Tips & Advice Uncategorized

Leveraging AI, Machine Learning, and Automation in 2021

Contractors collect a lot of data in the process of building a project. It comes from the field, office, and outside sources. Successful contractors are using this project data to improve the efficiency of their production and save admin time. Technology tools like artificial intelligence, machine learning, and automation are driving this focus and providing valuable insights that contractors can use to increase profitability.

Artificial intelligence

Artificial intelligence (AI) is more than robots and droids. It describes all the ways that machines are able to mimic human thinking, like problem-solving, pattern recognition, and learning. It is now being programmed into powerful software packages that are being used by contractors to analyze and assess their projects. This intelligence is a powerful way to coordinate with team members and provide project monitoring.

AI is being used to create predictive models based on past project data. These models can be used to analyze projects for cost overruns, schedule delays, and other onsite issues. The software reviews past projects and compares them to current work to determine the similarities. From there it can predict potential problems and bring them to contractors’ attention before they become issues.

IoT devices are increasingly being used on-site to assist with safety and health monitoring. Smartwatches, cameras, and drones are able to detect worker proximity to other workers and potential hazards. These devices became more popular in the past year and a half due to the pandemic and the need to keep workers spaced out. The software was able to monitor who workers were close to, allowing for quick and easy contact tracing. It’s also possible to track where workers are on-site and to analyze work patterns to help improve productivity and efficiency.
Some contractors are using AI to better analyze their project schedules so they can more efficiently perform work and meet deadlines. ALICE is a schedule analysis algorithm, started by ALICE Technologies, that many contractors are using to assess and predict delays. It analyzes schedules by extrapolating thousands of possible outcomes and running simulations of a project’s 4D schedule and BIM. By adjusting the inputs, contractors can quickly see what effect it will have on the schedule.

Machine learning

Machine learning is a subset of AI and has to do with using statistical technology and calculations to understand project data and gain insights. As more data is added to the machine learning model, the software is able to assimilate the information and refine its predictions in current situations.

Contractors are using machine learning and predictive analysis to assess projects ahead of time so they know the risks and can proactively manage them from the beginning of the project. This type of analysis can monitor RFIs, project issues, and potential change orders and alert contractors to critical issues.

This learning is also being used to take real-time measurements of completed work on-site and compare them to the scheduled work to determine if a project is on schedule or not. Based on the result of the analysis, the software alerts contractors to direct their resources to address the delay. This analysis can be done across a project portfolio, allowing contractors to be more effective in their crew and equipment resource use.

One application of machine learning uses visual and audio data to identify people in photos and videos and recognize if they are using the correct safety equipment, including PPE. This information is then used to notify safety officers, superintendents, and others who can act to correct the issue.

Selecting which projects to bid on and improving the accuracy and success of bids is another way that machine learning is being used to make contractors more successful. The software can analyze the results to predict which projects contractors will be most successful on and provide insight on bid successes.


Automation, or the use of technology to perform tasks, is gaining in popularity in construction. Contractors who can eliminate unnecessary work improve efficiency, saving time and money, which leads to more profits.

Construction management and accounting software uses automation to speed up data entry tasks and help prevent typing mistakes. For example, automated invoice entry in accounts payable allows workers to pull amounts and codes from commitment records and receive notifications when budget line items are over their limits. These automations help ensure data is entered accurately and cost overruns are detected and managed.

For recurring invoice entries, where the amounts and codes don’t change, automating these entries speeds up the process and ensures that costs are coded and allocated correctly. Using a template saves time by reducing the amount of data that needs to be entered, reducing the chance for errors.

When mistakes are made in invoice entry, or an invoice allocation needs to be changed, simple invoice correction helps workers quickly make the necessary changes and move on with the rest of their work. There’s no need to worry about debits and credits, as the system takes care of those entries on the backend. The goal is to make changing invoices quick and easy while maintaining the proper audit trail.

Get the right software

If you’re looking to leverage the latest in automation, AI, and machine learning to improve efficiency and increase profits, you’ll need software that can gather and analyze data quickly. Our construction management and accounting software provide teams with the tools they need to take advantage of these technologies. Click here to schedule a personalized product tour.

