Journal of Management and Business Solutions 3092-7226 In Press 2027 07 01 Artificial Intelligence–Driven Demand Forecasting and Inventory Performance in Omnichannel Supply Chains: The Contingent Roles of Data Quality, Demand Volatility, and Organizational Analytics Maturity Artificial Intelligence–Driven Demand Forecasting and Inventory Performance in Omnichannel Supply Chains: The Contingent Roles of Data Quality, Demand Volatility, and Organizational Analytics Maturity 1 23 EN Journal Article 2026 04 02 <p>This study examined the effect of artificial intelligence–driven demand forecasting on inventory performance in omnichannel supply chains and tested the moderating roles of data quality, demand volatility, and organizational analytics maturity. A quantitative cross-sectional study was conducted among 312 managers, supervisors, analysts, and specialists employed in omnichannel organizations in Tehran, Iran. Data were collected using a structured questionnaire measuring artificial intelligence–driven demand forecasting, inventory performance, data quality, demand volatility, and organizational analytics maturity. The measurement model and structural relationships were analyzed using partial least squares structural equation modeling with 5,000 bootstrap resamples. Reliability, convergent validity, discriminant validity, multicollinearity, explanatory power, predictive relevance, effect sizes, and interaction effects were assessed. Artificial intelligence–driven demand forecasting had a significant positive effect on inventory performance (β = 0.421, p &lt; 0.001). Data quality (β = 0.263, p &lt; 0.001) and organizational analytics maturity (β = 0.196, p &lt; 0.001) positively predicted inventory performance, whereas demand volatility had a significant negative effect (β = −0.148, p &lt; 0.001). Data quality strengthened the forecasting–performance relationship (β = 0.163, p &lt; 0.001), while demand volatility weakened it (β = −0.121, p = 0.001). Organizational analytics maturity also amplified the positive effect of artificial intelligence forecasting (β = 0.142, p = 0.001). The final model explained 61.2% of the variance in inventory performance and demonstrated acceptable predictive relevance and fit. Artificial intelligence–driven demand forecasting improves inventory performance in omnichannel supply chains, but its benefits are greatest when organizations possess high-quality integrated data and mature analytics capabilities and are reduced under highly volatile demand conditions.</p> https://www.journalmbs.com/index.php/jmbs/article/download/388/308