Artificial Intelligence Reshapes Asset Management
Executive Summary
Artificial Intelligence (AI) has become increasingly relevant over the last decade as computing power has increased alongside the development of AI methods such as deep learning. In industry, AI is discussed as part of a new frontier of automation and operational efficiency, but AI integration within industrial processes is still nascent. Existing applications of AI within asset management are varied, providing value across asset heavy industries. This study provides an overview of the sub-categories of AI applied within asset management, as well as a discussion of specific use cases and the benefits AI brings to asset management processes. This report is aimed at chief strategy officers, VPs of products and heads of marketing at firms who offer AI products for asset management, as well as VPs of operations, reliability engineering managers and heads of asset management to provide insight for purchasing decisions.
Table of contents
Business Case For AI Applications Within Asset Management EmergesMachine Learning Underlies AI Applied Within Asset Management
Industrial Firms Can Leverage AI For Numerous Applications Within Asset Management
AI Optimizes Approaches To Asset Management
Table of figures
Figure 1. Three Sub-Categories of Artificial Intelligence Applicable For Asset ManagementOrganisations mentioned
Alcoa, AVEVA, Bentley Systems, Boston Dynamics, BP, C3 AI, Cognite, Copperleaf, DNV , Enel, Énergie NB Power, Falkonry, GE Digital, Guardhat, Hamburger Hafen und Logistik, IBM, Industrial Scientific, Inform, Inspection², Librestream, Mechanica AI, Microsoft, National Grid, OMV, OverIT, Rail Cargo Group, Realwear, Senseye, Sierra Wireless, Sitch AI, Symphony Industrial AI, Telit, United Nations Conference on Trade and Development and VinsaAbout the authors
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