The growing use of artificial intelligence in engineering is changing how project teams approach analysis and routine technical work, increasing the importance of engineers who can combine digital skills with technical judgment. Burns & McDonnell has outlined several capabilities that early-career engineers should develop as AI becomes more common across the AEC sector.
The guidance centers on using AI as a support tool rather than a replacement for engineering expertise. For new professionals entering the industry, understanding how to evaluate technology-generated outputs can be as important as knowing how to operate the tools themselves.
AI applications can assist engineers with tasks such as analyzing information, comparing alternatives, supporting models and automating repetitive activities. These applications can potentially give project teams additional time for analysis and quality checks.
For students and recent graduates, practical AI experience can be demonstrated through projects where technology was used to analyze data, automate processes or evaluate design alternatives. The emphasis should remain on the engineering decisions made after using the technology.
Technical fundamentals continue to underpin AI-assisted engineering work. Engineers must be able to understand calculations, identify errors and evaluate results against applicable codes, standards and project requirements.
The increased use of AI also creates a need for engineers to understand the limitations of the technology. Outputs can depend on the quality of the underlying information, assumptions and data used by an AI system.
AI-generated information requires appropriate technical review before it is incorporated into engineering work. Engineers entering the profession can strengthen this capability by asking how a system operates, what information it uses and where its results may be unreliable.
Data literacy is particularly relevant because AI systems cannot compensate for poor or inconsistent source data. Engineers need to recognize potential weaknesses in datasets and understand how those weaknesses can affect downstream analysis.
The approach is similar to other technology transitions in engineering. Digital tools can change how work is performed while leaving responsibility for technical decisions with qualified professionals.
As engineering workflows become more digital, communication skills remain important. Engineers must be able to translate technical information into clear guidance for project teams and clients.
The ability to seek guidance, explain reasoning and recognize when a technical issue requires additional review also remains important for early-career professionals.
For construction owners and project teams, the development of AI-capable engineers could support broader adoption of digital workflows while maintaining technical oversight. The key consideration is not simply whether engineers can use AI tools, but whether they can evaluate their outputs and take responsibility for the resulting engineering work.
AI is becoming another component of engineering workflows, but the technology does not eliminate the need for technical knowledge, data evaluation or professional judgment. For construction and engineering organizations, developing employees who can combine AI literacy with established engineering practices will be important as digital tools become more integrated into project delivery.
Source: Burns & McDonnell.
