Global Artificial Intelligence (AI) in Construction Market Growth, Share, Size, Trends and Forecast (2025 - 2031)

• In 2023, generative AI adoption in the construction industry saw notable advancements. Companies utilizing generative AI for architectural design and building information modeling (BIM) reported a 27% increase in adoption compared to the previous year. This technology contributed to a 20% reduction in project delays and cost overruns. Additionally, generative AI for site logistics and resource allocation improved efficiency by 18% in 2023​

• In 2024, the industry is transitioning from experimenting with AI technologies to fully implementing them. AI-driven tools are increasingly being integrated into construction workflows, enabling automated design processes, real-time predictive analytics, and improved data management. These developments are expected to significantly enhance efficiency, reduce waste, and streamline construction projects across all phases​

The Global Artificial Intelligence (AI) in Construction Market is witnessing significant growth driven by advancements in machine learning, deep learning, and natural language processing (NLP) technologies. These AI technologies are revolutionizing the construction industry by optimizing various stages of construction projects, from pre-construction planning to post-construction maintenance. By leveraging AI, construction companies can enhance efficiency, accuracy, and decision-making across the project lifecycle, leading to improved project outcomes and cost savings.

The application of AI in construction is segmented across different stages of projects, including pre-construction, construction, and post-construction phases. During the pre-construction phase, AI solutions assist in design and planning tasks, revenue estimation, and demand forecasting. In the construction stage, AI technologies facilitate project management, field management, risk management, and schedule management, optimizing resource allocation and workflow efficiency. Post-construction, AI-driven predictive maintenance systems help monitor and maintain infrastructure assets, ensuring their longevity and performance over time.

The market is characterized by a diverse range of AI solutions and services. AI solutions include design and planning tools, virtual assistants, demand forecasting systems, and predictive maintenance software. On the services front, integration and deployment support, training and consulting services, and ongoing maintenance assistance are crucial for successful AI implementation in construction projects. Both large enterprises and small and medium-sized enterprises (SMEs) are adopting AI solutions to streamline operations and improve productivity across various segments of the construction industry, including residential, commercial, heavy construction, and specialized projects.

Geographically, the AI in construction market spans regions such as North America, Europe, Asia Pacific, Middle East and Africa, and Latin America. These regions are witnessing increasing AI adoption within the construction sector, driven by factors such as technological advancements, infrastructure development initiatives, and the growing demand for sustainable construction practices. With a timeline spanning from 2020 to 2030, the market analysis captures the evolving landscape of AI integration in construction, highlighting emerging trends, challenges, and opportunities shaping the industry over the next decade.

Global Artificial Intelligence (AI) in Construction Segment Analysis

In this report, the global artificial intelligence (ai) in construction market has been segmented by technology, stage, component, application, deployment type, industry type, organization size and geography.

Global Artificial Intelligence (AI) in Construction Market, Segmentation by Technology

The Global Artificial Intelligence (AI) in Construction Market has been segmented by Technology into Machine learning and deep learning and Natural Language Processing (NLP).

The Global Artificial Intelligence (AI) in Construction Market is witnessing significant growth driven by advancements in key technologies such as machine learning, deep learning, and natural language processing (NLP). These technologies are transforming the construction sector by enabling automation, optimization, and intelligent decision-making across various project stages. Machine learning algorithms analyze vast amounts of data to identify patterns and make predictions, enhancing project planning, resource allocation, and risk management processes. Deep learning techniques, inspired by the human brain's structure and function, empower AI systems to learn from complex data representations and perform tasks with human-like accuracy. Natural language processing (NLP) facilitates seamless communication between humans and machines, allowing construction professionals to interact with AI-powered systems through voice commands, text input, or other natural language interfaces. This integration of machine learning, deep learning, and NLP technologies is driving efficiency, productivity, and innovation in the construction industry, ultimately leading to improved project outcomes and cost savings.

In the Global Artificial Intelligence (AI) in Construction Market, the adoption of machine learning, deep learning, and NLP technologies is particularly evident across various stages of construction projects. These stages include pre-construction, construction, and post-construction phases. During the pre-construction phase, AI-powered solutions assist in design and planning tasks, revenue estimation, virtual assistance, demand forecasting, and predictive maintenance. Throughout the construction stage, these technologies facilitate project management, field management, risk management, schedule management, supply chain management, and other critical activities. Even in the post-construction phase, AI continues to play a vital role in monitoring building performance, analyzing data, and optimizing maintenance processes. By leveraging AI across these stages, construction companies can streamline operations, enhance decision-making, and deliver projects more efficiently.

