As AI enters engineering workflows, firms must strengthen human oversight, independent review, data governance, and professional judgment.
Artificial intelligence is changing engineering work, from finding information and developing designs to evaluating alternatives and managing projects. Yet Bentley Systems argues that greater AI involvement does not transfer responsibility from engineers to technology. Firms remain accountable for the quality, safety, and integrity of engineering decisions.
The article draws on an ACEC Research Institute report identifying eight interconnected AI risk areas, including technical reliability, professional liability, data governance, workforce capability, ethics, cybersecurity, commercial models, and leadership. These risks make AI governance an organizational issue rather than simply an IT responsibility.
Regulatory approaches differ internationally. The United States emphasizes AI adoption while relying heavily on existing professional responsibilities and voluntary frameworks such as the NIST AI Risk Management Framework. The European Union has established more formal statutory requirements through the EU AI Act, including obligations for certain high-risk applications. The United Kingdom is pursuing a principles-based approach using existing regulators and professional standards.
Despite these differences, engineering firms face similar practical responsibilities. They need to know where AI is being used, what project or client data enters AI systems, and whether important outputs are independently verified. Using the same AI model, data, or assumptions for both engineering work and peer review could undermine independent assurance by reproducing the same errors.
Increasing AI accuracy may actually make professional scrutiny more important. Obvious mistakes could become less common, while subtle errors and plausible but incorrect conclusions become harder to detect.
AI also creates a workforce challenge. If routine calculations, research, and document reviews are increasingly automated, younger engineers could lose opportunities traditionally used to develop professional judgment. Firms may therefore need to rethink training while improving AI literacy across their workforce.
Bentley concludes that competitive advantage will come not simply from adopting AI, but from combining it with trusted engineering data, domain expertise, independent assurance, and accountable professionals. AI can support engineering decisions, but responsibility for those decisions must remain human.
