Artificial intelligence’s impact on asset management  

Artificial intelligence (AI) is changing the financial services landscape as we know it. Boards and senior executives understand that technology is a crucial component of the DNA of their organizations to remain successful in the future. Generative AI (Gen AI) is an AI capable of creating new content and what most consumers think of when they hear about AI. This can be very valuable within the asset management industry. Gen AI has capabilities that can be leveraged as a tool to differentiate asset managers within the industry. Some important factors to consider when considering AI readiness are identifying goals for AI, considering current use cases in alignment with business goals and strategies, identifying stakeholders, existing processes, the current IT environment, governance and adoption.  

When considering use cases for AI, specifically Gen AI within asset management, participants focus on enhancing productivity, harnessing automated data to create enhanced decision-making and creating personalized products for customers. Specifically, AI can be leveraged to generate tailored reports and train AI models to generate marketing content or thought leadership tailored towards industries or segments. It can also auto-generate responses to proposals and due diligence. AI can significantly reduce the time required for due diligence processes, benefiting both investor and vendor evaluations. Both involve providing information to third parties that historically has been very manual and time-consuming. If AI could process requests, it would streamline and provide standard responses.  AI can also help predict when clients may be likely to churn or make significant assets under management (AUM) movements and simulate macroeconomic scenarios. Participants may also consider enhanced productivity through simplified tasks such as an automated summary generation from market events relevant to holdings or asset classes, or an automated extraction of insights from analyst reports, regulatory filings and compliance reports to provide enhanced data compilations to inform decisions. AI can also benefit a company by standardizing recordkeeping, accounting and operational tasks that will be executed in the same manner each time.  

Advanced use cases for AI include the utilization of bots or AI agents, which are autonomous software entities that can perceive their environment, perform tasks and make decisions on behalf of a user or another system. AI agents learn from interactions and adapt their behaviors based on changing conditions. At its core, agentic AI relies on large language models (LLMs) and machine learning algorithms trained on vast amounts of data with data pipelines continuously feeding the AI system with new data to ensure the agents remain updated with the latest information. To facilitate real-time interactions, agentic AI systems utilize techniques that enable the agents to understand and generate human-like responses while improving the agents’ performance over time by learning from past interactions and feedback. Understanding and responding to natural language inputs makes agentic AI agents highly versatile and capable of adapting to various scenarios. Specifically, in asset management, considerations include using AI agents to manage personalized portfolios based on client preferences and investment objectives, flagging potential investment opportunities based on specified criteria of a specific investment strategy, or autonomous data mining based on specific market considerations.