Abstract

This study examined the effect of artificial intelligence (AI) adoption on the working capital management of manufacturing firms in South-South Nigeria. The manufacturing sector in the region contends with persistent liquidity strain, erratic inventory turnover and drawn-out receivables cycles, yet the operational value of AI tools for treasury and short-term financial decisions remains thinly evidenced in the Nigerian context. Anchored on the Resource-Based View and the TechnologyOrganisation-Environment framework, the study adopted a cross-sectional survey design. Primary data were gathered from 312 finance and operations managers across manufacturing firms in the six South-South states using a structured questionnaire, and multiple regression was employed to test the hypotheses. Findings indicate that predictive analytics, process automation and machine-learning forecasting each exert a positive and statistically significant effect on working capital management, with process automation emerging as the strongest predictor. The study concludes that AI adoption is a meaningful lever for tightening the cash conversion cycle and strengthening liquidity, and recommends deliberate investment in AI capability, staff retraining and phased integration with existing enterprise systems.