Oil and gas firms reporting AI use and stronger strategic management also report large cost and performance gains, but results are correlational and limited to 14 companies in one Nigerian state.

Main Finding

AI adoption and sophisticated strategic management practices are strongly and positively associated with cost reduction and business improvement among oil and gas firms in Rivers State, Nigeria. All tested relationships were statistically significant: AI–cost reduction (ρ = 0.834, p < 0.001), AI–business improvement (ρ = 0.791, p < 0.001), strategic management–cost reduction (ρ = 0.763, p < 0.001), and strategic management–business improvement (ρ = 0.812, p < 0.001).

Key Points

• The study is grounded in Resource-Based View and Dynamic Capabilities Theory, framing AI and management practices as firm resources/capabilities that enable competitive advantage.
• AI tools considered include predictive maintenance, machine learning algorithms, and intelligent supply-chain analytics.
• Sample: 156 respondents from 14 oil and gas companies located in Rivers State, Nigeria.
• Design and analysis: cross-sectional survey with a structured questionnaire; Spearman’s rank correlation used to test four hypotheses at the 95% confidence level. All four null hypotheses were rejected.
• Effect sizes (Spearman’s rho) are large (0.76–0.83), indicating strong positive associations between AI/strategic management and operational outcomes.
• Practical recommendations from the authors: strategic investment in AI infrastructure, governance, talent development, and evidence-based management systems to sustain competitive advantage.
• Reported limitations (implicit from design): geography-limited sample, cross-sectional and survey-based data, potential self-report bias and endogeneity concerns.

Data & Methods

• Population and sample: Employees/representatives from 14 selected oil & gas firms in Rivers State; N = 156 respondents.
• Data collection: Structured questionnaire administered cross-sectionally.
• Variables: measures of AI adoption/usage, strategic management practices, cost reduction, and business improvement (self-reported).
• Statistical test: Spearman’s rank correlation to assess monotonic associations; hypotheses tested at α = 0.05.
• Results: Strong, statistically significant positive correlations for all four pairings (AI & cost reduction; AI & business improvement; strategic management & cost reduction; strategic management & business improvement).
• Methodological caveats: correlation (not causal); cross-sectional design; possible measurement/reporting biases; limited geographic and sectoral scope.

Implications for AI Economics

• Productivity and cost effects: The strong associations suggest that AI adoption in capital- and operations-intensive sectors (like oil & gas) can be economically meaningful for cost reduction and operational performance — consistent with AI as a directed technological change that complements firm capabilities.
• Complementarity with management: Strategic management practices appear complementary to AI — investments in AI may yield larger returns when combined with managerial capability and governance (supports literature on complementary assets).
• Investment priorities: Firms and investors in the energy sector should treat AI not as a stand-alone expenditure but as part of an ecosystem requiring talent, governance, and process redesign to capture economic value.
• Policy and capacity building: Policymakers aiming to boost sectoral productivity should couple incentives for AI uptake with support for skills development, standard-setting, and data infrastructure to reduce adoption frictions.
• Measurement and evaluation: Given the cross-sectional/correlational nature of the evidence, future economic assessments should use objective performance metrics and quasi-experimental or longitudinal designs to estimate causal returns to AI and compute ROI.
• Labor and distributional effects: While not directly studied, the findings imply potential labor impacts (task reallocation, upskilling needs). Economic analysis should consider wage, employment, and skill-upgrading consequences of AI-led restructuring.
• Research priorities: causal identification (panel, difference-in-differences, IV, or randomized trials), heterogeneous effects by firm size/type and specific AI technologies, cost–benefit analyses, and generalizability tests across regions and other extractive industries.