Artificial Intelligence and Strategic Management in Nigeria's Oil and Gas Industry: A Study of Cost Reduction and Business Improvement in Rivers State

Abstract

This study examined the interrelationship among Artificial Intelligence (AI), strategic management practices, cost reduction, and business improvement within the Nigerian oil and gas industry, specifically concentrating on firms in Rivers State. Grounded in the Resource Based View (Barney, 1991) and Dynamic Capabilities Theory (Teece et al., 1997), the research explored how AI-driven tools, such as predictive maintenance systems, machine learning algorithms, and intelligent supply chain analytics, combined with effective strategic management practices, facilitate organizational attainment of operational efficiency, cost reduction, and sustained competitive advantage amidst a volatile business environment. A cross-sectional survey design was employed. Data were collected from 156 respondents across fourteen selected oil and gas companies in Rivers State through the administration of a structured questionnaire. Spearman’s rank correlation analysis was utilized to test four null hypotheses at a 95% confidence interval. The results revealed significant positive relationships between AI and cost reduction (ρ = .834, p = .000), AI and business improvement (ρ = .791, p = .000), strategic management practices and cost reduction (ρ = .763, p = .000), and strategic management practices and business improvement (ρ = .812, p = .000). All four null hypotheses were rejected. The study concludes that both AI adoption and strategic management sophistication are critical determinants of organizational efficiency and performance improvement for oil and gas firms in Rivers State, and recommends that firms invest strategically in AI infrastructure, governance, talent development, and evidence-based management systems to achieve sustainable competitive advantage in an increasingly digitalized energy sector.