Executive Summary:
- Transformative Potential Across Sectors: AI is revolutionizing the traditionally slow-to-adopt construction, engineering, and commercial real estate (CRE) sectors through predictive analytics, automation, and enhanced project planning tools, enabling greater efficiency, safety, and cost reductions.
- AI-Driven Robotics and Smart Technology: The integration of robotics and AI in construction tasks, such as 3D printing, automated monitoring, and resource optimization, is accelerating timelines, improving accuracy, and supporting sustainable practices.
- Predictive Analytics and Risk Management: AI tools are enhancing decision-making in construction and engineering by using historical data to optimize timelines, predict risks, and streamline resource management, resulting in safer worksites and reduced delays.
- Commercial Real Estate Efficiency Gains: CRE benefits from AI-powered tools in market forecasting, tenant management, and energy efficiency, driving increased investment and operational improvements across portfolios.
- Investment and Future Growth: Venture capital investment in AI for these sectors is growing rapidly, with transformative technologies like generative AI, smart city initiatives, and sustainability-focused innovations poised to shape the future of urban development and infrastructure.
The construction and engineering sectors have been some of the slowest to adopt AI, but the technology is poised to have a large impact nonetheless, as well as in commercial real estate. Like other industries, AI can provide predictive analytics for risk analysis and project planning, market forecasting and smart environments, as well as improvements to efficiency and sustainability. These sectors can also suffer from similar drawbacks, such as data security and privacy issues, as well as regulatory and compliance challenges.
In the construction sector, AI also offers unique benefits such as in robotic construction, AI-driven design, and structural analysis. AI-driven robotics have introduced groundbreaking advancements, from autonomous machinery for excavation and demolition to precision 3D printing robots that can construct entire building sections, thereby accelerating project timelines and minimizing human error. The expansion of AI into urban development processes could greatly enhance the progress and potential of smart city initiatives.
With predictive modeling and real-time monitoring, AI has the potential to optimize urban infrastructure, manage resources efficiently, and improve the quality of life for residents by reducing traffic congestion, enhancing public safety, and promoting sustainable energy solutions.
When it comes to commercial real estate, investors can make use of AI tools for portfolio management and forecasting, asset management, and improving tenant experiences.
Like all our research, this piece is not meant to be exhaustive or the final say on these topics. We want our clients to understand the possibilities and to show them paths to the future. It’s our clients’ job to figure out which growth opportunity direction to take.
Adoption
The slowness of adoption in the construction and engineering industry is not a drawback when it comes to VC investment, but rather an opportunity. A survey conducted by MIT Management in 2024 found that around “12% of firms in manufacturing, information services, and health care were using AI, compared with 4% in construction and retail.” Of this, much of the investment is “low intensity”, as you can see in Figure 1 below.
Figure 1: AI Use Intensity and Testing Rates by Sector
This shows the huge growth potential for the construction and engineering industry, and the possibilities for investment and AI adoption in the sector. There is a lot of catch-up that can be carried out, with the creation and deployment of AI technologies. As we cover in this article, businesses and investors in this sector plan to increase their investments in the coming years, and the VC environment particularly in the commercial real estate sector has been described as “young and energetic”.
McKinsey found that investment in architecture, engineering and construction already grew significantly from 2020 to 2022, as seen in Figure 2 below:
Figure 2: Global investment in architecture, engineering, and construction tech grew to $50 billion between 2020 and 2022
This shows the increase in adoption that has already occurred in recent years. McKinsey notes that these industries have experienced “an explosion of investment and a wave of start-up launches.” Some of the factors prompting investment include high demand for global, long-term construction, desires to decarbonize existing portfolios, and regulatory pushes towards increasing digitization in the industry. The $1.2 trillion Bipartisan Infrastructure Law in the United States and the €800 billion NextGenerationEU fund in Europe both provided government stimulus for more construction in the coming years. These funds are expected to catalyze advancements in AI for infrastructure, as governments increasingly prioritize not only the completion of projects but also the quality, safety, and sustainability of these structures.
