Modern Contractor Solutions December 2024 - 39

metadata can help provide deeper insights.
This iterative process involves regular reviews
and enhancements, gradually building a
robust dataset that AI can leverage effectively.
BUSINESS INTELLIGENCE AND BIM
Companies that have already invested in
Business Intelligence and BIM (Building
Information Modeling) will enjoy a marked
advantage. These tools provide a solid
foundation for AI by ensuring data is
significantly richer and considerably easier
to make machine readable. A thoughtful
approach to " business intelligence " means
that specific metrics are collected to derive
actionable insights.
These tools will also form the foundation
of an effective AI roadmap, which requires
collaboration between software providers
and clients. In industries like construction,
where projects vary widely in scope and
regulatory requirements, AI solutions need to
be adaptable. This adaptability is achieved
through enriched data and continuous
feedback from real-world applications.
One of the primary applications of AI
in construction is risk assessment and
project planning. AI can use historical data
to quantify risks, such as underestimating
activity durations or identifying potential
delays. This proactive risk management
enables companies to address issues
early, improving project outcomes and
client communication.
FUTURE IN CONSTRUCTION
In the construction industry, AI can be a
game-changer, reducing the risk of errors,
enhancing project accuracy, and improving
bidding processes. Companies that adopt
AI tools will have a major advantage over
those that do not.
The integration of AI in construction
is akin to the evolution that took place
from performing manual computations
to the use of calculators. It's a subjective
perspective, but it is no exaggeration to say
that the leap from traditional construction
planning methods to AI-driven processes is
even more dramatic and transformative.
TIMEFRAME
AI represents a significant opportunity to
enhance efficiency and accuracy, but none of
this is going to be quick. The timeline for AI
integration varies based on the organization's
size and readiness. For larger organizations, the
process can take up to a year or more, whereas
smaller companies might complete it within a
quarter. This timeline includes setting up the
necessary infrastructure, cleaning data, and
iterating processes to ensure everything is in
place for AI deployment.
At the end of this extensive process, you'll
have a robust data set that is going to build over
time. That's when you'll realize it was worth it.
about the author
Daniel Hewson is the data capability manager
for Elecosoft. He has a strong background
in mathematics, computer science, and
engineering, with a focus on machine
learning and how to apply it to real-world
processes, including construction. For more,
visit www.elecosoft.com.
www.mcsmag.com
DECEMBER 2024
39
http://www.elecosoft.com http://emarsinc.com http://www.mcsmag.com
Modern Contractor Solutions December 2024

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