What does the term “Artificial Intelligence” (AI) mean to you? What’s the first thing that comes to mind? For some people, it incites a sci-fi-inspired fear of machines taking over the world as we know it. Others say AI doesn’t impact their profession. I hear all sorts of responses any time AI is brought up in conversation. Granted, none of our jobs are at risk of being taken over by machines anytime soon, but we do need to consider and evaluate the ways in which we can leverage technology to make our industry more efficient and effective.

Let’s look at AI for what it truly is. Simply put, AI enables humans to be more efficient and effective. The technology does this by simulating human intelligence as a means of enhancing our existing manual processes. As a result, we’re freed up to focus on areas where previously we were unable to allocate resources. I’d even go as far as to say AI is the driver of our history’s next technological revolution.

Not too long ago, a construction executive reached out to talk about improving overall operations by replacing theirs with automation. One executive shared a great comment. “I have more visibility into my $10 pizza order then I do my $300 load of #57 Stone.” 

While this exec’s frustration-fueled comparison isn’t exactly correlated to AI, we totally get where he’s coming from and what he’s getting at. Translation? He’s in utter disbelief at his company’s inability to leverage technology for the intent of improving operations. Automated dispatch, digitizing paper tickets, accessing real-time insights, leveraging in-depth business reporting and performance insights, the possibilities are endless.

The “Here” and “Now” of AI & Machine Learning

It’s only been within the last five years that construction technology has really started to drive major innovations, many of them leveraging AI and its subset, machine learning. This is thanks in part to the growth of software-as-a-service (SaaS) offerings and mobile apps. While AI refers to the broad concept of machines mimicking human intelligence, machine learning describes instances where machines are capable of taking information gathered from various data points and user behavior. From there, they predict and maximize outcomes, all without the requirement of reprogramming. That learned information is constantly fed into algorithms. Those algorithms are put in place to constantly monitor the environment, and they’re specifically designed to make decisions that increase the probability of achieving determined goals.

Seem complicated and complex? There’s nothing to fear. Machine learning already exists all around you. Whether you’ve noticed it or not, AI machine learning is immersed throughout our day-to-day. From automating record keeping for administrative assistants to optimizing laparoscopic surgery, machine learning is leveraged across every type of professional task. A few examples: Plugging a destination into your favorite GPS app. Your email inbox spam filter working quietly behind the scenes. Even weather projections become more accurate thanks to machine learning technology. And in an industry where we live and die by the weather, the insights afforded by machine learning’s predictive modeling have a meaningful impact on our ability to plan and execute.

There are countless daily-use applications built on the foundations of AI and machine learning. But it’s the construction industry in particular that’s uniquely primed to reap the most benefit.

Consider the results of this 2020 FMI Industry Report. Among the report’s more salient findings were the top three industry concerns: 1) maintaining a safe jobsite (66%), 2) attracting and retaining skilled labor (57%), and 3) maximizing field productivity (52%). Nevertheless, the construction industry remains slow to adopt tech-driven solutions that address these concerns. The report goes on to further support this claim by citing 68 percent of respondents stated technology has or will play a significant part in “sustaining superior financial performance”. Meanwhile, only 26 percent reported actually using construction-specific software to address hiring and retention, and less than half (41%) reported using technology to maximize field productivity. Technology is being used to solve both these challenges in other industries, so why not ours?

AI: Eleven Reasons Why

We’ve stated it once, but it warrants readdressing. One of the most common concerns associated with machine learning is that it will eliminate the need for human involvement within any given job. The reality? AI machine learning is strategically developed with the goal to enhance our existing processes, not to replace them. As a result, we’re able to focus our efforts in areas that no robot can execute – AI helps ensure we’ve reserved the best parts for us! Through all this, we create opportunity for expanded productivity. Still not convinced? Take these 11 Reasons into consideration: 

  1. Preventative servicing on equipment. By combining equipment-usage data, environmental conditions, global equipment failure rates, and maintenance records, AI-enhanced technology can easily calculate the correct service schedule for equipment, preventing unscheduled downtime.
  2. Analyzing job safety. Safety is the top priority in the construction industry. Fixed or drone cameras allow computers to quickly identify safety issues. By teaching the technology differences between a safety issue and a false-positive, you can quickly establish an on-site safety-monitoring solution.
  3. Dump truck dispatch optimization. Until recently, there hasn’t been a great deal of innovation when it comes to dispatching dump trucks. Even though we now communicate with our drivers via text, the process of optimizing scheduled or intra-day dispatching hasn’t improved much, if at all. Machine learning can have a massive impact on our fleet management, matching available trucks to geographically relevant jobs.
  4. Automated dispatch. Wouldn’t it be nice to automatically assign trucks based on the day’s job orders? Using machine learning technology, auto dispatch is not only feasible, it’s a reality. This technology assigns loads based on expected traffic (pick up or drop off locations), as well as minimizing empty hauls and other driver inefficiencies.
  5. Trucks/job allocation. Machine learning technology can also forecast how many trucks you will need on a job and adjust over time based on data mined from project yields, routes, and other similar jobs.
  6. Stockpile management. Large construction projects have many moving pieces and thousands of tons of product inventory. Machine learning helps determine when various materials need to be pulled from storage and available at the site, leading to smoother and more efficient operations.
  7. Analytics and reporting. By collating data points from multiple construction jobs, machine learning software can determine patterns, recurring issues, and cost overruns to help project managers find and reduce potential bottlenecks before they happen. Fewer slowdowns translate into higher productivity, lower costs, and a smoother workflow.
  8. Preventing cost overruns. I recently read a post that said 90 percent of construction projects have cost overruns. It’s these overruns that give the construction industry a black eye with the public. Technology allows us to apply previous learning at both the bidding phase and during the construction phase so we can better calculate and monitor project costs. And, because the software is continuing to make changes based on fresh data points, changes can be made in near real-time to enhance productivity and stay on budget.
  9. Project scheduling/planning/management. Do you factor in seasonal effects when putting together project schedules? Machine learning solutions have and can make sure you are properly estimating project timelines.
  10. Self-operating and self-driving construction machinery. Heavy equipment with self-driving/operating capabilities isn’t far off. In fact, these machines are being used in large, commercial construction sites today and will become more commonplace as the industry continues to struggle with labor shortages. Not only do these autonomous and semi-autonomous machines speed up repetitive tasks such as pouring concrete or demolition, they free up employees to focus on complex construction tasks.
  11. Building information modeling. 3-D modeling gives building lifecycle stakeholders greater insight, especially during the initial planning phases by allowing them to see architectural, engineering, mechanical, electrical and plumbing plans. It can also detect and prevent work clashes (and rework) early on.

What does the future hold?

One thing is clear – Our industry is at odds with itself. It’s no secret that we’re well behind adopting tech. When it comes to utilizing AI and machine learning in the construction industry, it’s time to fish or cut bait. Other industries, such as manufacturing, are already using it to their advantage.

For machine learning to truly work across the industry as a whole, construction companies of all sizes need to begin adopting these technologies now. And they’re more readily available than most construction professionals are aware.

Our challenge? The coming years will determine whether our industry remains a global economic leader, or if we continue engaging in a never-ending game of catch-up. The future of construction technology is in our hands. How will you support our evolution?