Purpose: This study examines the relationship between the level of artificial intelligence maturity and the quality of financial reporting and profitability of companies listed on the Tehran Stock Exchange.

Design/Methodology: We employ a sequential explanatory mixed-methods design. The quantitative phase analyzes panel data from 50 firms (150 firm-year observations, 2021-2023) using fixed-effects regression models. AI maturity is measured via a contextualized five-level framework (0=unaware to 5=fully institutionalized) derived from content analysis of annual reports and survey responses (n=42, 84% response rate). Dependent variables include return on assets (ROA), price-to-earnings ratio (P/E), and a composite financial reporting quality index (FRQ). To mitigate endogeneity concerns, we utilize propensity score matching (PSM) and instrumental variable (IV) estimation. The qualitative phase involves 18 semi-structured interviews with CFOs.

Findings: Regression results indicate AI maturity is positively associated with ROA (β=0.147, p<0.01), P/E (β=4.1, p<0.01), and FRQ (β=0.274, p<0.05), controlling for firm size, leverage, and age. Firms with AI maturity ≥3 exhibit significantly higher average P/E ratios (12.1 vs. 8.3, p<0.01). Qualitative findings reveal key barriers (sanctions, skill shortages) and enablers (top management support, localized platforms).

Originality: This is the first study to adapt the AI maturity framework to Iran's institutional context and provide rigorous empirical evidence on the AI-performance link in an emerging market characterized by technology sanctions and data infrastructure constraints, using advanced causal inference techniques (PSM, IV).

Practical Implications: Results guide firms in prioritizing AI investments and inform policymakers on technology disclosure requirements and workforce development strategies.