Construction is growing faster than the workforce can keep up. McKinsey projects data center spending will hit $7 trillion by 2030, and U.S. utilities are planning $1.4 trillion in power infrastructure spending through the same year, roughly double the pace of the past decade.
This growth isn’t concentrated on traditional commercial projects. Data centers, healthcare megaprojects and power infrastructure demand tighter deadlines and more labor at a moment when labor is already scarce. According to data from the Associated General Contractors (AGC), 82% of firms report they cannot find enough hourly craft workers and 80% cannot fill salaried roles. Associated Builders and Contractors projects the industry needs 349,000 net new workers in 2026 alone.
That combination, more complicated work with fewer people to do it, is making AI an operational necessity to meet demand. However the technology is still so new that a proven implementation playbook has yet to emerge—adding a layer of difficulty to an already challenging process.
Fortunately, best practices are starting to surface.
Here’s a preview of some tactics construction leaders can use to lay the groundwork for a smoother rollout and stronger returns, based on observations of over one hundred AI implementations across the construction supply chain.
Start With What You Know
Before evaluating a single vendor, leadership needs a clear picture of how much time is spent on manual tasks. Teams stretched thin on delivery rarely have the bandwidth to step back and diagnose their own bottlenecks, which means the most visible problem might be addressed before the most costly one.
The fix doesn’t require an elaborate audit. Simple, consistent reporting—hours spent per task, neglected work that’s piling up, recurring errors that cost time to fix—on any objective metrics can turn a gut feeling into a business case. That said, qualitative feedback is also important, especially from less vocal members of the team. The employees who are the least likely to speak up about a problem are also the most likely to suffer a new issue in silence.
Prepare Your Systems and Permissions
Even the best AI tool will underperform if it can’t integrate with the systems already in place. Contractors preparing for implementation need to loop in IT leaders early to sort out permissions, API access and technical integrations, since a plug-and-play connector and a custom-built one require different timelines and internal resources.
Skipping this step is one of the most common reasons rollouts stretch from weeks into months. A little technical due diligence up front, including optional steps like cleaning up duplicate vendor records, can be the difference between a smooth launch and a stalled one.
Adoption Goes Beyond the Announcement
Enabling field crews and back-office staff to adopt new technology takes a deliberate approach: Start with credible early adopters then let the results speak for themselves and amplify their success. When organic adoption plateaus, leadership can give it a boost by incorporating usage metrics into weekly meetings and by asking holdouts directly what’s holding them back. Framing resistance as an opportunity for feedback, rather than a reprimand, tends to surface very fixable barriers to adoption.
Not All AI Vendors Are Built the Same
With so many early-stage vendors overselling new technology, a thorough evaluation is critical. A vendor that won’t run a live demo on your own data, has a shallow engineering bench or can’t produce independent security validation is signaling that their solution will come up short in practice.
Going beyond standard software due diligence, into the technical and operational details vendors rarely volunteer, is what separates a solid demo from a solution that will deliver.
Where Contractors Are Already Seeing Results
Accounts payable has emerged as one of the clearest early wins for automation. According to the Dodge Construction Network, contractors who’ve adopted AI for managing payables and receivables report a 100% satisfaction rate with their results. Executives across the 2026 ENR Top 400 Contractors are echoing that momentum, describing a deliberate, phased move toward enterprise-level AI deployment starting with foundational tools.
Getting good results with any new technology depends on the same fundamentals: an honest read of your organization’s needs, systems ready for integration, a long-term change management plan and a vendor that’s earned your trust.
For a free and an in-depth look at the tactics construction leaders are using for smoother AI rollouts, read the complete “How to Audit Your Organization for an AI Implementation” guide linked below.
