This chapter explores the transformative role of artificial intelligence (AI) in investment decision-making, focusing on market sentiment analysis, strategy development and trading automation. Through a series of experiments using both proprietary and open source platforms, the study demonstrates how AI tools, ranging from large language models to custom-built sentiment analyzers, can improve portfolio construction and short-term trading performance. Key findings reveal that, while AI-generated strategies offer improved risk-return profiles, their effectiveness is closely tied to data quality, model interpretability, and the integration of human oversight. Notably, short-selling strategies and DeepSeek-based trading models produced promising results; however, technical limitations such as incomplete API integration and constrained cloud infrastructure posed challenges to achieving full automation. The chapter highlights the practical trade-offs between custom and commercial AI solutions, and suggests ways to improve model robustness, regulatory compliance, and strategic alignment with investor objectives in future research. To sum up, the findings emphasize the increasing potential of AI as a decision support and execution tool in modern investment practice.