The construction and infrastructure sector uses AI the least. This comes at a time when order books are growing, qualified employees are scarce, and productivity gains remain limited. The sector faces a clear challenge: delivering more work with increasingly limited capacity. In this article, we demonstrate how AI is already making a concrete contribution to solving this issue.
Of all the sectors measured by Eurostat, none uses AI less than construction and infrastructure. While an average of 20.0 percent of European enterprises with ten or more employees adopted AI in 2025, this figure stagnated at 10.8 percent in construction, placing the sector at the bottom of the European rankings. This pattern is mirrored in the Netherlands, where construction—together with hospitality and transport—remains at the absolute rear (CBS, 2025).
This is understandable: concrete cannot yet be poured by a language model, and earthmoving is not managed via a chatbot. For now, these physical activities remain human labor.
However, an extensive information chain has evolved around this physical work. Contracts, drawings, schedules, work plans, quotes, inspection reports, safety documents, meeting minutes, and handover dossiers must all be read, processed, verified, and interconnected. Furthermore, this already comprehensive chain is becoming increasingly burdensome. In May 2026, TNO concluded that the thirty-year-old system of building regulations has become so complex that it hampers construction targets and generates additional administrative and regulatory costs for both government bodies and commercial parties.
It is precisely within this information chain that bottlenecks occur, but also where opportunities lie. In April 2026, 71 percent of new job openings in construction and infrastructure were difficult to fill—the highest proportion of any sector (UWV, 2026). The sector will therefore need to accomplish more work with the same workforce in the coming years. AI can offer part of the solution by processing the growing volume of project information more efficiently, allowing professionals to spend more time on tasks where their expertise is truly needed.
In every phase of the construction chain, information is read, linked, and verified. Furthermore, this same information is often reprocessed in other documents, systems, or formats. This costs professionals valuable time that they would prefer to spend on project content and execution. This is precisely where AI provides relief: not by replacing their professional judgment, but by processing information faster, making it more fully accessible, and taking over preliminary work.
The applications below are based on a combination of client experiences and broader trends observed across the Dutch construction and infrastructure market, organized by project phase.
In the design and development phase, large volumes of requirements, baseline parameters, and design data converge. Manually checking all this information is time-consuming, while a missed requirement or inconsistency can have major consequences later in the project. AI helps design teams systematically evaluate information and identify discrepancies earlier.
Tenders often consist of a large volume of documents that must be reviewed within a short timeframe. AI helps tender teams digest, structure, and convert this information into actionable input for bidding and estimation more quickly.
During work preparation, project information is translated into concrete work plans, safety measures, and procurement choices. Much of the required information is already available but scattered across past projects and different documents. AI compiles this information into a preliminary draft for review and tailoring by the relevant professional.
During execution, extensive information must be recorded and processed directly from the job site. For site managers and project leads, this creates a heavy administrative burden alongside operational duties. AI processes and structures this information faster, freeing up time for project progress, quality, and safety.
As hand-off approaches, certificates, inspection reports, as-built documentation, and other proof of delivery must be compiled into a complete dossier. AI supports this process by identifying missing information early.
The preceding applications represent concrete use cases that enable construction and infrastructure organizations to take their first steps, achieve early wins, and build internal momentum. Organizations that are more mature—or looking ahead—can interconnect these applications. In doing so, AI no longer supports just a single task or project phase, but integrates information holistically to trigger autonomous actions.
This digital knowledge layer forms the foundation for AI agents capable of executing follow-up actions autonomously. Operating within defined boundaries, AI updates project information, assigns action items from site meetings, requests missing inspection reports, and updates progress reports. Professionals retain control and decide where human oversight remains necessary. The prerequisite for all of this is a reliable, well-organized data foundation, ideally structured around every individual project.
Initial successes with AI are important; they demonstrate value, build trust, and generate momentum. At the same time, organizations must address two key questions prior to scaling: how do we ensure AI works at both the individual and organizational level, and what changes once it succeeds?
A work planner who no longer starts with a blank document, a buyer who no longer compares quotes entirely by hand, and a site manager who no longer re-types receipts will see their responsibilities evolve. They must not only learn to work with the technology, but also understand where their added value lies within the new workflow. This requires clear priorities, appropriate skill sets, and conscious decisions about how liberated time is utilized.
The organization must also be prepared for this transformation. Are IT systems, data infrastructure, and AI capabilities adequately set up to deploy applications at scale? Do roles and responsibilities need to be redistributed? Which process steps can be eliminated, where are new controls required, and who remains accountable for output quality?
Construction and infrastructure may have adopted AI later than other industries, but this presents the greatest opportunity to innovate differently: not by overlaying technology onto existing workflows, but by redesigning the information chain supporting the work. Disruptiv guides construction and infrastructure companies through this end-to-end AI transformation—from strategy and vision to identifying and implementing use cases, process redesign, adoption, and change management—combining deep technological expertise with hands-on sector experience.
