AI in Construction Market - Forecasts from 2025 to 2030
Description
The AI in the construction market is set to witness robust growth at a CAGR of 23.08% during the forecast period, worth US$22.768 billion in 2030 from US$8.060 billion in 2025.
The artificial intelligence (AI) market in construction is experiencing rapid growth as the industry increasingly integrates intelligent technologies to enhance safety, planning accuracy, and project efficiency. AI applications, including machine learning (ML), computer vision, predictive analytics, and natural language processing, are transforming construction processes across design, implementation, and management. These technologies address longstanding industry challenges such as cost overruns, project delays, workforce shortages, and resource inefficiencies, while simultaneously supporting sustainability and environmentally conscious building practices. Global digitalization, the rising demand for efficiency, and advances in sustainable construction are key drivers accelerating AI adoption across the sector.
AI-powered solutions in construction enable real-time progress monitoring, risk mitigation, predictive maintenance, and resource optimization. Machine learning algorithms facilitate project management by analyzing historical data to improve scheduling, resource allocation, and cost estimation. Predictive analytics tools allow managers to anticipate potential delays, equipment failures, or safety hazards, while computer vision systems monitor on-site activity for compliance and safety. Furthermore, AI integration extends to autonomous construction equipment, augmented reality (AR), and virtual reality (VR) tools for enhanced monitoring, planning, and maintenance. These solutions not only improve operational efficiency but also support compliance with regulatory standards and sustainability targets.
The growing demand for AI is reinforced by the expansion of construction investment worldwide. In the U.S., for example, total construction spending reached US$2,139.1 billion in July 2025, including US$1,623.3 billion in private construction, reflecting a continued focus on infrastructure and residential development. AI adoption is particularly beneficial for addressing challenges such as resource shortages, equipment inefficiencies, weather disruptions, and complex project coordination. Predictive technologies enable preemptive decision-making, reduce idle time, optimize procurement, and enhance safety, directly impacting project cost and efficiency. Residential construction increasingly relies on AI for smart home integration, energy efficiency, and IoT-based monitoring, while commercial, industrial, and infrastructure projects leverage AI for planning, scheduling, and risk management.
Geographically, North America remains a leading adopter due to substantial federal investments, supportive policy frameworks, and a high concentration of construction activity in major Sun Belt and coastal states. Federally funded AI initiatives from agencies such as the Department of Transportation (DOT) and the Department of Housing and Urban Development (HUD) drive adoption by supporting infrastructure resilience, site automation, and workforce training. Europe follows closely, driven by stringent sustainability regulations and a mature construction sector emphasizing efficiency and quality. The Asia-Pacific region is witnessing rapid growth due to expanding infrastructure projects and urban development, while South America, the Middle East, and Africa are emerging markets benefiting from gradual industrialization and construction sector modernization.
The competitive landscape is dominated by companies like IBM, Autodesk, Oracle, Bentley Systems, Trimble, and Procore Technologies, which are integrating AI into project management, data analytics, automation platforms, and generative design tools. Recent innovations include AI-powered project monitoring systems, automated construction scheduling tools, and generative design capabilities for civil engineering, all aimed at improving productivity, accuracy, and sustainability. Companies such as Saint-Gobain are also leveraging AI for sustainable communication and project visualization.
Largely, AI in construction is reshaping the industry by enabling predictive, data-driven decision-making, improving operational efficiency, and supporting sustainable development. With growing investment in digitalization, AI-driven construction is poised to transform project execution across all stages, from planning and pre-construction to on-site operations and post-construction maintenance, offering significant value to developers, contractors, and stakeholders globally.
