Artificial intelligence in construction project management: Trends, challenges and future directions
DOI:
https://doi.org/10.47818/DRArch.2025.v6i2165Keywords:
construction project management, digital transformation in construction, smart construction technologies, artificial intelligenceAbstract
Contemporary construction projects are characterized by escalating complexity, voluminous data flows, and stringent sustainability requirements, rendering conventional project management methods increasingly inadequate. In response, artificial intelligence (AI) has emerged as a transformative enabler in construction project management, offering advanced capabilities in predictive analytics, process automation, and intelligent decision support. This paper explores the role of AI in the identified principal functions of construction project management, including time management, cost estimation, quality assurance, occupational health and safety, risk mitigation, resource optimization, and design management through a narrative literature review. Analysis demonstrates that AI-driven approaches significantly enhance operational efficiency and system resilience by enabling proactive identification of schedule delays, cost overruns, and safety hazards. For example, image-recognition systems integrated with Internet-of-Things sensors facilitate real-time monitoring of site conditions and adaptive response to disruptions, while neural-network models trained on historical project data yield more accurate cost forecasts than traditional estimation techniques. In the design management domain, generative design algorithms and AI-enhanced BIM integration have the potential to automate clash detection, optimize form and function, and generate innovative design alternatives that align with cost, energy, and sustainability objectives. Beyond efficiency gains, AI fosters a paradigm shift toward predictive, data-driven, and adaptive management practices that strengthen project resilience, enabling teams to anticipate, absorb, and recover from unforeseen challenges while improving project performance and sustainability. Critical barriers to widespread AI adoption are also identified in this study. Fragmented and non-standardized data ecosystems impede model training and interoperability with legacy systems, while organizational resistance and a shortage of professionals skilled in both AI and construction hinder implementation. Ethical and legal concerns—stemming from the “black-box” nature of many AI algorithms—further complicate accountability in safety-critical decisions. By synthesizing these challenges, the strategic role of AI is highlighted not only as a technological innovation but also as a catalyst for cultural and organizational transformation toward more resilient project delivery. Targeted future research directions include empirical validation of AI tools in live project environments, development of sector-specific AI frameworks tailored to the peculiarities of the construction industry, interdisciplinary collaboration among engineers, data scientists, and managers, and educational initiatives to upskill the workforce. Collectively, these steps will help bridge the gap between theoretical potential and real-world impact, positioning AI as a cornerstone of intelligent, resilient, sustainable, and high-performing construction project management.
Metrics
References
- Abioye, S. O., Oyedele, L. O., Akanbi, L., Ajayi, A., Delgado, J. M. D., Bilal, M., Akinade, O. O., & Ahmed, A. (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44. https://doi.org/10.1016/j.jobe.2021.103299
- Adebayo, Y., Udoh, P., Kamudyariwa, X. B., & Osobajo, O. A. (2025). Artificial intelligence in construction project management: A structured literature review of its evolution in application and future trends. Digital, 5(3), 26. https://doi.org/10.3390/digital5030026
- Ajmal, M. M., Helo, P., & Kekäle, T. (2010). Critical factors for knowledge management in project business. Journal of Knowledge Management, 14(1), 156-168. https://doi.org/10.1108/13673271011015633
- Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O., & Ahmed, A. A. (2020). Deep learning in the construction industry: A review of present status and future innovations. Journal of Building Engineering, 32. https://doi.org/10.1016/j.jobe.2020.101827
- Aladağ, H., Güven, İ., & Ballı, O. (2024). Contribution of artificial intelligence (AI) to construction project management processes: State of the art with scoping review method. Sigma Journal of Engineering and Natural Sciences, 42(5), 1654-1669. https://dx.doi.org/10.14744/sigma.2024.00125
- Alanne, K. (2004). Selection of renovation actions using multi-criteria ‘knapsack’ model. Automation in Construction, 13(3), 377-391. https://doi.org/10.1016/j.autcon.2003.12.004
- Alashwal, A. M., Abdul-Rahman, H., & Asef, A. (2017). Influence of organizational learning and firm size on risk management maturity. Journal of Management in Engineering, 33(6). https://doi.org/10.1061/(ASCE)ME.1943-5479.0000553
- Allam, Z., & Dhunny, Z. A. (2019). On big data, artificial intelligence and smart cities. Cities, 89, 80-91. https://doi.org/10.1016/j.cities.2019.01.032
- Al-Mhdawi, M. K. S., Qazi, A., Alzarrad, A., Dacre, N., Rahimian, F., Buniya, M. K., & Zhang, H. (2023). Expert evaluation of ChatGPT performance for risk management process based on ISO 31000 standard. SSRN Electronic Journal. https://dx.doi.org/10.2139/ssrn.4504409
- Al-Sinan, M. A., Almohamad, Bubshait, A. A., & Aljaroudi, Z. (2024). Generation of construction scheduling through machine learning and BIM: A blueprint. Buildings, 14(4), 934. https://doi.org/10.3390/buildings14040934
- Asadi, E., Gameiro de Silva, M. C., Antunes, C. H., & Dias, L. C. (2012). A multi-objective optimization model for building retrofit strategies using TRNSYS simulations, GenOpt and MATLAB. Building and Environment, 56, 370-378. https://doi.org/10.1016/j.buildenv.2012.04.005
- Autodesk. (n.d.). Generative design. Retrieved May 27, 2024, from https://www.autodesk.com/solutions/generative-design
- Bai, C., Dallasega, P., Orzes, G., & Sarkis, J. (2020). Industry 4.0 technologies assessment: A sustainability perspective. International Journal of Production Economics, 229. https://doi.org/10.1016/j.ijpe.2020.107776
- Bang, S., & Olsson, N. (2022). Artificial intelligence in construction projects: A systematic scoping review. Journal of Engineering, Project, and Production Management, 12(3), 224-238. https://doi.org/10.32738/JEPPM-2022-0021
- Bilal, M., Oyedele, L. O., Qadir, J., Munir, K., Ajayi, S. O., Akinade, O. O., Owolabi, H. A., Alaka, H. A., & Pasha, M. (2016). Big data in the construction industry: A review of present status, opportunities, and future trends. Advanced Engineering Informatics, 30(3), 500-521. https://doi.org/10.1016/j.aei.2016.07.001
- Blanco, J. L., Mullin, A., Pandya, K., & Sridhar, M. (2017). The new age of engineering and construction technology. McKinsey and Company. https://www.mckinsey.com/industries/capital-projects-and-infrastructure/our-insights/the-new-age-of-engineering-and-construction-technology
- Bock, T., & Linner, T. (2015). Robot oriented design: Design and management tools for the deployment of automation and robotics in construction. Cambridge University Press.
- Bölek, B., Tutal, O., & Özbaşaran, H. (2023). A systematic review on artificial intelligence applications in architecture. Journal of Design for Resilience in Architecture and Planning, 4(1), 91-104. https://doi.org/10.47818/DRArch.2023.v4i1085
- Burger, R. (2017). I, project manager: The rise of artificial intelligence in the workplace. Capterra Blog. https://blog.capterra.com/i-project-manager-the-rise-of-artificial-intelligence-in-the-workplace
- Chen, K., Zhou, X., Bao, Z., Skibniewski, M. J., & Fang, W. (2025). Artificial intelligence in infrastructure construction: A critical review. Frontiers of Engineering Management, 12(1), 24-38. https://doi.org/10.1007/s42524-024-3128-5
