Artificial Intelligence and Financial Performance in Moroccan Startups: The Mediating Effect of Financial Reporting Quality

Artificial intelligence (AI) is increasingly reshaping financial information systems by automating accounting processes, expanding analytical capabilities, and supporting managerial decision-making. However, evidence remains limited regarding the mechanisms through which AI adoption contributes to financial performance, particularly among startups operating in emerging economies. Drawing on the Resource-Based View and rational decision-making theory, this study examines the effect of AI adoption on the financial performance of Moroccan startups and tests the mediating role of financial reporting quality. A quantitative, cross-sectional design was adopted. Data were collected through a structured online questionnaire distributed to executives, financial managers, and management controllers working in Moroccan startups. Of 160 questionnaires distributed, 125 complete responses were retained for analysis. The proposed model was tested using partial least squares structural equation modeling (PLS-SEM) with SmartPLS 4. The findings show that AI adoption positively affects financial reporting quality (β = 0.792, p < 0.001) and financial performance (β = 0.441, p < 0.001). Financial reporting quality also has a positive effect on financial performance (β = 0.472, p < 0.001). In addition, the indirect effect of AI adoption on financial performance through financial reporting quality is positive and significant (β = 0.373, p < 0.001), indicating complementary partial mediation. These findings suggest that AI creates value not only through direct operational and strategic improvements but also, by enhancing the reliability, timeliness, and usefulness of financial information for decision-making. The study contributes to the literature by integrating technological, informational, and performance-related mechanisms within an underexplored Moroccan startup context. Practically, it highlights the importance of combining investments in AI with robust financial reporting processes, data-management practices, and internal control mechanisms.