Artificial Intelligence and Workplace Safety in the Oil and Gas Industry: Transforming Occupational Risk Assessment through Smart Safety Analytics

This study examined the role of artificial intelligence in enhancing workplace safety in the oil and gas industry by transforming traditional occupational risk assessment into a predictive and data-driven process. A quantitative research design was adopted, and primary data were collected through a structured questionnaire from industry professionals involved in safety management and AI implementation. Statistical techniques, including descriptive statistics, correlation analysis, regression analysis, and structural equation modeling, were applied to analyze the relationships among artificial intelligence adoption, smart safety analytics, predictive risk assessment, and workplace safety outcomes. The results showed that workplace safety improvement had the highest mean value (M = 4.25, SD = 0.59), followed by predictive risk assessment (M = 4.18, SD = 0.64). Correlation analysis indicated strong positive relationships between artificial intelligence adoption and workplace safety (r = 0.75), and between predictive risk assessment and workplace safety (r = 0.78). Regression analysis revealed that predictive risk assessment had the strongest impact (β = 0.36, p < 0.001), followed by artificial intelligence adoption (β = 0.32, p < 0.001) and smart safety analytics (β = 0.28, p < 0.001), with the model explaining 69% of the variance (R² = 0.69). These findings demonstrated that AI significantly improved hazard detection, risk prediction, and operational efficiency. However, data quality issues, high implementation costs, and limited expertise remained key challenges. The study concluded that AI has strong potential to enhance workplace safety through proactive risk management.