AI isn't necessarily leading PE funds to do more deals. Instead, it compresses the initial, time-consuming phase of diligence from weeks to a single day, allowing teams to reallocate their energy toward deeper debate on core value creation drivers.

By programming one AI agent with a skeptical persona to question strategy and check details, the overall quality and rigor of the entire multi-agent system increases, mirroring the effect of a critical thinker in a human team.

Venture capital firms are leveraging AI tools like Google's NotebookLM to process deal flow. They ingest investment memos and legal documents to analyze them against their investment thesis and even simulate a preliminary legal review.

To improve the quality and accuracy of an AI agent's output, spawn multiple sub-agents with competing or adversarial roles. For example, a code review agent finds bugs, while several "auditor" agents check for false positives, resulting in a more reliable final analysis.

Advanced AI tools can model an organization's internal investment beliefs and processes. This allows investment committees to use the AI to "red team" proposals by prompting it to generate a memo with a negative stance or to re-evaluate a deal based on a new assumption, like a net-zero mandate.

Passively reading consultant decks is insufficient for grasping AI's potential. True understanding comes from active experimentation. Firms and their portfolio companies should "get their hands dirty" by building their own AI agents and co-pilots to discover the art of the possible and apply it directly to their own operations.

The future of AI in finance is not just about suggesting trades, but creating interacting systems of specialized agents. For instance, multiple AI "analyst" agents could research a stock, while separate "risk-taking" agents would interact with them to formulate and execute a cohesive trading strategy.

Grok 4.20 uses "swarm intelligence," where multiple specialized AI agents collaborate and discuss problems before providing a solution. This approach, mirroring academic concepts, is now being commercialized to tackle more complex tasks than single models can handle.

By creating AI agents with distinct roles (CEO, CFO, Sales), individuals can simulate an executive team meeting. These agents argue from their perspectives, stress-test ideas, and collaboratively develop a robust business strategy that a single person might miss. This moves beyond simple content generation to complex strategic planning.