Questions & Answers

AI can concretely improve relations with investors and IR teams, provided it is used as a lever for reliability and consistency, and not as a tool for automated message production.
IR teams face a growing requirement to respond faster, provide accurate, consistent and contextualized information, while adapting to very different investor profiles. In this context, AI can play a structuring role.
In concrete terms, it can be used to prepare summaries from internal reports, to reformulate content according to the recipient's level of expertise, to quickly find information in the history of exchanges or in a data room, and to improve the overall consistency of documents sent. It can also assist in the production of standardized responses (FAQs, standard emails), while maintaining a high level of editorial quality.
But the real contribution of AI does not lie in speed of execution. It lies in the ability to align messages. High-quality IR communication relies on single, reliable data shared between teams. If AI is plugged into fragmented or poorly governed sources, it amplifies inconsistencies instead of correcting them.
The challenge is therefore to anchor AI in a controlled data chain: same figures between BI, reporting and investor communications, traceability of sources, and systematic editorial control before sending. In this context, AI becomes a powerful support tool for structuring, harmonizing and securing communication.
The right balance consists in using AI to prepare and make content reliable, while leaving IR teams responsible for tone, context and relationship. It is this combination that improves both operational efficiency and investor confidence.

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.

Yes, automating investment reporting is not only possible, it's also one of the most immediate ways of improving a fund's operations.
In the majority of organizations, the process is still based on manual data collection, with heterogeneous files transmitted by the investments, and consolidations carried out in Excel. This model introduces a number of weaknesses: dependence on non-standardized formats, risk of errors during reprocessing, lack of traceability and long production lead times.
Effective automation depends on structuring the data chain upstream.
The first step is to standardize inputs. This involves defining a common data dictionary with all participants, including clearly defined indicators, expected formats, explicit calculation rules and a reporting schedule. Without this standardization, all automation remains partial.
The second step is to organize data collection. This can involve dedicated portals, structured templates or connectors. The aim is to reduce format variations and limit manual intervention.
Third step: industrialize controls. Automatic rules are used to detect inconsistencies, variations, breaks in series or anomalies between related indicators. These controls must be systematic and traceable.
Fourth step: centralize in a single source of truth. Consolidated data must be fed directly into reporting, BI and investor communication tools, to avoid any duplication or local reprocessing.
In this context, automation helps to secure production, reduce lead times and significantly increase the reliability of deliverables.
The role of teams is changing. They move from a production logic to a control and analysis logic. The challenge is no longer to consolidate, but to interpret data, identify weak signals and prepare decisions.
Finally, governance remains the critical point. Automation without clear rules on data quality, responsibilities and validation processes can degrade overall reliability. Automation must be part of a rigorous framework, focused on control, traceability and consistency.