5 Ways Contractors Are Using AI in Construction

“How are contractors using AI?”

It seems like a simple question, but as businesses across the construction supply chain have begun implementing new AI-powered solutions, another, more pressing question has emerged:

“How should contractors use AI?”

With AI moving from early experimentation to widespread adoption, construction leaders can now look to a growing body of industry research and the experience of their industry peers to better understand AI’s capabilities and make more informed decisions on the best tools to meet their needs.

This article examines the empirical evidence behind AI adoption in the construction industry to date. We’ll take a look at data compiled by a variety of independent research associations, including consultancies such as the McKinsey Global Institute, trade publications like ForConstructionPros, and independent editorial outlets like the Trade Services Authority, to understand how contractors have already deployed AI and give other leaders in construction the hard numbers they need to make the best AI decisions for their business.

How Common Is AI in the Construction Industry?

According to research from the Dodge Construction Network from December 2025, 87% of the 235 U.S.-based contractors they surveyed believe AI will have a meaningful impact on the construction industry. Large contractors (at least $275M in annual revenue) are even more bullish on AI–while 72% of midsize contractors ($50M to $274M in annual revenue) believe AI will give their business a competitive advantage, that belief rises to 86% amongst large contractors specifically.

The same Dodge report found that a little over half of those surveyed are actively piloting AI, making AI adopters the majority, with 40% specifically allocating budget to AI initiatives. Notably, of those who have piloted an AI initiative, between 50 and 100% strongly preferred using AI and reported meaningfully better results than former methods.

AI adoption is growing amongst other players in the construction supply chain, too. According to the National Association of Wholesale Distributors (NAW), nearly 90% of distribution leaders are actively pursuing or investing in AI initiatives. Manufacturers are quickly adopting AI as well. Research from the Federal Reserve of all major U.S. industries shows that year-on-year growth in work-related AI adoption was actually strongest in the manufacturing sector at about 58%.

The McKinsey Global Institute estimates AI and automation could add roughly $228 billion in annual value to the U.S. Architecture, Engineering, and Construction (AEC) industry by 2030, and that AI could automate 39% of the sector's nonphysical hours, with invoicing and data entry among the areas expected to change most. (For comparison, the same study predicts AI has the potential to automate 15% of the work hours that require both physical and nonphysical hours.)

What Are Contractors Using AI to Do?

When looking at the role of AI in construction, the conversation has focused on the distinction between AI for work in the field, such as robotics and prefab advancements, versus AI for the back office. That framework is useful to a point, but deeper specificity is necessary to explore all the ways contractors have the opportunity to use AI. 

McKinsey’s research, for example, further subdivides areas for AI application as such:

• Communication, collaboration, and creativity
• Management skills
• Information skills
• Digital skills
• Handling and moving
• Working with machinery and specialized equipment
• Constructing

The Trade Services Authority’s categorization is another helpful way to understand the current US market landscape for contractor AI solutions:

• Pre-construction tools: For estimating, takeoff, bid management, and risk modeling
• Project execution tools: For scheduling, field service management, inspection, and safety monitoring
• Subcontractor and workforce tools: For subcontractor management, workforce coordination, and compliance tracking
• Document and communication tools: For document management, contract analysis, and client communication
• Business operations tools: For accounting, CRM, marketing automation, and reporting

Note that not all AI solutions will fit neatly into one of these categories. Many tools will span categories, and new categories may emerge as the technology evolves, but for contractors assessing their needs and determining where AI can offer value today, these basic categories can act as a helpful guide.

While the following is not an exhaustive list of the ways contractors are currently using AI, they do cover five common use cases specific to the construction industry.

How Are Contractors Using AI to Bid on Jobs?

Machine learning is a key technology that comprises what we think of as “AI.” (You can learn more about machine learning’s exact definition in this article from MIT Sloan.)

Machine learning is what offers the biggest leap from traditional software to AI-powered tools. Whereas traditional software programming requires creating detailed instructions for the computer to follow to cover every possible scenario (“If A happens, then do B”), platforms powered by machine learning can teach themselves from experience. Instead of relying on a specific set of well-defined rules, machine learning can handle “messy” inputs, and with enough training over time, produce the desired outcome. 

