Some small and midsize construction businesses have been slow to
embrace AI. Perhaps you're a tad skeptical yourself. Can the industry really
benefit from this exploding technology in all its many forms? One way to gauge
a potential innovation's staying power is to look at whether and how large
construction businesses are putting it to the test. And, increasingly, they're
using AI to streamline and improve the estimating process.
The case in a nutshell
U.S. construction spending totaled approximately $1.45 trillion
during the first eight months of 2026, down 3.1% from the same period last
year. However, demand for data centers, power plants and health care facilities
remains relatively strong. The large construction businesses that bid on these
complex jobs must process vast amounts of cost information quickly and
accurately. And many have been using AI to do it.
Introducing AI into your
estimating process may also boost efficiency and precision. Used well, it can
help eliminate bottlenecks and free your staff to focus on higher-value work,
such as evaluating scope, assessing risk and crafting more competitive bids.
The robot's role
To be clear, AI-driven estimating tools can't replace the
expertise and judgment of experienced human estimators. Rather, their role is
to handle certain repetitive, time-consuming tasks — particularly those related
to takeoff.
For example, depending on their capabilities and available data,
AI-driven estimating tools can quickly gather relevant cost data from various
sources and apply algorithms to perform calculations. Potential sources they
can draw from include:
- Previous bids and projects,
- Various details of building information models, and
- Market trends and statistics.
During the takeoff stage, some tools can identify design
elements in drawings, such as doors or ductwork, and automatically calculate
quantities to develop cost estimates. These tools may process information much
faster than traditional manual methods, potentially reducing completion time
for certain tasks from hours to minutes. Some AI-enabled systems can use predictive
analytics to flag potential risks, such as material price increases or supply
chain disruptions, and update projections as new information becomes available.
AI involvement in this area also reduces the risk of human error
— and not just by automating manual data entry. You can also use AI-driven
tools to do a "first pass" on an initial estimate and flag anomalies compared
with previous projects for your estimators to review. Pattern recognition and
other capabilities can help improve accuracy within individual estimates and
promote greater consistency across projects.
Ultimately, integrating AI into your estimating workflow may
speed up the process. And with the time and resource savings, you might be able
to bid on more projects without hiring more estimators.
Need for guardrails
However, a couple of significant caveats are worth noting.
You've no doubt heard the saying "garbage in, garbage out" — that's especially
true when it comes to AI-powered estimating.
AI can't compensate for poor-quality data. If the underlying
data is inaccurate, incomplete or outdated, the resulting estimates may be
unreliable. Sloppy cost-code application, failure to update information on
completed projects, or a lack of standards (or enforcement of standards) can
corrupt the output and hurt your bottom line.
You also may have heard the term "human in the loop." It refers
to the need for human involvement at some point in an AI workflow to ensure
accuracy and accountability. In an estimating context, this means that your
estimators should have the final say after reviewing and analyzing the output
to reach realistic conclusions.
Take it slow
AI isn't coming to construction — it is already here. But that doesn't mean you should go "all in" without carefully scrutinizing costs, reliability and other critical issues. We can help you evaluate the financial implications of investing in AI-driven estimating tools and determine whether the potential benefits justify the costs.
