The landscape of investment management is experiencing a significant transformation as firms increasingly adopt artificial intelligence (AI) technologies. While AI has become integral to the daily operations of portfolio managers, analysts, and compliance teams, many organizations struggle to articulate the nature of the AI systems they employ. Understanding the fundamental characteristics of these systems is crucial for effective governance and scaling.
One of the most advanced forms of AI is agentic AI, which transcends basic models like ChatGPT. Unlike conventional systems that merely respond to queries, agentic AI can observe, analyze, make decisions, and even act autonomously within set parameters. Investment firms must determine the role of this technology within their operations—whether it serves as a decision-support tool, an independent research analyst, or an automated trader.
The importance of AI classification
Implementing AI in investment management presents a unique opportunity to define boundaries and categorize tools effectively. Without a clear classification system, firms face challenges in governance and scaling AI systems. A research collaboration between DePaul University and Panthera Solutions has yielded a multi-dimensional classification framework for AI agents tailored for the investment sector.
This classification system serves as a vital resource for practitioners, boards, and regulatory bodies, offering a standardized approach to evaluate agentic systems based on their autonomy, functionality, learning capabilities, and governance structures. By establishing a common language, investment leaders can better navigate the complexities of AI deployment and develop strategies aligned with their organizational objectives.
Balancing innovation and oversight
There is a delicate balance between fostering innovation and ensuring proper oversight in AI adoption. A well-structured AI taxonomy should not stifle creativity; rather, it should empower firms to articulate the problems their AI agents address, assign accountability, and mitigate risks associated with model usage. Such clarity can elevate AI adoption from a tactical exercise to a strategic initiative.
Two approaches to AI integration
Investment management firms generally approach AI in one of two ways: treating it as a functional toolset or as an integral component of the investment decision-making process. The functional approach focuses on using AI for specific tasks, such as risk assessment, sentiment analysis through natural language processing, or portfolio summary tools. While these applications enhance efficiency, they do not fundamentally alter the decision-making framework, which remains centered on human judgment.
Conversely, a smaller yet growing number of firms are embracing a systemic integration of AI, viewing it as a participatory element in the investment design process. In this model, AI agents are endowed with specific autonomy, learning capabilities, and governance structures, effectively creating a decision ecosystem where human insight and machine intelligence coexist and evolve together.
Creating sustainable competitive advantages
This distinction between functional and systemic AI adoption is critical. While function-driven adoption may yield faster tools, systemic adoption fosters smarter organizations capable of adapting to market changes. Neuroscientist Antonio Damasio emphasized that all forms of intelligence seek homeostasis, striving for balance within their environment. Similarly, financial markets are complex adaptive systems that require equilibrium between data-driven insights and human judgment.
A robust AI framework in investment management should map agents across three core dimensions: the investment process, comparative advantage, and complexity range. Understanding where an AI agent fits within the investment value chain—whether in idea generation, execution, or compliance—provides clarity on accountability and governance.
Regulatory considerations and future implications
Forthcoming regulations, such as the EU AI Act and the OECD Framework, aim to codify principles of explainability and accountability for AI systems. A taxonomy that aligns these regulatory requirements with practical governance mechanisms will be essential for firms seeking to navigate the evolving landscape while ensuring compliance.
One of the most advanced forms of AI is agentic AI, which transcends basic models like ChatGPT. Unlike conventional systems that merely respond to queries, agentic AI can observe, analyze, make decisions, and even act autonomously within set parameters. Investment firms must determine the role of this technology within their operations—whether it serves as a decision-support tool, an independent research analyst, or an automated trader.0