How to put AI to work in your construction business

Start with the business problem, not the technology

Six years ago, we started our data journey at Cooper.  At the time, we had about 20 systems, each with its own reporting tools.  We had lots of information, but no easy way to bring it together and use it. 

We weren’t thinking about AI yet.  We were trying to solve a business problem. 

That experience shaped how I think about AI today.  Don’t start by asking what AI can do.  Start by asking what problem you’re trying to solve. 

I recently sat on a panel about AI adoption in construction and where companies are starting to see real returns.  The conversation reinforced something we’ve learned ourselves: AI becomes much more useful when it can connect information across your business and put it in front of people when they need it. 

Imagine a project manager trying to understand why a job is falling behind schedule.  The answer is spread across equipment systems, workforce data, the project schedule and daily field reports.  Pulling it together can take hours. 

Now connect those systems and put AI to work.  It flags underused equipment, lower crew productivity and a late material delivery.  It checks the schedule and shows which upcoming activities are at risk if nothing changes. 

The project manager can see the bottleneck, what’s affected and where resources can be shifted.  Less time gathering information, more time deciding what to do next. 

At Cooper, we believe technology should support our people, not replace them. The objective isn’t to replace judgment, but to give our people better information, remove unnecessary work and help them make decisions faster 

If you’re wondering how AI can make a real difference in your business, here’s where I’d start: 

1. Start with the problem 

Don’t start with, “Where can we use AI?”  Start with the friction in your business.  Where are people wasting time?  Where is information hard to find?  Where are customers waiting while your people chase down answers? 

Start there.  Then determine whether AI can help. 

2. Get your data in order 

AI can’t drive productivity if your information is scattered across systems that don’t talk to each other.  Take stock of what you already have, where it lives and how it’s connected.  The more AI understands your business, not just general information, the more useful it becomes. 

3. Make work easier 

A simple test for any technology is whether it makes someone’s job easier.  At Cooper, we recently built an AI tool that helps our teams find available equipment across our branch network.  It can identify available machines and suitable alternatives without our people having to make multiple calls or dig through reports. 

Less time searching means less work for our teams, and customers get what they need faster. 

4. Start small and prove value 

Look for repetitive tasks that eat up time, like building reports, entering the same information more than once or chasing down job information. 

A few small wins will do more for AI adoption than a big presentation about what AI might eventually do. 

5. Bring the people doing the work into it 

The best ideas often come from the people closest to the work.  Get them involved early.  Let them help identify the problem, test the tools and tell you what doesn’t work.  If people they trust see value in the tool, others will follow. 

6. Build in governance from day one 

Moving quickly doesn’t mean moving carelessly.  Know where your data lives, who can access it and what goes into the tools you use.  Set clear guidelines on what information can and can’t be shared with AI tools. 

The real opportunity 

There’s a lot of noise around AI right now, and it can create the impression that every business needs an AI strategy immediately. 

I think the opportunity is much more practical. 

Look at how work gets done.  Find where people are losing time, chasing information or making decisions without the information they need.  Then determine where technology can help. 

The companies that get the most from AI won’t necessarily be the ones spending the most money on it.  They’ll be the ones that get very good at finding real problems worth solving and putting better information in the hands of their people. 

Don’t start by asking, “What can we do with AI?” 

Start by asking, “What problem are we trying to solve?” 

Ready to find practical ways AI can improve your construction business? Talk to Cooper about putting your data to work.

Brian Spilak
CEO