10.1109/ICTCS65341.2025.10989401

Costantino, F., Di Gravio, G., & Nonino, F. (2015). Project Selection in Project Portfolio Management: An Artificial Neural Network Model Based on Critical Success Factors. International Journal of Project Management, 33(8), 1744-1754. https://doi.org/10.1016/j.ijproman.2015.07.003

Craveiro, M., & Domingues, L. (2025). Artificial Intelligence on Project Management Performance Domains. Procedia Computer Science, 256, 1583-1590. https://doi.org/10.1016/j.procs.2025.02.294

Dam, H. K., Tran, T., Ghose, A., Grundy, J., & Kamei, Y. (2019). Towards Effective AI-Powered Agile Project Management. In 2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) (pp. 41-44). https://doi.org/10.1109/ICSE-NIER.2019.00019

Feylizadeh, M. R., Mahmoudi, A., Bagherpour, M., & Li, D.-F. (2018). Project Crashing Using a Fuzzy Multi-Objective Model Considering Time, Cost, Quality, and Risk under Fast Tracking Technique: A Case Study. Journal of Intelligent & Fuzzy Systems, 35(3), 3615-3631. https://doi.org/10.3233/JIFS-18171

Goyal, A., & Gupta, S. (2025). The Impact of Risk Assessment on Project Success based on a Data-Driven Approach. In 2025, the 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) (pp. 1-7). https://doi.org/10.1109/ICDCECE65353.2025.11034967

Haddaway, N. R., Page, M. J., Pritchard, C. C., & McGuinness, L. A. (2022). PRISMA2020: An R Package and Shiny App for Producing PRISMA 2020-Compliant Flow Diagrams, with Interactivity for Optimised Digital Transparency and Open Synthesis. Campbell Systematic Reviews, 18(2), e1230. https://doi.org/10.1002/cl2.1230

Hanafi, A. G., Nawi, M. N. M., Abdul Rahim, M. K. I., Abdul Nifa, F. A., Omar, M. F., & Mohamed, O. (2022). Project Managers Selection in the Construction Industry: Towards the Integration with Artificial Emotional Intelligence and Technology. Journal of Advanced Research in Applied Sciences and Engineering Technology, 29(1), 160-176. https://doi.org/10.37934/araset.29.1.160176

Hazır, Ö. (2015). A Review of Analytical Models, Approaches, and Decision Support Tools in Project Monitoring and Control. International Journal of Project Management, 33(4), 808-815. https://doi.org/10.1016/j.ijproman.2014.09.005

Hughes, L., Mavi, R. K., Aghajani, M., Fitzpatrick, K., Gunaratnege, S. M., Shekarabi, S. A. H., Hughes, R., Khanfar, A., Khatavakhotan, A., Mavi, N. K., Li, K., Mahmoud, M., Malik, T., Mutasa, S., Nafar, F., Yates, R., Alahmad, R., Jeon, I., & Dwivedi, Y. K. (2025). Impact of Artificial Intelligence on Project Management (PM): Multi‐Expert Perspectives on Advancing Knowledge and Driving Innovation Toward PM2030. Journal of Innovation & Knowledge, 10(5), 100772. https://doi.org/10.1016/j.jik.2025.100772

International Project Management Association [IPMA]. (2015). Individual Competence Baseline for Project, Programme and Portfolio Management (version 4.0). Retrieved June 24, 2026, from https://products.ipma.world/wp-content/uploads/2016/03/IPMA_ICB_4_0_WEB.pdf

Isah, M. A., & Kim, B.-S. (2025). Question-Answering System Powered by a Knowledge Graph and a Generative Pretrained Transformer to Support Risk Identification in Tunnel Projects. Journal of Construction Engineering and Management, 151(1), 04024193. https://doi.org/10.1061/JCEMD4.COENG-15230

Jing, L., Marian, M. L., Junqi, W., Alturise, F., & Alkhalaf, S. (2025). Exploring the Association between Cultural Intelligence and Project Performance with the Mediation of Artificial Intelligence Abilities. Journal of Organizational and End User Computing (JOEUC), 37(1), 1-28. https://doi.org/10.4018/JOEUC.372070

Kozhakhmetova, A., Mamyrbayev, A., Zhidebekkyzy, A., & Bilan, S. (2024). Assessing the Impact of Artificial Intelligence on Project Efficiency Enhancement. Knowledge and Performance Management, 8(2), 109-126. https://doi.org/10.21511/kpm.08(2).2024.09

Kumar, V., Pandey, A., & Singh, R. (2021). Can Artificial Intelligence be a Critical Success Factor of Construction Projects? Practitioner Perspectives. Technology Innovation Management Review, 11(11-12), 17-32. https://doi.org/10.22215/timreview/1471

Łukasik-Stachowiak, K. (2025). The Use of Artificial Intelligence in Project Management. Scientific Papers of Silesian University of Technology. Organization and Management Series, (217), 281-299. https://dx.doi.org/10.29119/1641-3466.2025.217.17

Maher, M., & Alneamy, J. S. (2022). An Overview of Machine Learning Approaches to Software Development Cost Estimation. In 2022 8th International Conference on Contemporary Information Technology and Mathematics (ICCITM) (pp. 153-158). https://doi.org/10.1109/ICCITM56309.2022.10032008

Miller, G. J. (2018). Comparative Analysis of Big Data Analytics and BI Projects. In M. Ganzha, L. Maciaszek, & M. Paprzycki (Eds.), Proceedings of the Federated Conference on Computer Science and Information Systems (vol. 15, pp. 701-705). https://doi.org/10.15439/2018F125