The Global Artificial Intelligence (AI) in Construction Market comprises various components, including solutions and services. Solutions encompass design and planning tools, revenue estimation software, virtual assistants, demand forecasting systems, and predictive maintenance platforms. On the other hand, services offered in the market include integration and deployment, training and consulting, and support and maintenance. Together, these components form a comprehensive ecosystem that empowers construction companies to harness the power of AI and unlock new opportunities for growth and innovation. With AI-enabled solutions and services, organizations can address industry challenges, optimize workflows, and stay competitive in a rapidly evolving market landscape.

The deployment of AI in construction is not limited to a specific type of organization or industry vertical. It spans across various sectors, including residential, institutional/commercial, heavy construction, and others. Both small and medium-sized enterprises (SMEs) and large enterprises are embracing AI technologies to improve their operations, enhance project outcomes, and drive business success. By leveraging AI-driven solutions tailored to their specific needs, organizations can gain a competitive edge, minimize risks, and capitalize on emerging opportunities in the construction market. The global reach of AI in construction extends across different regions, with North America, Europe, Asia Pacific, Middle East and Africa, and Latin America experiencing significant growth in AI adoption within the construction sector.

Global Artificial Intelligence (AI) in Construction Market, Segmentation by Stage

The Global Artificial Intelligence (AI) in Construction Market has been segmented by Stage into Pre-construction, Construction stage and Post-construction.

The Global Artificial Intelligence (AI) in Construction Market is segmented based on project stages, encompassing pre-construction, construction stage, and post-construction phases. In the pre-construction phase, AI technologies are employed to enhance design and planning processes, estimate project revenues, provide virtual assistance, forecast demand, and predict maintenance requirements. These AI-driven solutions enable construction companies to optimize project blueprints, identify potential risks, and accurately estimate costs, thereby improving project efficiency and reducing delays.

During the construction stage, AI plays a crucial role in various project management activities, including resource allocation, scheduling, risk management, and supply chain optimization. AI-powered systems facilitate real-time monitoring of construction progress, analyze on-site data, and provide insights to project managers, enabling them to make informed decisions and mitigate risks promptly. AI-driven solutions assist in field management tasks such as workforce coordination, equipment tracking, and safety monitoring, ensuring smoother operations and adherence to project timelines.

In the post-construction phase, AI continues to deliver value by monitoring building performance, analyzing data, and optimizing maintenance processes. AI-powered predictive maintenance systems leverage historical data and machine learning algorithms to anticipate equipment failures, schedule maintenance activities, and optimize asset lifecycle management. By proactively addressing maintenance needs, construction companies can enhance building sustainability, minimize downtime, and prolong the lifespan of infrastructure assets, leading to improved operational efficiency and reduced maintenance costs. AI's integration across pre-construction, construction, and post-construction stages is driving innovation and transforming the construction industry landscape.

Global Artificial Intelligence (AI) in Construction Market, Segmentation by Component

The Global Artificial Intelligence (AI) in Construction Market has been segmented by Component into Solutions and Services.

The Global Artificial Intelligence (AI) in Construction Market is characterized by various components, including both solutions and services tailored to meet the industry's specific needs. In terms of solutions, offerings encompass design and planning tools that leverage AI algorithms to optimize architectural blueprints, streamline project workflows, and enhance decision-making processes.AI-driven solutions facilitate revenue estimation by analyzing project parameters, market trends, and historical data to provide accurate cost projections, aiding construction companies in budget planning and resource allocation.

Virtual assistant solutions integrate AI technologies to provide personalized support to construction professionals, offering insights, recommendations, and assistance throughout the project lifecycle. AI-enabled demand forecasting tools leverage predictive analytics to anticipate material and labor requirements, optimizing supply chain management and ensuring adequate resource availability. Predictive maintenance solutions leverage AI algorithms and IoT sensors to monitor equipment health, predict potential failures, and schedule maintenance activities proactively, minimizing downtime and optimizing asset performance.

