ADOPTION OF ARTIFICIAL INTELLIGENCE IN PREDICTIVE RISK ASSESSMENT IN SELECTED OIL AND GAS COMPANIES IN NIGERIA

Abstract

This study examined the role of Artificial Intelligence adoption in predictive risk assessment within Nigerian oil and gas companies, focusing on its effectiveness in enhancing risk identification, evaluation, and management processes. A descriptive cross-sectional survey design was adopted, employing a proportionate sampling technique to select 256 participants from professionals and decision-makers involved in risk assessment, safety management, and operational roles across selected companies. A total of 256 questionnaires were distributed, out of which 249 were properly filled and valid for data analysis, representing a high response rate of 97.3%. Data was analyzed using the Statistical Package for the Social Sciences (SPSS) version 27.0. The results revealed that the adoption of AI technologies such as machine learning, predictive analytics, and data modelling has a significant positive impact on predictive risk assessment by increasing confidence in risk-mitigation strategies (Mean = 3.8), and management support for AI adoption (Mean = 3.8). This is followed by proactive incident prevention compared to traditional methods (Mean = 3.7), alignment of AI use with operational objectives (Mean = 3.7), and measurable improvements in safety outcomes (Mean = 3.7). AI-powered tools enabling quicker response to threats (Mean = 3.8), enhanced safety compliance (Mean = 3.8), tangible improvements in safety outcomes from AI investment (Mean = 3.8), and AI-based monitoring identifying risks before escalation (Mean = 3.8). The study concludes that AI plays a transformative role in improving predictive risk assessment and overall risk management effectiveness in the Nigerian oil and gas industry.