The impact of artificial intelligence on labor investment efficiency: a study of non-financial firms in emerging markets

Purpose This study examines whether artificial intelligence (AI) adoption is associated with improved labor investment efficiency among non-financial firms in the Middle East and North Africa (MENA) region. Design/methodology/approach Using firm-level panel data from 2010 to 2024 for approximately 700 non-financial firms, the study measures AI adoption through a tailored AI-related textual lexicon and applies a bias-corrected method of moments (BCMM) estimator to address dynamic adjustment and endogeneity concerns. Findings The results show that AI adoption is positively associated with labor investment efficiency. This relationship is stronger in firms with better employee treatment, higher human capital intensity, greater knowledge capital, stronger governance, and higher product market competition. The findings also indicate that AI-adopting firms improved labor investment efficiency after the COVID-19 period, despite temporary labor inefficiencies during the crisis. Research limitations/implications The study suggests that AI can support workforce optimization in emerging markets when combined with human capital investment, sound governance, and competitive market conditions. Originality/value This study extends recent research on AI, digital transformation, and labor investment efficiency by providing firm-level evidence from publicly reporting MENA non-financial firms. The MENA context is important because firms operate under distinctive labor-market conditions, including dependence on expatriate labor, skill shortages, uneven digital readiness, governance differences, and policy-led economic diversification. The study further contributes by showing that the AI–labor efficiency relationship is stronger when firms possess complementary organizational resources, including human capital, knowledge capital, employee-oriented practices, strong governance, and competitive market pressure.