By digitizing 94 years of internal research, Capital Group uses AI to analyze an individual investor's own historical decisions. It identifies past mistakes made in similar market conditions, providing personalized insights to prevent repeating errors and mitigate behavioral biases.

AI's primary value in pre-buy research isn't just accelerating diligence on promising ideas. It's about rapidly surfacing deal-breakers—like misaligned management incentives or existential risks—allowing analysts to discard flawed theses much earlier in the process and focus their deep research time more effectively.

WCM avoids generic AI use cases. Instead, they've built a "research partner" AI model specifically tuned to codify and diagnose their core concepts of "moat trajectory" and "culture." This allows them to amplify their unique edge by systematically flagging changes across a vast universe of data, rather than just automating simple tasks.

AI is becoming a personal C-suite tool. Vasant Narasimhan uses an AI agent trained on Novartis's historical R&D decisions. This allows him to query past contexts and biases when facing a new decision, leading to more informed, data-driven leadership rather than relying solely on memory.

Founders are consistently and universally wrong about their financial projections, particularly cash runway. AI tools can provide an objective, data-driven forecast based on trailing growth, correcting for inherent founder optimism and preventing critical miscalculations.

Venture firms are building their own small language models trained on internal meeting notes and application data. This allows them to retroactively analyze deals they passed on to refine their investment thesis and identify companies for potential late-stage investments.

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.

M&A leaders can feed diligence findings and past deal notes into an enterprise AI tool to quickly generate risk logs and identify key focus areas. This saves significant time that can be reinvested into crucial, high-touch stakeholder alignment and communication.

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.