건설 분야 인공지능 시장 기회, 성장 촉진요인, 업계 동향 분석 및 예측(2026-2035년)

The Global Artificial Intelligence in Construction Market was valued at USD 3.9 billion in 2025 and is estimated to grow at a CAGR of 20.6% to reach USD 25.6 billion by 2035.

Construction companies increasingly view artificial intelligence as a practical response to persistent labor shortages, rising project complexity, and growing pressure to deliver projects more efficiently. The industry continues to face a shortage of skilled workers, while the available workforce has not expanded at the same pace as construction demand. AI-powered automation can help companies improve productivity by reducing repetitive work, strengthening project coordination, and supporting faster decision-making. AI technologies can also help contractors manage labor more effectively and reduce periods of underutilized resources. This shift is gradually changing the role of AI from a productivity-supporting technology into an important component of construction workforce planning. Meanwhile, public infrastructure programs are placing greater emphasis on digital project delivery and technology-enabled construction practices, creating additional opportunities for AI adoption. Safety also remains a major concern because construction continues to experience elevated workplace risks. As contractors seek stronger operational control, improved resource allocation, better project visibility, and safer working environments, demand for artificial intelligence in construction solutions is expected to increase substantially through 2035.

The solutions segment held a 74% share in 2025. AI solutions account for the largest portion of the market because they integrate artificial intelligence capabilities directly into construction-related workflows. These offerings support a broad range of activities involving design, planning, monitoring, cost management, resource allocation, and project execution. Growing interest in software-based automation is strengthening demand as construction companies seek to improve productivity, minimize project delays, support more accurate decisions, and manage resources throughout the project lifecycle. Contractors, engineering organizations, and project owners are increasingly incorporating AI-enabled platforms into their digital construction environments. The expanding use of intelligent software also reflects the industry's broader transition toward data-driven project management and automated workflows. As construction businesses continue to modernize their technology infrastructure, AI solutions are expected to maintain their dominant market position.

The commercial construction segment accounted for a 32.3% share in 2025. Commercial projects typically involve complex planning requirements, multiple stakeholders, extensive resource coordination, and demanding completion schedules, creating significant opportunities for AI-based technologies. Construction companies and developers are increasingly using artificial intelligence to improve project planning, estimate costs, optimize schedules, manage digital building information, monitor quality, and strengthen overall project visibility. AI can help project teams process large amounts of construction data and identify potential operational issues at an earlier stage. Technology also supports more efficient coordination across different phases of a project, allowing stakeholders to make decisions based on current project information. Increasing digitalization across commercial construction is therefore supporting the adoption of artificial intelligence and strengthening the segment's position within the overall market.

China Artificial Intelligence in Construction Market accounted for 47% share generating USD 0.4 billion in 2025. The country's leading position reflects the scale of its construction industry and continued activity across residential, commercial, industrial, and infrastructure development. Ongoing urban development and substantial investment in construction modernization are creating a favorable environment for AI-based technologies. Increasing digital transformation across the construction sector is supporting demand for intelligent project management, Building Information Modeling (BIM), computer vision, digital twins, and predictive analytics. China's extensive construction activity provides a substantial addressable market for artificial intelligence solutions even as adoption continues to develop across different areas of the industry. Continued investment in digital construction capabilities, combined with the country's large project pipeline, is expected to provide significant opportunities for AI technology providers throughout the forecast period.

Major companies operating in the global artificial intelligence in construction industry include Autodesk, ALICE Technologies, Trimble, SmartPM, Bentley Systems, nPlan, Procore Technologies, Nemetschek, Trunk Tools, and Document Crunch. Market participants are strengthening their positions in the artificial intelligence in construction market through product innovation, technology development, strategic collaborations, platform expansion, and broader integration with construction workflows. Companies are investing in AI capabilities that improve project planning, data analysis, automation, monitoring, forecasting, and decision-making. Providers are also enhancing software functionality and interoperability to make their platforms easier to integrate with existing construction technology ecosystems. Strategic partnerships allow companies to combine complementary technologies, expand distribution capabilities, and reach additional customer segments. Market participants are also increasing investments in research and development to improve AI accuracy, scalability, automation, and ease of use. Expanding applications across different construction activities provides another avenue for revenue growth and customer acquisition.

