AI in Construction Market Size, Share, Opportunities, And Trends By Application, Construction Stage, Deployment, Industry, Geography - Forecast From 2025 To 2030

Description

AI in Construction Market Size: 

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.

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AI in the construction market is rapidly growing, with the wide adoption of intelligent technologies for improved safety, planning accuracy, and project efficiency replacing traditional methodologies. Machine learning, Computer vision, and Predictive Analytics are applied across the various phases of construction work to reduce costs and delays. Global digitalisation, the shortage of skilled labor, and advances in sustainable building methods are accelerating this adaptation.

AI in Construction Market Trends:

Artificial intelligence for construction relies on AI systems for designing, implementing, and practicing AI technologies for enhancing all construction projects. Its technologies include solutions based on machine learning (ML) algorithms, computer vision systems, and language processing tools that enhance process functionality like project management, risk management, time management, supply chain management, quality management, and safety management. This arises from a major shift based on the increasing demand for sustainability, efficiency, and economy in construction projects. This is by far one of the fastest-growing areas of endeavor in construction, which has numerous possibilities for innovation and transformation.

AI in Construction Market Overview

The integration of artificial intelligence (AI) offers the potential to transform the whole construction project planning and distribution process. The use of AI can help prevent delays in construction or at least shorten them by using advanced algorithms and predictive analytics coupled with real-time data. Thus, a tremendous gain is granted to an industry that contributes fairly to the world’s GDP. The growing use of AI will continue to boost its adoption in the construction industry by providing solutions to long-standing challenges in the sector. This integration of AI will lead to improved project outcomes.

Moreover, according to the United States Census Bureau data of September 2025, the monthly construction spending was USD 2,139.110 billion in July 2025. This constitutes non-residential spending of USD 1,240,425 million and residential spending of USD 898,686 million. This investment trend in construction activity represents an increase in infrastructure projects, which can contribute to the adoption of an AI-powered project management platform in the construction sector globally during the forecasted period, thereby maximizing time and resources.

The demand for construction can arise from diverse factors, such as resource shortages, faulty equipment, inadequate planning, or sudden weather changes. These challenges highlight the requirement of AI in construction industry to identify and eliminate construction delays. Additionally, advancements in technology are facilitating real-time progress reporting and early risk detection, which enhance risk management in the construction sector. These developments are expected to promote market growth.

Major players in this market include IBM Corporation, Oracle Corporation, SAP SE, Autodesk Inc., Bentley Systems, Trimble Inc., and Procore Technologies. These companies are integrating AI into project management, data analytics, and automation platforms to streamline operations and improve decision-making in large construction projects.

AI in Construction Market Growth Drivers:

• Growing Need for Risk Management and Predictive Analytics: The construction industry operates under high uncertainty due to factors like fluctuating material prices, workforce shortages, design changes, weather disruptions, and safety risks. The conventional risk management techniques, which primarily rely on manual evaluation and historical data, are no longer adequate as the projects become larger and more intricate. This has increased the adoption AI-based risk management systems. These systems utilize machine learning, predictive analytics, and real-time integration of data to detect potential problems before they escalate into serious problems. To illustrate, the AI models can predict the occurrence of cost overruns and schedule slippages and the presence of safety hazards based on the project history, IoT sensors, and site cameras.

Aligning with this, about ten countries are already providing AI-enabled services to their citizens, and 75 nations plan to share their AI strategies by 2024. The United States has requested an impressive $3 billion for AI in the FY25 budget. Meanwhile, Singapore aims to spend $1 billion on AI over the next five years. South Korea is prepared to invest $6.94 billion in AI by 2027.

Construction predictive analytics also allows taking preemptive decisions based on analyzing the project performance indicators, equipment conditions, and the trends in labor productivity. Pattern recognition and simulation can be used to estimate the likelihood of project delays, equipment failure, or work-related accidents, which can be mitigated through AI tools by managers in advance. Not only does this boost operational resilience and compliance with safety, but it also promotes cost optimization and sustainability of the project. Therefore, the rising trend towards predictive and data-driven management of construction is emerging as one of the primary factors that contribute to the increased adoption of AI within the construction industry of the modern world.

• Growing demand for productivity in the construction industry: One of the driving factors of skill development in the construction industry is the significantly growing demand for increased productivity and efficiency. Construction projects are complex, require many sites, have tight deadlines, and potential financial constraints. Traditional project management processes often do not provide the desired results, leading construction projects to miss deadlines, underperform, and overrun costs. To solve this problem, companies are turning toward using AI solutions utilizing modern technologies such as computer-based vision, ML, and AI to inject automation, big data analysis, and decision support into their operations.  