Their survey of investors and industry players found that 77% of the respondents “expect to invest in AEC tech at similar or higher levels in 2023, and 64 percent see it generating higher returns versus other verticals.” These hopes are borne out by forecasting by Precedence Research, which shows that AI in the construction market is predicted to grow significantly, as shown in Figure 3 below:
Figure 3: Artificial Intelligence (AI) in Construction Market Size 2023 to 2033 (USD Million)
The investment focus is on AI tools that improve efficiency and enhance project delivery, resource management, and operational efficiency, as well as models that improve safety and sustainability within construction and engineering. For instance, machine learning algorithms enable real-time monitoring of resource usage, reducing costs associated with over-ordering and minimizing environmental impact. In addition, AI-enhanced safety monitoring systems help reduce workplace accidents by alerting site managers to potential hazards before they escalate, further emphasizing the role of AI in creating safer construction environments.
In addition, project management and portfolio management for commercial real estate show significant potential for VC investment and financial gains from more effective operations in this sector.
Let’s look at AI technologies in the construction and engineering sectors, as well as in commercial real estate, to examine how the industry will be transformed over the coming years.
The Role of AI in Construction
AI has a large role to play in the construction and engineering sector regarding predictive analytics, automation and robotics, building information modeling, design, and project planning. Schneider Downs, one of the largest accounting firms in the United States, says that the construction industry was “once thought of as an industry that lagged willingness to adopt and embrace new technology and processes,” but with AI technologies and their application in the sector, “now has a rare opportunity to change that narrative.” This shift is not only reshaping the perception of the industry but is also driving a competitive edge by improving project accuracy, reducing waste, and minimizing unforeseen project delays.
First, regarding predictive analytics, AI tools can assist with cost estimation, supplier selection, project timeline estimation, risk analysis, and worksite optimization. Predictive analytics enable construction firms to base decisions on empirical data rather than assumptions, which can transform initial planning stages. For example, by analyzing factors like supply chain disruptions or environmental impacts, AI-driven predictive models allow firms to choose more reliable suppliers, establish realistic timelines, and account for external risks before breaking ground on a project.
On the operational side, cost estimation can be done with AI tools that use data from previous and historical projects to determine material costs, labor costs, and can even integrate data such as inflation and regulatory information. This helps construction and development firms, as well as investment firms, to make well-informed decisions about whether a project is viable, or how costs can be reduced. Similarly predictive analytics tools can be used to determine project timelines, by comparing current projects to data on previous project schedules. AI can be used to identify challenges or time runovers that occurred in the past, so that mechanisms can be put in place to prevent those issues in the current project. Furthermore, these AI tools facilitate real-time schedule adjustments, helping project managers to proactively address bottlenecks and optimize resource allocation as projects progress.
Another major area in which AI is transforming construction projects is in safety and risk forecasting. Safety is a serious issue for construction workers, with the industry having one of the highest on-the-job fatality rates of any industry, as you can see in Figure 4 below.
Figure 4: Construction had more deaths than any other industry sector in 2022
AI can assist significantly with issues such as job safety and risk analysis. Some AI tools can continuously monitor the conditions of a worksite, with video and audio data, and can identify risks such as scattered cones, the presence of the public, scaffolding, loose fencing, busy roads, and so on. These risks can then be noted by project managers and addressed to reduce risks to workers on the site. AI can also analyze weather patterns and machinery data, to identify risks from extreme weather or failing machinery. Predictive analytics can alert managers ahead of time, so that workers are protected from harm. In addition to improving worker safety, these real-time safety insights can also prevent costly legal issues and delays associated with workplace accidents, ultimately enhancing the overall profitability of construction projects.
One construction company in Massachusetts, Suffolk, worked with a company called Newmetrix, formerly Smartvid, (now owned by Oracle, called “Oracle Construction Intelligence Cloud”), to analyze risks on the job site. This tool uses “construction-specific AI models [to] analyze data to predict the projects at the highest risk of safety incidents.” This tool determined that for Suffolk, increasing the number of safety observations carried out per work hour was more effective at preventing incidents than their previous approaches. After using the tool, Suffolk found “an overall downward trend closer to 60% in terms of incident rates.”
Another way in which the construction industry is being revolutionized by AI is in the use of robotics and automation. These tools are being further integrated into the construction industry, such as through technologies like 3D construction printing. AI is also being used for tasks such as bricklaying, excavation, remote-controlled demolition robotics, modular and prefab construction designed by AI, and robotic heat welding to improve project speed and quality.
The University of British Colombia has been undertaking projects to transform typical construction vehicles into automated robots that can speed up tasks and improve decision-making. Structural engineer Dr. Tony Yang, a professor of civil engineering with the Faculty of Applied Science, says “We see this technology fully maturing within the next decade. The next construction era will be fully digitalized, allowing real-time monitoring of construction site activities, and knowing where machines, labor and materials are located within a construction site at all times.”