You can learn more about the difference between traditional software and AI-powered platforms in this webinar from The User Group.

This is the same technological capability powering contractor tools like bidding tools. These solutions are trained on historical job data–material costs, labor hours, subcontractor bids, etc.–to generate cost predictions for new projects. Contractors using this technology are able to make more accurate bids and better ensure their margin will be protected.

How Are Contractors Using AI for Procurement?

AI-powered procurement tools for contractors rely on machine learning, including a specific type of machine learning known as Natural Language Processing (NLP). The same explainer from MIT Sloan defines NLP as a field of machine learning in which machines learn to understand natural language as spoken and written by humans, instead of the data and numbers normally used to program computers, allowing machines to recognize language that’s used in the real world.

How does NLP power procurement? Whether managing the initial buyout or supporting a foreman with day-to-day orders, NLP models can parse material lists written in a spreadsheet, handwritten in a notebook or on a 2x4, or captured in a text or phone call–without an exact match in terminology. Instead of relying on keyword search like traditional software might, NLP-powered procurement tools have the capacity for semantic understanding. Those solutions can turn a list of required material into an order in seconds, matching generic field terms for items (“¾ LB”) to formal supplier product names (like “3/4" LB COND BODY AL SC”).

How Are Contractors Using AI for Operations?

The category of technologies known as Robotic Process Automation (RPA) is not new, businesses across the construction supply chain have used automation tools for years to handle repetitive data-entry tasks like invoice processing and payroll calculations. 

However, with AI–specifically machine learning and capabilities like NLP–these RPA tools now have a layer that can intelligently process all use cases. Instead of the brittle templates that software solutions relied on for automation, AI-powered automation is able to handle a majority of these repetitive tasks by recognizing when an incoming document doesn't match expected patterns and routing it for human review.

This is one area in particular where contractors are already reporting high levels of satisfaction with new AI platforms. The same survey from the Dodge Construction Network found that 100% of their respondents rated AI as highly or very highly effective for:

• Managing accounts payables and receivables
• Managing employee contracts and benefits 
• Regulatory compliance and reporting

For accounting teams at construction companies, for example, they’re finding AI-powered tools can handle 95% of their invoices automatically, freeing up teams to focus on the exceptions only (and relieving much of the pressure those teams have felt from the industry’s lack of adequate headcount).

How Are Contractors Using AI to Enhance Field Safety?

Another emerging AI-powered technology for contractors is Computer Vision (CV). These kinds of systems process images or video feeds from job sites to detect safety hazards, measure material quantities from drone footage, read blueprint dimensions, or inspect finished work against design specifications (Learn more from the Trade Services Authority about site monitoring and quality control). Like other forms of AI machine learning, CV systems require site-specific training so it can “learn” to perform reliably. 

Instead of paper-based workflows that can lack consistency or cause disruption when a document goes missing, AI-assisted site inspection tools can provide structure, reliability, and even institutionalize knowledge that previously only experienced site managers held. With a complete digital record, contractors can compare current equipment conditions to historical data and detect early signs of mechanical decline, enhancing worker safety and enabling cost savings through preventative maintenance.

How Are Contractors Using AI for Forecasting?

While predictive modeling isn’t new either, AI makes these capabilities accessible to people without a degree in data science. With emerging AI-powered predictive analysis tools, contractors are forecasting schedule delays, cost overruns, material demand, and more without hours of regular manual analysis. 

The most successful tools are drawing on third-party weather data APIs, well-maintained subcontractor performance records, and accurate field progress logs.

What’s the Best Way for Construction Companies to Use AI?

While a flurry of new technologies coming to market and providing potentially transformative solutions is exciting, the best AI implementations come from identifying a real pain point or opportunity for enhancement. Today’s construction industry is managing:

• Labor scarcity 
• Supply cost volatility
• Project pipeline anxiety
• Administrative burdens

These are all real challenges that require solutions. With a better understanding of what AI can do, leaders in the construction industry can take a step back to assess their own unique business needs and strategic goals to make an informed decision on what initiatives–AI or otherwise–make the most sense for them.

‍

Learn more about how to prepare for an AI initiative with our guide: How to Audit Your Organization for an AI Implementation

‍