AI in Construction Market Segmentation:
By Application
Project Management
Planning and Design
Safety
Autonomous Equipment
Monitoring and Maintenance
By Construction Stage
Pre-Construction
Construction
Post-Construction
By Deployment
On-Premises
Cloud Based
By Industry
Residential
Commercial
Others
By Geography
North America
United States
Canada
Mexico
South America
Brazil
Argentina
Others
Europe
United Kingdom
Germany
France
Spain
Others
Middle East and Africa
Saudi Arabia
UAE
Others
Asia Pacific
China
Japan
India
South Korea
Taiwan
Others
The artificial intelligence (AI) market in construction is experiencing rapid growth as the industry increasingly integrates intelligent technologies to enhance safety, planning accuracy, and project efficiency. AI applications, including machine learning (ML), computer vision, predictive analytics, and natural language processing, are transforming construction processes across design, implementation, and management. These technologies address longstanding industry challenges such as cost overruns, project delays, workforce shortages, and resource inefficiencies, while simultaneously supporting sustainability and environmentally conscious building practices. Global digitalization, the rising demand for efficiency, and advances in sustainable construction are key drivers accelerating AI adoption across the sector.
AI-powered solutions in construction enable real-time progress monitoring, risk mitigation, predictive maintenance, and resource optimization. Machine learning algorithms facilitate project management by analyzing historical data to improve scheduling, resource allocation, and cost estimation. Predictive analytics tools allow managers to anticipate potential delays, equipment failures, or safety hazards, while computer vision systems monitor on-site activity for compliance and safety. Furthermore, AI integration extends to autonomous construction equipment, augmented reality (AR), and virtual reality (VR) tools for enhanced monitoring, planning, and maintenance. These solutions not only improve operational efficiency but also support compliance with regulatory standards and sustainability targets.
The growing demand for AI is reinforced by the expansion of construction investment worldwide. In the U.S., for example, total construction spending reached US$2,139.1 billion in July 2025, including US$1,623.3 billion in private construction, reflecting a continued focus on infrastructure and residential development. AI adoption is particularly beneficial for addressing challenges such as resource shortages, equipment inefficiencies, weather disruptions, and complex project coordination. Predictive technologies enable preemptive decision-making, reduce idle time, optimize procurement, and enhance safety, directly impacting project cost and efficiency. Residential construction increasingly relies on AI for smart home integration, energy efficiency, and IoT-based monitoring, while commercial, industrial, and infrastructure projects leverage AI for planning, scheduling, and risk management.
Geographically, North America remains a leading adopter due to substantial federal investments, supportive policy frameworks, and a high concentration of construction activity in major Sun Belt and coastal states. Federally funded AI initiatives from agencies such as the Department of Transportation (DOT) and the Department of Housing and Urban Development (HUD) drive adoption by supporting infrastructure resilience, site automation, and workforce training. Europe follows closely, driven by stringent sustainability regulations and a mature construction sector emphasizing efficiency and quality. The Asia-Pacific region is witnessing rapid growth due to expanding infrastructure projects and urban development, while South America, the Middle East, and Africa are emerging markets benefiting from gradual industrialization and construction sector modernization.
The competitive landscape is dominated by companies like IBM, Autodesk, Oracle, Bentley Systems, Trimble, and Procore Technologies, which are integrating AI into project management, data analytics, automation platforms, and generative design tools. Recent innovations include AI-powered project monitoring systems, automated construction scheduling tools, and generative design capabilities for civil engineering, all aimed at improving productivity, accuracy, and sustainability. Companies such as Saint-Gobain are also leveraging AI for sustainable communication and project visualization.
Largely, AI in construction is reshaping the industry by enabling predictive, data-driven decision-making, improving operational efficiency, and supporting sustainable development. With growing investment in digitalization, AI-driven construction is poised to transform project execution across all stages, from planning and pre-construction to on-site operations and post-construction maintenance, offering significant value to developers, contractors, and stakeholders globally.
