Artificial intelligence (AI) and machine learning (ML) are redefining how investment professionals interpret data, construct portfolios, and manage risk. AI in Asset Management: Tools, Applications, and Frontiers was published by CFA Institute Research Foundation and CFA Institute Research and Policy Center. Edited by Joseph Simonian, PhD, the book explores how these technologies are transforming the practice of investing. This new volume builds on the Handbook of Artificial Intelligence and Big Data Applications in Finance (2023), expanding it with timely insights from leading practitioners who are deploying AI in real-world investment contexts. This research arrives at a decisive moment. AI adoption is accelerating across finance, yet its full potential and limitations are still being tested. Investors face both immense opportunity and new complexity: vast data flows, opaque algorithms, and regulatory scrutiny. This volume offers clear, practical frameworks to help professionals navigate that landscape, moving beyond the hype to understand what AI can realistically deliver for asset managers today. Chapters Unsupervised Learning I: Overview of Techniques, by Joseph Simonian, PhD - Unsupervised Learning II: Network Theory, by Gueorgui S. Konstantinov, PhD, and Agathe Sadeghi, PhD - Support Vector Machines, by Maxim Golts, PhD - Ensemble Learning in Investment: An Overview, by Alireza Yazdani, PhD - Deep Learning, by Paul Bilokon, PhD, and Joseph Simonian, PhD - Reinforcement Learning and Inverse Reinforcement Learning: A Practitioner’s Guide for Investment Management, by Igor Halperin, PhD, Petter N. Kolm, PhD, and Gordon Ritter, PhD - Natural Language Processing, Francesco A. Fabozzi, PhD - Machine Learning in Commodity Futures: Bridging Data, Theory, and Return Predictability, by Tony Guida - Quantum Computing for Finance, by Oswaldo Zapata, PhD - Ethical AI in Finance, by Anna Martirosyan The book captures a defining shift in the investment industry: from theoretical exploration of AI toward measurable, practitioner-led implementation. Each chapter demonstrates what AI in asset management is, focusing on how machine learning and data-driven modeling are reshaping specific investment functions — from alpha generation and risk management to portfolio construction, trading, and client reporting. Contributors combine academic rigor with on-the-ground experience, giving readers a toolkit of methods they can adapt directly to their own workflows. Ultimately, this volume serves as both a technical companion and a source of professional inspiration. It encourages investment practitioners to view AI not as a black box but as a set of evolving tools for inquiry and insight. By learning to interpret these tools critically and apply them judiciously, readers can expand their analytical capabilities while staying grounded in the principles of sound investment practice.