Franklin Templeton Develops AI Workflows for Equity Research

Franklin Templeton is developing AI workflows for equity research to help portfolio managers compare research views, bring together risk and compliance perspectives, and identify investment risks.

The firm is starting with equities. Its research process has traditionally been led by people and handled in stages, including stock analysis, earnings call reviews and competitor assessments.

Katrina Dudley, senior vice president of Public Market Investments at Franklin Templeton, said an initial test connecting AI models directly to a database fell short because the models struggled to understand how the data related to one another. The team added a semantic layer to organize the data by its meaning and connections.

The planned system links trusted data sources with information about portfolios and work processes to create a reusable foundation for research. It is designed to bring research, risk, compliance and trading perspectives into the same workflow, present portfolio managers with varied views and flag risks tied to investment ideas.

Franklin Templeton has used OpenAI’s Codex in hackathons pairing technical experts with investment practitioners. The firm’s Investment AI champions are helping move prototypes toward production-ready workflows, with security reviews and data controls. The specific version of Codex used was not disclosed.

Portfolio managers remain central to investment decisions, with AI intended to organize information for their review. Franklin Templeton has not disclosed quantified performance improvements or deployment headcount for the work.