Miller, G. J. (2019). Quantitative Comparison of Big Data Analytics and Business Intelligence Project Success Factors. In E. Ziemba (Eds.), Information Technology for Management: Emerging Research and Applications (vol. 346, pp. 53-72). Springer. https://doi.org/10.1007/978-3-030-15154-6_4

Miller, G. J. (2021). Artificial Intelligence Project Success Factors: Moral Decision-Making with Algorithms. In M. Ganzha, L. Maciaszek, M. Paprzycki, & D. Ślęzak (Eds.), Proceedings of the 16th Conference on Computer Science and Intelligence Systems (vol. 25, pp. 379-390). https://doi.org/10.15439/2021F26

Miller, G. J. (2022). Artificial Intelligence Project Success Factors—Beyond the Ethical Principles. In E. Ziemba, & W. Chmielarz (Eds.), Information Technology for Management: Business and Social Issues (vol. 442, pp. 65-96). Springer. https://doi.org/10.1007/978-3-030-98997-2_4

Müller, W., Grassinger, R., Schnebel, S., Stratmann, J., Weitzel, H., Aumann, A., Bernhard, G., Gaidetzka, M., Heiberger, L., Kreyer, I., Schmidt, C., Uhl, P., Visotschnig, M., & Widmann, J. (2021). Integration of Digital Competences into a Teacher Education Program: A Sensitive Approach. In Proceedings of the 13th International Conference on Computer Supported Education (vol. 1, pp. 232-242). SciTePress. https://doi.org/10.5220/0010527202320242

Okika, M. C., Vermeulen, A., & Pretorius, J. H. C. (2025). A Systematic Approach to Identify and Manage Supply Chain Risks in Construction Projects. Journal of Financial Management of Property and Construction, 30(1), 42-66. https://doi.org/10.1108/JFMPC-09-2023-0057

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J., Akl, E., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M., Li, T., Loder, E., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ, 372. https://doi.org/10.1136/bmj.n71

Project Management Institute [PMI]. (2021). A Guide to the Project Management Body of Knowledge and the Standard for Project Management (7th edition).

Project Management Institute [PMI]. (2024). Artificial Intelligence and Project Management: A Global Chapter-Led Survey 2024. Retrieved June 24, 2026, from https://www.pmi.org/-/media/pmi/documents/public/pdf/artificial-intelligence/community-led-ai-and-project-management-report.pdf

Rakade, T. (2020). To Study the Emerging Trends in Project Management: AI in Project Management and Emotional Intelligence, and to Assess Their Effects in the Industry. In H. Keathley, J. Enos, & M. Parrish (Eds.), Proceedings of the American Society for Engineering Management 2020 International Annual Conference (pp. 1-6). American Society for Engineering Management.

Rehan, A., Thorpe, D., & Heravi, A. (2024a). A Framework for Leadership Practices and Communication in the Context of the Construction Sector. Project Leadership and Society, 5, 100142. https://doi.org/10.1016/j.plas.2024.100142

Rehan, A., Thorpe, D., & Heravi, A. (2024b). Project Manager's Leadership Behavioural Practices: A Systematic Literature Review. Asia Pacific Management Review, 29(2), 165-178. https://doi.org/10.1016/j.apmrv.2023.12.005

Singla, A., Sukharevsky, A., Yee, L., Chui, M., Hall, B., & Balakrishnan, T. (2025a). The State of AI in 2025: Agents, Innovation, and Transformation. McKinsey & Company.

Singla, A., Sukharevsky, A., Yee, L., Chui, M., & Hall, B. (2025b). The State of AI: How Organisations Are Rewiring to Capture Value. McKinsey & Company.

Siripurapu, A., Nowpada, R. S., & Rao, K. S. (2022). Improving Dijkstra’s Algorithm for Estimating Project Characteristics and Critical Path. Reliability: Theory & Applications, 17(4(71), 65-73. https://doi.org/10.24412/1932-2321-2022-471-65-73

Sravanthi, J., Sobti, R., Semwal, A., Shravan, M., Al-Hilali, A. A., & Bader Alazzam, M. (2023). AI-Assisted Resource Allocation in Project Management. In 2023, the 3rd International Conference on Advanced Computing and Innovative Technologies in Engineering (ICACITE) (pp. 70-74). https://doi.org/10.1109/ICACITE57410.2023.10182760

Titu, A. M., Pană, M. M., & Moldoveanu, A. M. (2025). The Impact of Artificial Intelligence in the Project Manager Role. In D. Cavallucci, S. Brad, & P. Livotov (Eds.), World Conference of AI-Powered Innovation and Inventive Design (vol. 736). Springer. https://doi.org/10.1007/978-3-031-75923-9_27

Vuorikari, R., Kluzer, S., & Punie, Y. (2022). DigComp 2.2: The Digital Competence Framework for Citizens: With New Examples of Knowledge, Skills, and Attitudes. Publications Office of the European Union. https://doi.org/10.2760/115376

Zarghami, S. A., & Zwikael, O. (2022). Measuring Project Resilience: Learning from the Past to Enhance Decision Making in the Face of Disruption. Decision Support Systems, 160, 113831. https://doi.org/10.1016/j.dss.2022.113831

Opublikowane

2026-07-23

Numer

Dział

Artykuły

Kategorie

Received 2025-12-03
Accepted 2026-03-18
Published 2026-07-23