AI in Construction Market Segmentation Analysis by Application:

• Project Management: The integration of ML algorithms is the main driving factor for this growth. These algorithms optimize scheduling, resource allocation, and overall project productivity by analyzing past data and offering crucial insights into costs, timelines, and risks.
• Planning and Design: AI is revolutionizing construction planning through advanced machine learning algorithms. These algorithms analyze vast amounts of data with remarkable precision, predict potential risks, and optimize resource use. This development seeks to fundamentally change the construction process beyond efficiency improvements. A key example is the adoption of virtual replicas for better planning, as detailed in the Digital Twin in Construction Market analysis.
• Safety: AI algorithms allow real-time monitoring of construction sites and workers, allowing early detection of possible risks and triggering preventative actions.
• Autonomous Equipment: Construction equipment can execute complex tasks with high accuracy and efficiency due to modern AI algorithms.
• Monitoring and Maintenance: Construction teams use AI to track developments, spot errors, and quickly make well-informed decisions by monitoring the construction work. Innovations like augmented and virtual reality tools are enhancing on-site monitoring, which is covered extensively in the Extended Reality (XR) in Construction Market study.

AI in Construction Market Segmentation Analysis by Construction Stage:

• Pre-Construction: Before one starts a construction project, AI-driven technologies enable the evaluation of risk and design optimization.
• Construction: The most direct application of AI technologies can be seen by optimizing scheduling, resource allocation, and quality control procedures.
• Post-Construction: It includes the application of AI analytics to maintenance scheduling and construction performance monitoring.

AI in Construction Market Segmentation Analysis by Deployment:

• On-Premises: The deployment of the on-premises segment in AI in the construction industry is expected to grow significantly due to its widespread use in data security and maintaining legal compliance.
• Cloud Based: The cloud features that provide advanced technologies are used by construction companies regarding data security and privacy.

AI in Construction Market Segmentation Analysis by Industry:

• Residential: The shift towards enhanced productivity and economic cost is compelling residential builders to implement AI in ways that have never been encountered before. Building projects are encountering more labor shortages, inflation of construction costs, and constrained margins. Machine learning-powered scheduling, drones or cameras to track assets, and predictive technology of ordering materials are all examples of AI applications that facilitate the optimization of builds, the reduction of idle time, and procurement optimization. With the incorporation of real-time data provided by job-site sensors, builders can detect bottlenecks at earlier stages and reassign crews or equipment even beforehand.

Additionally, increasing investment in the country's residential sector is anticipated to further fuel market growth in the coming years. Additionally, the US Census Bureau states that the total construction spending at a seasonally adjusted rate was USD 2,139.110 billion for the same duration, of which private construction accounted for USD 1.623.269 billion.

The other significant driver is the shift to more intelligent, data-driven households and environmentally friendly building methods. The residential building is becoming increasingly connected through IoT sensors, smart home devices, and energy management systems in which AI is a key factor. AI can optimize the floor plan design, forecast energy usage, help select materials to make the building thermally efficient, and lifecycle manage residential assets.

At the same time, sustainability, such as waste reduction, embodied carbon, and efficient energy consumption, is subject to regulatory and consumer pressures. This indicates that AI-enabled analytics and digital twins are useful for developers to stand out and comply with regulations. The intersection of smart home expectations and sustainability necessitates residential construction companies to invest in AI technology sooner than they might have otherwise.

Additionally, the introduction of a product spurred the market development as it brings new innovative solutions that can improve efficiency, performance, or user experience, and attract new customers and initiate demand. They also increase competition, making current players upgrade technologies and extend their product lines to be relevant in the market.

• Commercial: For commercial projects to be completed on schedule and within budget, processes like project management, planning, and budgeting are optimized with the aid of AI technologies.
• Others: Other segments, including industrial and infrastructure industries, are expanding the market due to the rising construction in these industries and increasing infrastructure complexities.

AI in Construction Market Geographical Outlook:

The AI in construction market report analyzes growth factors across the following five regions:

• North America: The market is expanding in the North American region owing to the increased investments in the AI sector and growing infrastructure. One of the largest concentrations of construction employment and establishments is in the Sun Belt and major coastal states: Texas, California, Florida, and New York. Together, these states account for a significant portion of construction labour and payroll. These states thus attract several federal pilot projects, workforce training, and AI investments related to infrastructure.

