Objective This study systematically reviews the integration of artificial intelligence (AI) with investment funds in the asset management industry and emphasizes its transformative impact. Aiming to bridge knowledge gaps, this study explores AI's position within the industry, analyzes its variety, and assesses the transformational implications of existing practices. Methodology Adhering to systematic review methodology, a comprehensive search was conducted across the Web of Science and Scopus databases, identifying 27 high-quality studies published from 2020 to 2024. The study was then analyzed thematically. Findings The first theme of the review classifies AI applications into front-end and back-end roles, illustrating the transition from traditional processes. On the front-end, AI assists simple activities by analyzing an investor's profile to determine a suitable fund, similar to a human financial consultant. The back-end sees AI performing autonomous trading and managing pooled fund investments, resembling a human fund manager. As a secondary theme, this review analyse AI deployment include using robo-advisors and chatbots for front-end tasks, screening analysis, predictive analytics, automated algorithmic trading, and automated trading technical analysis for back-end tasks. This study also includes a deductive discussion on the implications and transofrmation of AI deployment. Contribution/implications This review serves as an analytical framework to highlight AI in investment funds in asset management and serves as a springboard for future research and industrial applications.