AI in Engineering: Benefits, Risks, and an Intern’s Perspective

Artificial intelligence is moving into engineering practice at a pace that’s hard to ignore. AI in engineering is already influencing research, cost estimation, safety monitoring, and workflow efficiency. From compressed design schedules to predictive safety insights and measurable cost reductions, the potential advantages are substantial. Yet for a profession grounded in precision, verification, and professional liability, the adoption of AI raises a critical question: can tools built to enhance engineering performance also introduce new forms of risk?

As with any technology introduced into a controlled technical environment, effectiveness depends not just on capability, but on understanding. Properly implemented, AI can strengthen workflows, improve decision support, and elevate safety outcomes. Poorly integrated, it can undermine accountability, obscure assumptions, and create vulnerabilities that are difficult to detect until consequences emerge. For engineering organizations, the challenge is not whether to use AI, it is how to implement it in a way that preserves reliability and responsibility.

Using AI in Engineering Research

AI has quickly become a practical research tool within engineering workflows. Today, many professionals interact with AI-assisted analysis without consciously seeking it out. For example, using Google as a search engine often produces AI-generated summaries alongside traditional results. Beyond speed, one of AI’s most valuable contributions is the ability to support iterative inquiry. Engineers can pose a question, refine assumptions, and continue probing deeper through a conversational exchange.

Modern search platforms extend this capability by organizing complex questions into structured subtopics supported by linked technical sources. This connection between query, synthesis, and source material enables engineers to rapidly locate references that either support a design rationale or challenge an underlying assumption, both of which are essential to sound engineering judgment.

Another advantage lies in contextual understanding. Variations in terminology, phrasing, and technical vocabulary can influence search outcomes, but AI systems increasingly adapt to user-specific language patterns. By incorporating contextual cues and prior inputs, large language models can refine responses to better align with the user’s intent and technical perspective. As additional information is provided during an interaction, the system can continuously build upon that context, enabling more targeted and relevant outputs.

In practice, AI can function as an initial research accelerator. When preparing technical content, for example, AI-assisted queries scan quickly surface relevant publications, summarize key points, and identify how sources relate to a specific engineering argument. Used critically and verified against primary references, this capability can significantly improve research efficiency while preserving analytical rigor.

How AI Improves Engineering Productivity

One of the most significant advantages of AI in engineering and construction management is its ability to streamline project cost estimation. AI-powered estimating tools can process large quantities of historical project data, material costs, and labor requirements far faster than traditional methods, allowing engineers to meet tight deadlines without sacrificing accuracy.

By reducing the time required for research and bid preparation, AI not only minimizes project costs but also strengthens client relationships through efficient, reliable project planning. While concerns about workforce displacement persist, engineers who can effectively leverage AI in their workflow become highly valuable, as proficiency with these tools increasingly differentiates candidates in the modern construction and engineering job market. Faster pre-construction planning ultimately shortens project lead times, reduces overhead, and enables teams to complete more projects potentially creating new employment opportunities.

Safety on construction sites is another area where AI delivers measurable benefits. Engineering projects demand constant vigilance, as minor oversights can quickly escalate into major hazards. AI introduces an additional layer of monitoring that operates tirelessly, improving real-time situational awareness.

Tools leveraging 3D spatial tracking, computer vision, and sensor networks can automatically identify unsafe behaviors, detect potential hazards, and generate actionable alerts. Examples include workers entering restricted zones, failing to wear proper PPE, or approaching heavy equipment without adequate safeguards.

Beyond immediate hazard detection, predictive analytics enable AI to analyze historical safety data, weather conditions, equipment usage patterns, and workforce behaviors to forecast risks before they occur. This proactive approach allows engineering and construction teams to implement preventive measures rather than reacting after an incident. As AI becomes more deeply integrated into construction management systems, its impact is measurable: fewer workplace injuries, more consistent adherence to safety protocols, and a culture where hazards are identified and mitigated in real time. For engineering teams, this means projects can advance more confidently, efficiently, and safely.

AI is also transforming industrial automation workflows, from predictive maintenance to smarter SCADA analytics and data-driven control strategies. For a deeper look at how these tools are being applied in PLC systems and SCADA platforms, read our guide to AI in Industrial Automation: Tools, Use Cases & Insights.

Data Security Risks of AI in Engineering

While AI offers significant advantages for engineering workflows, it also introduces risks pertaining to data security. One of the most critical concerns is the potential for sensitive information to be inadvertently exposed. AI language models store and use the data you provide to generate responses, which means any confidential designs, source code, or project details entered into these systems could potentially be retained or used in future outputs.

There have been multiple instances in industry where engineers unintentionally shared proprietary information with AI tools such as pasting source code, requesting optimization, or uploading internal meeting content. While these tools can improve efficiency, accuracy, and workflow productivity, mishandling sensitive data can have serious consequences for project integrity, intellectual property, and overall company performance.

There are strategies to mitigate these risks. Some organizations develop proprietary AI models internally, ensuring company data never leaves secure servers. However, this approach comes with high costs, extensive development time, and ongoing maintenance requirements.

The most practical precaution is simple: avoid inputting confidential or proprietary data into external AI platforms. While this limits certain AI capabilities, it protects core project information and intellectual property, preserving both safety and accountability in engineering operations.

How Engineers Can Start Using AI Today

AI tools are becoming increasingly integrated into the daily routines of professionals, including engineers. Recent reports suggest that AI platforms like ChatGPT now reach hundreds of millions of users weekly, with most interactions focused on general guidance and writing support rather than technical problem-solving. While this highlights the technology’s current role in personal or administrative tasks, it also points to untapped potential for engineering applications.

For junior engineers, AI can serve as a valuable resource for accelerating learning and onboarding. Junior engineers, for example, often need guidance on fundamental processes or software tools, and AI can provide step-by-step explanations that help them integrate into projects more quickly. For experienced engineers, AI can streamline workflows by offering alternative methods, shortcuts, or best practices that improve efficiency. By bridging knowledge gaps across experience levels, AI supports faster skill development, reduces ramp-up time, and helps teams operate more effectively in fast-paced engineering environments.

Frequently Asked Questions About AI in Engineering

Is AI reliable for engineering decisions?
AI can accelerate research and analysis, but outputs must always be verified against primary sources, codes, and engineering standards. It should support decision-making, not replace it.

What are the risks of using AI in engineering?
The primary risks include inaccurate outputs, overreliance without verification, and potential exposure of confidential data when using public platforms.

How can engineering firms start using AI safely?
Begin with low-risk applications such as research assistance and documentation support. Avoid entering proprietary information into public tools and establish internal validation procedures.

Conclusion: Using AI Responsibly in Engineering

AI is a powerful addition to the engineer’s toolkit, capable of improving efficiency, safety, and learning across projects. However, like any tool, its effectiveness depends on thoughtful application. Engineers must continue to double-check results, distinguish between reliable and inaccurate outputs, and avoid overreliance on automated systems.

When implemented responsibly, AI becomes a force multiplier. Enhancing productivity, supporting better decision-making, and allowing engineering teams to operate at their full potential without compromising quality or accountability.

About the Author

Eoin Darlington served as an Electrical Engineering Co-op in the Mansfield, MA office, where he supported client projects with the engineering team. He studied Electrical Engineering at Wentworth Institute of Technology.

About Hallam-ICS

Hallam-ICS is an engineering and automation company that designs MEP systems for facilities and plants, engineers control and automation solutions, and ensures safety and regulatory compliance through arc flash studies, commissioning, and validation. Our offices are located in Massachusetts, Connecticut, New York, Vermont, North Carolina, and Texas and our projects take us world-wide.