Artificial intelligence is quickly becoming part of the conversation across the construction industry. Yet the most important question is not whether AI exists. The real question is whether it actually helps project teams build better projects.
Recently, Jeffrey Nesbit, Director of Partnerships at Slate Technologies, joins the ConTech Exec Podcast, hosted by Robert Zimmerman, to discuss how artificial intelligence is evolving in construction. During the conversation, Jeffrey explains how construction AI works best when it understands not only data, but also the human intent behind project workflows.
This post highlights several key insights from that discussion. You can watch the full episode on YouTube or listen on Spotify below.
Construction AI must understand context
Construction projects generate an enormous amount of data. RFIs, submittals, schedules, job cost reports, daily logs, and emails all play a role in how a project progresses.
However, much of this information lives in separate systems. Scheduling teams may work in one platform while field teams operate in another. Meanwhile, historical data often sits in spreadsheets, ERP systems, or older project databases and doesn’t actually get utilized.
As a result, important insights are frequently hidden across disconnected tools.
This is where AI construction software becomes valuable. Jeffrey explains that Slate Technologies builds its platform to connect information across the contractor’s technology stack rather than replacing existing systems. Slate’s system not only connects the data, it understands it through a construction industry lens. For example, if a contractor refers to Grasshopper, Slate knows they are likely referring to the scheduling platform, not the insect.
This type of contextual understanding is critical. Construction data is full of terminology, workflows, and relationships that only make sense when viewed through the perspective of how projects actually run. about a bug.
By analyzing project data, the system surfaces risks, conflicts, and opportunities that might otherwise go unnoticed. Identifying these relationships manually can be extremely difficult when information is fragmented across multiple tools. Because of this, connecting data in context becomes one of the most important capabilities of modern construction AI.
Human intent remains central to AI in construction
While technology continues to evolve, construction AI should support human expertise rather than replace it.
Instead of automating decisions, Slate analyzes large volumes of project data and surfaces insights that teams may not have time or visibility to identify on their own. Contractors then evaluate those insights and decide what actions to take.
This approach keeps experienced professionals firmly in control of project decisions while allowing technology to handle complex analysis behind the scenes. In an industry where judgment, coordination, and experience matter, maintaining that balance between machine intelligence and human decision making is essential.
Turning lessons learned into actionable insight
Construction companies often possess decades of valuable institutional knowledge. Unfortunately, that knowledge frequently remains scattered across project files, spreadsheets, or individual expertise. Because of this fragmentation, teams sometimes repeat the same mistakes across multiple projects.
Slate addresses this challenge by aligning historical project data with current activities. When patterns appear across past projects, the system surfaces them as potential insights before similar issues occur again.
Jeffrey describes this concept as creating a lessons learned database. As contractors apply the insights from previous jobs, fewer recurring issues should appear in future projects.
As a result, historical data becomes more than archived information. It becomes a tool for improving project performance.
Reducing the cost of rework
One of the most significant opportunities for construction AI lies in reducing rework.
Rework occurs when teams must redo work because of errors, miscommunication, or coordination issues. In addition to direct labor and material costs, rework also creates schedule delays, workflow disruptions, and increased safety risks.
Because of this, identifying potential issues earlier can have a major impact on project outcomes.
By connecting historical lessons learned with current project data, construction AI can highlight warning signs earlier in the project lifecycle. If similar challenges appear across previous jobs, those insights can surface before the same mistake occurs again.
Ultimately, the goal is not simply detecting problems faster. The goal is preventing them altogether.
People and processes still come first
Despite the growing excitement surrounding artificial intelligence, Jeffrey makes one point very clear during the podcast: technology alone cannot solve project challenges.
Successful digital transformation still begins with the right people and the right processes. Once those foundations are in place, AI becomes a powerful tool for analyzing data faster and identifying insights that were previously hidden.
In this way, construction AI works best when it strengthens the workflows teams already rely on.
The future of construction AI
Artificial intelligence continues to evolve across industries, and construction is no exception. However, the most valuable innovations will not come from automation alone.
Instead, they will come from tools that help project teams understand their projects more clearly and make better decisions earlier.
For construction leaders exploring AI today, the opportunity lies in turning complex project data into practical insight. And that begins by ensuring that construction AI understands not just the data itself, but the human intent behind it.