Finally, AI is also being used to optimize Building Information Modeling (BIM) and improve design in construction. BIM uses virtual models of buildings to improve design. AI enabled BIM programs can identify issues with the model such as clashes or conflicts between necessary elements of the building’s design. AI algorithms can also look at these virtual models and determine the most energy efficient and sustainable design. For instance, AI-driven BIM tools can recommend materials with lower environmental impact or structural alterations that could enhance a building’s energy performance, directly supporting sustainability goals and reducing operational costs.
A research paper from Cornell University’s arXiv on generative AI in the construction industry analyzed social media posts on LinkedIn from construction industry practitioners, finding that a large number of posts related to AI, and particularly generative AI. They found, as you can see in Figure 5 below, that there was an “overwhelmingly positive sentiment inherent within the analyzed corpus about GenAI in construction.”
Figure 5: Sentiment Analysis of Industry Practitioners’ Opinions
From their research, their paper also found that “generative AI shows huge potential to transform information workflows in architecture, engineering, and construction.” Recent startups and VC investment also show the perceived potential in this industry.
One example of a startup in this industry is FYLD, a fieldwork execution platform powered by AI. Their AI Risk Assessment tool uses AI driven analytics to analyze videos taken by site workers, including examining site conditions and hazards. They found that the use of their tool resulted in a “20% reduction in injuries and accidents”, with a “75% reduction in risk assessment completion time.” FYLD recently secured 10 million in funding from NatWest, one of the UK’s largest business and commercial banks.
Another example is Nextera Robotics, which makes autonomous construction site monitoring robots. These robots can assess sites for safety issues and produce a “safety index” for the site, as well as heatmaps of risk areas. The visual data captured by the robots can also monitor site progress, including progress on walls, ceilings, mechanical, electrical, and plumbing processes. Nextera Robotics was started at MIT and is funded by Y-Combinator and other tech investors.
Other tools like Doxel also use AI to automate construction progress tracking, through taking a video attached to a head-mounted camera, and then comparing the video of the real progress to the project BIM. Automated progress tracking enables real-time status updates, which can highlight any deviations from the project schedule early on, allowing project managers to make adjustments before minor delays escalate.
A project carried out by the Karlsruhe Institute of Technology (KIT) in Germany called “SDaC – Smart Design and Construction”, also set up an AI system to analyze and digitize project documents, to increase efficiency from the project management side. As part of their project, they also collected a large list of AI-driven construction technology companies, which you can browse through here.
AI in Engineering
In addition to the use of AI in construction projects, AI is also being used on the engineering and structural side. Forbes notes that AI is poised to “revolutionize these fields”, just as it is revolutionizing others. Engineers often use Computer-aided design (CAD) tools already, and the integration of AI into these is improving their efficiency and scope of use. Engineers and architects can use AI-powered CAD tools to create designs for buildings, generate virtual twins of buildings (digital replicas), and large language models (LLMs) can even analyze building codes and regulations.
When it comes to structural safety, virtual twins are one way in which engineers can analyze whether a building will be suitable for use, such as how it will respond to weight, footfall, weather, earthquakes, or other risks. Predictive analysis can also be carried out using building datasets, to analyze environmental stresses and material fatigue, such as when materials will need to be replaced or may become weak. AI building monitoring can also determine potential defects, deterioration, or damage in buildings that engineers may need to address. This can be done through sensors and AI analysis of that sensor data. This real-time monitoring can prevent small structural issues from escalating by alerting engineers to changes in the building’s integrity, ultimately enhancing long-term safety.
One study on artificial intelligence in civil infrastructure health monitoring explains how machine learning tools called convolutional neural networks (CNNs) can analyze vibration characteristics to determine the integrity of a structure. This has already been in use for quite some time. However, the addition of AI tools has allowed these to be used for autonomous damage detection, allowing high accuracy in detection reports and the integration of multiple data sets. An example of how these systems work is in Figure 6 below:
Figure 6: A general framework for vibration-based damage detection systems.
