Navigating AI Adoption for the Construction Industry

Artificial Intelligence (AI) is rapidly transforming numerous industries, and construction is no exception.

From automating routine tasks to improving safety, productivity, and decision-making, AI is proving to be a powerful force in reshaping how construction projects are planned, managed, and executed.

However, as exciting as the benefits of AI may be, the adoption process in the construction industry can be daunting due to unique challenges such as project complexity, risk management, safety concerns, and workforce readiness.

Navigating AI adoption in the construction industry requires education, careful planning and execution, with a keen focus on the technical, cultural, and ethical challenges that come with integrating AI capabilities into traditional workflows. In this article, we explore the key steps and considerations for successfully adopting AI in the construction industry, offering practical insights for stakeholders looking to maximize the benefits of AI while mitigating potential risks.

Understanding AI’s Potential in Construction

Although the concept of AI seems new to construction, some of the capabilities that are included under the label of AI have been around and in use in construction for many years. Take invoices for example. Pattern recognition and machine learning (ML) have been in use in Optical Character Recognition (OCR) tools for a long time in our industry to recognize, extract and code invoices automatically. Predictive modeling has been in use to determine preventative maintenance tasks for equipment management since the early 2000’s and computer vision to recognize potential safety issues or violations have been around for almost as long. All of which use different elements of AI technology.

While the rapid emergence and adoption of ChatGPT (and the underlying OpenAI) has driven a lot of the awareness of AI, it is the slow yet steady digital transformation of our industry that is creating the opportunity for construction to leverage AI capabilities across multiple aspects of the construction process.

Here are a few examples of how contractors are already leveraging AI capabilities either in vendor provided software, or through training different AI models provided by Google, Microsoft, AWS, and many others (both paid and opensource) against their own data:

Automation of Routine Tasks: AI-powered systems can automate repetitive tasks like site monitoring, quality control, and machinery maintenance scheduling. A large GC building a huge data center is using computer vision capabilities of AI to monitor time clocks, identity the number of humans present at a certain point in time, compare that with how many people are “clocking in” via the time tracking application and identify when “buddy punching” is happening, or security is being usurped by doors being held open to allow other to enter a secure site without the necessary security credentials. A large solar and wind farm contactor is using computer vision to compare video and images of electrical wiring on solar panels against images of optimized installations to identify quality control issues.  A medium sized MEP contractor used machine learning to identify which types of jobs took the longest and schedule those for the afternoon, so a greater number of shorter duration service work could be done in the morning. Each of these examples help to reduce human error and free up workers for more complex, higher-value tasks.

Navigating AI adoption in the construction industry requires education, careful planning and execution, with a keen focus on the technical, cultural, and ethical challenges that come with integrating AI capabilities into traditional workflows.@Chanelle Malambo/peopleimages.com – adobe.stock.com

Improved Safety: AI can monitor safety protocols in real-time and predict hazardous conditions by analyzing data from wearables, cameras, and sensors. A GC is using this technology to identify pinch points for hands on their job site and alerting the project team before any work is done. This has significantly reduced one of the most common injuries they experienced on the jobsite.

Enhanced Design and Planning: A large GC now uses an AI driven schedule analysis tool to analyze P6 schedules to identify areas of risk. They even tested the tool against old schedules that had already been optimized and analyzed for risks and it still found areas for improvement. These AI tools can simulate different scenarios, allowing teams to anticipate and solve problems before they arise on-site.

Predictive Analytics: By analyzing data collected during previous projects, AI can provide predictive insights on costs, timelines, and risks, improving overall project management. A GC who was told by one of their suppliers they couldn’t provide a large steel order in sufficient time for construction, was able to use Google’s ML tools to analyze historical data from other suppliers to determine which combination of smaller suppliers could produce the needed steel in time. By doing this they were able to save the project.

Robotics and Semi-Autonomous Vehicles: A combination of AI driven computer vision, machine and deep learning is being used to improve the capabilities of robotics and autonomous/semi-autonomous equipment on the jobsite, performing construction tasks with limited human intervention. One paving contractor is using this technology to solve a machine operator labor shortage. Operators in one location are able to control multiple pieces of equipment in multiple locations using this type of technology

Despite these examples and real-world use cases, AI adoption in the construction industry remains slow compared to other industry sectors such as manufacturing or healthcare. This can in part be attributed to the complexity of construction projects, which often involve multiple stakeholders, dynamic environments, and tight budgets. Additionally, the industry tends to be risk-averse, making it cautious about adopting new technologies. However, for many contractors, the challenge is figuring out where to start as there are so many choices.

Key Challenges in AI Adoption

Adopting AI in construction is not without its challenges. These challenges span technical, organizational, and ethical domains, and they must be carefully navigated to ensure successful AI integration.

Data Availability and Quality: Data is the foundation of AI, but the construction industry often struggles with fragmented and inconsistent data. Construction projects generate vast amounts of information, from architectural designs to project timelines and labor data. However, this data is often stored in silo applications, with different contractors, architects, and engineers using separate systems that do not communicate effectively. This also leads to inconsistencies with data quality as well as the timeliness of data captured. Ensuring that data is standardized, clean, and accessible across different platforms is critical for AI adoption. Contractors must invest in data management and data governance tools and practices to ensure their data is ready for use with AI. Much can be achieved through education and process improvement practices within contractor’s organizations to increase the accuracy and consistency of data collection.