Table of Contents

Chapter 1 Research Methodology

• 1.1 Research approach
• 1.2 Quality Commitments
  • 1.2.1 GMI AI policy & data integrity commitment
    • 1.2.1.1 Source consistency protocol
• 1.3 Research Trail & Confidence Scoring
  • 1.3.1 Research Trail Components
  • 1.3.2 Scoring Components
• 1.4 Data Collection
  • 1.4.1 Partial list of primary sources
• 1.5 Data mining sources
  • 1.5.1 Paid sources
    • 1.5.1.1 Sources, by region
• 1.6 Base estimates and calculations
  • 1.6.1 Base year calculation for any one approach
• 1.7 Forecast model
  • 1.7.1 Quantified market impact analysis
    • 1.7.1.1 Mathematical impact of growth parameters on forecast
• 1.8 Research transparency addendum
  • 1.8.1 Source attribution framework
  • 1.8.2 Quality assurance metrics
  • 1.8.3 Our commitment to trust

Chapter 2 Executive Summary

• 2.1 Industry 360° synopsis, 2022 – 2035
• 2.2 Key market trends
  • 2.2.1 Regional
  • 2.2.2 Component
  • 2.2.3 Deployment Mode
  • 2.2.4 Construction Type
  • 2.2.5 Application
• 2.3 TAM Analysis, 2026-2035
• 2.4 CXO perspectives: Strategic imperatives

