Abstract

This study examines the relationship between artificial intelligence (AI) adoption and the financial performance of oil and gas servicing firms operating in the Niger Delta region of Nigeria. The oil and gas servicing sub-sector faces persistent margin pressure, ageing equipment, unpredictable maintenance costs and a volatile operating environment, and firms have begun turning to AIenabled tools to steady these variables. Drawing on a survey of 312 respondents drawn from senior managers, operations engineers, finance officers and technology leads across servicing firms in Rivers, Delta and Bayelsa States, the study measures AI adoption through four dimensions: predictive maintenance analytics, machine learning and automation, AI-driven decision support and robotic process automation. Financial performance is captured through return on assets, return on equity, net profit margin and operating cost efficiency. Data gathered from the 2026 field survey were analysed using descriptive statistics, correlation analysis and multiple regression. Findings show a positive and statistically significant relationship between AI adoption and financial performance, with predictive maintenance analytics and AI-driven decision support exerting the strongest effects. Firm size and technological readiness moderated the relationship, meaning larger and better-prepared firms extracted more value from the same technologies. The study concludes that AI adoption is not a cosmetic upgrade but a genuine driver of financial outcomes in this sector, and it recommends deliberate investment in data infrastructure, workforce capability and phased deployment.