Artificial Intelligence Applications in Asset Management: Structure, Strategy, and Governance

This study examines artificial intelligence applications in asset management through analysis of 178 peer-reviewed studies and industry implementations. We identify six core domains: AI-driven portfolio management, financial forecasting, robo-advisory services, risk management, strategic decision-making, and adoption enablers. Research demonstrates that AI enhances operational efficiency while reconfiguring strategic practices within financial institutions. Key findings reveal successful AI implementation requires hybrid approaches combining machine intelligence with human expertise, particularly in portfolio management where pure algorithmic signals can increase trading costs. However, challenges including algorithmic opacity, ethical concerns, and talent shortages hinder widespread adoption. This framework provides guidance for practitioners, regulators, and researchers in leveraging AI for modern investment management.