<![CDATA[Background. The increasing integration of artificial intelligence (AI) in finance is reshaping investment decision-making, as AI provides tools for analyzing large datasets, forecasting trends, and automating trading processes. This shift toward AI-driven insights aims to enhance decision accuracy and reduce human error, ultimately transforming traditional investment practices. Purpose. This study investigates the impact of AI on investment decision-making, focusing on how AI algorithms influence investor behavior, market forecasting, and risk management. The objective is to assess whether AI-driven models improve decision quality and identify any limitations in their application. Method. A mixed-method research approach was employed, combining quantitative analysis of AI model performance with qualitative insights from industry professionals. Machine learning algorithms were used to analyze historical investment data and predict market trends, while interviews with investment managers provided perspectives on the practical benefits and challenges of AI in financial decision-making. Results. Results indicate that AI algorithms can improve predictive accuracy by up to 90%, with reduced response times in volatile markets. However, reliance on AI models also introduces risks, including over-reliance on algorithmic predictions and potential biases in data. Conclusion. The study concludes that while AI significantly enhances investment decision-making through improved forecasting and efficiency, its limitations necessitate careful oversight. Implementing AI in investment requires a balanced approach, combining human expertise with algorithmic insights to optimize decision outcomes. The findings underscore the potential for AI to support investment strategies while highlighting the need for ethical and transparent AI applications.]]>
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