Leveraging Artificial Intelligence in Supporting Remote Information Technology Project Management: A Systematic Literature Review
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Published
May 18, 2026
Abstract
The transition to remote IT project management presents unique challenges in collaboration, resource allocation, and adaptability. While Artificial Intelligence (AI) is increasingly demanded as a strategic enabler to manage these complexities, its specific application within remote IT environments, particularly through the principles-based PMBOK7 framework, remains underexplored. To address this literature gap, this study presents a Systematic Literature Review (SLR) analyzing peer-reviewed literature published between 2021 and 2026. The review identifies 11 empirically validated studies and extracted 8 unique AI implementations, namely machine learning, natural language processing, neural network, multi-agent systems, large language model, fuzzy logic, genetic algorithm, and probability graph models. These technological implementations are systematically mapped across the eight PMBOK7 performance domains and five primary areas of change in remote IT work. The findings indicate that while empirical AI implementation in this domain is still in its infancy, it fundamentally enhances aspects like stakeholder communication, team flexibility, data-driven project planning, and holistic view of the project for project managers. Ultimately, this study serves as a foundational stepping stone for global IT organizations transitioning toward AI-assisted distributed project management
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How to Cite
Prasetyo, S. A., Raharjo, T. and Fitriani, A. N. (2026) “Leveraging Artificial Intelligence in Supporting Remote Information Technology Project Management: A Systematic Literature Review”, Ranah Research : Journal of Multidisciplinary Research and Development, 8(3), pp. 1784-1798. doi: 10.38035/rrj.v8i3.2083.
References
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Aljohani, B., Aljuhani, A., & Alsanoosy, T. (2025). Enhancing agile requirements change management. International Journal of Advanced Computer Science and Applications, 16(3). https://doi.org/10.14569/IJACSA.2025.01603103
Bradbury, P., Jamil, T., Mills, C., Shermon, D., Murray-Webster, R., & Dalcher, D. (2019). APM body of knowledge. Association for Project Management.
Carrera-Rivera, A., Ochoa, W., Larrinaga, F., & Lasa, G. (2022). How to conduct a systematic literature review: A quick guide for computer science research. MethodsX, 9, 101895. https://doi.org/10.1016/j.mex.2022.101895
Choudhury, P., Foroughi, C., & Larson, B. (2021). Work-from-anywhere: The productivity effects of geographic flexibility. Strategic Management Journal, 42(4), 655–683. https://doi.org/10.1002/smj.3251
Cinkusz, K., Chudziak, J. A., & Niewiadomska-Szynkiewicz, E. (2024). Cognitive agents powered by large language models for agile software project management. Electronics, 14(1), 87. https://doi.org/10.3390/electronics14010087
Comprehensive project management framework using machine learning. (2019). International Journal of Recent Technology and Engineering, 8(2S3), 1373–1377. https://doi.org/10.35940/ijrte.B1256.0782S319
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
Danielak, W., & Wysocki, R. (2022). The impact of remote work during the COVID-19 pandemic on the development of competences in selected areas of project management. Annales Universitatis Mariae Curie-Skłodowska, Sectio H–Oeconomia, 56(2), 7–20.
de Souza Santos, R. E., & Ralph, P. (2022). A grounded theory of coordination in remote-first and hybrid software teams. In Proceedings of the 44th International Conference on Software Engineering (pp. 25–35). ACM. https://doi.org/10.1145/3510003.3510105
Dwivedi, Y. K., et al. (2025). GenAI’s impact on global IT management: A multi-expert perspective and research agenda. Journal of Global Information Technology Management, 28(1), 49–63. https://doi.org/10.1080/1097198X.2025.2454192
Gayathri, J., et al. (2025). AI-powered dynamic task allocation for agile work environments. In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV) (pp. 1253–1259). IEEE. https://doi.org/10.1109/ICVADV63329.2025.10961466
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. Campbell Systematic Reviews, 18(2). https://doi.org/10.1002/cl2.1230
Harini, P., et al. (2025). Agentic AI-driven decision orchestration system for real-time project coordination. In 2025 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) (pp. 1–7). IEEE. https://doi.org/10.1109/ICECONF65644.2025.11379478
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). https://doi.org/10.14569/IJACSA.2023.0140940
Hefny, A. H., Dafoulas, G. A., & Ismail, M. A. (2021). A proactive management assistant chatbot for software engineering teams. In 2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES) (pp. 295–300). IEEE. https://doi.org/10.1109/NILES53778.2021.9600547
Jariwala, M. (2024). Incorporating artificial intelligence into PMBOK 7th edition frameworks: A domain-specific investigation for optimizing project management performance domains. International Journal of Trend in Scientific Research and Development, 8(3), 63–71.