Chapter 3 Industry Insights

• 3.1 Industry ecosystem analysis
  • 3.1.1 Supplier landscape
  • 3.1.2 Profit margin analysis
  • 3.1.3 Cost structure
  • 3.1.4 Value addition at each stage
  • 3.1.5 Factor affecting the value chain
  • 3.1.6 Disruptions
• 3.2 Industry impact forces
  • 3.2.1 Growth drivers
    • 3.2.1.1 Escalating Construction Labor Shortages Driving Automation & AI Adoption
    • 3.2.1.2 Government-Backed Infrastructure Spending Mandating Digital Transformation
    • 3.2.1.3 Rising Demand for Real-Time Safety Compliance & On-Site Risk Mitigation
    • 3.2.1.4 Cost Overrun Pressures Accelerating AI-Driven Project Management Adoption
  • 3.2.2 Industry pitfalls and challenges
    • 3.2.2.1 High Implementation & Integration Costs Limiting Adoption Among SMEs
    • 3.2.2.2 Fragmented Data Ecosystems & Legacy System Interoperability Barriers
  • 3.2.3 Market opportunities
    • 3.2.3.1 Generative AI Disruption Unlocking New Design & Planning Workflow Efficiencies
    • 3.2.3.2 Expanding AI Deployment Across Emerging Market Infrastructure Programs
    • 3.2.3.3 AI-Powered Prefabrication & Modular Construction Optimization
• 3.3 Growth potential analysis
• 3.4 Technology and innovation landscape
  • 3.4.1 Current technological trends
    • 3.4.1.1 AI-Powered Construction Planning and Scheduling
    • 3.4.1.2 Computer Vision for Site Monitoring and Safety
    • 3.4.1.3 Machine Learning for Predictive Analytics and Risk Management
    • 3.4.1.4 AI-Enabled Cost Estimation and Project Management
  • 3.4.2 Emerging technologies
    • 3.4.2.1 Generative AI for Construction Design and Project Workflows
    • 3.4.2.2 AI Agents for Autonomous Construction Management
    • 3.4.2.3 AI-Powered Digital Twins for Construction Projects
    • 3.4.2.4 AI-Driven Robotics and Autonomous Construction Equipment
• 3.5 Pricing Analysis (Driven by primary research)
  • 3.5.1 Historical Price Trend Analysis, 2022-2025
  • 3.5.2 Pricing Strategy by Player Type
• 3.6 Regulatory landscape
  • 3.6.1 North America
    • 3.6.1.1 U.S. Occupational Safety and Health Administration (OSHA) Regulations Supporting AI-Based Construction Safety Monitoring
    • 3.6.1.2 U.S. National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF 1.0)
    • 3.6.1.3 U.S. Federal Acquisition Regulation (FAR) Digital Engineering and AI-Enabled Project Delivery Requirements
    • 3.6.1.4 Canada Personal Information Protection and Electronic Documents Act (PIPEDA) for AI and Construction Data Privacy
    • 3.6.1.5 Canadian Centre for Cyber Security Guidelines for Secure AI and Cloud-Based Construction Platforms
  • 3.6.2 Europe
    • 3.6.2.1 EU Artificial Intelligence Act for High-Risk AI Systems in Construction and Infrastructure
    • 3.6.2.2 EU General Data Protection Regulation (GDPR) for AI-Driven Construction Data Processing
    • 3.6.2.3 NIS2 Directive for Cybersecurity of AI-Enabled Construction Platforms and Critical Infrastructure
    • 3.6.2.4 ISO 19650 Standards for Building Information Modeling (BIM) Information Management Supporting AI Applications
    • 3.6.2.5 EU eIDAS Regulation for Digital Signatures and Electronic Construction Documentation
  • 3.6.3 Asia Pacific
    • 3.6.3.1 China Interim Measures for the Management of Generative Artificial Intelligence Services and Data Security Law
    • 3.6.3.2 India Digital Personal Data Protection Act, 2023
    • 3.6.3.3 Japan Act on the Protection of Personal Information (APPI)
    • 3.6.3.4 Australia National AI Assurance Framework and Privacy Act Requirements
    • 3.6.3.5 Singapore Building and Construction Authority (BCA) BIM Roadmap and AI-Driven Built Environment Initiatives
  • 3.6.4 Latin America
    • 3.6.4.1 Brazil General Data Protection Law (LGPD)
    • 3.6.4.2 Brazil BIM BR Strategy
    • 3.6.4.3 Mexico Federal Law on Protection of Personal Data Held by Private Parties (LFPDPPP)
    • 3.6.4.4 Chile National BIM Plan
    • 3.6.4.5 Regional Digital Infrastructure and Smart Construction Initiatives Promoting AI Adoption
  • 3.6.5 Middle East & Africa
    • 3.6.5.1 UAE Artificial Intelligence Strategy 2031 and Personal Data Protection Law (PDPL)
    • 3.6.5.2 Saudi Vision 2030 Smart Construction Initiatives and Personal Data Protection Law (PDPL)
    • 3.6.5.3 Dubai Building Information Modeling (BIM) Mandate
    • 3.6.5.4 South Africa Protection of Personal Information Act (POPIA)
    • 3.6.5.5 GCC Digital Government and Smart City Frameworks Supporting AI Adoption in Construction and Infrastructure
• 3.7 Porter’s analysis
• 3.8 PESTEL analysis
• 3.9 Patent analysis (Driven by primary research)
• 3.10 Cost breakdown analysis, 2025
• 3.11 Impact of AI and Generative AI on the Market
  • 3.11.1 AI Driven Disruption of Existing Business Models
  • 3.11.2 GenAI Use Cases and Adoption Roadmap by Segment
  • 3.11.3 Risks Limitations and Regulatory Considerations
• 3.12 Forecast assumptions & scenario analysis (Driven by Primary Research)
  • 3.12.1 Base Case- Key Macro & Industry Variables Driving CAGR
  • 3.12.2 Optimistic Scenarios- Favorable macro and industry tailwinds
  • 3.12.3 Pessimistic Scenario - Macroeconomic slowdown or industry headwinds

