The purpose of this study was to identify, analyze, and validate the patterns of cognitive biases among managers and investors in the Iranian capital market and to examine the role of artificial intelligence in reducing and managing these biases. This study was developmental in purpose and adopted an exploratory mixed-methods design with a dominant qualitative approach. Data were collected through in-depth semi-structured interviews with capital market experts, including managers, analysts, and investors with more than five years of professional experience. Through qualitative content analysis, 456 initial codes were extracted from the interviews and systematically categorized into 35 major components using thematic analysis. To validate the identified components, a two-round Delphi technique was employed involving 12 capital market experts. Consensus Ratio (CR) values were also calculated to determine the level of agreement among participants and identify the final components suitable for the conceptual model of the study. The findings revealed that fundamental cognitive biases obtained the highest mean score (4.8), followed by cognitive bias reduction strategies (4.7), investment and financial biases (4.6), and informational and analytical biases (4.5). Applications of artificial intelligence in reducing biases achieved a mean score of 4.4, while advanced AI mechanisms scored 4.1. In the second Delphi round, 10 final components were confirmed with consensus ratios ranging from 0.75 to 1. The results demonstrated that artificial intelligence significantly contributes to reducing cognitive biases through large-scale data analysis, behavioral pattern recognition, scenario simulation, generation of alternative perspectives, and intelligent warning systems, thereby improving investment decision quality and analytical accuracy The study concluded that cognitive biases substantially influence managerial and investment decision-making in the Iranian capital market and may lead to reduced analytical quality, irrational risk-taking, and emotionally driven behaviors. Artificial intelligence, through data-driven analysis, hidden pattern detection, and enhancement of human judgment, can serve as an effective tool for managing and mitigating these biases. Furthermore, integrating human intelligence with artificial intelligence can facilitate the development of smarter decision-making systems, improve investment efficiency, and strengthen the stability of financial markets.