In addition to solutions, the AI in Construction Market also offers a range of services to support the implementation and deployment of AI technologies. Integration and deployment services assist organizations in integrating AI solutions into their existing systems, ensuring seamless interoperability and functionality. Training and consulting services provide construction professionals with the necessary expertise and guidance to leverage AI effectively, enabling them to maximize the value of AI investments and accelerate adoption. Support and maintenance services ensure the ongoing reliability, performance, and optimization of AI systems, providing timely assistance and troubleshooting to address any issues that may arise. The combination of AI solutions and services empowers construction companies to enhance productivity, efficiency, and innovation across project lifecycles.

Global Artificial Intelligence (AI) in Construction Market, Segmentation by Application

The Global Artificial Intelligence (AI) in Construction Market has been segmented by Application into Project management, Field management, Risk management, Schedule management, Supply chain management and Others.

The application of artificial intelligence (AI) in the construction industry spans various key areas, each addressing critical aspects of project management and operational efficiency. Project management applications leverage AI algorithms to streamline workflows, optimize resource allocation, and enhance collaboration among project stakeholders. AI-powered project management tools facilitate task scheduling, progress tracking, and risk assessment, enabling construction teams to coordinate activities effectively and deliver projects on time and within budget.

Field management solutions harness AI capabilities to enhance on-site operations and ensure the smooth execution of construction activities. These applications leverage real-time data analytics and predictive modeling to optimize workforce management, equipment utilization, and safety protocols. By providing actionable insights and automated workflows, AI-driven field management tools empower construction professionals to address challenges promptly and maintain productivity on the job site.Risk management applications utilize AI algorithms to identify, assess, and mitigate potential risks throughout the construction project lifecycle. These tools analyze various factors, including project specifications, environmental conditions, and regulatory requirements, to anticipate and mitigate risks such as delays, cost overruns, and safety hazards. By proactively addressing risk factors, AI-powered risk management solutions help construction companies minimize project disruptions and protect their bottom line.

Schedule management solutions leverage AI algorithms to optimize project timelines and resource allocation, ensuring efficient use of labor, materials, and equipment. These applications utilize predictive analytics to forecast project milestones, identify potential bottlenecks, and optimize construction schedules for maximum efficiency. By automating schedule adjustments and providing real-time updates, AI-driven schedule management tools enable construction teams to adapt quickly to changing conditions and maintain project timelines.Supply chain management applications leverage AI technologies to optimize procurement processes, inventory management, and supplier relationships in the construction industry. These solutions analyze historical data, market trends, and supply chain dynamics to optimize procurement decisions, reduce costs, and minimize supply chain disruptions. By improving visibility and transparency across the supply chain, AI-powered supply chain management tools enable construction companies to streamline operations and enhance overall efficiency.

Global Artificial Intelligence (AI) in Construction Market, Segmentation by Deployment Type

The Global Artificial Intelligence (AI) in Construction Market has been segmented by Deployment Type into Cloud and On-premises.

The deployment of artificial intelligence (AI) solutions in the construction industry can occur through two primary models: cloud-based and on-premises deployment. Cloud-based deployment involves hosting AI applications and services on remote servers managed by third-party cloud providers. This model offers several advantages, including scalability, flexibility, and accessibility. Cloud-based AI solutions enable construction companies to access advanced analytics tools and computational resources without the need for significant upfront investments in hardware infrastructure. Cloud deployment allows for seamless integration with existing IT systems and provides real-time access to data and insights from any location with internet connectivity.

On-premises deployment, on the other hand, involves hosting AI applications and services within the organization's own infrastructure, typically within the company's premises or data centers. This deployment model offers greater control over data privacy and security, as sensitive information remains within the organization's network. On-premises deployment may be preferred in situations where regulatory compliance or data sovereignty requirements mandate localized data storage and processing. While on-premises deployment may require higher initial capital investment in hardware and IT infrastructure, it provides construction companies with greater autonomy and customization options over AI solutions' implementation and management.

The choice between cloud-based and on-premises deployment for AI in construction depends on various factors, including the organization's IT infrastructure, budget, data security requirements, and scalability needs. Cloud-based deployment offers agility and scalability, making it suitable for organizations seeking rapid deployment and access to cutting-edge AI capabilities without substantial upfront investments. On the other hand, on-premises deployment provides greater control and security over sensitive data and may be preferred by organizations with stringent regulatory or compliance requirements.