Kadu, A. N., & Kimmatkar, N. W. (2024). Ensembling of deep learning models to evaluate software effort estimation. In 15th International Conference on Advances in Computing, Control, and Telecommunication Technologies (pp. 1210–1219).
Katari, P., Thota, S., Chitta, S., Venkata, A. K. P., & Ahmad, T. (2021). Remote project management: Best practices for distributed teams in the post-pandemic era. Australian Journal of Machine Learning Research & Applications, 1(2), 145–167.
Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering.
Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in software engineering: A systematic literature review. Information and Software Technology, 51(1), 7–15. https://doi.org/10.1016/j.infsof.2008.09.009
Li, Y., et al. (2024). Fine-SE: Integrating semantic features and expert features for software effort estimation. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering (pp. 1–12). ACM. https://doi.org/10.1145/3597503.3623349
Lou, Y., et al. (2025). Addressing class imbalance with probabilistic graphical models and variational inference. In 2025 5th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA) (pp. 1238–1242). IEEE. https://doi.org/10.1109/AIITA65135.2025.11047653
Machado, D. S. M., de Sousa Pereira, R. F., & Bianchi, I. S. (2021). Remote project management: Challenges and best practices. https://www.proquest.com/
Mood, S. (2025). Practical AI in agile project management: Reducing friction in distributed teams. In 2025 IEEE 6th India Council International Subsections Conference (INDISCON) (pp. 1–4). IEEE. https://doi.org/10.1109/INDISCON66021.2025.11251566
Nankap, L. H., Bouchard, B., Imbeau, G., & Francillette, Y. (2025). Enhancing agile project management for remote teams. In Proceedings of the 2025 8th International Conference on Software Engineering and Information Management (pp. 17–23). ACM. https://doi.org/10.1145/3725899.3725902
Nenni, M. E., De Felice, F., De Luca, C., & Forcina, A. (2025). How artificial intelligence will transform project management in the age of digitization: A systematic literature review. Management Review Quarterly, 75(2), 1669–1716. https://doi.org/10.1007/s11301-024-00418-z
Nieto-Rodriguez, A., & Vargas, R. V. (2023). How AI will transform project management. Harvard Business Review. https://hbr.org/2023/02/how-ai-will-transform-project-management
Nilsson, M., Chervenova, A., Chazbeck, R., & Cardena, J. (2025). AI in project management: One year later – 2025 and beyond.
Packiam, B., et al. (2025). Enhancing project risk management through Bayesian networks and transformer-based time series forecasting framework. In 2025 International Conference on Responsible, Generative and Explainable AI (ResGenXAI) (pp. 1–6). IEEE. https://doi.org/10.1109/ResgenXAI64788.2025.11343992
Page, M. J., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, n71. https://doi.org/10.1136/bmj.n71
Project Management Institute. (2021). A guide to the project management body of knowledge (PMBOK® guide) – Seventh edition and the standard for project management.
Project Management Institute. (2025). The standard for project management and a guide to the project management body of knowledge (PMBOK guide).
Raharjo, T., Purwandari, B., Budiardjo, E. K., & Yuniarti, R. (2023). The essence of software engineering framework-based model for an agile software development method. International Journal of Advanced Computer Science and Applications, 14(7). https://doi.org/10.14569/IJACSA.2023.0140788
Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
Salimimoghadam, S., et al. (2025). The rise of artificial intelligence in project management: A systematic literature review of current opportunities, enablers, and barriers. Buildings, 15(7), 1130. https://doi.org/10.3390/buildings15071130
Sarkis-Onofre, R., Catalá-López, F., Aromataris, E., & Lockwood, C. (2021). How to properly use the PRISMA statement. Systematic Reviews, 10(1), 117. https://doi.org/10.1186/s13643-021-01671-z
Shastri, Y., Hoda, R., & Amor, R. (2021). The role of the project manager in agile software development projects. Journal of Systems and Software, 173, 110871. https://doi.org/10.1016/j.jss.2020.110871
Supriyadi, S., & Nasution, Z. (2024). Teknologi artificial intelligence (AI) dan literasi digital mahasiswa terhadap hasil belajar mata kuliah evaluasi pembelajaran. Jurnal Teknodik, 113–118. https://doi.org/10.32550/teknodik.vi.1185
Ugochukwu, E. S., Khan, S., Jonathan, G. M., & Aasi, P. (2025). IT project management in remote work environments. Procedia Computer Science, 263, 539–547. https://doi.org/10.1016/j.procs.2025.07.065
Wahono, P., et al. (2025). Digital nomad di era jarak jauh: Tren dan strategi. CV Widina Media Utama.
