Artificial intelligence is no longer just a futuristic idea it is a tool that safety leaders can use now to move from reacting to incidents toward predicting and preventing them. On jobsites with construction, manufacturing, or transportation operations, AI analytics that draw on real-time sensor data, video surveillance, and historical incident logs are beginning to transform how hazards are recognized and addressed.

The National Institute for Occupational Safety and Health has documented how machine learning algorithms applied to surveillance data, incident reports, and unstructured “narratives” of injury records can help surface risk patterns months before a serious incident occurs. One recent project used AI to classify and code injury data more quickly and accurately than manual review, which enables earlier intervention and trend detection (Lifrieri & Marsh, 2021).

DOE has spotlighted responsible AI applications elsewhere that translate into safety insights. The U.S. Department of Energy is developing AI tools to support environmental permitting (e.g. PermitAI) and to improve real-time monitoring of potential hazards in energy infrastructure projects, which can be adapted for safety analytics on jobsites (U.S. Department of Energy, 2023).

NIOSH’s “Exploring Approaches to Keep an AI-Enabled Workplace Safe” blog clarifies how deploying AI systems in real-time monitoring requires transparent, trustworthy AI, with explicit risk management around bias, error, and privacy. Worksite safety improves when AI tools generate actionable alerts such as identifying missing personal protective equipment, dangerous proximity to heavy machinery, or unsafe environmental conditions and facilitate immediate corrective actions (Howard & Schulte, 2024).

Academic and industry research backs this up. A recent peer-reviewed study in PMC examined computer vision systems on worksites and found that AI-powered surveillance and monitoring tools significantly reduce unsafe behavior and incidents when combined with human oversight (Shah & Mishra, 2024).

At Key Safety LLC, we help organizations adopt AI analytics responsibly. For new projects, our Document Development for Start-up Projects includes SOPs configured for sensor integration, video feed analytics, and alerting thresholds. Our Service on Demand provides immediate hazard trend analyses when incidents occur and helps correct blind spots. Our Regular Consultation Service ensures that AI tools are kept current, that data privacy and bias are managed, and that usage aligns with OSHA and NIOSH guidelines.

AI analytics do not replace human judgment they amplify it. When teams use data smartly, jobsites become safer, proactive, and more resilient against preventable hazards.

References

Lifrieri, G., & Marsh, S. (2021, August 19). Using machine learning to code occupational surveillance data: A cooperative effort between NIOSH and the Harvard Computer Society – Tech for Social Good Program. NIOSH Science Blog. https://blogs.cdc.gov/niosh-science-blog/2021/08/19/machine-learning-t4sg/

U.S. Department of Energy (2023). Artificial intelligence. U.S. Department of Energy. https://www.energy.gov/topics/artificial-intelligence

Howard, J., & Schulte, P. A. (2024, September 9). Exploring Approaches to Keep an AI-Enabled Workplace Safe for Workers. NIOSH Science Blog. https://blogs.cdc.gov/niosh-science-blog/2024/09/09/ai-risk-management/

Shah, I. A., & Mishra, S. (2024). Artificial intelligence in advancing occupational health and safety: An encapsulation of developments. J Occup Health, 66(1), uiad017. https://pmc.ncbi.nlm.nih.gov/articles/PMC10878366/