The construction industry, despite being a cornerstone of the global economy, remains one of the least digitized and least venture capital invested sectors relative to its size and economic impact. It is a cornerstone of economic growth and, paradoxically, one of the least digitized sectors. A major challenge lies in the fragmented and unstructured nature of construction data, which spans disparate formats: textual documents, visual designs, schedules and even 3D models. This complexity, coupled with siled workflows in design, preconstruction and construction management, creates inefficiencies that AI is uniquely positioned to solve.
In this article, I explore how AI (specifically knowledge graphs, generative AI, and agentic AI) can fill these gaps, transforming construction processes into streamlined and intelligent autonomous systems. By leveraging AI at different phases of the construction lifecycle, the industry can move towards greater efficiency, cost savings and smarter decision-making.
Fragmented Data in Construction: A Problem to Solve
The construction process generates large amounts of data, but its diversity and lack of structure often hinder its usefulness. The main data sources include:
- Text information: Contracts, RFI (Requests for Information), specifications and project manuals.
- Visual data: Plans, design drawings, 3D models and reality capture.
- Dynamic Inputs: Project schedules, cost data and live site updates.
The challenge lies not only in collecting this information, but also in integrating and interpreting it in a coherent manner. For example, a change in a design drawing can have cascading effects on costs and schedules, but without structured systems, these dependencies often go unnoticed until it is too late. This lack of interoperability between tools and workflows leads to inefficiencies, cost overruns and delays.
Current Applications of AI in Construction
Below, I discuss specific applications tailored to each phase, showing how emerging technology startups are leveraging AI innovations to solve industry problems.
1. Design phase: knowledge graphics for drawing review
During the design phase, construction teams deal with complex sets of drawings and models. AI-based knowledge graphs are emerging as a solution in this area. By linking data from various sources (architectural plans, technical drawings and regulatory guidelines), knowledge graphs create a network of relationships between design elements.
- Example use case: An AI model can flag inconsistencies, such as a mismatch between the location of a structural beam in a drawing and the accompanying load calculations in the specifications.
- Technical advantage: Knowledge graphs excel at contextualizing data, making it easier to trace dependencies and detect problems early.
2. Preconstruction: Generative AI for proposal management
The preconstruction phase involves assembling complete proposals, which include budgets, schedules and resource plans. Generative AI tools can automate and improve this process by analyzing historical project data and generating detailed proposals in minutes.
- Example use case: A generative AI model trained on past RFPs (requests for proposals) can automatically generate cost estimates, risk assessments and milestone schedules, while tailoring proposals to meet specific project requirements. customers.
- Technical advantage: Generative AI enables faster turnaround times and reduces manual errors, giving teams more bandwidth to focus on strategic planning.
3. Construction Management: Agentic AI for Real-Time Project Coordination
Once construction begins, complexity increases. Site inspections, resource allocation and schedule management require constant monitoring. Agentic AI (autonomous agents that act and learn dynamically) offers a reasonable alternative solution to administrative project teams.
- Example use case: Agentic AI can integrate with ERP systems to track and update project documentation, providing instant access to drawings, installation guides and compliance checklists for building elements. It can also update schedules and inform stakeholders, streamlining workflows and reducing administrative delays.
- Technical advantage: By automating documentation management, agentic AI ensures accurate, real-time access to critical information, reducing errors and allowing project teams to focus on execution.
Bringing It All Together: The Future of AI-Driven Construction
What makes AI particularly transformative for construction is its ability to connect disparate data sources and workflows to achieve cohesive, actionable insights.
However, adopting AI in construction requires more than just technical expertise: it requires a mindset shift. Stakeholders must embrace AI not as a replacement but as a complement to human ingenuity, amplifying the capabilities of architects, engineers and project managers.
The construction sector is at a pivotal moment. By leveraging AI to process its fragmented and unstructured data, it can enter a new era of efficiency and innovation. From knowledge graphs for design reviews to generative AI for preconstruction proposals and agentic AI for dynamic project management, these technologies aren’t just theoretical—they’re already reshaping the way buildings are designed and constructed.
The foundations have been laid. It’s time to build the future.
About the author
Omar Zhandarbekuly, co-founder of Surface.prois an innovator at the forefront of construction technology, focused on improving the way projects are planned, managed and delivered. With a career spanning over a decade, Omar has led the development of over 7 million square feet of large-scale projects across the globe. He has collaborated with world-renowned companies such as SOM, Werner Sobek and AS+GG, gaining recognition for his expertise in large-scale complex developments.
During his tenure at Katerra and Rivian, Omar demonstrated his ability to drive innovation at scale. At Katerra, he introduced a block planning methodology that significantly improved project efficiency, enabling delivery of the K90 project in just 90 days. At Rivian, he played a key role in developing a construction cost intelligence platform for real estate and construction operations during the company’s rapid expansion.
A graduate of the University of Nottingham, Duke University and member of CELI 2024, Omar combines technical excellence with strategic vision, contributing to the advancement of sustainable and technological solutions in the construction sector.
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