Using AI to Analyze Construction-Project Data

Artificial intelligence might be most useful when it sticks to what it knows: data.
By Aviv Leibovici
November 14, 2023

Artificial intelligence is a hot topic in today’s world. From self-driving vehicles to generative AI, many industries are experiencing, or at least beginning to experience, this once-in-a-century transformation.

But, how about the construction industry? Robotics technology is nowhere near being able to fully and autonomously handle the complex environment and conditions of a construction project, and ChatGPT is hardly useful when trying to install drywall.

So what, if anything, can AI bring to the industry? Well, the answer is, of course, data and analysis.


A typical construction business has mountains of data: health and safety records, financial reports, HR data and more. But what about data describing the actual construction process?

How many glass panels can the tower crane lift in a day? How long does it take to install sprinkler pipes on a floor? How many laborers is the ideal number for installing drywall in an apartment?

This lack of this sort of data is easy to explain. The construction-project process comprises tens of thousands of tasks, being undertaken by hundreds of people from dozens of different businesses. To collect this particular set of data from processes which, for the most part, are conducted manually in a physical, non-digital—and sometimes temporary—environment, is virtually impossible.

Those dozens of businesses would all be required to contribute, instruct the hundreds of people to report on what is happening, and then consistently deliver against those expectations.

Another way to collect that data would be to outsource a third-party team to do so, but collecting even just a fraction of that data could take hours.


One might argue that certain data that projects are missing is not all that important anyway. After all, the industry has been delivering projects for decades without such data.

However, the results from the analysis of 64 construction projects showed just how inefficient that project-delivery process is. That analysis revealed an average area utilization rate of only 46%—meaning that half of a project is standing idle at any point in time. Without measurements or data detailing or explaining these inefficiencies, any attempt at tackling them seems futile.


The great thing about computers is that they do not have opinions, they do not get tired and they can work overtime without a problem. If AI is put in place to track these manual, physical and sometimes temporary processes, it will do so with precision and not get bored or distracted.

Certain AI systems can track the installation of elements onsite, providing exact progress percentages and deviations from the design by looking for every single element in every single room, installed by any subcontractor, and comparing the reality onsite with the design and schedule.

There is a variety of technologies available to collect data from the jobsite in this manner. Examples include solutions that help track the exact performance and activities of the tower crane, or supply information on exactly how much time workers spend on installation versus other tasks, such as transporting materials across the site. On an average week, these AI systems can determine the status of tens of thousands of elements per project. No human is capable of that, nor should they be.


Construction is one of the most complex processes in the world. Sure, building a wall might not be all that complex, but making sure the entire project is ticking along correctly, with all parts are moving together and in the right direction, is extremely difficult.

With many things going on and many different dependencies, it is often hard to identify not only what the current gaps are, but also their root cause. Is the drywall delayed because not enough work happened this week, or is it because the pace of the electrical work caused the pace of the drywall work to drop? The level of scrutiny, communication, collaboration and sometimes interrogation across all parties involved required to solve issues like this would be out of reach—or at the very least too time consuming—for the average construction site leader.


Once again, AI can quickly and automatically analyze all the information and reach the correct conclusion—allowing the managers to arrive at the correct decision. Using AI for these data-collection purposes means not only reaching more conclusions, but reaching conclusions that can be acted on more readily and accurately. This is because the data becomes the third party, showing the entire project team the objective truth of what is happening and has happened, enabling the team to deal with problems more efficiently.


The construction industry is also continuing to face a major challenge—there still aren’t enough workers. What AI can do is make sure that everyone has access to the right information, enabling the experienced managers to utilize that experience effectively and allowing them to support those reporting to them, helping both parties to not only succeed in their roles, but also to teach and to learn more quickly.

AI for construction still has much unrealized potential, but major benefits can already be reaped. Understanding how to fully utilize AI and what problems it can help solve enables construction companies to increase efficiency and profitability, helping the entire industry tackle major challenges.

by Aviv Leibovici
Chief Product Officer, Co-Founder, Buildots

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