In summary, both cloud-based and on-premises deployment models offer unique advantages and considerations for AI implementation in the construction industry. While cloud deployment provides scalability, flexibility, and accessibility, on-premises deployment offers greater control over data security and compliance. Ultimately, the choice between deployment models depends on the organization's specific needs, priorities, and resources.

Global Artificial Intelligence (AI) in Construction Market, Segmentation by Industry Type

The Global Artificial Intelligence (AI) in Construction Market has been segmented by Industry Type into Small and Medium-sized Enterprises (SMEs) and Large enterprises.

The adoption of artificial intelligence (AI) in the construction industry varies across different types of enterprises, including small and medium-sized enterprises (SMEs) and large enterprises. SMEs typically refer to companies with fewer employees and lower revenue compared to large enterprises. In the context of AI in construction, SMEs face unique challenges and opportunities in leveraging AI technologies to enhance their operations and competitiveness.

For SMEs in the construction sector, AI adoption may present significant benefits, including improved project efficiency, cost savings, and enhanced decision-making capabilities. AI solutions tailored for SMEs often prioritize ease of use, affordability, and scalability to accommodate the resource constraints commonly encountered by smaller firms. These solutions may include AI-powered project management tools, predictive analytics platforms, and virtual assistant technologies designed to streamline workflows and optimize resource allocation.Large enterprises, on the other hand, typically have greater financial resources, infrastructure, and organizational capacity to invest in and deploy AI solutions at scale. These companies may adopt more sophisticated AI technologies for construction management, such as advanced predictive modeling, autonomous equipment, and robotics systems. Large enterprises often leverage AI to optimize complex construction projects, improve safety and compliance, and gain a competitive edge in the market.

Despite the potential benefits of AI adoption, both SMEs and large enterprises in the construction industry face challenges in integrating AI technologies into their existing workflows. These challenges may include high upfront costs, limited access to AI expertise, data privacy and security concerns, and resistance to organizational change. With the right strategies and support, both SMEs and large enterprises can overcome these barriers and harness the full potential of AI to drive innovation and growth in the construction sector.In summary, AI adoption in the construction industry is influenced by the size and resources of enterprises, with SMEs and large enterprises facing distinct opportunities and challenges. While SMEs may benefit from AI solutions tailored to their specific needs and constraints, large enterprises have the capacity to invest in more advanced AI technologies to optimize complex construction projects. By addressing common barriers to adoption and leveraging AI to enhance operational efficiency and competitiveness, both SMEs and large enterprises can capitalize on the transformative potential of AI in construction.

Global Artificial Intelligence (AI) in Construction Market, Segmentation by Organization Size

The Global Artificial Intelligence (AI) in Construction Market has been segmented by Organization Size into Residential, Institutional commercials, Heavy construction and Others.

Artificial intelligence (AI) applications in the construction sector are not uniform but vary according to different stages of a project. These phases typically include pre-construction, construction, and post-construction activities, each presenting unique challenges and opportunities for AI integration.

In the pre-construction phase, AI technologies are utilized for tasks such as project planning, design optimization, and cost estimation. Through advanced algorithms, AI can analyze vast datasets to identify optimal site conditions, simulate various design options, and provide accurate cost projections. This streamlines the pre-construction process, improves decision-making, and enhances project outcomes.

During the construction stage, AI plays a crucial role in project execution by facilitating real-time monitoring of progress, resource allocation, and quality control. AI-driven analytics enable construction teams to identify potential delays or bottlenecks, optimize resource allocation, and address quality issues promptly. This enhances project efficiency, minimizes risks, and ensures timely project delivery.

In the post-construction phase, AI technologies continue to deliver value by optimizing facility management, maintenance, and operations. Predictive maintenance systems powered by AI help anticipate equipment failures and maintenance needs, reducing downtime and optimizing asset performance. AI-enabled building management platforms leverage sensor data and analytics to optimize energy usage, improve occupant comfort, and enhance overall building performance.

By integrating AI across the entire lifecycle of a construction project, companies can realize significant benefits in terms of efficiency, cost-effectiveness, and sustainability. Leveraging AI technologies from pre-construction planning to post-construction facility management allows for streamlined processes, risk mitigation, and higher-quality project delivery. It also enables ongoing optimization of operations and maintenance, ensuring long-term sustainability and value for stakeholders.

Global Artificial Intelligence (AI) in Construction Market, Segmentation by Geography

In this report, the Global Artificial Intelligence (AI) in Construction Market has been segmented by Geography into five regions; North America, Europe, Asia Pacific, Middle East and Africa and Latin America.

