Questions & Answers

The introduction of AI and Data into a fund doesn't create an abrupt disruption of business lines, but does give rise to new roles around data structuring, governance and operational use.
The first key role is that of business Data Owner. He or she is responsible for a critical perimeter of data, such as shareholdings, investors or the pipeline. He or she defines indicators, management rules, expected formats and quality standards. Without this role, data remains diffuse and difficult to exploit.
The second role is that of Data / AI Lead. He/she steers the fund's Data and AI roadmap, prioritizes use cases, arbitrates tool choices and ensures overall consistency. He acts as a point of convergence between the investment teams, Middle Office, IR and support functions.
A third role is emerging around the Operational Data Manager. Located at the heart of operations, often in the Middle Office, he or she ensures that data flows are correctly collected, controlled, consolidated and disseminated. He or she is responsible for the operational quality and fluidity of the data chain.
With AI, a more specific role of Business AI Champion is also emerging. This profile is not necessarily technical, but has a thorough command of the tools and their uses. He or she supports teams in their adoption, identifies relevant use cases and formalizes best practices, particularly with regard to the supervision and limits of AI agents.
Finally, a cross-functional role for Data and AI Governance is becoming essential. This covers traceability, security, compliance and control. In an LP and regulatory context, the ability to explain a piece of data or a decision becomes as important as producing it.
It's not necessary to set up a dedicated team right away. In most funds, these roles emerge gradually from existing teams. The challenge is to identify responsibilities, clarify scopes of work and structure targeted skills development.
The key point is to ensure that they are rooted in the business. These roles must not be isolated in a purely technical logic, but integrated into the heart of the investment and management processes. It is this proximity that enables data to be transformed into a real performance lever.

Hallucinations are the main risk when using AI agents in a financial context. They are not the result of random errors, but of the structural behavior of models in the absence of reliable or sufficiently constrained information.
Three levels of control can greatly reduce this risk.
The first is strict anchoring on a certified source of truth. The agent must not rely on general knowledge or implicit data, but only on a controlled internal repository. Each answer must be associated with an identifiable, accessible and verifiable source. An unsourced answer must be considered invalid by default.
The second level is the restriction of the functional and informational perimeter. An agent must intervene in a precise domain, with a limited and controlled set of data. The wider the scope, the greater the risk of approximate interpretation. In practice, an agent who specializes in a subset of financial data, such as a shareholding's KPIs or Middle Office flows, is significantly more reliable than a generalist agent.
The third level is the implementation of systematic controls on outputs. All numerical information intended for external use, in particular for investors, must be validated by a human being. This validation must be based on complete traceability: initial request, sources used, transformations applied and response generated.
Beyond these principles, technical architecture plays a decisive role. A structured approach consists in isolating the AI layer from raw data, using a centralized repository, then exposing only validated data to agents. This allows precise control over what the agent can see and use.
Finally, it's important to treat hallucinations as a governance issue, not just a technical problem. This means defining rules of use, levels of responsibility and supervisory mechanisms adapted to the financial stakes involved.
A reliable agent is not one that "responds well", but one whose every response can be explained, traced and verified.

AI can significantly improve the preparation of an investment committee, provided that it does not degrade what makes a quality decision: clarity of reasoning, prioritization of information and solidity of conviction.
The main risk is well identified: AI increases the amount of information available: more data, more scenarios, more signals, without guaranteeing a better decision. This abundance can even create cognitive overload and dilute the truly structuring points. It is therefore essential not to confuse information wealth with quality of judgment.
The right approach is to organize AI around the decision-making process, and not the other way around. This means identifying upstream the key questions that will be debated in committee, then producing targeted summaries, calibrated to shed light on these questions without seeking exhaustiveness. The aim is to reduce the noise to enhance the signal.
AI is particularly useful for preparing these supports: structuring an investment memo, synthesizing a data room, reconciling different sources of information, or reformulating analyses to improve readability. But it should not take the place of the teams. The responsibility for analysis and recommendation remains with the individual.
A key point is traceability. Every figure or assertion used in committee must be traceable to an identifiable source. AI can help structure this traceability, but it must be based on a reliable, governed data chain.
In meetings, its role is more tactical: quickly retrieving specific information, verifying a point, exploring an alternative scenario on request. Used in this way, it becomes a support tool, without interfering with the decision-making process.
Finally, the right performance indicator is not the preparation time saved, but the quality of the decisions made. Well-used AI must improve understanding of the issues at stake, the robustness of exchanges and the ability to reach decisions, not simply speed up document production.