AI in Construction Market Segmentation:
By Application
Project Management
Planning and Design
Safety
Autonomous Equipment
Monitoring and Maintenance
By Construction Stage
Pre-Construction
Construction
Post-Construction
By Deployment
On-Premises
Cloud Based
By Industry
Residential
Commercial
Others
By Geography
North America
United States
Canada
Mexico
South America
Brazil
Argentina
Others
Europe
United Kingdom
Germany
France
Spain
Others
Middle East and Africa
Saudi Arabia
UAE
Others
Asia Pacific
China
Japan
India
South Korea
Taiwan
Others
Table of Contents
188 Pages
- 1. Executive Summary
- 2. Market Snapshot
- 2.1. Market Overview
- 2.2. Market Definition
- 2.3. Scope of the Study
- 2.4. Market Segmentation
- 3. Business Landscape
- 3.1. Market Drivers
- 3.2. Market Restraints
- 3.3. Market Opportunities
- 3.4. Porter’s Five Forces Analysis
- 3.5. Industry Value Chain Analysis
- 3.6. Policies and Regulations
- 3.7. Strategic Recommendations
- 4. Technological Outlook
- 5. AI in Construction Market By Application (2020-2030)
- 5.
- 1. Introduction
- 5.2. Project Management
- 5.3. Planning and Design
- 5.4. Safety
- 5.5. Autonomous Equipment
- 5.6. Monitoring and Maintenance
- 6. AI in Construction Market By Construction Stage (2020-2030)
- 6.
- 1. Introduction
- 6.2. Pre-Construction
- 6.3. Construction
- 6.4. Post-Construction
- 7. AI in Construction Market By Deployment (2020-2030)
- 7.
- 1. Introduction
- 7.2. On-Premises
- 7.3. Cloud-Based
- 8. AI in Construction Market By Industry (2020-2030)
- 8.
- 1. Introduction
- 8.2. Residential
- 8.3. Commercial
- 8.4. Others
- 9. AI in Construction Market By Geography (2020-2030)
- 9.
- 1. Introduction
- 9.2. North America
- 9.2.1. By Application
- 9.2.2. By Construction Stage
- 9.2.3. By Deployment
- 9.2.4. By Industry
- 9.2.5. By Country
- 9.2.5.1. USA
- 9.2.5.2. Canada
- 9.2.5.3. Mexico
- 9.3. South America
- 9.3.1. By Application
- 9.3.2. By Construction Stage
- 9.3.3. By Deployment
- 9.3.4. By Industry
- 9.3.5. By Country
- 9.3.5.1. Brazil
- 9.3.5.2. Argentina
- 9.3.5.3. Others
- 9.4. Europe
- 9.4.1. By Application
- 9.4.2. By Construction Stage
- 9.4.3. By Deployment
- 9.4.4. By Industry
- 9.4.5. By Country
- 9.4.5.1. United Kingdom
- 9.4.5.2. Germany
- 9.4.5.3. France
- 9.4.5.4. Spain
- 9.4.5.5. Others
- 9.5. Middle East and Africa
- 9.5.1. By Application
- 9.5.2. By Construction Stage
- 9.5.3. By Deployment
- 9.5.4. By Industry
- 9.5.5. By Country
- 9.5.5.1. Saudi Arabia
- 9.5.5.2. UAE
- 9.5.5.3. Others
- 9.6. Asia Pacific
- 9.6.1. By Application
- 9.6.2. By Construction Stage
- 9.6.3. By Deployment
- 9.6.4. By Industry
- 9.6.5. By Country
- 9.6.5.1. China
- 9.6.5.2. Japan
- 9.6.5.3. India
- 9.6.5.4. South Korea
- 9.6.5.5. Taiwan
- 9.6.5.6. Others
- 10. Competitive Environment and Analysis
- 10.1. Major Players and Strategy Analysis
- 10.2. Market Share Analysis
- 10.3. Mergers, Acquisitions, Agreements, and Collaborations
- 10.4. Competitive Dashboard
- 11. Company Profiles
- 11.1. IBM
- 11.2. Autodesk, Inc.
- 11.3. Oracle Corporation
- 11.4. SAP SE
- 11.5. ALICE Technologies Inc.
- 11.6. The Access Group
- 11.7. Doxel
- 11.8. eSUB, Inc.
- 11.9. Procore
- 11.10. Buildots
- 11.11. Dusty Robotics, Inc.
- 11.12. OpenSpace
- 11.13. AI Clearing
- 11.14. Bentley Systems
- 11.15. Trimble
- 12. Research Methodology
Pricing
Currency Rates
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