- Cheng, M.-Y., Vu, Q.-T., & Gosal, F. E. (2025). Hybrid deep learning model for accurate cost and schedule estimation in construction projects using sequential and non-sequential data. Automation in Construction, 170. https://doi.org/10.1016/j.autcon.2024.105904
- Clark, J. (2015). Why 2015 was a breakthrough year in artificial intelligence. Bloomberg. https://www.bloomberg.com/news/articles/2015-12-08/why-2015-was-a-breakthrough-year-in-artificial-intelligence
- Doxel AI. (n.d.). Physical intelligence for construction. Retrieved April 11, 2025, from https://www.doxel.ai/
- Eadie, R., Browne, M., Odeyinka, H., McKeown, C., & McNiff, S. (2013). BIM implementation throughout the UK construction project lifecycle: An analysis. Automation in Construction, 36, 145-151. https://doi.org/10.1016/j.autcon.2013.09.001
- Ekanayake, B., Wong, J. K. W., Fini, A. A. F., Smith, P., & Thengane, V. (2024). Deep learning-based computer vision in project management: Automating indoor construction progress monitoring. Project Leadership and Society, 5. https://doi.org/10.1016/j.plas.2024.100149
- Elghaish, F., Abrishami, S., Hosseini, M. R., Abu-Samra, S., & Gaterell, M. (2019). Integrated project delivery with BIM: An automated EVM-based approach. Automation in Construction, 106. https://doi.org/10.1016/j.autcon.2019.102907
- Flyvbjerg, B., Holm, M. K. S., & Buhl, S. L. (2003). How common and how large are cost overruns in transport infrastructure projects? Transport Reviews, 23(1), 71-88. https://doi.org/10.1080/01441640309904
- Hasan, M. M., Kasedullah, M., Ripon, M. B. B., & Khan, M. M. H. (2025). AI-driven quality control in manufacturing and construction: Enhancing precision and reducing human error. Applied IT and Engineering, 3(1), 1-10. https://doi.org/10.25163/engineering.3110270
- Hinze, J., Thurman, S., & Wehle, A. (2013). Leading indicators of construction safety performance. Safety Science, 51(1), 23-28. https://doi.org/10.1016/j.ssci.2012.05.016
- Jiang, Y., Su, S., Zhao, S., Zhong, R. Y., Qiu, W., Skibniewski, M. J., Brilakis, I., & Huang, G. Q. (2024). Digital twin-enabled synchronized construction management: A roadmap from construction 4.0 towards future prospect. Developments in the Built Environment, 19. https://doi.org/10.1016/j.dibe.2024.100512
- Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25. https://doi.org/10.1016/j.bushor.2018.08.004
- Kerzner, H. (2017). Project management: A systems approach to planning, scheduling, and controlling (12th ed.). John Wiley and Sons Inc.
- Khosrowshahi, F., & Arayici, Y. (2012). Roadmap for implementation of BIM in the UK construction industry. Engineering, Construction and Architectural Management, 19(6), 610-635. https://doi.org/10.1108/09699981211277531
- Kovačević, S. M., & Bouder, F. (2023). The use of AI algorithms in architecture, engineering and construction: A tool for crisis prevention? The uncertainty perspective. Journal of Design for Resilience in Architecture and Planning, 4(SI), 39-50. https://doi.org/10.47818/DRArch.2023.v4si108
- Kowalski, M., Zelewski, S., Bergenrodt, D., & Klüpfel, H. (2012). Application of new techniques of artificial intelligence in logistics: An ontology-driven case-based reasoning approach. Proceedings of the European Simulation and Modelling Conference (ESM 2012). FOM, Essen, Germany.
- Lee, J., & Lee, S. (2023). Construction site safety management: A computer vision and deep learning approach. Sensors, 23(2), 944. https://doi.org/10.3390/s23020944
- Love, P. E. D., Edwards, D. J., & Irani, Z. (2012). Moving beyond optimism bias and strategic misrepresentation: An explanation for social infrastructure project cost overruns. IEEE Transactions on Engineering Management, 59(4), 560-571. https://doi.org/10.1109/TEM.2011.2163628
- Love, P. E. D., Edwards, D. J., & Smith, J. (2016). Rework causation: Emergent theoretical insights and implications for research. Journal of Construction Engineering and Management, 142(6). https://doi.org/10.1061/(ASCE)CO.1943-7862.0001114
- Luger, G. F. (2004). Artificial intelligence: Structures and strategies for complex problem solving (5th ed.). Addison-Wesley.
