The construction supervision model was created for a different era, and hiring more supervisors won’t close the gap.
Projects are getting bigger, more complex and more geographically dispersed than ever before. At the same time, contractors are dealing with a growing labor shortage and a shrinking number of experienced field leaders. By 2025, 92% of contractors report having difficulty finding skilled workers, and nearly half say labor shortages are already causing delays.
However, supervisors are expected to keep work moving, catch quality problems before they become costly, ensure compliance, protect worker safety, and deliver projects on time and on budget.
The traditional model does not scale.
We assumed the solution was expanded monitoring: more site visits, logging and reporting. But even the most experienced supervisor can only be in one place at a time. The problem we should all be trying to solve is how to extend the reach of the supervisors we already have.
This is where artificial intelligence has the potential to fundamentally change the way construction projects are managed.
Supervisors need help knowing where to focus their limited time
Many organizations initially think that remote monitoring is the challenge. In my experience, it is not. The challenge is knowing where our attention is needed. The reality is that most crews do not need intervention at any time.
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Supervisors spend too much time looking for signals among disconnected updates, phone calls and paperwork. Deloitte’s Outlook to 2025 describes it as a “reactive project control model” in which teams are burdened with a constant stream of deliverables, change events and schedule updates that require manual intervention.
This makes it difficult to distinguish genuine risk from routine activity. One team calls repeatedly with minor questions, while another finds a major problem but never asks for help. A project seems to be progressing normally until a small deviation turns into a costly rework days later.
It’s impossible to manage based on your gut alone. Managers need operational visibility.
This is especially true when organizations try to increase control intervals. With experienced supervisors in short supply, simply adding more layers of management is not sustainable. Organizations are finding ways to help one person effectively support more crews by giving them better insight into where they can have the biggest impact, at a time when the outcome can still be changed.
We have seen what this looks like in practice. In a deployment with utility SGN, remote managers were able to monitor substantially more field equipment using our desktop supervisor module. After a few weeks, they had enough capacity that the organization assigned them responsibility for a second depot.
Agentic AI changes the equation
Where most construction technology has documented what already happened, agent AI can respond as conditions continue to change.
Construction software has largely served as a system of record. It has helped organizations standardize workflows, capture documentation and understand what happened. More recently, AI has made it easier to search, summarize and analyze this information.
But projects are not won or lost after the fact. They are won or lost while work is in progress, when an emerging problem can still be corrected.
Specialist agents can compare live field activity against multiple sources simultaneously: project plans, company procedures, permit requirements, operational data and external regulations. Then emerge the situations where a supervisor’s judgment is most needed.
One of the biggest surprises we’ve seen is that the most valuable agents don’t do the most complicated tasks. They monitor lead signs of deviations.
For a utility contractor, we are developing an agent that reviews field video along with permit information, company-specific rules, and legal right-of-way work requirements. Instead of asking teams to spend 15 minutes documenting every possible compliance detail, the agent focuses on the handful of conditions most likely to result in fines and flags only the issues that require corrective action.
In another deployment, an agent compares what teams say they’re doing on the field to the approved plan. If the work begins to deviate from the plan, supervisors are notified before the work needs to be redone.
We saw the importance of this during a deployment where a manager manually caught a crew describing a particular placement that differed significantly from the approved specifications. If left unchecked, diversion in the poor could have resulted in millions of dollars in rework. In the future, this is exactly the kind of comparison agents can perform automatically on thousands of jobs.
Equally important, we have learned that these agents must know when not to make a judgment call. If the information needed to verify compliance is incomplete, an agent should not assume that all is well. I should say, “I can’t confirm that.” In a workplace, an honest “I don’t know” is far more valuable than a fake “all clear.”
Rethink the operating model
Construction has never lacked data. He has not been able to connect this information quickly enough to support better decisions. However, no agent understands the project context, client relationships, or field realities like an experienced supervisor.
If agent AI allows a supervisor to support more crews, the opportunity is to decide what to do with the capacity it creates. Organizations can take on more work, respond faster, improve quality or redeploy experienced people where they create the most value. They may even choose to reinvest some of those earnings back into the field, creating a better experience for people who do physically demanding work every day.
Agentic AI enables construction leaders to rethink their own operating model. And this may be his greatest contribution to the industry.
Shelley Copsey is the founder and CEO of FYLD, the AI-powered operating system for high-risk fieldwork. Under his leadership, FYLD has become a trusted partner for global infrastructure, utilities, energy and construction organizations, helping frontline teams and managers reduce risk, improve productivity and deliver more predictable results through real-time field intelligence.
