Franklin Templeton is building AI workflows to help portfolio managers weigh competing research views
The asset manager is starting with equity research and uses Codex among several tools, but has shared no results, costs or deployment scale.
In brief
Franklin Templeton rebuilds stock research with AI
The asset manager is building AI workflows, starting with equity research. They help portfolio managers compare research views and weigh risks. No results or costs have been disclosed.
Franklin Templeton says it is building AI workflows that help portfolio managers compare differing research views and weigh investment risks. The news comes from OpenAI's own post, dated 5 November 2026.
The work starts with equity research, the study of company stocks. Codex is one of several tools the firm's investment teams use to build these workflows.
From a chain of hand-offs to a connected system
The post describes the old process as led by people and run step by step. An analyst does the first pass on a stock, goes through earnings calls and studies competitors.
The planned system is different. It is meant to be a governed, reusable base of intelligence. It combines trusted data connectors, context about portfolios and workflows, and agentic features, meaning tools that can act on tasks, to surface insights for analysts and portfolio managers.
What the firm says
Dudley, named in the post, says the firm has moved from adding AI to its research process to redesigning the research workflow. The aim, Dudley says, is access to a range of research and opinion, rather than tools that keep showing one provider's reports.
Franklin Templeton has also run hackathons, where outside experts worked alongside investment staff to build tools.
Why it matters
Imagine two analysts reading opposite takes on the same company. A system that lays both views side by side, with the risks attached, could save a manager time. That is the kind of job this project points at.
What comes next
The longer-term goal is to make this connected intelligence available across investment teams, from analysts to group leaders.
The post frames the work as still in development. It gives no figures on results, costs or deployment scale, and no independent report confirms how it performs.
Sources
Source: OpenAI News