- Microsoft. (n.d.). What is artificial intelligence? Microsoft Azure cloud computing dictionary. Retrieved April 11, 2025, from https://azure.microsoft.com/tr-tr/resources/cloud-computing-dictionary/what-is-artificial-intelligence#s%C3%BCr%C3%BCc%C3%BCs%C3%BCz-ara%C3%A7lar
- Mohamed, M. A. S., Al-Mhdawi, M. K. S., Ojiako, U., Dacre, N., Qazi, A., & Rahimian, F. (2025). Generative AI in construction risk management: A bibliometric analysis of the associated benefits and risks. Urbanization, Sustainability and Society, 2(1), 196-228. https://doi.org/10.1108/USS-11-2024-0069
- Nousiainen, L. (2008). Improving internal information flow: Case: The case company [Master’s thesis, Tampere University]. Tampere Polytechnic Business School. https://www.theseus.fi/bitstream/handle/10024/8506/Nousiainen.Leena.pdf;jsessionid=6B9%20A4F4A7976E1A0B7446D6445B95636?sequence=2
- Oti, A. H., Tah, J. H. M., & Abanda, F. H. (2018). Integration of lessons learned knowledge in building information modeling. Journal of Construction Engineering and Management, 144(9). https://doi.org/10.1061/(ASCE)CO.1943-7862.0001537
- Perera, S., Nanayakkara, S., Rodrigo, M. N. N., Senaratne, S., & Weinand, R. (2020). Blockchain technology: Is it hype or real in the construction industry? Journal of Industrial Information Integration, 17. https://doi.org/10.1016/j.jii.2020.100125
- Project Management Institute (PMI). (2017). A guide to the project management body of knowledge (PMBOK guide) (6th ed.). Project Management Institute.
- Riter. (2019). Artificial intelligence in today's project management. Riter Blog. Retrieved May 23, 2024, from https://riter.co/blog/artificial-intelligence-in-today-s-project-management
- Russell, S. J., & Norvig, P. (2009). Artificial intelligence: A modern approach (3rd ed.). Prentice Hall.
- Sacks, R., Girolami, M., & Brilakis, I. (2020). Building information modelling, artificial intelligence and construction tech. Developments in the Built Environment, 4. https://doi.org/10.1016/j.dibe.2020.100011
- Salimimoghadam, S., Ghanbaripour, A. N., Tumpa, R. J., Kamel Rahimi, A., Golmoradi, M., Rashidian, S., & Skitmore, M. (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
- Schneeweiss, S. (2014). Learning from big health care data. The New England Journal of Medicine, 370(23), 2161-2163. https://doi.org/10.1056/nejmp1401111
- Shamim, M. M. I., Hamid, A. B. b. A., Nyamasvisva, T. E., & Rafi, N. S. B. (2025). Advancement of artificial intelligence in cost estimation for project management success: A systematic review of machine learning, deep learning, regression, and hybrid models. Modelling, 6(2), 35. https://doi.org/10.3390/modelling6020035
- Smith, O., & Kanner, J. (2017). AI for construction safety-Reducing job site risk. Autodesk. Retrieved May 20, 2024, from https://www.autodesk.com/autodesk-university/class/AI-Construction-Safety-Reducing-Job-Site-Risk-2017
- Tian, K., Zhu, Z., Mbachu, J., Ghanbaripour, A., & Moorhead, M. (2025). Artificial intelligence in risk management within the realm of construction projects: A bibliometric analysis and systematic literature review. Journal of Innovation and Knowledge, 10(3). https://doi.org/10.1016/j.jik.2025.100711
- Toor, S. U. R., & Ogunlana, S. O. (2010). Beyond the ‘iron triangle’: Stakeholder perception of key performance indicators (KPIs) for large-scale public sector development projects. International Journal of Project Management, 28(3), 228-236. https://doi.org/10.1016/j.ijproman.2009.05.005
- Tran, S. V.-T., Yang, J., Hussain, R., Khan, N., Kimito, E. C., Pedro, A., Sotani, M., Lee, U.-K., & Park, C. (2024). Leveraging large language models for enhanced construction safety regulation extraction. Journal of Information Technology in Construction (ITcon), 29, 1026-1038. https://doi.org/10.36680/j.itcon.2024.045
- United Nations Environment Programme (UNEP). (2021). 2021 global status report for buildings and construction. Retrieved May 20, 2024, from https://www.unep.org/resources/report/2021-global-status-report-buildings-and-construction
- Walker, A. (2015). Project management in construction (6th ed.). Wiley-Blackwell.
- Zhai, X., Liu, A. M. M., & Fellows, R. (2013). Role of human resource practices in enhancing organizational learning in Chinese construction organizations. Journal of Management in Engineering, 30(2), 194-204. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000207
- Zou, P. X. W., Zhang, G., & Wang, J. (2007). Understanding the key risks in construction projects in China. International Journal of Project Management, 25(6), 601–614. https://doi.org/10.1016/j.ijproman.2007.03.001
Downloads
Published
How to Cite
License
Copyright (c) 2025 Sezer Savaş
License

This work is licensed under a Creative Commons Attribution 4.0 International License.