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’s a cornerstone of economic growth, and is paradoxically among the least digitized sectors. A key challenge lies in the fragmented and unstructured nature of construction data, which spans across disparate formats—textual documents, visual designs, schedules, and even 3D models. This complexity, coupled with siloed workflows in design, preconstruction, and construction management, creates inefficiencies that AI is uniquely positioned to address.
In this article, I’m exploring how AI —particularly knowledge graphs, generative AI, and agentic AI—can bridge these gaps, transforming construction processes into streamlined, intelligent standalone systems. By leveraging AI at different phases of the construction lifecycle, the industry can move toward greater efficiency, cost savings, and smarter decision-making.
Fragmented Data in Construction: A Problem Worth Solving
The construction process generates vast amounts of data, but its diversity and lack of structure often hinder its utility. Key sources of data include:
- Textual Information: Contracts, RFIs (Requests for Information), specifications, and project manuals.
- Visual Data: Blueprints, design drawings, 3D models, and reality capture.
- Dynamic Inputs: Project schedules, cost data, and live site updates.
The challenge lies not only in collecting these inputs but also in integrating and interpreting them cohesively. For example, a change in a design drawing might have cascading effects on costs and schedules, but without structured systems, these dependencies often go unnoticed until it’s too late. This lack of interoperability across tools and workflows results in inefficiencies, cost overruns, and delays.
Current applications of AI in Construction
Below, I delve into specific applications tailored to each phase, showcasing how emerging tech startups leverage AI innovations to address industry pain points.
1. Design Phase: Knowledge Graphs for Drawings Review
In the design phase, construction teams deal with intricate sets of drawings and models. AI-powered knowledge graphs are emerging as a solution in this space. By linking data from various sources—architectural plans, engineering 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 a structural beam’s placement 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 issues early.
2. Preconstruction: Generative AI for Proposal Management
The preconstruction phase involves assembling comprehensive proposals, which include budgets, schedules, and resource plans. Generative AI tools can automate and enhance 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 auto-generate cost estimates, risk assessments, and milestone schedules, while also tailoring proposals to meet specific client requirements.
- 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, the complexity escalates. Site inspections, resource allocation, and schedule management require constant oversight. Agentic AI—autonomous agents that act and learn dynamically—offer 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 construction elements. It can also update schedules and notify 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 freeing project teams to focus on execution.
Bringing It All Together: The Future of AI-Driven Construction
What makes AI especially transformative for construction is its ability to connect disparate data sources and workflows into cohesive, actionable insights.
However, adopting AI in construction requires more than just technical expertise—it demands a shift in mindset. 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 industry stands at a pivotal moment. By harnessing AI to address its fragmented and unstructured data, it can leapfrog into 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 are not just theoretical—they are already reshaping how buildings are conceived and constructed.
The foundation has been laid. It’s time to build the future.
About the Author
Omar Zhandarbekuly, co-founder at Surfaice.pro, is an innovator at the forefront of construction technology, focusing on improving how projects are planned, managed, and delivered. With a career spanning over a decade, Omar has spearheaded the development of more than 7 million square feet of high-profile projects around the globe. He has collaborated with globally renowned firms such as SOM, Werner Sobek, and AS+GG, earning recognition for his expertise in complex, large-scale developments.
During his tenure at Katerra and Rivian, Omar demonstrated his ability to drive innovation at scale. At Katerra, he introduced a block scheduling methodology that significantly improved project efficiency, achieving the 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 University of Nottingham, Duke University and 2024 CELI Fellow, Omar combines technical excellence with strategic insight, contributing to the advancement of sustainable and technology-driven solutions in the construction sector.
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