AI can 5-10x the speed and quality of your investment process if you know where to use it.
Over the past 6 months, I have had conversations with some of the top hedge fund analysts working across the most prestigious hedge funds on Wall Street to better understand how they use AI in their diligence process
My sample set spanned different strategies (long/short vs macro), sizes (large cap vs small cap), exposures (market neutral vs net long), and types (single manager vs multi manager).
Here are the most common answers of how analysts are already using AI today and where pain points still exist.
## AI as a Screening Tool
AI platforms can be a fantastic funnel that turns human intuition and pattern recognition into actionable ideas. As an analyst, a lot of your job revolves around forming a view of what the world looks like in a year.
This part of the job is still inherently human. As good as AI is, it still cannot correctly predict the future. And even if you ask it to, the most common answers will involve things that are either inherently priced into the market or a moonshot that requires binary bets.
Here is an example of an answer where I asked Claude to give me predictions for 1 year out from today (April 2026)
Even more importantly, Claude is providing the same answers to everyone using this prompt. As a hedge fund analyst, this part of the idea generation is where there is still alpha today.
If you are outsourcing your first step of idea generation to AI, you have already failed. You just gave up your most valuable edge when it comes to investing today.
Only once you have a general sense of what you are looking for, these tools give you incredible research capabilities that make you 10x more efficient in the initial screen.
A favorite tool here is Perplexity Finance, which has a built-in screener that you can prompt like any other LLM.
In the example below, I gave Perplexity a prompt to find me companies with at least $10bn market cap that grew revenues >20% and EBITDA margins >20%.
However, most AI platforms still lag far behind when it comes to qualitative screeners. For example, if you ask for a list of companies that have said the word "AI" at least 20x in their recent earnings call, these tools run into trouble. The context window and amount of data ingestion required to solve for that are too large for the capabilities that exist today.
As a hedge fund analyst, your firm might provide you with additional enterprise tools that solve for this issue. From my conversations, the best tools for this type of screening are AlphaSense, Rogo, and Hebbia.
These are native AI platforms built for financial analysts. Each of them can pull data from earnings or research reports, making it a curated platform for banks, private equity firms and hedge funds.
Incumbent AI tools such as the "ASKB" function in Bloomberg are also targeting this same market.
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