AI in Construction: 4 Use Cases & What You Need To Get Started

AI in Construction Equipment Management

You have no doubt heard about, and probably read about, artificial intelligence and how this technology has started to make impacts on businesses in nearly every sector of the economy.

But maybe you’ve wondered how something a fast-food chain can use to customize and target drive-thru menu displays1 could apply to the construction industry. Or specifically, how it could apply directly to equipment management.

This month we are examining AI in construction and will show how it can directly benefit both the equipment management function and the construction industry overall.

What do we mean by AI

First, what do we mean by AI? IBM provides a concise and comprehensive answer2: “At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving.” It is really those two key elements, data and problem-solving, that are at the heart of AI.

AI encompasses many sub-fields you may have also heard of, including machine learning, deep learning, and neural networks. What all of these have in common is that they take data and connect patterns. Often, due to the size of the data, these are patterns that would have been too time-consuming or tedious for humans to tackle.

Image explaining what AI means and what is involved
Source: IBM

What does AI in construction look like right now?

Construction has a reputation for typically being behind the curve with technology. While this isn’t completely unwarranted, at least according to a McKinsey analysis3 construction is not the only industry a bit behind the curve with AI adoption.

More importantly, though, construction is behind in terms of current investment in developing future AI applications. So, there is plenty of work to do in construction before it is truly harnessing the power of AI.

Graph showing the demand for AI in construction
Source: Mckinsey

While it may not be at the forefront, what is construction doing with AI now? Here are three examples from the design, pre-construction, and operations phases of construction.


Autodesk is an obvious example when it comes to using AI with building design. The firm’s Toronto office, completed in 2017, was one of the first buildings to be constructed using generative design4. With this process, AI was used to run a number of designs alongside each other, factoring in things such as sustainability and employee configuration.

Image showing how AUtodesk are using AI in construction
Source: Autodesk


There have been many startups with promising developments who are trying to tackle the estimating process in pre-construction. Two examples are briq and InEight. These firms are using AI to make financial modeling in the estimation and bidding phases more predictive and ultimately improve margins for their customers.


One interesting example in the operations space is Buildots. This firm is using hardhat-mounted cameras to bring live images back from the construction site. They then apply AI to that image data and look, for example, at where accidents have occurred in the past to predict in advance where issues could happen on that site in the future.

Image showing how Buildots is using AI in construction
Source: Buildots

Where else could AI be used in construction?

There is certainly still a lot of space for AI to grow within construction, particularly within the equipment management function. Here are four specific areas we see that AI could make a big impact on equipment management:

Internal Rates

When was the last time you evaluated and adjusted your internal charge rates? We all know this is an important task, but we also know that many firms do not do this on a regular basis. Accurate rates can be absolutely crucial, however, in ensuring that you are not over or under-bidding on projects.

By employing AI, setting accurate internal rates could be even more streamlined and more specific to the individual construction firm in the future. AI engines could take into consideration asset purchase prices, meter reads, maintenance events, telematics data, location considerations, fuel costs, and more. The culmination of combining all of these data points would be a much more accurate rate.

Of course, a firm would not want to dynamically adjust rates once they have already locked them in for a project, but knowing how these factors are changing over the duration of a project could help the team course correct if necessary.

Equipment Valuation

Hopefully, we are all aware that the process of properly valuing equipment has more to it than simply looking at what it is currently selling for. With as much market, usage, and other data as there is to consider, an AI engine could make this process much more manageable.

By interrogating the data, the AI engine could look for pricing volatility, consider differences between resale and auction patterns, parse data by manufacturer, factor in age, utilization, and location trends, and manage for seasonality. This is the benefit of using AI over and above human number crunching—the machine can ingest many more data points and identify patterns more quickly. It also helps to remove bias, as it is taking in so many data points across so many sources.

All of this data crunching could help the end-user produce highly accurate fleet valuations instantly, across their whole fleet or for certain categories of interest.

Disposition Decisions

When is the opportune time for you to sell each asset in your fleet? While that question may seem overwhelming, with the help of AI-generated metrics it could be greatly simplified.

