AI Applications And Risks In Construction – Construction & Planning

In July 2023, the Construction Management Association of
America hosted a webinar discussing the merits and use of
artificial intelligence (AI) in construction. This article
summarizes the panel discussion and contains considerations for AI
adoption based on that discussion. The panel was a cross-section of
experienced AI users and firms beginning to explore AI’s use in
practice. Panel members using AI in practice included Kylan Greenville,
assistant virtual design and construction manager with Balfour
Beatty, and Mike Giacco, associate
vice president responsible for new technology and IT at AI
Engineers. New AI adopters exploring AI’s place in their
workflow included Sharnette Tucker,
program manager with HNTB, and Mark Bloom, partner
with ArentFox Schiff, co-leader of the firm’s national
construction practice, and member of the firm’s
interdisciplinary AI team. HKA Partner John
Paolin moderated the panel.¹

How Is AI Applied in Construction?

The construction market is changing before our eyes. The speed
of adopting new technology is accelerating, making construction
safer and more efficient with higher-quality results. But there is
another side to the AI coin. As AI becomes a more significant
component of construction-related technologies, new risks,
considerations, and challenges are surfacing. The concept of AI can
be ambiguous and is often interchangeable with automation. For this
discussion, AI can be defined as any computer-based system that can
understand natural language, recognize patterns, make predictions,
draw conclusions, and learn from evolving data sets. In their
highest form, AI systems attempt to simulate human
intelligence.

AI applications can be grouped into three categories: proven
applications accepted as best practices, newly developed
applications awaiting acceptance in the mainstream, and exploratory
applications that exist in more of a beta-testing environment. The
panel discussed the considerations for each application and
addressed potential risks.

Balfour Beatty’s use of OpenSpace.AI is an example of AI in
a proven, accepted application. The firm has been using
OpenSpace.AI to connect building information modeling models,
plans, and weekly site walk-through 360 imagery to analyze and
compare as-built with as-planned conditions since 2019.

These applications predict discrepancies and allow the
construction team to address problems before or as they occur. The
AI tool can also monitor schedule progress; however, that portion
of the tool is primarily used by self-performing contractors.

AI Engineers’ use of drone imagery and AI algorithms is a
good example of a newly developed AI application for a traditional
inspection process. The firm uses drone imagery of complex
structures such as bridges combined with AI algorithms to create 3D
models that can highlight and classify structural deficiencies.

HNTB, an infrastructure solutions firm, works alongside
transportation agencies across the United States, offering a broad
range of services to meet its clients’ needs. This includes a
digital transformation solutions team that works with clients
interested in analyzing AI adoption and implementation to help them
learn to use AI predictive analytics and machine learning for asset
management and assessment processes. The HNTB team, which supports
AI in the infrastructure and architecture design processes, is in
the early prototyping stage of how AI is being implemented for
project control applications such as risk management, schedule
development, submittals, data analysis (KPI dashboards), and
reporting. Clients are beta testing technologies such as AI bots to
address the speed and efficiency of various construction
processes.

ArentFox Schiff, a law firm with a national construction
practice and interdisciplinary AI team, is advising clients on
AI-related considerations and risks for their businesses.
Additionally, ArentFox Schiff is using a sandboxed version of
ChatGPT to test the merits of the AI tool for research while
keeping its data safe in a secure, confidential environment (the
firm calls its tool ChatAFS). The term sandboxed refers to
the fact that no data is shared outside ArentFox Schiff, and the
firm retains full data ownership and control.

HKA, an expert witness and litigation consulting firm, is
keeping a close eye on AI in practice, noting where AI has factored
into potential risks during construction while at the same time
working through concerns regarding client confidentiality and other
issues that must be considered when implementing AI in a consulting
environment.