Author Biography:

Dawn Killough is a construction writer with over 20 years of experience with construction payments, from the perspectives of subcontractors and general contractors. Dawn has held roles such as a staff accountant, green building advisor, project assistant, and contract administrator.  Her work for general contractors, design firms, and subcontractors has even led to the publication of blogs on several construction tech websites and her book, Green Building Design 101

Trends & Technology

How is AI & Machine Learning Transforming the Construction Industry

In recent years, more and more construction companies have started to utilize artificial intelligence (AI) and machine learning in various ways to optimize efficiency, reduce costs, mitigate risks and improve safety. We often hear the terms used interchangeably and while they are closely linked, they’re not quite the same thing. Let’s begin by defining and differentiating the two concepts, and then we’ll discuss how their applications are already being used to transform the construction industry as we know it.

What is Artificial Intelligence (AI)?

According to McKinsey & Company, artificial intelligence (AI) is typically defined as “the ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem solving, and even exercising creativity. Examples of technologies that enable AI to solve business problems are robotics and autonomous vehicles, computer vision, language, virtual agents, and machine learning.” 

Simply put, AI is used to describe technology that is capable of performing, or augmenting, cognitive tasks that once had to be done by humans.

What is Machine Learning?

Machine learning is a type of AI in which “algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction. The algorithms also adapt in response to new data and experiences to improve efficacy over time.” (McKinsey & Company)

The four types of machine learning are: supervised learning, unsupervised learning, reinforcement learning, and semi-supervised machine learning. 

Examples of AI & Machine Learning in Construction

The possibilities of utilizing AI and machine learning in construction are vast, as are the benefits. Below are five ways construction companies are already implementing these technologies to improve processes and recognizing the benefits:

1. Increased productivity on job sites

Self-driving construction machinery is being used to perform necessary but repetitive tasks like bricklaying, concrete pouring, welding and demolition in lieu of human labor given the technology’s ability to complete the tasks quicker and more efficiently. Autonomous or semi-autonomous bulldozers can be programmed to exact specifications to perform excavation and prep work needed to get a site ready for construction. Project managers are also able to track progress in real-time using cameras, facial recognition software and other technology to assess productivity and adherence to protocol.

2. More efficient project planning

Industrial Engineering works in front of monitoring screen in the production control center. technology concept.

Some companies are already using AI-powered robots to capture 3D scans of construction sites, which are then uploaded into a deep neural network that can classify the status of various sub-projects. This allows for early intervention if something seems off-track, so the team can handle small issues before they turn into more complex problems.  

3. Risk mitigation

Every project phase has inherent risks associated with quality, safety, time or cost. Today there are AI and machine learning solutions that can be used to assess risk and automatically assign priority to complex issues or areas most prone to threats. Time and resources are limited, and this helps project teams understand where to focus their time and effort in order to mitigate the most high-risk situations. 

4. Prevent budget overrun

Project managers meeting to analyze and discuss on the status of projects, cost to date, and estimate at completion

Even with careful planning and skilled project teams, it’s not uncommon for projects to run over budget given the number of variables that can change and directly impact costs or the schedule. Artificial neural networks—brain-inspired systems intended to replicate the way humans learn—can be employed to predict potential budget pitfalls by assessing factors like project size, contract type and performance of team members, as well as analyzing other patterns too complex for the human mind, to reduce the likelihood of budget overruns. 

5. Improved worker safety

The construction industry is witnessing a rise of machines and sensors that are capable of increasing jobsite safety and reducing risk—for example, sensors that can detect worksite weather conditions to assess risk factors. These tools, when combined with AI, are able to intelligently monitor job sites by analyzing data in real-time to predict the likelihood of problems or malfunctions that may occur and translating that data into actionable insights. AI-enabled surveillance systems with facial and object recognition software also make it possible to detect unsafe behavior and alert team members of potential hazards. 

A Look to the Future

According to a recent Research Dive report, “the artificial intelligence in construction market size was $408.1 Million by end of 2018, and is anticipated to reach $ 2,642.4 Million by 2026, growing with a CAGR of 26.3% during the forecast period.” The continued growth will largely be driven by the cost efficiency and availability of advanced AI products, as well as the ability to more accurately predict needs, assess risks and improve safety.

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Author Biography:

Kathryn Dressler is a content strategist with more than 10 years of experience across the spectrum of marketing services, including blogging, social media, public relations, copywriting and editorial services.