Global Artificial Intelligence (AI) in Construction Market Share (%), by Geographical Region, 2024

In North America, including the United States and Canada, robust technological infrastructure and a strong focus on innovation are driving the adoption of AI solutions in the construction sector. Europe, with its emphasis on sustainable development and digital transformation, is witnessing a surge in AI implementation to optimize construction processes and enhance project outcomes.

The Asia Pacific region, encompassing countries like China, India, Japan, and South Korea, is experiencing rapid urbanization and infrastructure development, leading to increased demand for AI-enabled construction solutions. Governments in these countries are investing heavily in smart city initiatives and infrastructure projects, creating favorable conditions for the growth of AI in construction.

In the Middle East and Africa (MEA) region, countries such as the United Arab Emirates (UAE), Saudi Arabia, and South Africa are investing in large-scale construction projects, including smart cities, transportation networks, and commercial developments. AI technologies are being leveraged to improve project efficiency, reduce costs, and address sustainability challenges in these markets.

Latin America, including countries like Brazil, Mexico, and Argentina, is witnessing a growing interest in AI adoption across various industries, including construction. Economic development, coupled with infrastructure modernization initiatives, is driving the uptake of AI solutions to address construction challenges and capitalize on emerging opportunities in the region.

The global AI in construction market is expected to witness significant growth and innovation across all regions, fueled by technological advancements, increasing infrastructure investments, and a growing awareness of the benefits of AI-driven construction solutions. This trend is likely to continue throughout the forecast period, positioning AI as a key enabler of efficiency, productivity, and sustainability in the construction industry worldwide.

This report provides an in depth analysis of various factors that impact the dynamics of Global Artificial Intelligence (AI) in Construction Market. These factors include; Market Drivers, Restraints and Opportunities.

Drivers, Restraints and Opportunity

Drivers:

• Increasing Adoption of Building Information Modeling (BIM) Technology
• Growing Demand for Automation and Efficiency in Construction Processes
• Rising Investments in Smart Infrastructure Projects
• Need for Improved Safety and Risk Management Practices

• Emergence of Advanced AI Technologies for Construction Applications:The construction industry is witnessing a transformative shift with the emergence of advanced AI technologies tailored specifically for construction applications. These technologies, including machine learning, deep learning, and natural language processing (NLP), are revolutionizing traditional practices by enabling construction companies to harness the power of data and automation. Through AI, construction firms can optimize various aspects of their operations, from project planning and design to execution and maintenance. By leveraging AI algorithms, companies can analyze vast amounts of data, predict project outcomes, and make informed decisions, ultimately leading to improved project efficiency and quality.

  One of the key advantages of advanced AI technologies in construction is their ability to automate routine tasks and processes. Machine learning algorithms can learn from historical data to identify patterns and trends, enabling construction companies to streamline workflows and reduce manual efforts. Deep learning techniques, on the other hand, enable computers to recognize complex patterns and make decisions without human intervention, paving the way for autonomous construction processes. By automating tasks such as scheduling, resource allocation, and quality control, AI can help construction firms save time and resources while minimizing errors and delays.

  AI-driven solutions are enhancing collaboration and communication among project stakeholders, leading to improved project coordination and efficiency. Through virtual assistants and communication platforms powered by natural language processing (NLP), construction teams can communicate more effectively, share information in real-time, and resolve issues promptly. This seamless exchange of information fosters greater transparency and accountability, enabling construction projects to stay on schedule and within budget.AI-powered tools for project management and scheduling enable construction companies to optimize resource allocation, identify potential risks, and adapt to changing project conditions, ensuring smoother project execution and delivery.

  Looking ahead, the adoption of AI technologies in the construction industry is expected to continue growing as companies recognize the potential benefits of these advanced solutions. With ongoing advancements in AI algorithms and technologies, construction firms have the opportunity to further enhance their operations, improve project outcomes, and drive innovation in the industry. As AI becomes increasingly integrated into construction processes, companies that embrace these technologies stand to gain a competitive advantage, positioning themselves for success in the rapidly evolving construction landscape.

