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By Adam Petersen – Director of Strategic Innovation

Everyone has an opinion about AI at work. Will it replace jobs? Eliminate promotions? Outsmart the experts?

We have heard this before. The fax machine changed how we communicated. The iPhone changed where and when we worked. Neither replaced the people doing the work.

AI will follow the same path. It will help people work faster, make better decisions, and spend more time where their judgment matters.

Construction makes the opportunity clear. Every project produces a mountain of information. Drawings. Specifications. RFIs. Meeting notes. Cost data. Safety records. Closeout documents. The issue is not a lack of information. The issue is finding the right information when we need it.

I left the ENR FutureTech conference earlier this year with one clear takeaway: AI can give project teams time back by handling work that can be automated.

That does not remove people from the process. It gives them more time to manage risk, strengthen communication, solve problems, and deliver a consistent client experience.

At Â鶹¹ÙÍø, that is the goal. We use technology to help good people perform at a higher level.

Recently, I continued that conversation at the AI Adoption in Construction conference in New York. Owners, contractors, designers, and risk and legal specialists shared what is working as the industry evolves and adoption progresses.

Where AI Can Create Value? Better information, delivered faster

Our people and completed projects hold years of knowledge. Too much of it remains trapped in separate files, systems, and individual experience.

AI can make that knowledge easier to find and reuse. One owner demonstrated an agent that searches warranties, specifications, and maintenance schedules while remaining read-only and respecting existing file permissions.

That is the right model. Increase access without increasing exposure.

Stronger risk management

Better access to information can improve how we manage safety, schedule, cost, performance, and client concerns.

One contractor built a proprietary system around five core risk categories. The system now operates across approximately 400 projects. As adoption increased, recordable incidents decreased.

The result matters more than the technology. AI creates value only when it changes an outcome.

Better pricing and budget decisions

AI can help teams find relevant cost and schedule information from past projects and apply it to current decisions.

The control point matters. One contractor uses a deterministic engine to perform the calculations. AI explains the results and supports what-if scenarios. Their team found that AI alone was not reliable enough for the math.

That is the right division of labor. Use proven systems for calculation. Use AI to find information, explain results, and support decisions. Keep people accountable for the final call.

A smoother warranty process and stronger lessons learned

Warranty issues often require teams to search years of project records to understand what happened, how it was resolved, and what should have been done differently.

AI can shorten that process. Teams can quickly find similar conditions, understand the decisions that were made, and see the outcome. More importantly, those lessons do not stay buried in archived files. They become accessible to the next project team facing a similar challenge.

The result is faster responses for clients, fewer repeated mistakes, and a stronger culture of continuous improvement. Every project becomes a source of knowledge for the ones that follow.

Trust built into the process

Every panel reached the same conclusion: trust requires evidence.

AI-generated answers must trace back to reliable source information. The system must say when it does not know. Human review must increase with the risk of the decision.

A good demonstration is not enough. We need to know where the information came from, who reviews the output, and what happens when the system gets it wrong.

Faster development of useful tools

AI also changes how quickly companies can build applications.

Rogers O’Brien started with two developers and expanded to five. Within one year, 88 percent of its employees were using the internal platform the team created.

Their goal was simple: give people back their attention.

They also made a harder point. The technology is the easy part; getting an organization to agree on the one problem worth solving is difficult.

The real work comes down to these core points.

• Start with a business problem.
• Define the outcome.
• Build the smallest useful solution.
• Measure whether it works.

People First, Always

AI can backfire when it overwhelms employees, adds another disconnected platform, or automates work without understanding its value.

Meeting notes are a good example. Automated notes save time for the person running the meeting. They can also remove an important learning opportunity for the project engineer who used to write the notes and build context in the process.

Both outcomes are real. We need to account for both.

At Â鶹¹ÙÍø, the goal is not automation for its own sake. The goal is better project delivery, stronger client service, and more meaningful work for our people.

New platforms will continue to arrive. Some will create value. Others will create noise.

The decision is not whether to use AI. The decision is where AI can solve a real problem, how we manage the risk, and how we prove the result. The technology will change. The importance of good people doing the actual work will not.

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