Workforce Readiness: For many contractors, the construction workforce is not yet fully prepared to embrace AI technologies. Many workers are unfamiliar with digital tools, let alone advanced AI systems. This can lead to resistance to AI adoption, as employees are fearful that automation will replace their jobs or require them to learn new skills that they find intimidating. To mitigate this challenge, construction companies should prioritize workforce education and training. This may involve partnering with educational institutions or technology providers to develop targeted training that focuses on both digital literacy (the ability to find, evaluate, organize, create and communicate information safely and responsibly) and AI-specific skills and understanding.

Resource Availability, Cost and ROI Considerations: Adopting AI can require significant upfront investment in technology infrastructure, software, and training if contractors plan to develop their own internal AI initiatives. This can be a major barrier for companies operating on tight margins, especially smaller contractors who may lack the resources to invest in cutting-edge technologies. However, plenty of vendors that they use on a day-to-day basis for managing their business operations are incorporating AI capabilities into their products. Unfortunately, many vendors, seeing the interest that AI generates, are incorporating AI terminology into their product messaging without having actual AI capability. To mitigate the risks of this, contractors will need to make sure they question the vendors carefully to understand what their AI capabilities are, what AI models (deep learning, machine learning, natural language processing, computer vision, etc.) they are using and how frequently they retrain the models to ensure the quality and consistency of AI value. This approach, combined with testing AI capabilities on smaller projects, or a proof-of-concept initiative will help contractors to evaluate the return on investment (ROI) of AI.

Ethical and Legal Risks: AI adoption raises ethical and legal questions, particularly in areas such as data privacy, worker displacement, and accountability. For example, AI systems that monitor workers or predict potential hazards based on biometric data could be seen as intrusive, raising concerns about privacy and surveillance. In addition, the use of AI in construction decision-making brings up questions about accountability. Who is responsible when an AI system makes an error or a wrong decision? Is it the developer of the AI system, the construction company, or the end user? These are important issues that need to be addressed through clear guidelines and policies that define the role of AI in construction projects. For most contractors already using AI, they have policies that state AI will never be used to make decisions, without human intervention.

Steps for Navigating AI Adoption in Construction

Successfully adopting AI in the construction industry requires a strategic approach. Here are key steps to follow:

Assess Current Capabilities: Before jumping into AI adoption, construction companies need to assess their current digital capabilities. This includes evaluating their existing data management tools and processes, and digital skills within the workforce. Companies should identify gaps that need to be addressed, whether that means upgrading software, improving data collection processes, or offering additional training for employees. A digital readiness assessment can help identify areas where AI adoption will have the most significant impact and inform the development of a roadmap for AI integration.

Start Small with Pilot Projects: AI adoption should not be approached as an all-or-nothing initiative. Companies should start small by implementing AI on pilot/proof-of-concept projects where the technology can be tested and refined before being scaled up across the organization. For example, a contractor might start by using available AI tools to optimize equipment maintenance schedules, reducing downtime and repair costs. Once the system proves effective, it can be scaled up to other areas, such as project management or safety monitoring.

Create a Data Strategy: A strong data strategy is key to successful AI adoption. This involves ensuring that data is collected in a consistent and standardized manner across all projects and making sure that this data is stored securely and is easily accessible to AI systems. Implementation of a data governance program designed to share the responsibility for ensuring high quality, consistent data collection across the organization is another key component of a data strategy. Contractors should also consider partnering with construction industry technology consultants who have experience developing data and AI strategies to provide the necessary guidance. This can significantly reduce the time it takes to adopt AI.

Invest in Workforce Development: AI adoption is not just a technical challenge; it is also a cultural one. Construction companies need to invest in workforce development to ensure that employees are ready to work alongside AI systems. This means providing targeted education programs and fostering a culture of innovation that encourages workers to embrace new technologies. Contractors should also involve employees in the AI adoption process by soliciting their input and addressing any concerns they may have about how AI will impact their roles.

Ensure Ethical AI Deployment: As contractors implement AI, it is crucial to address the ethical implications of these technologies. This includes establishing clear guidelines for data privacy, ensuring transparency in AI decision-making processes, and implementing measures to prevent AI from introducing bias into project management or workforce evaluations. By being proactive about these ethical considerations, construction companies can mitigate risks and ensure that AI is deployed in a responsible and fair manner.

The Road Ahead for AI in Construction

The road to widespread AI adoption in the construction industry will be a gradual one, but the potential benefits are too significant to ignore. From increased productivity to improved safety and better project outcomes, AI has the power to revolutionize construction processes. However, to realize these benefits, companies must navigate the challenges of AI adoption carefully. By assessing current capabilities (both internally within the contractor and externally within the vendors providing the software they use on a day-to-day basis), piloting AI projects, investing in workforce development, and addressing ethical considerations, construction companies can position themselves to capitalize on the advantages of AI. Contractors not leveraging AI in the future will likely find themselves falling significantly behind their peers and competitors.

The key to success lies not in rushing the adoption process but in taking a thoughtful, measured approach that considers both the opportunities and risks associated with AI in construction. With the right strategy in place, construction companies can embrace the future of AI and transform their operations for the better.