Franklin Templeton is developing AI workflows, starting with equity research, to help portfolio managers compare different research viewpoints and examine the risks behind investment ideas. The firm is using several tools, including Codex, to create these research-supporting processes.
From early experiments to a connected intelligence layer
When Franklin Templeton first experimented with AI, the team placed data into a database and connected models to it. That setup did not deliver the needed answers because the models did not grasp how the data related. After adding agents and a semantic layer — a structured model of what the firm’s data means and how it connects — the outcomes improved. "We've been able to build what we think of as a connected intelligence layer," Dudley says. "We're bringing the data into the tools, and we're surfacing insights that make a difference."
Integrating multiple perspectives for decision-makers
Research, risk, compliance and trading each offer different perspectives on an investment. Franklin Templeton aims to combine those inputs so portfolio managers get more complete insights. "We're able to bring that all in and then use that to surface insights to the people who are making decisions, which are our portfolio managers," Dudley says.
Dudley describes the earlier equity-research workflow as human-led and sequential: an analyst performs a first-pass stock analysis, reviews earnings calls and studies competitors in each business line. The goal is to surface useful information as analysts prepare new ideas to pitch to portfolio managers or provide updates on existing portfolio positions. As she puts it, "It's just continuous intelligence combined with that human judgment."
Characteristics of the envisioned workflow
Under Franklin Templeton’s vision, investment teams will access a governed, reusable intelligence foundation that brings together trusted connectors, portfolio and workflow context, and agentic capabilities that surface actionable insights for analysts and portfolio managers.
The firm has used Codex in hackathons, pairing subject-matter experts with investment practitioners as they develop tools. Its Investment AI champions are working to move experiments into production-ready workflows; security review, expert support and data controls are integral to that process.
Dudley says, "We have moved from augmenting our research process to reimagining the investment research workflow."
Challenges in asset management and avoiding bias
Some AI features that help in other settings can be problematic in asset management. Many tools learn a user’s preferences and then show more of the same content, so if someone favors one research provider, the tool surfaces more reports from that provider. For an investment team, that narrowing can introduce bias into the investment process. Franklin Templeton aims to do the opposite. "We want to make sure that they're accessing that diversity of research and diversity of opinion," Dudley says.
The firm also wants its AI to emphasize investment risks. "We need AI to understand that a great investment idea is one that is well reasoned, but it also has analyzed the risks and makes our portfolio managers aware of the risk," Dudley says. "If you’re looking at a research note, focusing on the fact someone has identified the investment risks is not a bad thing from our perspective. It makes for a more informed investment decision."
Practical best practices emerging from experience
Franklin Templeton’s experience points to several practical principles:
- Leadership message: Leadership encourages innovation and experimentation with AI within clearly defined risk guardrails; not every initiative will succeed, but each should generate insights that strengthen capabilities, inform future decisions and drive continuous improvement, Dudley says.
- Get tools into people's hands quickly: The firm has provided practical labs to teams shortly after new technologies launch so they can test fit with their work.
- Help teams learn the tools: "The goal is getting them to actually understand the power of the technology," Dudley says.
- Build with appropriate controls: Security review, expert support and data agreements form part of the route from experimentation to production-grade tools.
Preserving human expertise
Dudley’s longer-term goal is to make the connected intelligence accessible across investment teams, from analysts to the leaders of each investment group. She wants analysts to spend more time in the field, speaking with a range of experts and professionals, including executives at private companies, and to bring those human insights back to their investment teams. For Dudley, human judgment remains essential alongside evolving tools.
Summary
Franklin Templeton is combining Codex and other AI tools with a semantic, agent-enabled layer to create a governed intelligence foundation for investment research. The firm emphasizes experimenting under risk guardrails, promoting diverse research perspectives, highlighting risks in analyses and scaling successful pilots into production with security and data controls.
