Almost every research team we speak with believes they’ve already “adopted AI.”
When you look closely, that usually means summarizing PDFs, asking ad-hoc questions, searching news, or rewriting notes. Useful, sure. Transformative, no.
We see this gap repeatedly because we sit inside real workflows. We talk to buy-side teams, boutique funds, family offices, and institutional research groups every week. What’s being said externally about AI adoption and what’s actually happening internally are very different things
The uncomfortable truth: most analysts are using AI as a convenience layer, not as a structural change to how research gets done.
The Wrong Questions are Being Asked
“What can AI do?”, “Will AI replace analysts?” and “Can it do end-to-end research?”
The real question is: what parts of analyst work should humans no longer be doing at all?
Very few teams are willing to ask that question honestly.
As a result:
Analysts still build financial models from scratch, cell by cell.
Quarterly updates are still manual and time-consuming.
Data extraction from filings is still copy-paste work.
News tracking is either manual or tied to expensive subscriptions.
AI tools exist, but no firm-wide frameworks or workflows exist to use them properly.
AI has dramatically lowered the cost of automation and integration, but most research teams are still operating as if nothing fundamental has changed.
Where AI Clearly Works and Everyone Agrees
There are areas where AI is already incredibly effective, and almost no one disputes this:
