DAMPAK ARTIFICIAL INTELLIGENCE TERHADAP TRANSFORMASI BISNIS ARSITEKTUR: STRATEGI MANAJEMEN ADAPTIF DI INDONESIA (A SYSTEMATIC LITERATURE REVIEW) - Muhammad Ridhani; Risca Damayanti; Akhmad Hariadi; Aly Syaipullah; Akbar Rahman; Ira Mentayani (CV. Adiba Aisha Amira, 2026)
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.