Exploring the appropriate use of AI in a construction workflow
is the first step in adopting AI. Companies need to recognize that
AI is here to stay and will continue to evolve. The idea of using
data to predict the life cycle of assets brings tremendous
benefits, such as allowing companies to gather information in an
expedited time frame. “When it comes to project controls,
it’s all about quality data,” said Sharnette Tucker of
HNTB. “It is critical that we are ‘in the know’ about
AI tools. AI can expedite the data collection process, improve
reaction time, and put us in a better position to help our clients
deliver successful projects.” Simply put, AI is a tool that
can be leveraged to improve construction and will continue to
impact the industry exponentially.

Evaluating AI Tools

One of the most daunting tasks for a firm considering AI is
evaluating various tools and deciding whether they will fit into
traditional workflows. According to the contractors participating
in this panel, buy-in, transparency, and bias are three big
considerations in getting an AI application off the ground. Their
comments are not intended to be advice per se but can be referenced
as lessons learned by other AI users.

ArentFox Shiff’s Considerations

When engaging with an AI provider, the user agreement is a
critical document for protection. Unlike phone applications, where
user agreements are often glossed over and accepted, AI user
agreements can present a significant risk if not carefully
reviewed. A user agreement is a contract that defines the
relationship between an AI provider and a user. Until recently, AI
applications were treated like traditional software, and user
liability was limited. User agreements set the parameters for
future privacy, use of data, and liability. A key parameter is data
ownership.

“We surveyed 30–40 user agreements and found clauses
that, among other things, addressed data ownership and intellectual
property, imposed varying state laws that would be applicable
should there be a dispute, and provided for different levels of
user liability and indemnification,” said Mark Bloom of
ArentFox Schiff. “You need to thoroughly understand what you
are signing up for before you click accept.”

Indeed, in many instances, AI software uses your data to
learn, which benefits other users of the platform.
Important questions need to be addressed, such as whether the data
is proprietary and who can access it. The user agreement will
define whether an AI vendor can legally use your data in other ways
and who owns the intellectual property. “It is important that
you keep control of your data when using AI,” said Mark Bloom.
“The sandboxed implementation of ChatAFS allows our firm to
reap the benefits of testing a cutting-edge AI platform while
keeping control of our data. It also enables us to measure the
progress of AI learning against our own input.”

AI Engineers’ Considerations

AI Engineers (AIE) had some skepticism from their inspectors
when adopting an additional process for collecting data for an AI
application. However, AIE was encouraged despite the skeptics,
adding an extra step and more time to its inspection process. After
using the application for a while, inspectors found that they spent
significantly less time tagging and cataloging inspection photos,
making the overall inspection process more efficient and
effective.

Fear of change is often a big component of buy-in. “You
certainly don’t want to push technology on your team if they
deem it as gimmicky,” said Michael Giacco of AI Engineers.
“It is up to leadership to sell the benefits and build trust
in its use. For AIE, it started with a pilot program and developed
internal champions for the application. The second level of buy-in
was with our clients – the state agencies who certify our
inspections.”

“We don’t get paid until we have buy-in from our
clients,” added Michael. “At this point in time,
AI-driven inspections are not certifiable; however, AI augmentation
(tagging and logging) on a traditional inspection process is
accepted.”

Transparency in the use of AI can only be addressed in one way
– with early and frequent communication. Lack of
transparency, or withholding the fact that AI played some role in a
process, could result in legal consequences or loss of trust (and
business) if discovered after the fact. Full transparency after a
process has begun could cause unnecessary concern, skepticism, or
removal of AI from the process. Stakeholders should know what AI
is, why it is being used, and how it will benefit the process.
Anticipated risks or downsides should be identified and addressed.
All AI users should be on the same page in terms of how they
explain the AI application to colleagues and clients.

