Franklin Templeton is building AI-powered equity research workflows on OpenAI's models, targeting two specific functions: comparing research views across analysts and surfacing investment risks for portfolio managers.
The integration matters because equity research is one of the last high-margin, human-gated workflows in asset management. Automating view comparison and risk examination does not replace analysts. It compresses the time between data and decision, which is where alpha leaks.
The full case study details how the workflow is structured and where human judgment remains in the loop. That architecture question, not the headline, is what serious readers should dig into.
[READ ORIGINAL →]