Organizational factors influencing the adoption of Artificial Intelligence by petroleum companies in Nairobi County

Abstract

Artificial Intelligence (AI) in the petroleum sector gained significant attention globally, as it became essential in various innovative operations. However, the pace of AI adoption varied, with specific factors influencing the readiness and willingness of organizations to integrate AI. This study explored the organizational factors influencing the adoption of AI by petroleum companies in Nairobi County. The research focused on different models and factors, and outlined various theories guiding the AI adoption process. Different models were used in this analysis including TAM as well as DOI theory. Data was gathered through a survey system, targeting 179 senior management and technical staff from petroleum companies operating in Nairobi County. Additionally, secondary data was obtained from company reports, industry publications, and regulatory documents. The analysis employed a combination of quantitative techniques, particularly multinomial logistic regression, to assess the factors influencing AI adoption. The findings revealed that leadership support, technological infrastructure, employee skills, organizational culture, and financial resources were all significant factors influencing AI adoption in petroleum companies. Leadership support and organizational culture were found to play particularly significant roles in driving AI adoption, while technological infrastructure and employee skills were also crucial enablers. The study concluded that organizations that invested in leadership support, created a culture open to technological change, and ensured their employees were adequately trained were more likely to achieve successful AI adoption. The study recommended that policymakers in the petroleum sector should create an enabling environment that encourages AI integration through supportive regulations, incentives for technological infrastructure development, and partnerships between public and private sectors. Industry leaders and practitioners were advised to prioritize AI-related employee training and invest in necessary technological infrastructure. It was further recommended that researchers and theorists continue to explore AI adoption in the petroleum sector, with a focus on the interaction between organizational culture, leadership, and technological readiness. These recommendations were expected to guide organizations in enhancing their AI adoption strategies, ultimately improving their competitiveness and sustainability in the industry. The study also contributed to expanding the theoretical and practical understanding of AI adoption in the energy sector.