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What AI is doing on construction sites
Some of the most visible examples start with a camera. A worker or drone captures site imagery; software then organizes the images, compares them with project information or makes them easier to review remotely. The camera is a data-capture device, not an AI system by itself. The analysis depends on software and, in some workflows, project plans, schedules or building information modeling (BIM) data.
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Other applications are more experimental: robots applying materials, or computer vision analyzing aerial footage for potential hazards. These examples matter, but they should not be mistaken for routine deployment across construction projects.
Where the technology is being applied
| Workflow | What it does and what it uses | Evidence and maturity |
|---|---|---|
| Visual site records | OpenSpace’s workflow, as described by Autodesk’s integration directory, uses off-the-shelf 360-degree cameras with computer vision and AI to create a visual record, support remote progress tracking and connect visual references to issues or requests for information (RFIs). OpenSpace also lists smartphone images, 360-degree walks and drone flights as capture inputs. | A vendor-described workflow. The imagery helps people review the site; it does not independently establish that a task is complete or that an issue has been resolved. |
| Progress analysis | Buildots says it compares captured site conditions with BIM and schedule data. Its product page lists drone, 360-degree-camera and laser-scan inputs. Autodesk’s integration listing says detected issues can be exported to Autodesk Build. | Vendor and integration-directory descriptions of product capabilities, not independent proof of project-wide results. |
| Robotic material application | The European Commission Joint Research Centre’s RoBUILT project explores a mobile robot applying reinforced mortar for façade renovation and retrofitting, including work on irregular or historic masonry. | An exploratory demonstration with structural testing planned or underway; it is not evidence of a general-purpose façade robot in widespread use. |
| Computer-vision safety sensing | NIOSH describes research analyzing aerial construction video to identify workers, equipment and physical openings that may present hazards. | Ongoing research into possible earlier warnings, not a guarantee that hazards will be detected or prevented. |
Adoption is real, but still uneven
RICS’s 2025 global construction report draws on more than 2,200 respondents and points to a gap between experimentation and routine use. RICS reports that 78% described current AI adoption as low and 74% said their organization had limited or no preparation. In the same survey, 34% reported pilot projects and 15% active use. RICS’s summary in its 2025 Global Performance Report says only 1% of firms had successfully implemented AI across their projects. These are survey responses, not a census of construction firms.
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Respondents saw potential in functions including scheduling, progress tracking and design optioneering. But potential is not an outcome measure: the reported adoption figures do not demonstrate that AI has raised productivity or improved safety across the industry. RICS also found that 46% named a lack of skilled personnel as the main barrier to adoption, making training and implementation capability central to whether a tool can be used effectively. Read the RICS report.
Why jobsite safety is a difficult AI problem
Construction sites change as work proceeds. Trades overlap, access routes shift and materials or equipment can obstruct a camera’s view. NIOSH identifies weather, dust and lighting as additional conditions that can affect sensing and visibility. It also notes hazards such as struck-by incidents, caught-in or crushing situations, and unexpected movement, alongside technology risks including sensor degradation, communication failures and limits in object recognition.
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The stakes are significant: the European Commission’s Joint Research Centre says construction accounted for 24% of workplace fatalities in the EU in 2023. That figure describes the sector’s share of EU workplace fatalities; it does not show that AI can prevent them. A camera system may flag a possible concern, but workers still need a clear process for reviewing alerts and responding to hazards.
“Safe implementation depends not only on the capabilities of the robot but also on how work is organized, how workers interact with robotic systems, and how changing site conditions are managed throughout the project.”
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— NIOSH, Safety in Construction Robotics
What a contractor should check before adopting a tool
Start with a specific field problem, not the promise of AI in general. A progress-recording workflow, for example, needs a way to capture imagery consistently and a plan for connecting observations to the relevant schedule, BIM model or issue-management process. A safety-monitoring pilot needs a response procedure as well as sensing equipment. In either case, someone must be accountable for reviewing outputs and acting on them.
- Define the task: Decide whether the goal is documentation, progress tracking, quality checks, safety monitoring or physical work. These are different applications with different evidence and risks.
- Check inputs and integration: Confirm which capture methods the software accepts and whether outputs connect to the project’s plans, schedule or issue system. A camera, drone or laser scanner captures information; it does not guarantee a useful analysis.
- Test in actual site conditions: Check how the workflow performs as work areas change and under the site’s lighting, weather, dust and obstruction conditions. Set out who handles missed detections, questionable alerts or communication failures.
- Keep human oversight: Treat automated findings as information for qualified people to assess, not as proof that work is complete or a hazard has been eliminated. Coordinate the system with site safety procedures and worker communication.
- Measure the result: Agree in advance what would count as a useful outcome for the chosen task. The cited surveys, vendor descriptions and research projects do not establish a universal productivity or injury-reduction effect.
AI may address a productivity problem, but it has not solved it
McKinsey’s 2026 analysis says construction productivity improved 10% from 2000 to 2022, or 0.4% annually, compared with manufacturing’s 90% improvement, or 3.0% annually. Those figures help explain interest in new tools, but they do not show that AI caused a productivity gain. The practical question for a project is narrower: can a particular tool improve a defined workflow, with reliable inputs, trained users and suitable human checks?
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