Like in other industries, AI can also be used to improve supply chain management and logistics for large engineering projects. Supply chains that use AI analysis of routes, workflows, and resource management, can perform more efficiently and with less waste. With AI, these tasks and monitoring can be done in an increasingly automated manner. Some supply chains make use of Internet of Things (IoT) devices to collect data and improve analytics. Inventory and resource levels can be tracked with AI, so that shortages or resource oversupplies can be monitored and adjusted for.
These benefits allow engineering teams to reduce costs, reduce waste, make real-time decisions with better information, manage inventories and warehouses better, and continue to optimize these processes.
Engineers are also able to use AI-driven tools for environmental impact assessments and product life cycle assessments. Tools like OneClick LCA use AI to provide life-cycle assessments for building materials and environmental product declarations. It uses large amounts of both local and global data to make these assessments. It also allows engineers and designers to adjust based on sustainability measures, and to find additional decarbonization opportunities. Tools like this allow engineers to quickly improve costs, efficiency, and sustainability when building.
Another example is Augmenta, which uses Generative AI (GenAI) to create efficient, code-compliant, error-free building designs. The Augmenta Construction Platform initially focuses on electrical components for buildings, but will soon integrate plumbing, mechanical, structural, and prefab components. Augmenta’s GenAI platform helps streamline the design process by automating complex calculations and validating compliance requirements, reducing human error and enhancing design accuracy.
Finally, nPlan offers another type of tool for predictive analytics and AI to determine engineering risks. The platform analyzes the project and determines how long it will take, potential risks to completion, and how to mitigate those risks.
These tools show high potential in the engineering space to rapidly improve the efficiency of projects, implement better planning and management, as well as improving safety and structural monitoring. Now let’s take a look at commercial real estate.
AI in Commercial Real Estate (CRE)
Commercial real estate is another key area that is picking up on the transformative potential of AI. A study carried out by JLL (Jones Lang LaSalle), a global real estate services company, found in 2023 that “AI and generative AI were ranked among the top 3 technologies that were expected to have the greatest impact on real estate over the next three years.” These technologies are anticipated to reshape property management by introducing efficiencies, reducing human error, and improving client satisfaction through enhanced automation and predictive tools.
One of the interesting potential impacts of AI, they found, could be that companies involved in the creation of AI and AI components, specifically “semiconductor hardware, cloud computing platforms, model hubs and application development”, for example, can all increase the potential for commercial real estate occupancy. This growth would center around major tech hubs, potentially, and in areas where talent related to AI is concentrated. As you can see in Figure 7 below, a significant proportion of AI companies are concentrated in the United States:
Figure 7: 37% of AI companies are based in the U.S.
AI is also able to analyze market trends for accurate property valuation and demand forecasting. This makes property valuation faster and more efficient, with reduced errors and higher reliability. Key property information can be extracted from datasets and used to calculate property profiles that can be priced and assessed.
AI can also help companies to optimize asset portfolios based on real-time market and operational data. EY explains that generative AI has a role to play in “creating opportunities for greater efficiency, mitigating risk through its ability to rapidly scan data and identify any concerns that may exist, and potentially paving the way for new business models” in real estate more generally. With respect to efficiency, AI can manage both finding, buying and selling recommendations, through predictive analytics.
In addition, AI can provide support for commercial real estate with forecasting, budgeting, and managing properties from the administrative side. As well as this, properties themselves can be handled more efficiently regarding their energy management, security and access management, maintenance reports, and tenant management such as through AI chatbots in which tenants can make requests for assistance.
In another study also by JLL, they found that “among all AI-powered PropTech companies, over 70% are VC-backed. About 20% of companies are in the very early incubator, angel or seed stage; 25% are at early-stage VC rounds; and 15% are at late-stage VC rounds. Overall, this ecosystem is young and energetic.” In addition, they found that over 80% of real estate investors and developers “plan to increase their real estate technology budget in the next three years.” This shows a strong indicator for potential investment into these technologies, with uptake from a large proportion of industry participants.
One recent startup, Enertiv, has an AI technology that can track systems in a building, including boilers, fans, elevators and waste. It provides real-time fault detection, reports, sensor data analysis, and recommendations for improving building efficiency. Alongside this AI tool, Enertiv also provides asset management tools for CRE, allowing assets to be digitized and managed, so that the CRE portfolio can be made more energy efficient, with lower tenant turnover, smoother billing, and less complicated maintenance.
Another example is Verdigris, a smart building and energy management company that uses AI to continually capture sensor data on energy use and system health, as well as automation tools to optimize power and cooling operations.