Restraints:

• High Initial Investment Costs and Implementation Challenges
• Lack of Skilled Workforce in AI and Construction Industries
• Data Privacy and Security Concerns
• Compatibility Issues with Existing Construction Technologies and Systems

• Resistance to Change and Traditional Work Practices:Resistance to change and adherence to traditional work practices present significant challenges for the adoption of artificial intelligence (AI) in the construction market. Many stakeholders in the construction industry are accustomed to traditional methods and may be hesitant to embrace AI-driven technologies due to concerns about disruption and unfamiliarity.There may be a lack of awareness or understanding about the potential benefits of AI, leading to skepticism and resistance among construction professionals.

  The construction industry has traditionally been slow to adopt new technologies, often relying on established practices and processes that have been in place for decades. This resistance to change can create barriers to the implementation of AI solutions, as construction firms may be reluctant to deviate from familiar methods or invest in new technologies without a clear understanding of their benefits. There may be a perception that AI will replace human workers, leading to concerns about job security and resistance from labor unions and workers.

  The complexity and fragmented nature of the construction industry can pose challenges for the integration of AI technologies. Construction projects typically involve multiple stakeholders, including architects, engineers, contractors, subcontractors, and suppliers, each with their own workflows, systems, and preferences. Coordinating these disparate entities and aligning their processes with AI-driven solutions can be challenging and may require significant collaboration and communication efforts.

  Addressing resistance to change and traditional work practices in the construction industry will require proactive measures to educate stakeholders about the benefits of AI and foster a culture of innovation and openness to new technologies. Companies may need to invest in training and development programs to upskill their workforce and ensure that employees are equipped with the necessary knowledge and skills to leverage AI effectively.Collaboration and engagement with industry associations, regulatory bodies, and other stakeholders can help to build consensus and facilitate the adoption of AI-driven solutions in the construction sector.

Opportunities:

• Adoption of AI for Predictive Maintenance and Equipment Monitoring
• Integration of AI with Building Information Modeling (BIM) Systems
• Enhanced Safety and Risk Management Through AI-driven Analytics
• Optimization of Construction Processes and Project Management

• Expansion of AI Applications in Sustainable Construction Practices:The expansion of AI applications in sustainable construction practices signifies a significant trend in the global artificial intelligence (AI) in construction market. AI technologies are increasingly being leveraged to optimize construction processes and promote sustainability across various stages of the construction lifecycle. From design and planning to materials selection, energy management, and waste reduction, AI-driven solutions offer valuable insights and capabilities to enhance environmental performance and promote green building practices.

  One key area where AI is making an impact is in building design and optimization. AI-powered design tools can analyze complex datasets and generate innovative design solutions that prioritize energy efficiency, daylighting, natural ventilation, and other sustainable design principles. These tools enable architects and engineers to explore a wider range of design options and make more informed decisions that minimize environmental impact and maximize resource efficiency.In addition to design optimization, AI is also being used to improve construction processes and reduce waste. AI-driven project management systems can analyze real-time data from construction sites to identify inefficiencies, streamline workflows, and optimize resource allocation. By predicting potential delays, identifying safety hazards, and optimizing material usage, these systems help to minimize project costs and environmental impact while improving overall project performance.

  AI technologies are being deployed to enhance energy management and sustainability in buildings throughout their operational lifecycle. AI-powered building management systems can continuously monitor energy consumption, analyze data patterns, and optimize building performance in real-time to minimize energy usage and reduce carbon emissions. These systems can also facilitate predictive maintenance, enabling proactive identification and resolution of equipment issues to prevent energy waste and improve building efficiency.The expansion of AI applications in sustainable construction practices reflects a growing recognition of the importance of environmental sustainability in the construction industry. By harnessing the power of AI, construction companies can achieve greater efficiency, reduce waste, and minimize environmental impact, ultimately contributing to a more sustainable built environment for future generations.

Key players in Global Artificial Intelligence (AI) in Construction Market include:

• IBM
• Microsoft
• Oracle
• SAP
• Alice Technologies
• eSUB
• SmarTVid.Io
• DarKTrace
• Aurora Computer Services
• Autodesk
• Jaroop
• Lili.Ai
• Predii
• Assignar
• Deepomatic
• Coins Global
• Beyond Limits
• Doxel
• Askporter
• Plangrid
• Renoworks Software
• Building System Planning
• Bentley Systems

In this report, the profile of each market player provides following information:

• Company Overview and Product Portfolio
• Key Developments
• Financial Overview
• Strategies
• Company SWOT Analysis