Artificial Intelligence (AI) is increasingly being leveraged across the asset and portfolio management value chain, spanning return forecasting, portfolio construction, risk monitoring, compliance and investor communication. This article reviews the major AI techniques used in this domain — machine learning, deep learning, natural language processing, reinforcement learning, generative AI and explainable AI — and their applications in stock selection, portfolio optimization, robo-advisory and risk management. Beyond a technical review, the study empirically examines whether investors are actually willing to adopt AI-supported portfolio management services, using a structured survey of 120 respondents. Consistent with the Technology Acceptance Model, the results show that perceived usefulness has a significant positive effect on investors’ willingness to adopt AI-supported portfolio management, while perceived risk has a significant negative effect. The article concludes that the future of AI in asset and portfolio management depends less on further technical sophistication and more on transparency, explainability and continued human oversight, and offers suggestions for asset managers, technology providers and regulators.