Most U.S. construction firms are interested in AI. Far fewer are actually putting it to work. For many construction businesses, there isn’t a question whether AI matters. The conversation surrounds why growing interest has not yet translated into measurable business value, better project intelligence or more resilient operations.
Despite growing investment in AI within the AEC sector, actual adoption remains limited. A 2025 RICS survey of more than 2,200 construction professionals globally found that 45% of firms have no AI implementation in place, and another 34% are still conducting early pilots. A 2025 Dodge Construction Network survey of U.S. contractors tells a similar story — fewer than 1 in 5 are actively adapting their workflows for AI.
The gap isn’t simply a technological problem. Firms aren’t struggling because AI tools are unavailable. They’re struggling because their operational model, digital foundations and internal controls aren’t ready to support meaningful AI use.
The Big Data Problem
AI readiness often starts with a crucial question: Is your company’s data actually useful?
Too many firms approach AI as an isolated innovation initiative, meaning they neglect essential data groundwork. A recent Dodge survey shows 74% of U.S. contractors rate their data quality as poor or only moderate, reporting it’s unreliable, inaccurate and inconsistent. Decades of project data sit across fragmented platforms that were never designed to work together. Drawings, schedules, RFIs, change orders, specifications and site records have historically lived in separate systems with little standardization between them. The interoperability problem in AEC tools isn’t new, but AI makes it more consequential. AI is only as useful as the data it runs on, and construction data was almost never structured with machine use in mind.
For construction firms, this shows up in very practical ways: inconsistent cost histories, incomplete project records, disconnected field documentation, siloed storage environments and systems that make it difficult to turn past project information into useful business intelligence.
This isn’t purely an AI readiness issue. Fragmented platforms and data also create problems with storage, backup, recovery and compliance regardless of whether a firm is thinking about AI. But for firms that want to move AI from experimentation into operations, getting data and infrastructure in order must come first.
Governance and Security Aren’t Optional
Without clear policies around data ownership, access and accountability, firms are understandably hesitant to move AI out of controlled test conditions. The RICS report found that 25% of respondents cited a lack of standards and guidance as a barrier — a sign that many firms don't know how to govern AI use responsibly even when they want to move forward.
More than 40% of firms cite data-sharing security and IP risk as a barrier to AI adoption. The hesitation is well-founded. Construction data carries real exposure. Project specifications, bids, schedules, client information and financial records are sensitive in any context. Feeding proprietary design data into external AI models can also compromise competitive advantage. Firms involved in national infrastructure, transportation networks, energy systems and other critical assets are especially high-value targets for cyberattacks.
This raises immediate IT and governance questions: Which AI tools are approved? What project or client data can be used? Where is that data stored? Who has access? What happens if an AI-generated output is wrong, incomplete or based on outdated information? Without clear answers, adoption remains limited.
A governance model that controls which AI tools are used, how they access company data, who can use them and what they can be used for makes it safer to expand AI use beyond a sandbox environment. Without one, firms risk commercial exposure in pursuit of efficiency.
The Business Case and the Human Factor
The majority (87%) of U.S. contractors claim to believe AI will have a meaningful impact on the industry, boosting productivity, new business opportunities and growth. However, broader business research shows only about 1 in 5 business organizations consider themselves highly prepared to address the skills and organizational changes required for AI, highlighting a wider readiness gap that construction firms are also facing. Leaders that haven’t yet structured their data or identified where AI would enhance their operations are not in a position to calculate a realistic return. The ROI case becomes clearer once firms have defined their use cases and identified where AI would replace manual effort.
AI adoption also depends on people. Construction companies need to ensure employees understand both the technology and how it integrates into the workflows it’s supposed to support. A tool that works in theory won’t deliver value if it doesn’t apply to the ways estimators, project managers, superintendents or field teams realistically work.
Even when the right skills are in place, adoption is not automatic. The people doing the work have to believe that AI tools can be trusted to deliver consistently and in their interests, enhancing their work rather than replacing it. According to the RICS survey, 2 in 5 construction professionals are worried about the impact of AI on their own roles, while accuracy of AI outputs is their primary concern. That skepticism makes sense. Tools that produce unreliable outputs don’t get used, and in construction and engineering, unreliable information has real consequences in project delivery.
The Execution Gap
Running a successful proof of concept is not the same as integrating AI into the processes that a business runs on. Bridging that gap requires documentation, training, and the management and integration of change with existing systems. Most AEC firms have not yet done that work. The firms that are furthest along recognized early that deployment is an organizational challenge as much as a technological one, and built accordingly.
AEC leaders don’t need to overhaul their entire technology environment at once. Start by focusing on one or two workflows where AI could have the most immediate impact, then work backward to the decision points and data that shape them. Most construction leaders know what AI can do. The next step is to create the conditions that make the tools work.
