The investment management industry is at an evolutionary crossroads in adopting artificial intelligence (AI). AI agents are increasingly getting used within the each day workflows of portfolio managers, analysts and compliance officers, but most firms cannot accurately describe the form of “intelligence” they’ve deployed.
Agentic AI (or AI agent) takes large language models (LLMs) many steps further than widely used models like ChatGPT. It’s not nearly asking an issue and getting a solution. Agentic AI can observe, analyze, make decisions and sometimes act on an individual’s behalf inside defined limits. Investment firms have to make your mind up: is it a choice support tool, an autonomous research analyst or a delegated trader?
Every AI introduction and implementation offers the chance to set boundaries and differentiate the tools. If you possibly can’t classify your AI, you possibly can’t control it, let alone scale it. To this end, our research team, a collaboration between DePaul University and Panthera Solutions, developed a multi-dimensional classification system for AI agents in investment management. This article is an excerpt from a scientific paper recently submitted to a scientific journal.
This system provides practitioners, boards, and regulators with a standard language for evaluating agent systems based on autonomy, function, learning, and governance. Investment leaders gain an understanding of the steps needed to design an AI taxonomy and create a framework for mapping the AI agents deployed of their firms.
Without a standard taxonomy, we risk over-trusting and under-utilizing a technology that’s already changing the way in which capital is allocated, which may result in further complications down the road.