Chapter 4 Competitive Landscape, 2025

• 4.1 Introduction
• 4.2 Company market share analysis
  • 4.2.1 North America
  • 4.2.2 Europe
  • 4.2.3 Asia Pacific
  • 4.2.4 LATAM
  • 4.2.5 MEA
• 4.3 Competitive analysis of major market players
• 4.4 Competitive positioning matrix
• 4.5 Key developments
  • 4.5.1 Mergers & acquisitions
  • 4.5.2 Partnerships & collaborations
  • 4.5.3 New Product Launches
  • 4.5.4 Expansion Plans and funding
• 4.6 Company tier benchmarking
  • 4.6.1 Tier classification criteria & qualifying thresholds
  • 4.6.2 Tier positioning matrix by revenue, geography & innovation

Chapter 5 Market Estimates & Forecast, By Component, 2022 - 2035 (USD Bn)

• 5.1 Key trends
• 5.2 Solutions
  • 5.2.1 Machine Learning & Deep Learning Solutions
  • 5.2.2 Computer Vision Solutions
  • 5.2.3 Natural Language Processing (NLP) Solutions
  • 5.2.4 Generative AI (GenAI) Solutions
  • 5.2.5 Others
• 5.3 Services
  • 5.3.1 Professional Service
  • 5.3.2 Managed Service

Chapter 6 Market Estimates & Forecast, By Deployment Mode, 2022 - 2035 (USD Bn)

• 6.1 Key trends
• 6.2 Cloud
• 6.3 On-Premise
• 6.4 Hybrid

Chapter 7 Market Estimates & Forecast, By Construction Type, 2022 - 2035 (USD Bn)

• 7.1 Key trends
• 7.2 Residential Construction
• 7.3 Commercial Construction
• 7.4 Industrial Construction
• 7.5 Infrastructure & Civil Engineering
• 7.6 Others

Chapter 8 Market Estimates & Forecast, By Application, 2022 - 2035 (USD Bn)

• 8.1 Key trends
• 8.2 Project Management & Planning
• 8.3 Safety & Risk Management
• 8.4 Design Optimization & BIM
• 8.5 Field & Operations Management
• 8.6 Supply Chain & Procurement Management
• 8.7 Others

Chapter 9 Market Estimates & Forecast, By Region, 2022 - 2035 (USD Bn)

• 9.1 Key trends
• 9.2 North America
  • 9.2.1 US
  • 9.2.2 Canada
• 9.3 Europe
  • 9.3.1 Germany
  • 9.3.2 UK
  • 9.3.3 France
  • 9.3.4 Italy
  • 9.3.5 Spain
  • 9.3.6 Russia
  • 9.3.7 Norway
  • 9.3.8 Netherlands
  • 9.3.9 Sweden
• 9.4 Asia Pacific
  • 9.4.1 China
  • 9.4.2 India
  • 9.4.3 Japan
  • 9.4.4 Australia
  • 9.4.5 South Korea
  • 9.4.6 Singapore
  • 9.4.7 Thailand
  • 9.4.8 Indonesia
  • 9.4.9 Vietnam
• 9.5 Latin America
  • 9.5.1 Brazil
  • 9.5.2 Mexico
  • 9.5.3 Argentina
• 9.6 MEA
  • 9.6.1 South Africa
  • 9.6.2 Saudi Arabia
  • 9.6.3 UAE
  • 9.6.4 Turkey

Chapter 10 Company Profiles

• 10.1 Global Players
  • 10.1.1 ALICE Technologies
  • 10.1.2 Procore Technologies
  • 10.1.3 Autodesk
  • 10.1.4 Trimble
  • 10.1.5 Bentley Systems
  • 10.1.6 Nemetschek
  • 10.1.7 Hexagon
  • 10.1.8 Oracle
  • 10.1.9 Microsoft
  • 10.1.10 DroneDeploy
  • 10.1.11 OpenSpace
  • 10.1.12 Buildots
• 10.2 Regional Players
  • 10.2.1 Trunk Tools
  • 10.2.2 Document Crunch
  • 10.2.3 nPlan
  • 10.2.4 SmartPM
  • 10.2.5 Versatile
  • 10.2.6 AI Clearing
  • 10.2.7 viAct
  • 10.2.8 FYLD