AI is starting to show up in more and more construction conversations. But with growing interest comes a new wave of questions: Where do we start? What’s worth investing in? How do we make sure this actually helps our people—not overwhelm them?
That’s what we set out to unpack during The AI Reality Check, a live webinar hosted by Quantum Rise. We brought together four people who are deep in this work—Jen Suerth from Pepper Construction, Samantha Boyle from Superior Bowen, Dennis DiPalma from Kahua, and Quantum Rise CFO Michael Latiner—for a candid discussion on what AI adoption really looks like inside construction companies today. Hugh Seaton, an author, podcast host, and recognized authority in AI for construction, moderated the event.
“AI is going to change our business. If we don’t leverage it, we’re going to fall behind,” Suerth said.
She also called out the importance of finding the right balance between being on the cutting edge and not the bleeding edge. “You want to be ahead, but not too far ahead.”
That thought perfectly sums up the moment construction is in right now—recognizing the importance of AI while staying grounded in what’s practical, realistic, and most valuable today.
One of the clearest takeaways from the conversation is that everyone is still figuring this out. Some companies are further along than others, but no one has all the answers—and that’s okay.
“I was not an early adopter,” Boyle said. “But now I'm a heavy ChatGPT user and rely on it as a thought partner."
What started as a curiosity became a daily habit, helping her brainstorm meeting frameworks or learn enough about a topic to lead a conversation with confidence. “That was really where the snowball started rolling,” she said.
On the other hand, Suerth described herself as a proud early adopter. She’s been leading innovation efforts at Pepper for years, and was quick to admit how fast things are changing. “I’m in a lot of peer groups. I joined advisory boards for some AI startups, and part of the reason I did that is not necessarily to help them; it’s to help me. I think it’s important for us to recognize we can’t know everything.”
DiPalma shared how he’s using tools like ChatGPT, Claude, and Gemini to speed up research, summarize long documents, and pressure-test ideas. “Instead of being the author, I can become the editor,” he said.
He also described how he and others are starting to think about these tools—not as experts but as capable junior team members. DiPalma shared the concept of an "artificial intern" he'd heard at a conference: you can offload tasks you might give to an intern, but you still need to maintain oversight.
Suerth agreed. "Interns provide value. You're still checking their work. But they take something off your plate, and they get better with time."
That framing helped shift the conversation away from fears of replacement and toward something more realistic: support that improves over time but still relies on human oversight.
Everyone is moving, testing, and learning. Some tools stick. Others don’t. But the willingness to explore—and to talk openly about what’s working and what isn’t—is what’s driving real progress.
While many companies are still exploring what’s possible, the panelists shared a few clear examples of how AI is already making a difference in their day-to-day operations:
These implementations succeeded because each started with a real problem, not a shiny new tool. That focus made all the difference. When teams start small and see how a solution solves something meaningful, buy-in often follows. It also helped surface internal champions who could guide others and keep the momentum going.
The panelists didn’t just talk about what they’re doing with AI; they shared what it takes to do it responsibly. Three themes came up in the conversation: trust, data readiness, and security.
The panelists emphasized that successful AI adoption requires honesty about what these tools can and can't do. Whether it's field workers worried about job replacement or office staff concerned about accuracy, the solution is the same: educate teams upfront, keep humans in control of decisions, and position AI as a support tool—not a replacement. When people understand the real role AI plays, resistance turns into adoption.
To get the most out of AI, companies need to understand where their data lives and how clean and accessible it is. But that process isn’t linear. As DiPalma explained, working with the data often surfaces gaps that need fixing. The tools help reveal what’s messy, and cleaning it up helps the tools work better.
In other words, this process is cyclical; it’s not a single project.
The panelists stressed that protecting sensitive information is critical when testing AI tools. DiPalma emphasized that security and privacy must be "forefront" considerations, noting the importance of trusting both the systems and the vendors behind them. While these safeguards may slow implementation, they're non-negotiable for responsible AI adoption.
One thing this conversation made clear: there’s no single roadmap to AI implementation. Some companies are testing tools on their own, while others are moving faster with help. In fact, Pepper Construction and Superior Bowen are already working with Quantum Rise to build smart, tailored AI strategies that meet their specific goals.
No matter where you start, the key is to focus on real problems, move thoughtfully, and learn as you go.
That’s what we help teams do at Quantum Rise—turn interest into action in a grounded, strategic, and sustainable way.
If you missed the live conversation (or want to rewatch it), you can catch the full replay here.
And if you’re ready to explore what AI could look like in your organization, we’d love to connect.
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