In the first episode of this series on artificial intelligence in venture capital, the question was that of adoption. Why should funds be interested in AI? What risks do they take in remaining spectators? Who should manage the subject internally?
In the second episode, we explored the uses. Tools are multiplying, agents are specializing, workflows are becoming automated. From sector monitoring to the preparation of investment memos, including portfolio monitoring or reporting to investors, artificial intelligence is starting to be integrated into the daily operations of investment teams.
The debate is now moving towards a more sensitive subject: that of governance.
Because after the demonstrations of productivity and the promises of automation, a fundamental question arises. What happens when funds start to connect their databases, their CRMs, their document spaces, their portfolio management tools or their exchanges with entrepreneurs to external artificial intelligence systems?
For Adrien Chaltiel, Founder of Board Project, who has just launched a toolbox for entrepreneurs with Join Ultimate, the real issue is no longer technological, but organizational, legal and strategic. Artificial intelligence is forcing management companies to rethink subjects that until now seemed relatively under control: data confidentiality, protection of business secrets, traceability of decisions, regulatory compliance and even liability in the event of an error.
The subject is all the more important as funds manipulate some of the most sensitive data in the digital economy. Valuations, financial performance, due diligence, growth strategies, negotiations with LPs, acquisition or refinancing projects: this information constitutes the heart of their competitive advantage. As AI becomes an increasingly prevalent operational layer in investment processes, the question is no longer just what it enables, but what it exposes.
This development comes as the European AI Act gradually comes into application and the first documentation, traceability and governance obligations begin to concern a growing number of organizations. For funds, the challenge is no longer simply about adopting the right tools. It consists of building a framework allowing them to benefit from the productivity gains of AI without losing control of the data, decisions and responsibilities that underlie their profession.
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