Creating an AI-Driven Model to Enhance Safety, Efficiency, and Risk Mitigation in Energy Projects

Increasing complexity and scale of energy projects necessitate innovative approaches to address critical safety, efficiency, and risk mitigation challenges. This paper proposes an AI-driven model tailored to enhance decision-making and operational outcomes across all phases of energy project management. The model addresses key gaps in existing frameworks by leveraging advanced technologies such as machine learning, predictive analytics, and real-time data processing, offering a holistic approach to identifying and mitigating risks, automating safety protocols, and optimizing resource utilization. The conceptual framework integrates AI across planning, execution, monitoring, and closure phases, emphasizing its transformative potential to reduce human error, improve cost efficiency, and bolster operational resilience. A detailed discussion highlights the implications of this model on safety enhancement, efficiency gains, and risk management, while addressing barriers to adoption, such as workforce adaptation and regulatory compliance. The paper concludes by outlining actionable recommendations for industry adoption, future research directions, and the development of supportive policy frameworks to facilitate the integration of AI in energy projects. This work underscores the pivotal role of AI in driving innovation and sustainability in the energy sector, paving the way for more secure, efficient, and resilient project management practices.