Employing AI could garner a much more accurate read on the depreciation of an asset class. When combined with location and seasonality, it could become very powerful. Armed with this data the equipment manager could make better decisions about when to sell an asset—perhaps one month is shown to have a better return—and where the sale should happen.

Besides just the when and where, AI-derived metrics could also be quite powerful when looking at cost-based disposition. By ingesting data for maintenance events and combining that with operating, ownership, and repair costs specific to the business, it could show an optimal disposition window for each asset. This would help the fleet manager understand when the soonest an asset should be disposed of and when the optimal time would be. It could also drive more accurate economic forecasts and projections for where the construction industry, or a particular niche of the construction industry, is headed.

Using Owned vs. Rented Equipment

When you need a piece of equipment for a job, how do you know if you should use what you already own or if you should consider renting the asset instead?

An AI engine could pull in local rental rates, your own fleet’s data, and market data to get a more nuanced picture to help frame this decision. The recommendations would likely be directly tied to projected utilization for the project, which would also be predicted by the AI. Using this data, a fleet manager may decide to rent equipment even if it is already owned if the usage would be below a certain threshold of operating hours, or would decide to employ their own fleet if the projected usage was higher than the threshold.

How to Access this AI Capability?

Overall, the benefits of AI in construction equipment management are quite exciting. In each use case presented, a large amount of data could be fed into the engine and a set of tailored, nuanced metrics would be returned, enabling the user to make better decisions. The AI would not be making the decisions but would be giving its users the tools to act on data they previously would not have had time to consider.

But how can the average construction firm harness the potential of these AI engines? To get to this AI-driven, data-rich future the construction industry will need to invest. However, it may not be as much of a lift as it would seem on the surface. Here are three avenues we would suggest looking into to start your firm on this path.

Internal Resources

Getting dedicated data scientists or data programmers into the equipment management function would be a very direct route to leveraging AI capabilities. These data personnel may be new to your organization or perhaps they could be repurposed from other areas already at your company.

Another opportunity could be in hiring interns from local colleges to get the ball rolling or to bolster other full-time staff. The current generation of graduates are particularly data-savvy and programming-literate and could have an outsized impact on the efforts. It is not necessarily about having the largest team—a small team with the right tools and data could make a big difference.

External AI Specialists

There are a large variety of external AI specialists that can set up dedicated projects tailored to your business. Many of these external AI specialists are based offshore and can offer these services at a fraction of the price of domestic providers. Domestic or offshore, however, these providers are highly skilled and extremely qualified and have experience leveraging these types of data-solutions across sectors, including construction.

Startups and Market-Specific Applications

Given where we are in the development of AI in construction, there are a great number of startups coming online with market-specific applications. These are specific to the construction sector and for the equipment management function. It is still early days for these startups, but it will not be long before there are specific applications that the typical equipment manager could deploy.

What’s to Gain?

Technology in construction remains quite fragmented across design, pre-construction, operations, and equipment deployment, but we are starting to see that change. We see a desire in the industry for the dots to be connected and an opportunity for the equipment management function to be the driver of this change. By investing in these AI-driven analytics, equipment managers will have the insights and answers which means they can start to correlate the performance of a project in the operating phase right back to how it was conceived when it was still being bid on. It can be that catalyst. It can move the equipment management function from simply being a cost center to being a real profit driver and strategic contributor to the business.

Being Practical

While the potential of AI in construction is vast, it is best to start with specific problems you would like to solve. Are you trying to get more accurate internal rates? More timely fleet valuations? Focused on the best time to sell assets? Pick which would have the biggest immediate impact to your organization and start there.

Getting Started Now

EquipmentWatch can help you with the data you need now to set accurate rates, value your fleet, make disposition decisions, and understand when to rent equipment. We would also be honoured to be your principal data provider for any AI-focused projects you take on in the future. For more information on the data solutions offered by EquipmentWatch call us at (888) 307-1713 or click here to request a product demo.