Elise AI also provides a different tool for property managers: an AI-powered chatbot and property management tool that is used by hundreds of the top property management companies. With this AI interface you can collect and analyze resident information, leasing reports, and operational workflows, to reduce costs and streamline operations.
Deloitte notes, as you can see in Figure 8 below, that property transaction enablement is also another big area of VC investment:
Figure 8: Real estate firms are investing primarily into artificial intelligence and machine learning services for property transaction enablement
They also note that “commitment without a plan” could lead to problems, as the industry is still relatively new in terms of AI adoption. Deloitte says that the industry is now at “a pivotal juncture when it comes to generative AI adoption. Some real estate firms are likely already beginning their generative AI journey, hiring talented individuals who can spearhead transformation and making investments in companies championing emerging capabilities.”
Key Challenges and Barriers
While these industries are poised to increasingly adopt AI technologies to improve numerous performance metrics and daily workflows, there are also risks involved.
The research paper mentioned above from Cornell University’s arXiv found several challenges in the implementation of AI in the construction industry. You can see some of these issues in Figure 9 below, including construction regulatory challenges, domain knowledge, hallucinations, accuracy, and cost. Another issue not mentioned is the issue of data security and privacy.
Figure 9: Challenges of GenAI in Construction
First, the construction and engineering industries have significant domain knowledge, with overlapping areas of expertise such as electrical, plumbing, mechanical, structural, and other areas. The spatial aspect of construction and engineering projects can also push the current limits of AI and its modeling capabilities. GenAI and LLM tools can help to integrate domain knowledge into other AI tools, with learning capacity and the ability to interpret texts, regulations, and best practice guides. However, creating AI models with sufficient expertise to manage these cross-disciplinary challenges is complex and may require extensive retraining and customization to capture the nuances specific to each project.
Like other complex domains, engineering, construction, and commercial real estate rely on a diverse array of systems. Achieving interoperability or integration among these systems can be challenging, particularly when digital integration hasn’t previously occurred at the level or scale required for effective AI functionality. [GS1] Bridging these systems requires not only technical compatibility but also standardized data structures, which are still developing across the construction, engineering, and real estate industries.
A risk with all AI programs is the risk of “hallucinations”, in which AI decides or produces a convincing output that is nonetheless false or based on faulty reasoning. When it comes to construction and engineering, these false decisions could be costly, dangerous, or even fatal. High quality training data, enriched data sets, and running many simulations can help to overcome this challenge.
One of the other major challenges is cost. As the construction industry has been relatively slow to take up these technologies until this point, R&D funding could be quite high. Some of these AI programs and tools may also be prohibitively expensive for smaller construction or engineering firms to purchase. As development continues however and prices come down, these issues will be resolved.
Another challenge, as with all AI systems, is the issue of privacy and security. When it comes to commercial real estate, for example, information such as behavioral data that could be used to optimize heating systems or air conditioning, could violate privacy laws. This will depend significantly on the jurisdiction that any company is operating in, whether office occupants have given consent to this data collection, and more. Security issues relating to breaches of data, whether in commercial real estate, construction, or engineering, could result in dangerous outcomes if security breaches result in tampering, stolen data, or hacks that overtake smart building systems, IoT, or smart city infrastructure.
Finally, regulatory and compliance issues may make it difficult for emerging firms and tools to make progress, or to fit into existing frameworks. However, there is some evidence that laws and regulations are already shifting in a direction to accommodate new technologies, such as the United Kingdom’s Building Safety Act. This law states that “building information must now be kept digitally and securely, and it also must be readily available for people who need the information to do a job (firefighters or building maintenance, for example) whenever that person needs the information.”
While the Cincinnati Bar Association notes that there is “still much uncertainty regarding the legal standards, responsibilities, and expectations of parties when integrating this technology to construction,” they also state that “early adopters stand to gain a competitive advantage over others who lag behind.”
Impact on Sustainability and Green Building Practices
Despite risks and challenges, AI in the construction and engineering industry also has a lot of potential to make a difference in terms of sustainability and decarbonization. Green building practices and energy efficiency are firmly supported by AI tools.
Materials for construction need to have relatively specific material properties, regarding durability, heat resistance, and flexibility. A lot of these materials are particular mixes of relatively carbon-intensive components, such as cement. The process of making these materials is also very carbon intensive. The production of alternative materials that have the same or sufficiently similar properties, is one way to reduce carbon emissions. AI can carry out simulations for material testing, and can be used for chemistry for developing new, climate-friendly materials for building.