Federal policy is creating a path to practical implementation. The 2025 Executive Order on AI infrastructure, along with the NIST AI Risk Management Framework, encouraged agencies to implement funding for applied AI for infrastructure resilience, safety, and standards, work that supports construction applications such as site sensors, automated inspection, and risk scoring. Therefore, federally supported R&D and standards are important components of general adoption in those areas that have national labs, large federal contracts, or HUD/DOT pilot-type programs.

The DOT and HUD agencies are creating specific regional activity: DOT’s plans for AI and the recent ARPA-I reports project a focus on mapping, subsurface utilities detection, and construction safety tools for funding; HUD’s building technology grant programs specifically fund modular/automated solutions using AI, concentrating available funds where there are needs for housing affordability and federal partnership. These funds are bottlenecked in metro areas that have pipelines of necessary infrastructure.

Coastal megaregions and large Sun Belt metros provide the test sites (high employment, more federal program presence, better-skilled labour). Rural areas still struggle to incorporate AI, except when federal grants support them. The policy implications are clear: national standards, continued federal funding, and reprogramming of the workforce.

In July 2025, total U.S. construction spending reached $2,139,110 million, with private construction accounting for $1,623,269 million. This steady investment reflects the sector’s continued expansion and modernisation efforts. The growing scale of projects increases the demand for AI-driven solutions that enhance safety, automate planning, and optimise resource use. As construction activity rises, companies are integrating AI tools for project monitoring, cost estimation, and design modelling. This surge in spending provides a strong foundation for AI technologies to gain deeper integration across all stages of construction, driving efficiency and smarter infrastructure development across the U.S.

• Europe: The European region is expected to attain a greater market share during the forecasted timeline because of an established construction sector that prioritizes sustainability, effectiveness, and quality, which propels the use of AI technologies.
• Asia Pacific: The rapid infrastructure development and increasing investment in the construction industry are expected to boost the regional market expansion.
• South America & MEA: The South American, Middle East, and African regions are expected to witness a significant growth rate, owing to the gradual advancement in the construction industry.

AI in Construction Market Competitive Landscape:

• IBM - IBM adopts sustainable building operations using AI algorithms and advanced data analytics to determine building patterns.
• Autodesk Construction Cloud - Artificial Intelligence is integrated into Autodesk Construction Cloud to improve various construction processes. When performing construction takeoffs, estimators can save time and minimize errors by using Autodesk's symbol detection feature, which uses machine learning to detect and identify symbols during takeoff.
• Oracle - Oracle analyzes data and forecasts which projects are most likely to experience safety incidents using AI models tailored to the construction industry. It estimates the likelihood of project and activity delays, which helps to take preventive measures.

The following companies are among the global leaders in the research, development, and advancement of AI in the construction industry.

AI in Construction Market Latest Developments:

• In October 2025, Saint-Goblin announced the launch of its AI avatar for application as the face and voice of a digital series named “Voice of the Future” for advanced sustainable communication in the construction sector. Through this series, the company is highlighting construction-related sustainability issues, such as resilience, accessibility, and digitalisation in diverse countries, including India, China, South Africa, and Australia, among others.
• In March 2025, Buildots introduced its first automated balance project control tool for eliminating construction-related workflow delays, along with finding the actor and balancing the major trade activities. This is an on-site, specific AI tool that works to manage and adjust the project to limit construction delays.
• In February 2025, AI Clearing launched the AI-powered construction monitoring system that is developed to offer same-day progress and quality reports in under six hours for large-scale energy and infrastructure projects.
• In November 2024, IBM created a new AI model with IBM Watsonx AI and data platform, and operates on IBM Cloud. It determines whether a building is residential or non-residential based on building-specific information, such as its footprint, number of floors, roof image, location, and other map data.
• In October 2024, Bentley Systems, Inc. announced new generative AI capabilities for civil site design, such as a design copilot, site layout optimizations, and automated drawing production, resulting in increased productivity and accuracy.

AI in Construction Market Scope:

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

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Table Of Contents

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

12. Research Methodology

List of Figures

List of Tables

Companies Profiled

IBM 

Autodesk, Inc.

Oracle Corporation 

SAP SE 

ALICE Technologies Inc.

The Access Group

Doxel

eSUB, Inc.

Procore

Buildots

Dusty Robotics, Inc.

OpenSpace

AI Clearing

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