DAMPAK ARTIFICIAL INTELLIGENCE TERHADAP TRANSFORMASI BISNIS ARSITEKTUR: STRATEGI MANAJEMEN ADAPTIF DI INDONESIA (A SYSTEMATIC LITERATURE REVIEW)
DOI:
https://doi.org/10.5281/zenodo.20502314Keywords:
Architecture, Artificial Intelligence, Business Model, Digital Transformation, Ethical Governance, Project Management, IndonesiaAbstract
This study analyzes the development and application of Artificial Intelligence (AI) in architectural practice, its implications for business model transformation and work systems, and adaptive management strategies relevant to architectural firms in Indonesia. The research employs a Systematic Literature Review (SLR) following the PRISMA protocol. Articles were retrieved from Scopus, Web of Science, IEEE Xplore, and ScienceDirect using Boolean combinations of keywords related to AI, architecture, AEC industry, BIM, generative design, digital twin, business model, and digital transformation. The review focuses on peer-reviewed publications from 2021 to 2025. From an initial pool of 456 articles, 20 articles were selected through a rigorous multi-stage screening process for in-depth analysis. The findings indicate that AI is increasingly integrated into architectural design through generative design, building performance optimization, and data-driven decision support systems. In project management, AI is closely connected with BIM, digital twin, 4D simulation, IoT, and predictive analytics. A critical synthesis of the literature reveals consensus on AI's capacity to improve efficiency and transform professional roles, alongside notable contradictions regarding the threat versus empowerment of architects and the feasibility of AI adoption in resource-limited contexts. These developments encourage architectural firms to shift from conventional design services toward data-driven, performance-based, and technology-enabled services such as building performance simulation, lifecycle cost analysis, and smart facility management. However, implementation in Indonesia remains constrained by uneven digital capability among human resources, limited infrastructure, fragmented project data, investment barriers, and the absence of comprehensive implementation guidelines. Therefore, adaptive management strategies are required, encompassing digital upskilling, phased technology integration, collaborative ecosystems, value-based innovation, and ethical governance of AI use.
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References
Agri, M. E., Le, A., & Phung, Q. (2025). AI integration in architectural design and management: Professionals' perspectives. Architectural Engineering and Design Management, 1–16. https://doi.org/10.1080/17452007.2025.2548911
Albukhari, I. N. (2025). The role of artificial intelligence (AI) in architectural design: A systematic review of emerging technologies and applications. Journal of Umm Al-Qura University for Engineering and Architecture, 16, 1457–1476.
Ariono, B., Wasesa, M., & Dhewanto, W. (2022). The drivers, barriers, and enablers of Building Information Modeling (BIM) innovation in developing countries: Insights from Indonesia. Buildings, 12(1912), 1–22.
Baduge, S. K., Thilakarathna, S., Perera, J. S., Arashpour, M., Sharafi, P., Teodosio, B., Shringi, A., & Mendis, P. (2022). Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Automation in Construction, 141, 104440.
Bercic, T., Bohanec, M., & Momirski, L. A. (2024). Integrating multi-criteria decision models in smart urban planning: A case study of architectural and urban design competitions. Smart Cities, 7, 786–805.
Bock, T., & Linner, T. (2016). Robotics and automation in construction. Cambridge University Press.
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
Eastman, C., Teicholz, P., Sacks, R., & Liston, K. (2018). BIM handbook: A guide to building information modeling for owners, designers, engineers, contractors, and facility managers (3rd ed.). John Wiley & Sons.
Gadalla, A., Tracada, E., & Hamza, O. (2025). The role of AI in driving effective sustainable architecture design. Springer Nature, 2, 451–460.
Habibi, S. (2023). Smart BIM and digital twin for sustainable buildings: A review of integrated frameworks and applications. Buildings, 13(4), 902.
Kementerian Pekerjaan Umum dan Perumahan Rakyat. (2018). Peraturan Menteri PUPR No. 22/PRT/M/2018 tentang Pembangunan Bangunan Gedung Negara. Kementerian PUPR Republik Indonesia.
Li, J., Liu, Z., Han, G., & Demian, P. (2024). The relationship between Artificial Intelligence (AI) and Building Information Modeling (BIM) technologies for sustainable building in the context of smart cities. Sustainability, 16(10848), 1–38.
Liang, C. J., Le, T., Ham, Y., & Shealy, T. (2023). Ethics of artificial intelligence and robotics in the architecture, engineering, and construction industry. Journal of Construction Engineering and Management, 149(7).
Liu, Y., Li, T., Xu, W., Wang, Q., & Huang, H. (2023). Building information modelling-enabled multi-objective optimization for energy consumption parametric analysis in green buildings design using hybrid machine learning algorithms. Energy and Buildings, 300, 113665.
Mayouf, M., Jones, J., & Elghaish, F. (2024). Revolutionising the 4D BIM process to support scheduling requirements in modular construction. Sustainability, 16(2), 476.
Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. A. (2020). Investigating the effects of big data analytics capabilities on firm performance: The mediating role of dynamic capabilities. Information & Management, 57(2).
Ogunseiju, O. R., Akanmu, A. A., Nwosu, K. I., & Huang, Y. (2023). Emerging technology-based learning environments for construction workforce upskilling and reskilling in the era of Industry 4.0. Automation in Construction, 145, 104629.
Pacheco, A., & Pacheco-Pumaleque, L. (2024). Transforming construction management in Peru: The role of BIM in innovation and efficiency. Sage Journal, 14(1), 21582440241233400.
Pan, Y., & Zhang, L. (2021). Roles of artificial intelligence in construction engineering and management: A critical review and future trends. Automation in Construction, 122, 103517. https://doi.org/10.1016/j.autcon.2020.103517
Quan, S. J. (2022). Urban-GAN: An artificial intelligence-aided computation system for plural urban design. Sage Journal, 49(9), 2500–2515. https://doi.org/10.1177/23998083221100550
Rane, N. L. (2023). Integrating leading-edge Artificial Intelligence (AI), Internet of Things (IoT), and big data technologies for smart and sustainable Architecture, Engineering and Construction (AEC) industry. International Journal of Data Science and Big Data Analytics, 3, 73–95.
Rani, H. A., Al-mohammad, M. S., & Rajabi, M. S. (2023). Critical government strategies for enhancing Building Information Modeling implementation in Indonesia. Infrastructures, 8(57), 1–16.
Tiburcio, V. A. (2024). Digital management methodology for building production optimization through digital twin and Artificial Intelligence integration. Buildings, 14(2110), 1–23.
Zhang, Y., Li, X., Wang, Z., & Chen, H. (2025). Generative AI in architectural design: Application, data, and evaluation methods. Automation in Construction, 174, 106174.
Zhao, Y., Wang, N., Liu, Z., & Mu, E. (2022). Construction theory for a building intelligent operation and maintenance system based on digital twins and machine learning. Buildings, 12(87), 1–17.
