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.

Venture capitalists will leverage AI by meticulously documenting their reasoning for each investment decision in "pre-mortems." This structured data will train personalized models to identify biases, surface blind spots, and cross-examine future decisions against their own track record.

A powerful, practical use of AI in investment research is to verify management's track record. By feeding all historical earnings call transcripts into a large language model, an analyst can quickly ask whether management's past promises and guidance materialized, automating a crucial but time-consuming due diligence step.

The discipline of writing down your thought process is crucial for decision analysis. AI now amplifies this by creating a searchable, analyzable record of your thinking over time, helping you identify blind spots and get objective feedback on your reasoning.

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.

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.

Private equity firms are adopting AI to quickly screen initial investment memorandums (CIMs) for red flags. This automation handles low-level review tasks, freeing up investment professionals to focus on higher-value activities like building relationships with executives and industry experts.

Instead of solely focusing on AI fallibility, a major application is using AI agents to audit human work. Perplexity's "Final Pass" feature analyzes documents for factual errors and internal inconsistencies, finding glaring mistakes in things like Gartner's earnings press releases and work done by professional accountants.