In recent years, the application of artificial intelligence in financial analysis has attracted significant attention as a key tool for improving forecasting accuracy and detecting financial fraud. This study examines the impact of artificial intelligence on financial analysis and identifies the challenges and barriers associated with its adoption in organizations. Using both qualitative and quantitative methods, data were collected from 22 experts in the field of artificial intelligence and 300 respondents in the financial sector. The results indicate that artificial intelligence can significantly enhance price forecasting accuracy and improve the rate of financial fraud detection. However, challenges such as the lack of high-quality data, distrust in new systems, and ethical concerns related to privacy hinder the widespread adoption of this technology. Furthermore, the findings show that machine learning algorithms—particularly neural networks—have substantial potential for predicting financial outcomes. Based on these findings, recommendations are proposed to improve the adoption of artificial intelligence and manage existing challenges. This research serves as evidence for organizations to adopt artificial intelligence as an effective tool in financial processes, with particular emphasis on the importance of addressing ethical challenges. Ultimately, this study can be used as a practical guide for financial managers and decision-makers to optimize the use of artificial intelligence in financial analysis and enhance organizational processes.