Direct answer
What should you evaluate before choosing an AI investment research tool?
Test whether the tool makes claims reviewable. Check source provenance, counter-evidence, date handling, coverage limits, saved context, privacy, exports, usage controls, and whether the system keeps the final decision with the user.
Run the same evaluation question
- Does every material claim have a usable source?
- Are publication and retrieval dates visible?
- Does the answer surface conflicting and missing evidence?
- Can you inspect files and prior conclusions later?
- Can you set hard usage or spend limits?
- Does the product avoid signals, execution, custody, and return promises?
Evaluate the second session
A polished first answer is not enough. Return with a new filing or changed assumption and test whether the tool can compare the evidence with the prior thesis without reconstructing the whole context.
Check data boundaries
Determine what is retained, who can access it, how sharing works, and whether payment authority or write-enabled financial credentials enter the research runtime. Do not upload secrets that the workflow does not need.
Limits
- No benchmark represents every research task.
- Source access can vary by jurisdiction and publisher.
- AI output still requires human verification and judgment.
Common questions
Questions about this workflow
What is the most important feature in an AI investment research tool?
Reviewability: the ability to inspect the sources, dates, assumptions, conflicting evidence, and prior reasoning behind an answer.
Should an AI research tool make investment decisions?
No. Research software can organize evidence and challenge assumptions, but suitability, allocation, execution, and final decisions should remain human-owned.