________________________________________________________________________________________________ Main
Interview
Most of our team comes from the field or from other ConTech firms, so we understand our customers’ needs quite well.
My PhD gave me a robust analytical framework to understand where the technology is and where it could be in five-to-ten years, which helps inform our product roadmap.
As one of the few women CEOs in construction, what leadership perspective do you think the industry is missing more broadly? I actually don’ t think the fact that I’ m a female CEO in construction matters all that much. What matters more is that our industry doesn’ t have enough leaders who have deep knowledge of the industry, AI, and human behavior. To build technology that will meet the demands of the future, leaders in our industry need to account for all three of these.
What misconceptions do you see about AI in construction, and what should executives better understand before investing in it? Most technology firms have been slapping“ AI” and“ agent” labels all over their marketing for several years now, even when the product has barely changed. Additionally, many who have adopted AI— whether legacy businesses with code bases not built for the processing demands of AI, or new startups trying to capitalize on the AI economy— are merely white-labelling foundational models from OpenAI, Anthropic, Google, and others. These foundational models are not trained on construction data for construction-specific workflows.
I always advise our customers and prospects to test truly agentic, industryspecific solutions like our submittal and drawing revision agents against“ custom agent builders” from other potential vendors and evaluate what will actually help them accomplish their technology innovation goals. Do you want your teams to spend time building agents that may or may not work? Or would you prefer ready-to-deploy solutions that make an immediate impact?
Integrating AI into legacy systems is slow, complex, and often constrained by architecture that wasn’ t built for real-time intelligence. AI moves fast. Construction teams can’ t wait. Legacy systems weren’ t designed for the intensive, real-time data processing required for real AI. Fusing the two has been known to lead to poor data quality, degraded performance, and technical debt.
Finally, the composition of the team building these solutions matters. Pay attention to the depth and breadth of experience and education of the AI / ML engineers at every potential vendor you evaluate. A bigger company might not necessarily have a heavier-hitting AI / ML team.
Looking ahead, how do you see AI changing the relationship between the physical jobsite and digital project management over the next five years? Given the conflicting factors of high demand for new construction( data centers, housing, infrastructure) and low supply of skilled white- and blue-collar construction workers, AI agents that can handle the most timeintensive, paperwork-heavy, bureaucratic processes will be a critical resource to augment project teams, making them more productive, faster, and more accurate. I am not at all concerned about AI replacing workers. Everyone who works in construction knows that we need more talented people, not less. However, the efficiency and cost savings that truly agentic AI offers will make construction companies more profitable and help address the labor shortage, at least in part. ■
www. trunktools. com
construction-today. com 15