Unveiling The Role of Artificial Intelligence In Optimizing Training And Development Strategies For The Oil Sector

Authors

  • Hassan Hadi M. A. Al-Fatlawi Petroleum Research and Development Centre
  • Qaeser Mohsin Khayoon Petroleum Research and Development Centre
  • Mohammed Hamdi Al-Rubaye Training and Development Directorate

DOI:

https://doi.org/10.47134/pslse.v2i3.387

Keywords:

Artificial Intelligence, Workforce Optimization, Machine Learning, Oil Industry, Training and Development, Skills Enhancement

Abstract

This study investigates at how artificial intelligence, or simulated intelligence, is helping the oil and Gas industry improve its methods for training and development. As the companies deal with growing challenges and technological obstacles, enhancing personnel capabilities becomes essential for realistic growth and operational efficiency. This study sheds light on how computer-based intelligence-driven systems provide personalized training modules, tailored learning experiences, and ongoing performance evaluations by means of a thorough investigation of simulated intelligence applications, such as machine learning, natural language processing, and predictive analysis. Oil and gas companies may control artificial intelligence to bridge capability gaps, enhance security protocols, and streamline workflows. This research also looks at the potential challenges and ethical considerations associated with the use of simulated intelligence in training and development initiatives. In general, the oil industry can adapt to changing market needs and foster a culture of continuous learning by adopting computer-based intelligence advancements, ensuring a skilled and adaptable workforce for any challenges that may arise in the future.

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Published

2025-04-08

How to Cite

Al-Fatlawi, H., Khayoon, Q., & Al-Rubaye, M. (2025). Unveiling The Role of Artificial Intelligence In Optimizing Training And Development Strategies For The Oil Sector. Physical Sciences, Life Science and Engineering, 2(3), 11. https://doi.org/10.47134/pslse.v2i3.387

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