AI can also help to reduce the carbon footprint of construction projects more generally, through the efficiency and waste-reduction impacts that data-driven analysis has. The World Economic Forum notes that “from change orders and inaccurate design plans to an overestimation of materials needed, AI can be especially insightful to help reduce waste during projects.” It does this by using forecasting and analytics to predict more effectively what materials are needed for a project, and at what rate. This reduces material waste and reduces costs. Additionally, AI enables precise tracking of supply chain emissions, allowing construction firms to identify and reduce carbon-intensive stages in procurement and logistics.
As discussed further below, smart energy management can reduce waste and carbon emissions for buildings and for urban areas more generally. For commercial buildings in particular, these savings can be significant.
One company that has received significant funding recently is ICON, a 3D printing robotics company that uses automation to build. With materials that are sustainable and highly energy efficient, one of ICON’s goals is to produce sustainable housing for the future. ICON has even partnered with NASA to develop 3D printed buildings that can be built on the moon. This technology has implications for Earth-based construction as well, allowing rapid production of durable, sustainable housing in areas affected by natural disasters or with limited access to traditional building materials.
Cove.Tool is another AI technology that uses a simulation engine to provide analysis on sustainability and compliance for buildings. It looks at CO2 emissions, energy efficiency, certifications and compliance, to provide net-zero homes, energy efficient buildings, and reduced carbon footprints for developments.
HolonIQ found in 2023 that climate tech VC had been increasing in strength into 2022 and onwards, with sector breakdowns as displayed in Figure 10 below.
Figure 10: Global Climate Tech Venture Capital Funding 2010-2022 in USD Billions and number of funding rounds
Here you can see the potential of the built environment industry to increase VC investment to support climate technologies that aid sustainable buildings and reduce carbon emissions.
Many opportunities in the sustainable building space are still to be taken: the US Green Building Council found that the level of green building activity has increased significantly from 2021 to 2024, with a shift from 28% to 42% of respondents saying that more than 60% of their projects involve green building activity. This is shown in Figure 11 below.
Figure 11: Level of Green Building Activity
The integration of AI into green building technologies, with increased efficiency and cost reductions is also likely to improve the attractiveness of this space for investors. Most respondents to the survey said that their main reasons for building green were client demands, environmental regulations, and it being “the right thing to do”, none of which are likely to change soon as drivers.
Future Growth Opportunities
One future growth opportunity is in AI-driven robotics. Crunchbase data shows that “investors have poured hundreds of millions into startups at the intersection of construction and robotics,” with a long list of AI construction robotics companies that have received funding in the last couple of years, as you can see in Figure 12 below.
Figure 12: Construction Robotics Companies Last Funded In 2022-2024
The increase in funding in this area and confidence in these technologies shows a lot of potential for research, growth, and development, to transform the industry significantly.
In addition, several smart buildings are leveraging AI and IoT for real-time management and automation. This is often used in relation to ventilation, air conditioning, lighting, heating and energy use in buildings, but can also be used for other things such as security and access management. The smart building sector is expected to grow significantly, according to a report from Fortune Business Insights, which you can see in Figure 13 below:
Figure 13: North America Smart Building Market Size, 2019-2032 (USD Billion)
One example of technology in this area is Infogrid, a startup that uses AI analysis for managing smart buildings, particularly in terms of energy and heating, cleaning, and asset management for portfolios. Infogrid’s system leverages IoT sensors to collect real-time data on air quality, temperature, and space usage, allowing for more efficient building operations and enhanced tenant comfort.
Another example is Safehub, a company that installs IoT sensors in buildings to instantly gather data on structural damage following natural disasters like earthquakes. The AI analysis of this sensor data provides real-time results on whether a building is safe to enter. This data also helps businesses to quickly gain information and act, also in relation to making insurance claims.
The growth of smart buildings is connected to the growth of smart city development more broadly. Urbanization is increasing rapidly on a global scale, and the United Nations predicts that by 2050 more than two thirds of the world’s population will be living in cities.