William, P., Kumar, P., Chhabra, G. S., & Vengatesan, K. (2021). Task allocation in distributed agile software development using machine learning approach. In 2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON) (pp. 168–172). IEEE. https://doi.org/10.1109/CENTCON52345.2021.9688114
Zhi, H., & Liu, S. (2019). Face recognition based on genetic algorithm. Journal of Visual Communication and Image Representation, 58, 495–502. https://doi.org/10.1016/j.jvcir.2018.12.012
Zhou, Y., & Zhao, L. (2025). Research on schedule optimization of information system project based on genetic algorithm. In 2025 8th International Symposium on Big Data and Applied Statistics (ISBDAS) (pp. 363–369). IEEE. https://doi.org/10.1109/ISBDAS64762.2025.11116864
Aljohani, B., Aljuhani, A., & Alsanoosy, T. (2025). Enhancing agile requirements change management. International Journal of Advanced Computer Science and Applications, 16(3). https://doi.org/10.14569/IJACSA.2025.01603103
Bradbury, P., Jamil, T., Mills, C., Shermon, D., Murray-Webster, R., & Dalcher, D. (2019). APM body of knowledge. Association for Project Management.
Carrera-Rivera, A., Ochoa, W., Larrinaga, F., & Lasa, G. (2022). How to conduct a systematic literature review: A quick guide for computer science research. MethodsX, 9, 101895. https://doi.org/10.1016/j.mex.2022.101895
Choudhury, P., Foroughi, C., & Larson, B. (2021). Work-from-anywhere: The productivity effects of geographic flexibility. Strategic Management Journal, 42(4), 655–683. https://doi.org/10.1002/smj.3251
Cinkusz, K., Chudziak, J. A., & Niewiadomska-Szynkiewicz, E. (2024). Cognitive agents powered by large language models for agile software project management. Electronics, 14(1), 87. https://doi.org/10.3390/electronics14010087
Comprehensive project management framework using machine learning. (2019). International Journal of Recent Technology and Engineering, 8(2S3), 1373–1377. https://doi.org/10.35940/ijrte.B1256.0782S319
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
Danielak, W., & Wysocki, R. (2022). The impact of remote work during the COVID-19 pandemic on the development of competences in selected areas of project management. Annales Universitatis Mariae Curie-Skłodowska, Sectio H–Oeconomia, 56(2), 7–20.
de Souza Santos, R. E., & Ralph, P. (2022). A grounded theory of coordination in remote-first and hybrid software teams. In Proceedings of the 44th International Conference on Software Engineering (pp. 25–35). ACM. https://doi.org/10.1145/3510003.3510105
Dwivedi, Y. K., et al. (2025). GenAI’s impact on global IT management: A multi-expert perspective and research agenda. Journal of Global Information Technology Management, 28(1), 49–63. https://doi.org/10.1080/1097198X.2025.2454192
Gayathri, J., et al. (2025). AI-powered dynamic task allocation for agile work environments. In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV) (pp. 1253–1259). IEEE. https://doi.org/10.1109/ICVADV63329.2025.10961466
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. Campbell Systematic Reviews, 18(2). https://doi.org/10.1002/cl2.1230
Harini, P., et al. (2025). Agentic AI-driven decision orchestration system for real-time project coordination. In 2025 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) (pp. 1–7). IEEE. https://doi.org/10.1109/ICECONF65644.2025.11379478
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). https://doi.org/10.14569/IJACSA.2023.0140940
Hefny, A. H., Dafoulas, G. A., & Ismail, M. A. (2021). A proactive management assistant chatbot for software engineering teams. In 2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES) (pp. 295–300). IEEE. https://doi.org/10.1109/NILES53778.2021.9600547
Jariwala, M. (2024). Incorporating artificial intelligence into PMBOK 7th edition frameworks: A domain-specific investigation for optimizing project management performance domains. International Journal of Trend in Scientific Research and Development, 8(3), 63–71.
Kadu, A. N., & Kimmatkar, N. W. (2024). Ensembling of deep learning models to evaluate software effort estimation. In 15th International Conference on Advances in Computing, Control, and Telecommunication Technologies (pp. 1210–1219).
Katari, P., Thota, S., Chitta, S., Venkata, A. K. P., & Ahmad, T. (2021). Remote project management: Best practices for distributed teams in the post-pandemic era. Australian Journal of Machine Learning Research & Applications, 1(2), 145–167.
Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering.
Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in software engineering: A systematic literature review. Information and Software Technology, 51(1), 7–15. https://doi.org/10.1016/j.infsof.2008.09.009
Li, Y., et al. (2024). Fine-SE: Integrating semantic features and expert features for software effort estimation. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering (pp. 1–12). ACM. https://doi.org/10.1145/3597503.3623349
Lou, Y., et al. (2025). Addressing class imbalance with probabilistic graphical models and variational inference. In 2025 5th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA) (pp. 1238–1242). IEEE. https://doi.org/10.1109/AIITA65135.2025.11047653
Machado, D. S. M., de Sousa Pereira, R. F., & Bianchi, I. S. (2021). Remote project management: Challenges and best practices. https://www.proquest.com/
Mood, S. (2025). Practical AI in agile project management: Reducing friction in distributed teams. In 2025 IEEE 6th India Council International Subsections Conference (INDISCON) (pp. 1–4). IEEE. https://doi.org/10.1109/INDISCON66021.2025.11251566
Nankap, L. H., Bouchard, B., Imbeau, G., & Francillette, Y. (2025). Enhancing agile project management for remote teams. In Proceedings of the 2025 8th International Conference on Software Engineering and Information Management (pp. 17–23). ACM. https://doi.org/10.1145/3725899.3725902
Nenni, M. E., De Felice, F., De Luca, C., & Forcina, A. (2025). How artificial intelligence will transform project management in the age of digitization: A systematic literature review. Management Review Quarterly, 75(2), 1669–1716. https://doi.org/10.1007/s11301-024-00418-z
Nieto-Rodriguez, A., & Vargas, R. V. (2023). How AI will transform project management. Harvard Business Review. https://hbr.org/2023/02/how-ai-will-transform-project-management
Nilsson, M., Chervenova, A., Chazbeck, R., & Cardena, J. (2025). AI in project management: One year later – 2025 and beyond.
Packiam, B., et al. (2025). Enhancing project risk management through Bayesian networks and transformer-based time series forecasting framework. In 2025 International Conference on Responsible, Generative and Explainable AI (ResGenXAI) (pp. 1–6). IEEE. https://doi.org/10.1109/ResgenXAI64788.2025.11343992
Page, M. J., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, n71. https://doi.org/10.1136/bmj.n71
Project Management Institute. (2021). A guide to the project management body of knowledge (PMBOK® guide) – Seventh edition and the standard for project management.
Project Management Institute. (2025). The standard for project management and a guide to the project management body of knowledge (PMBOK guide).
Raharjo, T., Purwandari, B., Budiardjo, E. K., & Yuniarti, R. (2023). The essence of software engineering framework-based model for an agile software development method. International Journal of Advanced Computer Science and Applications, 14(7). https://doi.org/10.14569/IJACSA.2023.0140788
Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
Salimimoghadam, S., et al. (2025). The rise of artificial intelligence in project management: A systematic literature review of current opportunities, enablers, and barriers. Buildings, 15(7), 1130. https://doi.org/10.3390/buildings15071130
Sarkis-Onofre, R., Catalá-López, F., Aromataris, E., & Lockwood, C. (2021). How to properly use the PRISMA statement. Systematic Reviews, 10(1), 117. https://doi.org/10.1186/s13643-021-01671-z
Shastri, Y., Hoda, R., & Amor, R. (2021). The role of the project manager in agile software development projects. Journal of Systems and Software, 173, 110871. https://doi.org/10.1016/j.jss.2020.110871
Supriyadi, S., & Nasution, Z. (2024). Teknologi artificial intelligence (AI) dan literasi digital mahasiswa terhadap hasil belajar mata kuliah evaluasi pembelajaran. Jurnal Teknodik, 113–118. https://doi.org/10.32550/teknodik.vi.1185
Ugochukwu, E. S., Khan, S., Jonathan, G. M., & Aasi, P. (2025). IT project management in remote work environments. Procedia Computer Science, 263, 539–547. https://doi.org/10.1016/j.procs.2025.07.065
Wahono, P., et al. (2025). Digital nomad di era jarak jauh: Tren dan strategi. CV Widina Media Utama.
William, P., Kumar, P., Chhabra, G. S., & Vengatesan, K. (2021). Task allocation in distributed agile software development using machine learning approach. In 2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON) (pp. 168–172). IEEE. https://doi.org/10.1109/CENTCON52345.2021.9688114
Zhi, H., & Liu, S. (2019). Face recognition based on genetic algorithm. Journal of Visual Communication and Image Representation, 58, 495–502. https://doi.org/10.1016/j.jvcir.2018.12.012
Zhou, Y., & Zhao, L. (2025). Research on schedule optimization of information system project based on genetic algorithm. In 2025 8th International Symposium on Big Data and Applied Statistics (ISBDAS) (pp. 363–369). IEEE. https://doi.org/10.1109/ISBDAS64762.2025.11116864