The landscape of the investment management industry is undergoing a significant transformation as it embraces the power of artificial intelligence (AI). While AI tools are being integrated into the daily workflows of portfolio managers and compliance officers, many firms struggle to define the specific type of intelligence they are utilizing. This lack of clarity hampers their ability to govern and scale these technologies effectively.
At the core of this technological evolution is agentic AI, which extends beyond traditional models like ChatGPT.
Unlike simpler applications that merely provide responses to queries, agentic AI can observe, analyze, decide, and even act on behalf of humans within set parameters. Investment firms must determine whether these AI systems serve as decision-support tools, autonomous research analysts, or delegated traders.
Table of Contents:
Establishing a clear AI taxonomy
To harness the full potential of AI, firms must develop a clear taxonomy that categorizes these systems. Without a well-defined classification, it becomes challenging to manage and govern AI implementations. A research initiative led by experts from DePaul University and Panthera Solutions has produced a comprehensive classification system designed specifically for AI agents in the investment sector. This framework aids practitioners, boards, and regulators in evaluating agentic systems based on various dimensions such as autonomy, functionality, learning capacity, and governance.
Investment leaders can utilize this taxonomy to understand the necessary steps for designing AI systems that align with their organizational goals. By categorizing AI effectively, firms can avoid the pitfalls of over-relying on technology or underutilizing its capabilities, thereby paving the way for a more strategic approach to AI adoption.
The dual approach to AI adoption
In the current landscape, investment managers typically adopt AI in one of two ways: as a collection of functional tools or as an integral component of the investment decision-making process. The functional approach involves employing AI for specific tasks such as risk scoring, sentiment analysis through natural language processing, and summarizing portfolio exposures. While this method enhances efficiency, it fails to fundamentally alter the decision-making framework, keeping the organization primarily human-centric.
Conversely, a smaller yet growing segment of firms is embracing a systemic approach, wherein AI agents are woven into the investment design process as adaptive participants. This integration allows for explicit definitions of autonomy, learning capabilities, and governance structures, transforming the firm into a dynamic decision ecosystem that promotes collaboration between human judgment and machine reasoning.
Creating a balanced decision-making ecosystem
As noted by neuroscientist Antonio Damasio, all forms of intelligence strive for homeostasis—a balance with their environment. Financial markets, being complex adaptive systems, necessitate equilibrium between various elements such as data analysis and human judgment, automation and accountability, as well as profitability and sustainability. An effective AI framework should mirror this ecological balance by categorizing AI agents across three key dimensions.
Understanding the investment process
The first dimension involves examining the investment process. It is essential to identify where within the value chain an AI agent operates. Typically, the investment process encompasses five key stages: idea generation, assessment, decision-making, execution, and monitoring. AI agents can enhance each of these stages, but the delegation of decision rights should correspond with the level of interpretability associated with the AI’s outputs.
Mapping AI agents to these stages clarifies accountability and minimizes governance gaps. Additionally, the classification can extend to compliance and stakeholder reporting, enabling AI systems to recognize patterns and flag breaches while translating complex performance metrics into accessible narratives for clients and regulatory bodies.
Evaluating competitive advantages
The second dimension focuses on the comparative advantage that AI agents provide. It is crucial to assess whether an AI system enhances informational, analytical, or behavioral advantages. While AI does not inherently generate alpha, it can amplify existing strengths. For instance, a quantitative investment firm might apply reinforcement learning for deeper analytical insights, while a discretionary firm could utilize AI co-pilots to maintain reasoning integrity and behavioral discipline.
At the core of this technological evolution is agentic AI, which extends beyond traditional models like ChatGPT. Unlike simpler applications that merely provide responses to queries, agentic AI can observe, analyze, decide, and even act on behalf of humans within set parameters. Investment firms must determine whether these AI systems serve as decision-support tools, autonomous research analysts, or delegated traders.0
Designing for decision quality
At the core of this technological evolution is agentic AI, which extends beyond traditional models like ChatGPT. Unlike simpler applications that merely provide responses to queries, agentic AI can observe, analyze, decide, and even act on behalf of humans within set parameters. Investment firms must determine whether these AI systems serve as decision-support tools, autonomous research analysts, or delegated traders.1
At the core of this technological evolution is agentic AI, which extends beyond traditional models like ChatGPT. Unlike simpler applications that merely provide responses to queries, agentic AI can observe, analyze, decide, and even act on behalf of humans within set parameters. Investment firms must determine whether these AI systems serve as decision-support tools, autonomous research analysts, or delegated traders.2