Balfour Beatty’s Considerations

With any data-driven application, variations in how data is
collected (by humans) can create bias and compromise data quality.
For Balfour Beatty, it was critical to develop a data collection
process early on to ensure consistency among the personnel
collecting the 360 images. “It sounds simplistic, but the most
important element of adopting AI for quality control is
implementing a solid process for its use early on,” said Kylan
Greenville of Balfour Beatty. “We mapped out the course of how
our team would capture data on-site, we trained each user in the
same fashion, and we monitored the data quality to assure
consistency.”

When it comes to imagery, machine learning kicks in with higher
capture volume, so it is important to set up capture processes
early on to ensure consistency of collection. “Human
interaction and foresight are imperative when creating paths for
data collection through OpenSpace,” said Kylan. “If our
data collectors disregard where pillars or walls will be as the
structure is built, it will confuse the AI algorithms.
Understanding the model and landmarks is crucial early on while the
AI is learning the correct pathways.”

HKA’s Considerations

Verifying that the data is correct has been a challenge for many
AI tools, including ChatGPT. “There have been reported
instances where documents referenced content generated by ChatGPT,
and the references didn’t exist,” said Tyler Donnelly, HKA
Director and forensic accounting specialist. “On the other
hand, it is because of this vast and evolving repository of
information that it is possible to significantly reduce the time
required for market research.”

“AI tools could be particularly valuable when preparing
estimates, evaluating shifts in the labor market, or tracking bulk
material prices, for example,” adds Seyran Celik, HKA Partner
and claims expert. “AI tools may be particularly adept at
quickly identifying hard-to-find information such as the top ten
differences between two revisions to codes of federal regulations.
The human element value lies in the interpretation and evaluation
of those differences.”

Seyran goes on to emphasize the importance of further
verification to avoid false hallucinations.
“Hallucinations is a term used to describe when AI
provides false or non-existing references,” she explained.

The construction industry is a large user of legal services,
which is an area already being impacted by AI. According to a
recent article published in Law360, when Phi Finney McDonald was
conducting discovery in shareholder litigation over a decrease in
the company’s stock price following bad business news, it used
an AI tool developed by legal technology/e-discovery provider
Relativity, called sentiment analysis, to identify key documents.
“E-discovery software providers may be early adopters of AI
because construction disputes often tend to involve a significant
volume of records,” said Daniel Kwon, HKA Partner, and claims
expert. “The key will be matching AI technology with human
interaction and verification.”

Conclusions

One of the key concerns when using an AI platform is security
and privacy. Beyond having a sound approach to user agreements,
implementing a sophisticated cybersecurity protocol is critical. A
firm’s IT leader should be actively involved throughout the AI
implementation process, and the application should be tested for
weak links and potential data compromise.

Much of the data collected for AI is generated from imagery.
Large volumes of imagery could impose a greater chance of privacy
risk. A drone or 360 camera collecting site information on-site
could compromise private information such as trade secrets. Images
of uninvolved participants that are inadvertently collected could
be in violation of privacy laws.

Another key risk for many AI adopters is the lag in regulation
catching up to technology. Even drone regulations are still
antiquated, and they have been in the mainstream for 5 years. As an
example, American Association of State Highway and Transportation
Officials regulations do not reference or account for AI. As a
result, the state agencies who accept AI work products must be
willing to take on the risk so that the federal agencies who may be
paying their bills will not balk at the work product.

AI is not just the future of technology; it is the here and now.
When legislation and regulations catch up to the progress of
technology, we anticipate that the implementation of AI will soar.
We asked our panel what the construction workflow will look like in
3–5 years, and all agreed that AI will be part of almost
every construction tool and process. But at the end of the day, AI
will not replace people or human interaction, rather it is simply
another tool in the toolbox. In terms of job opportunities, some
roles and skill sets will become obsolete, but the new roles,
skills, and expertise that are created will offer even more
employment opportunities. In that vein, a Blueprint for an AI Bill of Rights | OSTP | The
White House is already underway.

Footnote

1. HKA does not use AI in performing its expert services, and
this article is not intended as an endorsement of its use.

Originally published 26th March 2024

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