Artificial intelligence (AI) is rapidly transforming many industries, and the architecture, engineering, and construction (AEC) sector is no exception. From improving project efficiency to enhancing design capabilities, AI offers exciting potential to revolutionize the way we build. 

However, its adoption is not without challenges, including legacy systems, security concerns, and data complexity. Here we explore the current state of AI in AEC, examine its potential applications, and discuss the key factors driving its adoption and success.

AI’s Potential to Solve AEC’s Most Pressing Issues

According to a 2024 survey by the Associated General Contractors of America (AGCA), a majority of U.S. construction firms struggle with finding skilled labor, with nearly 9 out of 10 firms reporting difficulty filling open positions. AI-powered tools offer solutions for site monitoring, autonomous earthwork, and even ensuring workers are adhering to safety protocols. AI is also being applied to back-office tasks, with platforms like Document Crunch reviewing lengthy contracts for compliance, significantly reducing engineering hours and potentially lowering project costs by 10% to 15%.

Despite this potential, there is still hesitancy within the industry. As of 2024, 70% of U.S. construction firms had yet to invest in AI, even for pilot projects. The biggest barriers, according to a Deloitte study, are knowledge, skill, and data gaps. Business leaders remain uncertain about how best to apply AI to achieve meaningful results.

According to Deltek’s annual report looking at the AEC industry, AI is on the rise in AEC, with leaders expecting it to help improve project delivery and timelines, reduce overhead costs, improve internal communication, increase profits, and reduce project expenditure. Survey respondents expect AI to help:

• Create proposals/marketing content 

• Summarize document and/or drawing data

• Develop 3D models of renderings 

• Analyze and explore data 

• Automate repetitive drawing/modeling tasks

• And more

Barriers to AI Adoption: Legacy Systems and Security Concerns

While AI presents opportunities, it also faces obstacles—with legacy systems and security concerns at the forefront. Research by IFS found that legacy-based technology landscapes, ethical and safety concerns, and data complexity are slowing AI adoption in the AEC sector. Nevertheless, optimism persists, with many in the industry confident that AI will soon drive significant improvements in product and service innovation.

For instance, IFS reports that companies with AI-driven sustainability plans are more optimistic about their ability to meet sustainability goals, such as reducing waste and improving demand visibility. However, Christian Pedersen, Chief Product Officer at IFS, warns that lofty expectations for AI may lead to disillusionment if businesses fail to understand how AI can deliver value across interconnected systems. Pedersen advocates for a comprehensive strategy, where structured datasets enable businesses to unlock the full potential of AI.

The AEC industry can work to overcome these challenges by:

• Addressing Legacy Systems: Modernizing legacy systems and investing in data infrastructure are crucial for successful AI implementation.

• Prioritizing Security: Implementing robust security measures is essential to protect sensitive data and mitigate risks associated with AI.

• Overcoming Data Complexity: Cleaning, organizing, and integrating data from various sources is necessary for AI algorithms to provide accurate insights.

• Building Internal Expertise: Investing in training and development to acquire AI skills within the organization is vital for successful adoption.

• Cultivating a Culture of Innovation: Fostering a culture that embraces change and encourages experimentation with new technologies is essential.

AI for Safety and Efficiency

One of the most significant advantages of AI in construction is its ability to improve safety and manage fleets and assets efficiently. Motive, an AI technology company, conducted a survey that revealed safety is the top area where AI has made a substantial impact. AI-powered tools can provide real-time monitoring of job sites, ensuring that workers wear proper safety gear and identifying hazards before they result in accidents. In addition to worker safety, AI helps detect fraud, track assets and vehicles, and even predict demand, which allows companies to better plan their operations and reduce waste.

Moreover, AI-based fleet management tools can reduce administrative workloads, streamline expense planning, and mitigate rising costs associated with supply chain disruptions. Despite these advancements, many construction companies still struggle with fragmented data and outdated technologies, which limits their ability to integrate AI effectively across their operations.

Targeted AI Use Cases in AEC

While the potential of AI in AEC is vast, industry leaders caution against trying to apply it too broadly. During a recent industry conference panel, Alex Belkofer, Senior Director of Virtual Design and Construction (VDC) at McCarthy, emphasized the need for targeted AI applications that solve specific problems, rather than attempting to overhaul entire systems at once. According to Belkofer, AI should be used to optimize existing processes, such as preconstruction planning or scheduling, where it can have the most immediate and measurable impact.

Belkofer advised firms to “put AI in a box,” focusing on data sets that can drive tangible improvements, rather than applying AI indiscriminately to vast amounts of data. Other experts echoed these sentiments, with Daniel Dart, a partner at Rock Yard Ventures, pointing out that AI should complement human ingenuity by handling repetitive tasks and streamlining workflows, allowing workers to focus on high-value activities.

AI in Construction: A Promising but Cautious Future

The conversation on AI in construction has become a balancing act between optimism and caution. AI’s potential to revolutionize safety, operational efficiency, and sustainability in the AEC sectors is clear, but its adoption must be measured and deliberate. AI can deliver impressive results, but the key to success lies in targeted, practical applications.

The future of AI in construction is promising, but it requires a calculated approach, focusing on solving the most pressing issues—labor shortages, safety, and cost efficiency—before scaling up to larger, more complex implementations. With the right strategies and tools, AI can help reshape AEC for the better, delivering safer, more efficient, and more innovative projects across the board.