The application of smart technology to this urban development is intended to support efficient urban planning and sustainable growth and is often done using sensors and real-time data that can be increasingly seen in places such as bus stops and train stations with real-time arrival data. Air quality sensors are increasingly used to monitor pollution in cities, and similar technologies are likely to grow. These sensors can also feed into digital platforms accessible by city dwellers, allowing them to make informed decisions about transportation, avoid high-pollution zones, and reduce their environmental footprint.
S&P Global says that “over the next five years, we expect AI/generative AI to impact cities through integration into digital government services, smart transportation and interactive digital twins.” Many smart city technologies are already so integrated, that people don’t necessarily notice them. S&P found that smart city technologies are already appealing to urban dwellers, such as public Wi-Fi, smart street lighting, and improvements to public transportation, as shown in Figure 14 below:
Figure 14: Citizens’ interest in smart cities driven by quality-of-life enhancements, services
In the United States, a number of smart cities initiatives are being driven by the GovAI Coalition, which connects city administrations and the public sector to promote purposeful and responsible AI use. The group has over 1,500 members and over 500 local, state, and federal agencies. This group hopes to support the use of AI and smart city technologies to promote positive urban living and a high quality of life for residents.
Built Robotics is one example of an AI-enabled construction company that is focused on building in the solar industry. They have developed a few autonomous construction robots that can also be cloud monitored and controlled, with AI-powered smart cameras to monitor progress, actions, and safety.
Another example is the relatively well-known Sidewalk Labs, which had proposed to develop a new smart city district on the Toronto Waterfront. It is now owned by Google and named “Google Earth”. Sidewalk Labs ran into issues when locals pushed back against their data being used for smart cities purposes. This shows some of the pitfalls with emerging technology in this space, and the privacy and security issues that developers need to be careful of. With more careful planning and better public engagement, Sidewalk Labs could have potentially avoided the backlash that it received.
McKinsey’s survey of industry professionals and investors also found that the VC funding scene for construction and engineering technology is evolving, shifting from angel and seed investment and early-stage VC to late-stage VC, with increases in deal sizes and funding for all types of investment in these industries. You can see these changes in Figure 15 below:
Figure 15: Funding sources for architecture, engineering and construction tech are evolving, with late-stage venture capital investors gaining prominence
Each of these future growth opportunities shows the potential of AI in the construction, engineering and commercial real estate industries. There are numerous different directions in which the technology and accompanying VC investment can continue to develop.
Now let’s finish with a look at a few case studies that showed the effects of these AI-enabled tools in real construction projects.
Case Studies
One case study for the use of AI in construction comes from nPlan, mentioned above. nPlan used their AI-powered technology to support the building of an extension for a hospital in Boston. This was an 11-story building with numerous areas needed for hospital beds, a NICU, and an MRI suite. With nPlan’s tool, Suffolk, the contractor, was able to identify a particular risk area that could cause significant delays. Working with nPlan’s recommendation, Suffolk could prevent 20 days of potential delay, and potential losses of $1.25 million USD.
Another example of an AI-powered tool, this time in property market analysis, is Zillow’s “Zestimate”. The Zestimate uses both public and proprietary information to determine the value of a home, such as tax data, listing price, market trends, and historical data. Tools like this can be used throughout the market in both residential and commercial property markets for determining pricing and valuation. Zestimate has a self-published median error rate of 2.4%.
Real I.S., a property investment group in Germany, used the AI tool “Recognizer” and applied their AI-assisted control of building services for sustainable building operation. The Realizer AI system includes a self-learning component, and was able to significantly reduce the carbon footprint of heating and other energy systems in Real I.S.’s buildings, reducing “energy consumption and emissions by an average of more than 20 percent.” They also saw more than “20 percent energy and emissions reduction in a German shopping center and in an office building” using Recognizer AI technology.
These case studies show the real-world applicability of these technologies, and the impacts they can have.
Conclusion
The potential for AI to transform the construction, engineering, and commercial real estate industries is significant, especially given the slow uptake of technology in these sectors (until now). This leaves high potential for growth, and VC investment seems to be accelerating in this area. The use of AI technologies in construction and engineering can have large impacts on project efficiency and cost reduction, project management, inventory and supply chain management, and result in savings for firms working in this space. It also has the potential to improve safety for workers in the industry, and sustainability both in the process of building and in the end product.
For commercial real estate, AI can add to portfolio management success and efficiency, investment choices, and building management, all of which increase the economic return for those in the sector. AI is a significant catalyst for innovation and growth in these sectors and will have a large role to play in building the infrastructure of tomorrow.
