agchouston.org Fall 2025 Cornerstone 11 agchouston.org Fall 2025 Cornerstone 11 imaging systems can analyze concrete placement in real-time to detect con- solidation or finishing problems before they become costly defects. Additionally, through machine learning, these systems continuously refine their detection algo- rithms and progressively improve their ability to ensure optimal outcomes with each project. 3. Safety enhancements enable active risk management by continuously monitoring environmental conditions, worker behaviors and equipment oper- ations to predict and prevent dangerous situations. Given that construction is one of the world’s most hazardous industries, this capability proves invaluable. For example, computer-vision AI sys- tems can detect workers lacking proper protective equipment, identify unsafe behaviors near machinery and analyze patterns to predict high-risk situations. Real-time alerts, too, allow supervisors to intervene before accidents occur and change shift safety management from reactive to preventive. 4. Proactive solutions transform reactive business practices into for- ward-looking strategies by analyzing historical patterns to anticipate future needs and opportunities. Construction companies leverage predictive mainte- nance programs that forecast equipment failures before they occur, preventing costly downtime. For example, AI-powered drone sur- veys can be used to generate data-rich progress reports that enable decisions based on predictive analytics rather than historical data. This shift from solving problems to preventing them represents a fundamental change in construction operations and creates competitive advantages through anticipation rather than reaction. What Potential Risks Can AI Pose to My Company Of course, knowing the risks of any tool is just as important as knowing its benefits. This isn’t because the risks are certain; it’s because they are avoidable, but only if you know what to look for and then plan for them. Successfully implementing AI into your business requires a thorough understanding of its risks and an intentional, purposeful plan on how to address them. In a broad sense, there are four catego- ries for which most risks can be assigned, although some could be in multiple cat- egories: business risks, operational risks, legal risks and third-party risks. Like the overviews for benefits dis- cussed above, below are some overviews of the risks your company could encoun- ter. There are no “one-size-fits-all” solu- tions for these risks, but starting to think about them is the first step. 1. Business risks encompass stra- tegic and financial uncertainties that affect your margins, market position, stakeholder relationships and reputation. These risks extend beyond the initial investment to influence long-term com- petitive standing. Adopters of AI systems often discover a noticeable return takes longer than anticipated. For example, a contractor investing in AI-powered bidding software could lose projects to traditional competitors while its teams navigate learning curves, illustrating how important it is to have a structured roll-out plan coupled with continuing education. Reputation presents equally critical considerations. While AI adoption might signal innovation to some clients, others may question the reliability of automated decision-making. Market perception varies significantly across client segments and requires careful analysis before implementation. 2. Operational risks emerge when companies implement AI systems into their day-to-day activities in an irrespon- sible manner. These risks can materialize as workflow disruptions, system failures or a decline in human capabilities. The core issue is blind trust without proper training, protocols and verification pol- icies, which creates outcomes directly adverse to AI’s intended benefits. For example, efficiency can turn into costly delays when untrained staff mem- bers accept flawed AI outputs without question. Quality improvements can become disasters when teams skip ver- ification. Safety enhancements can fail when workers rely on AI monitoring without maintaining personal awareness. Proactive solutions turn reactive when over-dependence causes skill atrophy — the loss of problem-solving abilities through disuse. In short, without ade- quate human oversight and with mis- placed overreliance on AI, companies risk poor work results. Adam Robertson is a shareholder at Andrews Myers, P.C. Before you start implementing AI into your business, you’ll need to understand the potential benefits of AI, and how those benefits do and do not apply to your specific business.
