A drawing can show a duct, but the real world doesn’t always work that way. BIM can show a route, but there’s no guarantee that it’ll be installed that way. There’s often a readily available answer to a construction question, but it’ll take a while to find it.<\/p>\n\n\n\n
Fragmented information causes these conditions. Construction teams can have the information, but may have to search in different documents or software.<\/p>\n\n\n\n
Closing this gap is the focus of the next wave of construction AI technologies. Instead of automating tasks or creating reports, the focus will be on how AI technologies can support the construction team in integrating project information, finding conflicting project information, and focusing on project information prioritization.<\/p>\n\n\n\n
Groundbreak 2026 will allow construction professionals to examine the integration of these new technologies into construction project workflows. The iFieldSmart AI<\/a> team will demonstrate technology for joining fragmented construction information, integrating AI with construction industry expertise, and presenting analyses that project teams can evaluate and take action on.<\/p>\n\n\n\n Here are five construction AI developments worth watching and what you can explore when you visit iFieldSmart AI<\/a> at booth #1216.<\/p>\n\n\n\n Construction work produces a huge amount of documentation. This includes various styles of drawings at different stages of completion, notes, specifications, models, requests, submittals, RFI, coordination logs and field reports. The ability to integrate various data sources is the key to working effectively.<\/p>\n\n\n\n An example: Data integration is the resolution of a design conflict where the proposed routing of a mechanical system duct is obstructed by a steel beam in a structural drawing. The relevant dimensions may appear across separate drawings, while the specifications contain additional installation requirements. Unless someone connects those details, the conflict may go unnoticed until coordination or construction exposes it.<\/p>\n\n\n\n AI-assisted analysis allows teams to compile data from the various sources to uncover pertinent relationships.<\/p>\n\n\n\n The platform unlocks data silos across the construction industry by combining construction data from various sources into a unified layer of intelligence. Teams can link pertinent project information across various data sources without having to switch or leave their existing construction management tools.<\/p>\n\n\n\n This intelligence changes how the construction team looks for answers. Rather than reviewing isolated documents, construction teams can look into related project information and examine potential impacts across the project.<\/p>\n\n\n\n It utilizes connected construction data to help build a foundation for AI to meaningfully engage with construction professionals. The potential value of AI is greatly enhanced with more robust project data.<\/p>\n\n\n\n Constructability problems often arise when materials and crews are brought to the project site. Additional problems can arise when other trades work around a constructability problem. Traditional constructability reviews often require detailed reviews of construction drawings by multiple trades. This process requires a significant amount of time and close coordination.<\/p>\n\n\n\n Problems with AI-supported constructability reviews are often located by the system and directed to a particular location.<\/p>\n\n\n\n As an example, the clubhouse constructability review identified that, with a floor-to-floor height of a thick slab and a ceiling height. A concrete beam was located in the duct space, and an insulated outside air duct<\/p>\n\n\n\n With the identified constraints, the duct could not be routed in the plenum without conflict.<\/p>\n\n\n\n The BIM coordination and duct routing needed to be resolved before construction.<\/p>\n\n\n\n Conflicts like these can be easily identified by AI, but to be useful, AI needs to focus on issues like why the available space is a problem and what the team should review or change to resolve the problem. AI-supported review systems can allow project teams to address design conflicts earlier, when options are available to change the design.<\/p>\n\n\n\n A BIM model represents design intent. The site shows what’s actually built. When there’s a disparity, the reasons for the difference need to be identified before they impact subsequent work.<\/p>\n\n\n\n Several things on the site can differ from the model. Sleeves can be missing or misplaced. Modeled fittings can differ from what’s installed. Connection configurations can be different. Each of these can’t be adequately documented with general comments.<\/p>\n\n\n\n Reality Capture can be used to electronically document job site conditions with several different technologies. Evidence can be combined with digital building information to reconcile issues.<\/p>\n\n\n\n Through the iFieldSmart Lens360 app, field teams can verify sleeves, report deviations in the field vs. BIM, perform thermal inspections, and capture progress.<\/p>\n\n\n\n The main purpose of an inspection or verification report is to bring focus to the discrepancies to allow the responsible professionals to investigate and\/or correct the sleeves before the next restrictive work activities on the project.<\/p>\n\n\n\n Construction situations require both analysis of massive data sets and the application of contextual judgment.<\/p>\n\n\n\n Anomalies aren’t always issues. What the construction team does next is determined by the scope of the current project. Artificial intelligence evaluates large sets of data as part of the system. Construction experts determine the best alternative based on the outcomes.<\/p>\n\n\n\n The AI solution reviews data sets and offers potential analyses, for example, identifying conflicts, gaps, deviations, and risks. The experts review the data, evaluate the significance of the AI’s conclusions, and recommend the most appropriate steps to resolve the issue.<\/p>\n\n\n\n In one example, AI can review field-installed piping against the building information modeling (BIM) model and flag differences. A construction expert reviews the evidence, considers design intent, and offers the best response.<\/p>\n\n\n\n Construction professionals review findings, determine significance, and render judgment based on the context of the project. A list of flagged issues is relatively worthless to a construction team.<\/p>\n\n\n\n Construction teams are given several types of reports and logs. But simply providing a report of an issue does not help solve it.<\/p>\n\n\n\n There are findings that basically state an issue and do not help articulate a course of action. An example of this type of finding is “Sleeve missing.”<\/p>\n\n\n\n An actionable finding provides information that aids in advancement towards a solution.<\/p>\n\n\n\n Useful construction intelligence should answer three questions: Where is the issue? Why does it matter? What should happen next?<\/p>\n\n\n\n iFieldSmart AI applies this principle across three connected areas:<\/p>\n\n\n\n Each tool helps the user identify and analyze information to understand a situation and suggest the most appropriate next step.<\/p>\n\n\n\n While evaluating construction AI, one should question whether the system helps the end user locate the relevant information and offer insights to meaningfully assist in understanding the situation.<\/p>\n\n\n\n Groundbreak 2026 covers various construction-related topics and provides a forward-looking view of how construction-related sectors and industries will adapt to and integrate emerging technologies in the built environment.<\/p>\n\n\n\n Design and construct-related challenges occur at many levels and for many different reasons. Challenges may include: expended effort on specification searches, inadequate tools for VDC-related virtual coordination, or a means for field supervisory personnel to ascertain if work installed conforms to BIM.<\/p>\n\n\n\n The most valuable thing to learn at the conference is the opportunity to better define the challenge you need to solve.<\/p>\n\n\n\n The most valuable AI demonstration is the one that changes the way your team thinks about an existing problem. At Groundbreak 2026, hear how connecting construction data will help provide insights into construction activities and get access to construction solutions.<\/p>\n\n\n\n Learn how AI Skills<\/a> can help with document-intensive construction activities and see how Lens360<\/a> integrates Construction Reality-Capture data with Building Information Modeling (BIM). Explore how VDC and Mixed Reality<\/a> support the integrative coordination of construction digital models with the physical construction site.<\/p>\n\n\n\n1. Connecting Project Information Across Platforms<\/h2>\n\n\n\n
2. Identifying Constructability Issues Before Construction<\/h2>\n\n\n\n
3. Connecting BIM With Actual Jobsite Conditions<\/h2>\n\n\n\n
4. Combining AI Analysis With Construction Expertise<\/h2>\n\n\n\n
5. Moving Beyond Reports to Actionable Construction Intelligence<\/h2>\n\n\n\n
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What Construction Professionals Should Explore<\/h2>\n\n\n\n
Bring Your Toughest Construction Challenge to Booth #1216<\/h2>\n\n\n\n